samba wp 6 report - statnett · this report is a result from wp6 "risk monitoring centre"...

Risk monitoring function in Statnett SAMBA WP 6 report

Report Subject: SAMBA WP 6 report

Dokumentet sendes til:

Arne Smisethjell / UPX

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Maren Istad/ SINTEF Energy Research

Eivind Solvang/ SINTEF Energy Research

Maria Catrinu-Renström /Statnett

Jørn Johnsen/ Statnett

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Til orientering:

/ UPX

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/ UPX

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Dokument ID: [000000]

Date: 23. november 2018

Risk monitoring function in Statnett – Results from WP6 in the SAMBA-project

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Executive summary This report is a result from WP6 "Risk monitoring centre" of the SAMBA-project. The report describes the scope for risk monitoring in Statnett, with main focus on asset-related risks, i.e. the risks emerging from the degradation of the assets' condition.

Statnett will in the future have a continuously updated and quality assured assessment of the current and future condition and risks associated with the failure of components, substations and overhead lines. Statnett will hence always have an updated risk overview valid and available throughout the organisation. This risk overview will be dynamic and based on simulations using information from system operation and planning, asset management and relevant external data such as weather data. Prerequisites for this is the development of degradation models and access to high quality data, like updated maintenance information, system configuration and online data received from sensors.

The risk monitoring function in Statnett will have an active role in alerting throughout decision levels, i.e. system operations or asset management decision-makers, to take appropriate actions when risk is changing/increasing. The risk monitoring function will contribute to improving data quality through discovering and investigating deviations and diverging observations in data sets and models.

The risk monitoring function will also have an important role as the "eyes" of the organisation in the field using cameras and drones. Security issues, like trespassing, vandalism and safety issues, i.e. ensuring that it is safe to enter a station after a failure.

The main recommendations to Statnett regarding a risk monitoring function are:

• Establish a risk monitoring function in Statnett and define what to monitor, how (for example using sensors), who (define roles and responsibilities), and how the information will be used in decisions (develop algorithms to process data and information at different decision levels).

• Main tasks for this function should be: • Aid risk assessment and support decision-making for asset management, system operation/planning

and 1st line field operations including: o Performing quality assurance for both data and models o Improving models for risk assessment

• Monitor and enhance safety and security • The risk monitoring function will require new competences which will combine system knowledge,

component knowledge and analytic competence. It will be crucial for Statnett to maintain and develop existing competence and experience from system and asset operation and maintenance management

• A platform (specialist ICT-systems) for risk assessment and monitoring should be developed. This platform must have dashboard functionality suitable for the risk monitoring function as well as interfaces for asset management and system operation.

• Implementation of the risk monitoring function must be stepwise, starting with monitoring asset condition (health) and developing towards monitoring asset risk

• The stepwise implementation of the risk monitoring function will also imply the exploration of new tools, such as virtual test beds, digital twins, VR/3D products and drones, to get an overview of potential benefits.


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Contents

Abbreviations ............................................................................................................................... 6

1 Introduction .......................................................................................................................... 7 1.1 Risk monitoring function ....................................................................................................... 7 1.2 Risk monitoring centre .......................................................................................................... 7 1.3 Background and motivation .................................................................................................. 7 1.4 Methodology ......................................................................................................................... 9

2 Scope for the risk monitoring function in Statnett ................................................................ 10 2.1 Asset management ............................................................................................................. 10 2.2 System operation and planning .......................................................................................... 11 2.3 1st line field operation ......................................................................................................... 12 2.4 Analytics services ................................................................................................................ 13 2.5 Quality assurance ................................................................................................................ 13 2.6 Safety and Security.............................................................................................................. 14

3 Dashboard for risk monitoring ............................................................................................. 15 3.1 Overview ............................................................................................................................. 15 3.2 Processes and data ............................................................................................................. 15 3.3 Visualization ........................................................................................................................ 17

4 Recommendations for the risk monitoring function .............................................................. 19 4.1 Main recommendations ...................................................................................................... 19 4.2 Implementation process ..................................................................................................... 20

V1 Definitions .......................................................................................................................... 22

V2 Relevant literature and experiences gained from visits ......................................................... 25

V3 Risk analysis in Statnett today ............................................................................................. 30 V3.1 Status for risk analysis today ............................................................................................... 30 V3.2 SAMBA-project .................................................................................................................... 32 V3.3 Data science initiative in Statnett ....................................................................................... 32 V3.4 GARPUR-project .................................................................................................................. 33 V3.5 BUDV-project ...................................................................................................................... 33 V3.6 ICT projects in Statnett ....................................................................................................... 33 V3.7 Virtual test bed and digital twin .......................................................................................... 33

References .................................................................................................................................. 35


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Abbreviations ICT Information and communication technology

ISO International organization for standardization

PFA Asset management plan ("Plan for anleggsforvaltning" in Norwegian)

RCM Reliability centered maintenance

DSO Distribution system operator

TSO Transmission system operator

DDP System operation planning office

VR Virtual reality

3D Three dimensional


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1 Introduction

1.1 Risk monitoring function

This report is a result from WP6 "Risk monitoring centre" of the SAMBA-project. The report describes the scope for risk monitoring in Statnett, with main focus on asset-related risks, i.e. the risks emerging from the degradation of the assets' condition.

Statnett will in the future have a continuously updated and quality assured assessment of the current and future condition and risks associated with the failure of components, substations and overhead lines. Statnett will hence always have an updated risk overview valid and available throughout the organisation. This risk overview will be dynamic and based on simulations using information from system operation and planning, asset management and relevant external data such as weather data. Prerequisites for this is the development of degradation models and access to high quality data, like updated maintenance information, system configuration and online data received from sensors.

Risk monitoring is an important part of the overall risk management process in a company, being defined in the ISO 31000 standard [1] as: "continual checking, supervising, critically observing or determining the status in order to identify change from the performance level required or expected".

The risk monitoring function in Statnett will have an active role in alerting throughout decision levels, i.e. system operations or asset management decision-makers, to take appropriate actions when risk is changing/increasing. The risk monitoring function will contribute to improving data quality through discovering and investigating deviations and diverging observations in data sets and models.

The risk monitoring function will also have an important role as the "eyes" of the organisation in the field using cameras and drones. Security issues, like trespassing, vandalism and safety issues, i.e. ensuring that it is safe to enter a station after a failure.

The most important tasks of the risk monitoring function can be summarized as follows:

• Aid risk assessment and support decision-making for asset management, system operation/planning and 1st line field operations including:

o Performing quality assurance for both data and models o Improving models for risk assessment

• Monitor and enhance safety and security

1.2 Risk monitoring centre

The organization of a risk monitoring centre is beyond the scope of this report. The report focuses on what the risk monitoring function can provide and what Statnett can gain from performing systematic risk monitoring.

1.3 Background and motivation

Statnett has already established a monitoring centre in Sunndalsøra that provides 24/7 access control and camera monitoring of a number of substations. Currently, there is competence on operation and maintenance of components at this centre. This is the first step in providing support to 1st line field crews. The establishment of this centre is an opportunity for Statnett to contribute to the development of a risk monitoring function. In the future, the risk monitoring function should imply many tasks that can be performed online, independent of the physical location of the operators and decision-makers.


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A risk monitoring function will contribute to achieving the goal of Statnett concerning security of supply: "Securing power supply through operations, monitoring and preparedness1". As a risk monitoring function can aid decision-making for both asset management and system operation/planning, hence increase the utilization of component, improve maintenance and optimize reinvestments.

When the system operation margins decrease, the system operators must dynamically adapt to more information about the variability of probabilities of failure and the socio—economic impact of service interruptions, in addition to the traditional N-1 criterion, as described in the GARPUR-project2. Monitoring thus becomes more important, as it can reduce the uncertainty in the risk assessments. A closer cooperation between the risk monitoring function, asset management and system operation/planning are an important motivation for establishing a risk monitoring function, see Figure 1-1. In asset management, the monitoring aspect is gaining increasing attention due to the new possibilities to optimize the lifetime of components and predict/prevent failures offered by new sensors, communication and analytical tools (machine learning). More information into the effect of operation on the condition and risk of assets will also be valuable as i.e. overloading can reduce the lifetime quite significantly for a transformer.

Figure 1-1 Relation between asset management, risk monitoring and system operation

The digitalization3 trend has hit all industries and can be quite disruptive as manual work/operations are replaced with machines, more data is gathered, more analyses are performed, and automated analysis/decision-making is becoming a necessity as the amount of information is increasing. This trend will influence the way the transmission system will be maintained and operated in the future. Statnett could take into full use online monitoring with new sensors, as well as to develop new maintenance practices (physical inspections and monitoring of installations in the field, on demand with less human interactions or replacing human operators) using drones/robotics. In parallel with the SAMBA-project Statnett is working on strategy for the use of sensors. Two alternatives of such a strategy could be to only monitoring of critical component or monitor "everything", as the prices of sensors are decreasing. The latter alternative will obviously call for a higher degree of automated analysis and prioritising of alarms/warnings. The reliance on sensors will

1 http://statnett.no/en/About-Statnett/ 2 https://www.sintef.no/projectweb/garpur 3 Strategy document of Energi21 defines digitalization on page 17 as " an increasing number of physical components are equipped with sensors. These sensors measure physical parameters related to use of energy and the condition of the component. The sensors are connected through bidirectional communication networks. The data is gathered and analyzed, and control signals are sent back to optimizes, i.e. use of energy. The new sensor layers in the systems make solutions based on pattern recognition and machine learning possible (authors translation from Norwegian):https://energi21.no/prognett-energi21/Artikkel/HORINGSRUNDE/1254033869473

http://statnett.no/en/About-Statnett/

https://www.sintef.no/projectweb/garpur

https://energi21.no/prognett-energi21/Artikkel/HORINGSRUNDE/1254033869473


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increase as less personnel is physically present at the substations and this presupposes quality assurance of sensors.

Statnett is also developing a digitalization strategy which will define the way the company will gather, process/structure, analyse and make data available. Analytic methods and tools will be developed to get information out of the data and enable predictive/prescriptive maintenance and improved preparedness. All these are solid premises for the development of a risk monitoring function.

1.4 Methodology

This report has been drafted by a working group consisting of Jørn Johnsen (leader), Maria Catrinu-Renstrøm, Arne Smisethjell, Ivar Bullvåg Hansen, Per Ådne Bergfjord, Eivind Solvang and Maren Istad. The main authors of the report are Maren Istad and Eivind Solvang from SINTEF Energy Research. The working group had internal discussions and arranged workshops with relevant stakeholders in Statnett; asset management, system planning and operation, representatives from Sunndalsøra and data science group to get input on what a risk monitoring function could provide for Statnett in the future. In addition, visits to ABB at Billingstad, First Energy and AEP (American Electric Power) in Texas and Kahramaa in Qatar have contributed to a better understanding on how other TSOs perform risk monitoring. A small number of relevant literature reports have been reviewed, see appendix V2. This shows that there are many companies that are in the process of developing health indices, but there is no report on the development of a similar risk monitoring function as proposed here. Chapter 2 presents the scope for the risk monitoring function in Statnett, while chapter 3 presents framework for a risk monitoring dashboard. Chapter 4 contains the recommendations for the risk monitoring function. In appendices relevant definitions, summary of relevant literature and experiences gained from visits and an overview of risk analysis in Statnett today can be found.


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2 Scope for the risk monitoring function in Statnett This chapter describes the scope for the risk monitoring function in Statnett. This scope is described in relation to the following existing functions in Statnett: asset management, analytics services, 1st line operation and system operation and planning, see Figure 2-1. In addition, two chapters on quality assurance and safety and security are included. These are prominent crosscutting issues in Statnett that can benefit from the risk monitoring function.

Figure 2-1 Risk monitoring in relation to other functions

2.1 Asset management

Asset management consists of many elements as illustrated in Figure 2-2. Asset management aims at optimizing the whole lifetime of assets. Hence, the asset condition and risk of component failure is crucial information for asset management.

The risk monitoring function will focus on critical assets and the development of condition and risk for these assets, see also chapter 3. Information and analysis from asset management will be an important contributor to this. Asset management will benefit from the risk monitoring function through the increased interaction with system operation/planning. As the risk monitoring function can bridge the existing gap between asset management and system operation and planning, see also Figure 1-1.

There are many events, like short circuits and overloading, which may have an impact on the condition and remaining life of components, thus leading to slower or faster degradation than predicted. The risk monitoring function will in the future provide important insights into events, such as information about coming periods of overload that may affect the condition of components, correlations between increased failure probabilities and short circuits, changing operational patterns that need to be included in the analysis and further assessment of asset management plans. An example can be a more rapid decline in health index


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than predicted. This will initiate contact between the risk monitoring function and asset management to discuss the reasons behind this development. The unexpected development in the health index might be errors in the data collection or an actual unwanted development in condition. This again will affect the predictive maintenance scheme for the component and might also accelerate reinvestments. Again, postponed reinvestments may have system consequences. In the future, the cost for special regulations, due to capacity restrictions imposed by components in bad condition, should be a part of reinvestment analysis as the system consequences of postponements are not systematically included today. This is illustrated by the bidirectional arrow in Figure 2-1.

The risk monitoring function needs access to the same tools as asset management to get the required information about the assets.

Figure 2-2 Elements in asset management with a supporting ICT-architecture [2]

Relevant SAMBA use cases are T3.6 Health index, T3.4 Periodic oil and gas analysis, O2.5 Event detection, A3.1 Estimation of residual lifetime, probability and risk, A3.2 Technical-economic analysis of maintenance and reinvestment and A3.5 Condition assessment through sample testing. More information on the use cases can be found in [3].

2.2 System operation and planning

System operation and planning includes the daily operation of the network and contingency planning, as well as planning the operation ahead, up to a year. The operation planning requires information about the transport capacities that are available on a short time horizon; now or in the next few days up to one week. Information about reduced capacities due to i.e. bad technical condition of components and external stress like wind, ice loads etc are important for system operation/planning.

In addition to the information they have today, system operation and planning could benefit from having more accurate and updated information especially if the transmission network components are rapidly degrading. This is knowledge that the risk monitoring function will provide in the future, thus linking asset management and system planning. In the future, dynamic line ration (DLR) will be implemented. This


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paradigm shift in system planning and operation will demand more information about condition of components, if the benefits of DLR should be harvested.

Another possible task for the risk monitoring function, suggested by system planning, is a follow-up activity related to the restrictions reported by DSOs and Statnett to system planning. These restrictions impose risks for the power system and DSOs and Statnett needs to be informed and reminded of this, as postponed reinvestment decisions have system consequences. The risk monitoring function will have an overview of capacity restrictions caused by technical conditions or any other reasons both at Statnett and DSOs. This is illustrated by the bidirectional arrow in Figure 2-1.

The risk monitoring function needs access to the same tools as system operation and planning to get information about i.e. capacity restrictions.

During normal operation conditions the risk monitoring function will have updated information about the condition of the assets and can detect rapid component failure development. Transformer gas monitoring can indicate when the amount of hydrogen in the transformer oil is rapidly increasing. This can be due to sensor error or a real failure approaching. Another example can be that some earth faults can be observed on camera as smoke. The risk monitoring function will identify measuring/sensor error from real failures and quickly notify system operation. The network is also monitored by PMU4s and power quality instruments. Models are currently being made for failure prediction based on such measurements for instance in the Earlywarn5-project. These models combined with condition information can provide additional information to system operation. The risk monitoring function can contribute with condition information to supplement failure prediction models.

The risk monitoring function will be available for system operation during challenging conditions, like bad weather, large planned maintenance operations (N-0) and/or strained power situations and provide information on i.e. high-risk components that should not be overload.

Relevant SAMBA use cases are T2.1 Online gas data analysis and O2.5 Event detection. More information on the use cases can be found in [3].

2.3 1st line field operation

1st line field operation is the personnel that troubleshooting for failures mainly based on distance measurement from protection equipment, repair failures in the network and performs maintenance tasks. 1st field operations are now testing the use of drones for inspection of powerlines. The risk monitoring function can provide information to 1st line field operations using available measurements, camera and dispatchable drones. This can prepare the personnel better for the situation in the field.

4 Phasor measurement unit (PMU) is a device which measures the electrical waves on an electricity grid using a common time source for synchronization. 5 https://www.sintef.no/en/projects/earlywarn/

https://www.sintef.no/en/projects/earlywarn/


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Historical information about past events that resembles the current situation can also be retrieved quickly and experiences and learnings can be utilised to improve the outcome of the current situation. Hence, the risk monitoring function can aid the implementation of "lessons learned" and continuous improvements. This assumes that historical information is available in a usable format. In addition, cameras and other sensors can be used to determine if it is safe to enter an area/station for maintenance or repair purposes. The use of on-demand drones can also be coordinated by the risk monitoring function. This is illustrated by the one directional arrow in Figure 2-1 as the main flow of information is from the risk monitoring function to 1st line field operations.

2.4 Analytics services

The future tasks of the risk monitoring function should be supported by assessment tools and dashboards that will have interfaces also for asset management and system operation and planning. The development of algorithms behind the tools and dashboards should primarily be triggered by needs to better perform the core tasks in asset management and system operation/planning. The algorithms themselves should be made in close cooperation with analytics services.

In addition, the risk monitoring function will require specific tools (for example to process large amounts of data from sensors) which will most probably require advanced analytics services based on e.g. machine learning. The risk monitoring function can be the owner and user of selected models from analytic services. These analyses can run continuously with alarms at deviations, at given times or on-demand. Problems not suitable for machine learning, i.e. due to lack of appropriate data or poor data quality can temporarily be analysed manually by the risk monitoring function. This is illustrated by the one directional arrow in Figure 2-1 as the main interaction is from models from analytics services to the risk monitoring function.

Relevant SAMBA use cases are C2.4 Thermal condition (DTS measurements) [3] and NTNU master thesis supported by the SAMBA-project [4].

2.5 Quality assurance

The risk monitoring function can help closing the loop (Plan-Do-Check-Act) as shown in Figure 2-3 , especially the "check" part as quality issues, both concerning data and models, can be discovered by the risk monitoring function when working with the data and models. Large deviations from degradation models can indicate improper modelling or unwanted condition development. The setting up of quality alarms, i.e. alarm if the temperature of a component has not changed for a given time period, is an efficient way of identifying quality issues. The risk monitoring function can receive and investigate such quality alarms.

Figure 2-3 Plan-do-check-act


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Ensuring quality of historical data can also be a task for the risk monitoring function. The correctness, calibration and traceability of these data needs to be taken care of if asset management analysis is to be based upon these data.

Relevant SAMBA documents related to quality issues are the work performed by Martine Ukkelberg, in her summer job [5], project thesis and master thesis [4]. The final reporting from SAMBA WP5 will include information on the data quality issues encountered when testing SAMBA use cases.

2.6 Safety and Security

An increase in installed cameras and use of drones will enable Statnett to monitor trespassing and unwanted activities close to installations. Tasks related to area supervision and admission control can be a task for the risk monitoring function.

Safety for personnel can also be increased by using drones and cameras to ensure that it is safe to send in personnel or replace human interactions.


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3 Dashboard for risk monitoring

3.1 Overview

A dashboard for risk monitoring must contain relevant information about critical assets and operational situations and weather condition relevant for the degradation of asset condition and corresponding risk related to economy, quality of supply, health/safety and environment. Figure 3–1 illustrates fictional examples of visualization of information in a risk monitoring dashboard.

Figure 3–1 Dashboard for risk monitoring

One important motivation for creating a dashboard for risk monitoring is that it will contribute to a shared perception of "what risk is" today, and in the future. A dashboard can have several layers with customized information for different users in the company, i.e. risk monitoring, management, component experts and planners of maintenance and reinvestments. Information must be available for all, but access should be adjusted to the user needs. Relevant standard for making dashboards/HMIs (human machine interfaces) can be:

• ANSI/ISA-101.01-2015 - Human Machine Interfaces for Process Automation Systems • ISO 11064-5 - Ergonomic design of control centres — Part 5: Displays and controls • IEC 62682:2014 - Management of alarm systems for the process industries

3.2 Processes and data

There are three main processes for the estimation of the information presented in the dashboard, see also Figure 3–2:

• Estimation of risk • Estimation of health indices for critical assets • Selection of notifications

Notifications include information about upcoming operational situations or weather conditions that may affect critical assets, e.g. weather forecasts (wind, snow, temperature, lightning, etc.) and temporary operating conditions that result in increased stresses on components and reduced redundancy. Such


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notifications are predefined and related to given situation. Health indices and notifications are relevant additional information in a risk monitoring dashboard for understanding and evaluating the risk.

Figure 3–2 shows six main categories of data that are required for the estimation of the information presented in the dashboard:

• Critical assets and related failure models (probability and consequences of critical failures) • Technical condition for critical assets (online and offline measurements) • Operational stresses (switching and loading) • External stresses (expected weather, lighting, etc.) • Maintenance (performed and planned) and reinvestments (planned) • Event history (events in the past that may have led to significant weakening of the technical condition

of critical assets)

Figure 3–2 Processes and data

Criteria must be specified for when the information in the dashboard shall be automatically updated. That can for example be:

• when new information (measurements) of technical condition are available • when measured value of technical condition is of a certain size or has reached a given level • after events in the network that may affect the condition of critical components (lightning stresses,

short circuits)


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The dashboard is also useful when estimation of risk is used to support preventive decisions and actions. Such risk assessments can be performed:

• prior to bad weather • prior to switching operations that may result in increased stresses on components and reduced

redundancy • prior to switching and work operations that may affect the safety of personnel • in assessing the need for maintenance and reinvestments

The selection of critical assets, relevant failure modes and parameters to monitor is very important for the value of risk monitoring. This work must be based on good theoretical and methodological knowledge and best practice. Statnett's RCM framework will provide the guidelines for such specifications. Analytic services resources will be important for the development of failure models and associated functions (tools).

3.3 Visualization

The visualization in a risk monitoring dashboard can include quantitative as well as qualitative information presenting graphs, traffic lights (red, yellow, green), tables, pointers, etc. (see Figure 3–1). Background maps and single line diagrams can be used to link the information to substations, power lines and assets. The information can be shown as momentary values, changes, trends (e.g. last five years) and prognoses (e.g. next five years).

Risk matrices are often used to visualize risk, see Figure 3–3. Estimated risk can be quantitative as well as qualitative. Statnett quantifies risk in matrices today for substations and overhead lines as part of the asset management plan (PFA). This information will be important input. A critical failure is plotted in the matrix according to its failure probability and consequence of failure. Figure 3–3 includes three failures, plotted in (2,M), (3,L) and (4,X).

In the figure the combination of failure probabilities and consequences of failure that are in the red part of the matrix represent unacceptable risk while combinations in the green part represent acceptable risk. Combinations in the yellow part is not unacceptable, but neither acceptable.

Figure 3–3 Risk matrix (example)


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New assessments of technical condition may result in a reassessment of asset's probability of failure and remaining lifetime, thus an increased risk. Actions like immediate interventions, additional preventive maintenance activities and component replacement can reduce the risk. Figure 3–4 illustrates two examples of changed risk. Such risk matrices can be presented on several detailed layers in the dashboard than the "overview" layer illustrated in Figure 3–1. The "overview" layer will present a summary of information from the risk matrices (number and type of unacceptable risk, etc.).

Figure 3–4 Two examples of changing risk

Economic risk can be presented in tables and by graphs related to a substation, power lines as well as individual assets. Estimated costs can include:

• Cost of preventive maintenance and reinvestments • Cost of energy not supplied (CENS) due to failures and planned actions • Cost of corrective maintenance (repair after failure) • Total costs


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4 Recommendations for the risk monitoring function The working group recommends that Statnett establish a risk monitoring function. The main recommendations are provided in chapter 4.1. The implementation of the risk monitoring function should by stepwise, see chapter 4.2.

4.1 Main recommendations

The main tasks for the risk monitoring function should be (descriptions can be found in chapter 2):

• Aid risk assessment and support decision-making for asset management, system operation/planning and 1st line field operations including:

o Performing quality assurance for both data and models o Improving models for risk assessment

• Monitor and enhance safety and security

In order to perform risk monitoring Statnett will need new competences that combine system and component knowledge. It will be of outmost importance to preserve and develop existing competence, i.e. personnel with practical experience from system operation and maintenance management. A Statnett internal exchange program between different functions could be an option to learn from each other. In addition, Statnett will need to develop and invest in analytic and ICT skills.

The risk monitoring function can contribute to better asset management decisions, i.e. maintenance and reinvestment management and failure analysis. The organizational borders between these functions must be retaught in order to find the best possible organisation and ways to cooperate. It is recommended to build a common ICT-system for asset management and risk monitoring which will aid to form a holistic perspective. This IT-system must have dashboard functionality suitable for risk monitoring, as described in chapter 3.

The exploration of new tools to get an overview of potential benefits for the risk monitoring function should be prioritized. The concepts of virtual test beds and digital twins should be investigated, as these can be used


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to simulate component degradation or system responses and hence aid the estimation of future risks. Visualization tools, like VR/3D products, and drones should be investigated for their potential to make cooperation between the risk monitoring function and 1st line field crews more precise and efficient. The risk monitoring function can, in the future, be given the authority to dispatch drones to investigate failures or safety/security issues.

4.2 Implementation process

The process of implementing the risk monitoring function in Statnett must be a stepwise process starting with asset condition/health, through more monitoring and better registration of information from visual inspections, and step by step evolve towards monitoring of risk and integration with system operation.

The following implementation process is recommended, see Figure 4-1:

1. Develop a road map for the implementation of the risk monitoring function including: • Identification of roles and responsibilities. • Clarification of tasks and cooperation between existing functions in the company • Identification of organisation of the risk monitoring function • Specification of the appropriate platform for cooperation with asset management and

system operations/planning • Specification of ICT needs for the risk monitoring function, including dashboard functionality • Specification of the critical assets and events to be covered by the risk monitoring function

2. Implementation of monitoring of condition (health indices) 3. Implementation of monitoring of risk

Figure 4-1 Suggested implementation process

Step number 2 above is monitoring of asset condition based on information from asset management. The work on health indices for transformers has started in SAMBA and would be a good starting point as Statnett can have operative models for transformer health estimation up and running on a short time scale. Then health indices can be established for other components.

The risk monitoring function needs input on both probability (health indices) and consequences of failures. As a starting point, the latter can be modelled as risk assessment based on consequences assuming that the network has "normal service conditions" (N-1). Then, information about the actual service conditions, i.e. components taken out due to maintenance or failure, weather conditions, redundancy and operational alternatives for supplying customers can be included.

The consequences of failure are provided for different parameters like security of supply, health, economy and so on, hence the overall risk for a component will have to be based on the importance of these parameters. In order to do this, trustworthy models for both the current and future development of asset condition and risk must be implemented. These models must be accepted as relevant (the best we have) at the different decision levels and by the different decision-makers in the organization. In addition, important


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work has to be done in Statnett on how to assess the condition and risk of substations and overhead lines6. Statnett needs tools that can help with these risk assessments and specifically to incorporate information that would show rapid changes in the risk overview, i.e. faster or slower degradation than expected due to i.e. operational patterns. New analytic tools should be acquired and/or developed in close cooperation with i.e. those performing RCM-analysis and analytics services.

Data quality is a premise for both condition and ultimately risk monitoring used as basis for decision-making. Quality of data increases in importance as Statnett becomes a data driven organisation. New routines to ensure data quality must be implemented at all levels, from calibration to automatic checks of measurement values. The risk monitoring function will have an important role in the quality assurance as this function will work with the data and models and hence detect quality issues. The risk monitoring function can also be the receiver of quality alerts.

In parallel with the implementation of the tasks mentioned above, the risk monitoring function can enhance safety and security through the use of cameras, drones and other tools.

6 Work on estimation of the probability of failure of overhead lines is currently performed at Statnett: https://datascience.statnett.no/2018/04/23/estimating-probability-of-failure-overhead-line-lightning/ and setting up a forecast service for weather dependent failures on power lines in one week and ten minutes https://datascience.statnett.no/2018/04/27/from-idea-to-deployment-a-service-for-estimation-of-failure-probability-on-overhead-lines-based-on-the-current-weather-forecast/

https://datascience.statnett.no/2018/04/23/estimating-probability-of-failure-overhead-line-lightning/

https://datascience.statnett.no/2018/04/27/from-idea-to-deployment-a-service-for-estimation-of-failure-probability-on-overhead-lines-based-on-the-current-weather-forecast/

https://datascience.statnett.no/2018/04/27/from-idea-to-deployment-a-service-for-estimation-of-failure-probability-on-overhead-lines-based-on-the-current-weather-forecast/


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V1 Definitions Risk is often expressed as the combination of the consequences of an event and the associated likelihood/probability of occurrence. Risk management is all the coordinated activities to direct and control an organization with regard to risk [1].

Risk monitoring is important part of the overall risk management process. Monitoring is in the vocabulary of the ISO 31000 standard [6] defined as:

continual checking, supervising, critically observing or determining the status in order to identify change from the performance level required or expected

Monitoring and review should be done for all parts of the risk management process. The aim of this is, according to [1]:

• ensuring that controls are effective and efficient in both design and operation; • obtaining further information to improve risk assessment; • analyzing and learning lessons from events (including near-misses), changes, trends, successes and

failures; • detecting changes in the external and internal context, including changes to risk criteria and the risk

itself which can require revision of risk treatments and priorities; and • identifying emerging risks.

Data science is the extraction of actionable knowledge directly from data through a process of discovery, or hypothesis formulation and hypothesis testing [7]. Knowledge needed in data science is illustrated below; this can be one person, or more likely a team of several persons. Data science is an interdisciplinary field and aim at getting value out of the data.

Figure V1.1 Skills needed in data science [7]

Data science projects can be structured based on a The Team Data Science Process (TDSP) by Microsoft [8], inspired by the Cross Industry Standard Process for Data Mining (CRISP-DM). The process is illustrated in Figure V1.2.


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Figure V1.2 Team data science process lifecycle

A data science project requires:

1. Business understanding

There are two main tasks of business understanding;

• Identify business problems that the data science project should solve and value of solving this problem. • Identify data sources needed to solve the problems

2. Data acquisition and understanding

There are three main tasks in data acquisition and understanding:

• Set up the process to move the data from source locations to the target locations where analytics operations like training and predictions are to be executed

• Explore the data to determine if the data quality is adequate o Cleaning of data

• Set up a data pipeline to score new or regularly refreshed data

3. Modelling

Different types of modelling can be done, but TDSP focus on machine learning. The tasks described below are also valid for other types of modelling.

Machine learning

The goal of machine learning is to develop methods that can automatically detect patterns in data, and then use the uncovered patterns to predict future data or other outcomes of interest [9]. Possible task in relation to machine learning methods:

1. Method selection: The right machine learning method must be chosen for the problem at hand, as there is no universally best model/method. This is called the "no free lunch theorem" [9].

2. Training of the model


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4. Deployment, use, maintenance and improvement.

Deployment is the operationalizing of the model. This will typically be the task for the data scientists. Other parts of the companies can be the owner and user of the machine learning models and hence also have the responsibility for suggesting maintenance and improvements of the models. The actual model maintenance and improvements can be performed by the data scientists.


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V2 Relevant literature and experiences gained from visits ABB Bilingstad

ABB has an integrated operation center for ship control at Billingstad in Norway [10], [11]. At this centre ABB monitors and can connect to equipment onboard ships at sea. The aim is to monitor more in order to make online failure analysis and informed decisions on maintenance. This is a service offered to the shipowners. ABB has challenged their component experts in order to model degradation in a manner that is possible to monitor.

AEP and First Energy

Both AEP (Americal Electic Power) and First Energy have invested in ABB Ability Ellipse (formerly called Asset Health Centre) [12]. AEP and First energy both started the process towards asset health monitoring by assessing the health of power transformers. First Energy is still in the process of assessing the health and data quality assuring, while AEP concluded the work on transformers and started assessing the health of other components. Understanding the models for health indices included in the ABB Ability Ellipse required cooperation with ABB experts as the models had to be "tuned" to fit the individual company's needs. The implications for the companies' IT solutions were huge and resources had to be used in gathering and quality assuring data.

AEP has documented their experiences in [13]. Here, AEP reports on testing of sensors in the 765kV transmission network for transformers and circuit breakers. Transformer monitoring included:

1. Gas monitors a. Single gas monitor b. Multi-gas monitor

2. Temperature monitor 3. Partial discharge monitor 4. Transformer bushing monitor 5. Frequency Response Analysis (FRA) monitor 6. Oil monitors

a. Moisture in oil monitor b. Particle in oil monitor c. Oil dielectric strength monitor d. Oil interfacial tension monitor e. Furan in oil monitor f. Other oil monitors

7. Cooling accessory monitors. a. Cooling pump monitor b. Cooling fan monitor c. Cooling flow monitor d. Heat exchanger efficiency/clogged coolers monitor

8. Geomagnetic Disturbances (GMD) monitors a. Geomagnetically Induced Current (GIC) monitor b. Harmonics monitor c. Geomagnetic field monitor (Magnetometer)

For circuit breakers the following were monitored:

1. Interrupter wear monitors a. Contact wear monitor b. Nozzle wear monitor


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2. Operating mechanism monitors a. Trip coil current signature monitor b. Contact and/or mechanism travel monitor c. Energy storage source monitor (spring, hydraulic or pneumatic)

3. Breaker timing monitor 4. Leak detection/gas density monitors

a. Leak detection and gas leakage measurement b. Gasket health

5. IED relays a. Number of through faults b. Loading profile

AEP chose the ABB Ability Ellipse to perform analyses on the data and produce health indices, suggest actions and in some cases workorders directly. The architecture of the asset health center is shown in Figure V2.1. The different data sources, including the sensors on transformers and circuit breakers, are shown to the left in the figure. All these data are prepared for analysis in a central data warehouse and then sent to the ABB system for analysis. The analyzed and health indices, reliability predictions, remaining life is calculated for components. In addition, there are models for aggregation of the health into substations and fleet condition. This information is available for the grid development, maintenance and renewal programs in order to support decisions. The expected benefits are avoiding failures through increased knowledge about condition, perform condition-based maintenance, informed replacement of assets and ability to document the decisions made based on traceable information from sensors and deterioration models.

Figure V2.1 AEP Asset Health Center Architecture [13]


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Kahramaa in Qatar

Kahramaa, the TSO and DSO, of Qatar has chosen the APM from GE Grid to establish health indices for all of their components in the network. They have published some of their experiences in [14]. The TSO has 326 substations and base their health indices on information from visual inspections, oil sampling for transformers, thermography and partial discharge measurements in substations. Their goal is to have online monitoring of the asset health in the same location as the operation center. They have chosen to customize the APM from GE to suit their needs, i.e. changing degradation models and weighing to suit their components and climate. One additional benefit from the assessment of component health is the overview of weaknesses in particular designs, which has been used in specifications and discussions with vendors when purchasing new equipment.

Asset monitoring centre in Australia – Transgrid

Transgrid has invested in an asset monitoring centre in Sidney [15]. Transgrid has an organizational model with asset owner, asset manager and service provider. The asset manager function owns the asset monitoring centre and works within guidelines set by the Asset Strategy Managers which are experts who look after specific types of assets such as Substations, Transmission Lines or Control Systems, to manage the assets. The asset monitoring centre has day to day reviews of component condition and coordinate activities related to the components. The asset health centre focus on incidents or condition issues that need attention now or within a month. Non-urgent issues are handled by asset strategy managers who make long term plans. The competence of the crew at the asset health centre is diverse; component specialists, specialists in communications, protection, control, automation and SCADA. All these experts cooperate to find the cause of a failure. If the cause of failure is found, a strategy is made to prevent the same failure from happening again.

An important value from the monitoring center is that collected data is brought together and reviewed centrally in a more consistent manner enabling more informed decisions. Transgrid has historically performed time-based regional maintenance, but one challenge with this approach is maintaining consistency in the entire company when different teams preform condition assessment and maintenance. The asset health center is a part of system operations and is located in the same control room, see Figure V2.2. Transgrid is ISO50001 certified.

Figure V2.2 Asset health centre at Transgrid, Australia [15].


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Small literature survey

In [16] a decision-support model for asset managers based on risk, remaining life and failure impact is presented. The aim of this model is to support maintenance and reinvestment decisions. The illustration at the top of Figure V2.3 shows the decision support model including both health index and a risk index. Cost analysis is also taken into consideration in the decision taking. The health index is used to identify the need for maintenance and reinvestment, while the risk index is used to estimate the impact of the failure. The cost analysis is a cost/benefit analysis to find the correct measure to be performed, comparing cost of measures (maintenance, replacement etc) with the corresponding risk reduction. This can be net present value calculation. The illustration at the bottom of Figure illustrated the different components in numbered bobbles in the risk matrix based on remaining life and impact. The 3D diagram illustrates the number of assets in the different risk categories (red, orange, yellow and green). In addition, the intensity of the color illustrates the uncertainty of the results where brighter color indicates more certain results than diffuse colors.

Figure V2.3 Health and risk index from [16]

The health and risk index explained in this paper is made for maintenance and reinvestment purposes and the network is often assumed to be in a normal state when the impact of a component failure is estimated. The actual state of operation besides normal operation, like lack of redundancy and bad weather is not included. Hence, this risk index does not provide the short-term risk needed in for risk monitoring defined in this report. But as a starting point for risk monitoring as a more "static" risk overview the explained indices is interesting.

In [17] a method for risk assessment in distribution networks is described. This method based on a combination of a health index and an importance index. The risk identified in this paper is for operation purposes and aims at improving system performance. The product of these indices is the risk. The health and importance indices are made on two levels; equipment and system level. A health index for a transformer includes among other things nameplate information, failure information, load factor, temperature and operating years. Less focus, compared to health indices in the transmission network, is put on the condition of the components revealed by condition monitoring in the paper. This corresponds with the current lack of condition measurements performed in the distribution network. An importance index for a transformer


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includes rated capacity, load factor, number of important customers and so on. Low indices mean that the risk is low. Bad health and/or high importance increases the risk. At system level, the weakest equipment is in focus, in addition to historical information about interruptions and system vulnerabilities. In Figure V2.4 the risk matrices for eight feeders in a normal situation is shown to the left and with increased load factor and increase in the importance of the costumers for one of the feeders. The feeder gets a higher health index due to increased load factor and increased importance due to both load factor and increase in importance of customers.

Figure V2.4 Risk matrices for normal (left side) and increased load factor and importance of customers (right) for eight feeders in a distribution system

SP Energy in Scotland has developed a condition monitoring program based on the health and criticality of assets. This is described in [18]. The making of deterioration curves for the assets and the impact of failure defines the risk for the assets. Historically, the deterioration curves were based solely on age, but now the effect environment and other factors in slowing and accelerating the aging is included. The health index of an asset is based on condition data and goes from 1 to 5. HI 1 assets are in very good condition, and HI 5 are at the end of life. Then the health index is aged to calculate the deterioration curve of that asset and therefore the future expected HI. Consequences of failures are added to identify risk and asset strategy for the coming years. The process of making health index for transformers, TFM (transformer management) is shown in Figure V2.5. The index is a combination of internal, i.e. oil analysis, and external factors, i.e. information from inspections. Comments from experts and manufactures and information concerning design are also included when identifying the correct maintenance and time for reinvestment.

Figure V2.5 SP Energy method for making of health index for transformers [18]


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V3 Risk analysis in Statnett today

V3.1 Status for risk analysis today

Risk management is important in Statnett and risk analyses are performed at various levels. Measures are continuously being implemented to treat, mainly to reduce risk, i.e. maintenance, reinvestments, system planning, emergency preparedness, etc. This chapter summarizes the discussions, in WP6, regarding practices for risk analysis with different departments in Statnett.

Risk monitoring at system level

The load dispatching centre performs some risk monitoring today at system level. For instance, system simulations and preparations for bad weather conditions, i.e. by notifying local resources when bad weather is coming, to increase emergency preparedness. The load dispatching center's goal is mainly on the reduction of system consequences i.e. interruptions and blackouts. The economic or technical consequences, i.e. increased asset degradation, is not the main responsibility of the load dispatching center. They have limited knowledge7 of the condition of components and do not have the time to consider operation options aiming at reducing impact on component condition.

The system operation planning office (DDP) is another important part of system operation. DDP coordinates all outages in transmission (Statnett) and distribution networks as well as power plants outages. All outage requests should be planned and submitted to DDP before 1st of October the year before. DDP received 8000 outage requests for 2017. In addition, there are always changes to the announced plans that have to be planned again and unplanned outages. Unplanned interruptions are reported by the load dispatching centre. When permanent failures have occurred, new capacities for the network are set by the system operators in northern and southern Norway. The capacities are reported through FOSWEB8. DDP evaluates the reported capacities and decides the valid capacities limits and the network is operated according to these limits. Components with reduced capacity are recommended replaced to the asset owners and also reported to the regulator (NVE). DDP cannot decide when the components that cause reduced capacity in the network are replaced (not even for Statnett), but the regulator can request the asset owner to make a replacement. DDP has some communication with the DSOs having assets that limit the transmission/distribution capacity. Reduced capacity in the market is solved by the activation of special regulation reserves9. These are system costs that should be taken into consideration in reinvestment planning, especially when there is the option to increase the lifetime of an asset by keeping it in function, but with reduced capacity. Today, the plan for next week is sent to the load dispatching centre on Thursday the week before. In the future, there is a drive towards more dynamic analysis and plans updates closer to the operation of the network. This will enable the operator to perform more correct and efficient preventive actions in operation while taking into account updated information on capacity limits both on individual assets and network sections. In the event of bad weather, all planned outages are cancelled and capacity limits re-analysed and adjusted to limit the consequences (overload of lines and transformers) especially in areas that are or might be operated separately.

Risk monitoring at asset management level

Reinvestment planning

Statnett has a long-term reinvestment plan (PFA) which is updated every second year. The plan is made based on a qualitative risk analysis of asset reaching end of life or reported in bad condition. The methods used is

7 The load dispatching centre knows about operation restrictions on transformers and overhead lines 8 http://www.statnett.no/Fosweb/ 9http://www.statnett.no/Global/Dokumenter/Kraftsystemet/Systemansvar/Systemdrifts-%20og%20markedsutviklingsplan%202017-2021_Oppslag.pdf

http://www.statnett.no/Fosweb/

http://www.statnett.no/Global/Dokumenter/Kraftsystemet/Systemansvar/Systemdrifts-%20og%20markedsutviklingsplan%202017-2021_Oppslag.pdf

http://www.statnett.no/Global/Dokumenter/Kraftsystemet/Systemansvar/Systemdrifts-%20og%20markedsutviklingsplan%202017-2021_Oppslag.pdf


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to interview local asset operators and component experts about estimating the probability of failure for asset groups in substations and for overhead lines/cables. The asset groups analysed in substations are: switchgear AIS and GIS (separated on voltage levels), the control systems (substation automation systems), transformers, compensation equipment, substation cables, converters (HVDC), auxiliary facilities. For overhead lines the asset groups analysed are: lines crossing fjords, cables, connections/stress relief, conductors, insulators/ suspension/conductor joint, phase spacers/damping, towers, foundations, aircraft warning spheres.

The following consequence categories are evaluated for each potential failure: personnel safety, environmental impact (i.e. leakages, damage to the landscape), energy not supplied (MWh), socio-economic costs (mNOK), time to restore the supply (hours), if the failure may cause a severe capacity situation (MW) at system level.

Acceptance criteria for the risk (combination of probability and consequences) are established and used to find substations and overhead lines that must be prioritized for replacement. The risk assessments for PFA are updated every other year with the purpose to find the optimal timing for and prioritize reinvestment projects. The information about the system and the assets used in PFA is rather static and of qualitative nature. In the future, the PFA plans can definitely be improved by using updated maintenance and condition information, including parameters that will be monitored online (by risk monitoring).

Maintenance planning

Statnett uses an RCM philosophy/method to analyse the assets and to decide the correct time and type of maintenance, focusing on critical functions, and system consequences. The probability of failure estimated in RCM-analysis is based on failure statistics of varying quality and expert evaluations. These estimations are rather static and uncertain to be used as a method in 'risk monitoring'. Ideally, the probability of failure use for RCM and PFA should be the same; it should be updated based on the most up to date information about the assets' condition, the system operation conditions that may influence remaining lifetime (or time to next failure) and the environment (external stress), and should be depicted in a risk monitoring dashboard.

The asset management department and component specialists have knowledge about the current condition of components based on inspections and measurements, but today this knowledge is not systematized and documented or easily available for analysis. Statnett does not have and integrated analysis function nor analyses tools to support asset management decisions something that may result in sub-optimal decisions with cumbersome and ling decision processes, incorrect risk evaluations, parallel work to extract and analyse data in different departments, etc. In addition, it is of outmost importance that Statnett reviews what information is collected about components to make sure enough data is available (and of good quality) to support analyses and further decisions. For example, from inspections only failure reports are recoded, i.e. if the condition or the asset examined is not as expected (in the RCM analysis). These failure reports are the closest Statnett has to risk monitoring because the reports should be classified in terms of their criticality: how quickly the failure should be repaired given the condition of the asset and the expected consequence of the failure. However, given the rather high responsibility for failure reporting, little guidance is given to the field personnel regarding how to describe and classify these failures. To improve this process in the future, in addition to the findings during maintenance, Statnett should use expert knowledge, failure statistics and new algorithms to better estimate the probability of failure and corresponding consequences (risk). This knowledge must be systematically gathered, updated and visualized to reach the aim of continuous risk monitoring.


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V3.2 SAMBA-project

The SGAM framework [14] has been used in the SAMBA-project to explain the project's central R&D challenges and scientific methods, see [1]. All the layers of the SGAM framework have to to be interoperable, as information from components in the component layer must be available for analysis in the function layer to support the business objectives of optimized lifetime of the components, high reliability etc.

Figure V3.1 SGAM framework [19]

In SGAM's function layer depicted in Figure V3.1, functions and services are represented as use cases independent of the physical realisation in systems and components. This level ensures that the right information enters the right process and the right actor. This is why the main research work in SAMBA consists of the description and testing of use cases with focus on data (information layer) and the description of the future ICT architecture. However, the implementation of use cases, data models and architecture at Statnett is outside the scope of SAMBA. The results and recommendations for further work from SAMBA are the premises for further development of analysis tools and decision support for risk assessment and monitoring.

V3.3 Data science initiative in Statnett

The data scientist group is organized as a part of Statnett ICT department, offering support for data management to the entire organization. The group works with locating data, quality assurance, suggesting and/or setting up machine learning algorithms for different types of analysis. They use data science to extract knowledge from the vast amounts of data gathered about the power system and suggest new data-driven approaches to improve power system operation, planning and maintenance10.

10 https://datascience.statnett.no/

https://datascience.statnett.no/


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V3.4 GARPUR-project

GARPUR was a four-year project funded by the European Commission FP7 programme, started in September 2013 and finished in October 2017. The acronym GARPUR stands for “Generally Accepted Reliability Principle with Uncertainty modelling and through probabilistic Risk assessment”.

The GARPUR project is a relevant reference for this report because it provides ideas and the framework for an integrated risk assessment and monitoring across decision levels and time dimensions. The methodology proposed has been designed to seamlessly fit in with existing TSO processes.

The main objectives of the project were to define, evaluate and compare new classes of probabilistic reliability criteria, away from the N-1 traditionally used in planning and power system operation. The new criteria are developed for system development, asset management and system operation in order to ensure a consistent treatment of reliability (and risk assessment) across all time horizons.

V3.5 BUDV-project

BUDV (Bedre utnyttelse av drift og vedlikeholdsdata) is an abbreviation for better exploration of operation and maintenance data in Statnett. The project is an internal Statnett initiative to align maintenance (work orders) data and system performance data (power system disturbances and failure statistics) visualize it and make it available for analysis and use for different purposes. BUDV is a relevant reference because it is the first form of visualizing the effect of failures: duration, costs to repair and restore the component in operation and the system related costs of the interruption. The future risk monitoring dashboard platform will have to integrate the algorithms for data integration developed in BUDV.

V3.6 ICT projects in Statnett

FIA

FIA is an abbreviation for common information architecture in Norwegian. The FIA-project will ensure that Statnett has a common data model, handling of master data, data flow and life cycle.

Finbeck

The Finbeck-project aim at creating an ICT-architecture to support the information needs of the future: easy access to data for different analysis purposes and enabling advanced analysis with multiple data sources. The focus is both on the ICT system architecture (data storage, system, integration and functionality) as well as the infrastructure (servers, network, gateways, routers and security barriers). Finbeck is relevant for this work because it sets the premises and a vision for big data analysis in Statnett.

V3.7 Virtual test bed and digital twin

A digital twin is a "virtual substitute of real world objects" according to [20]. This can be a virtual power network or a virtual component. Virtual testbeds can represent digital twins according to the same source. Figure below shows a virtual testbed, where the digital twin is 𝑠𝑠𝑒𝑒𝑒𝑒𝑒𝑒𝑠𝑠𝑠𝑠𝑠𝑠(𝑡𝑡). The virtual test bed also consists of a data processing system (DPS) receiving sensor data form the real world and influencing the real world through actuators. Through the use of the switches Tsense or Tact the DPS can be used to simulate or on the real world. This makes it possible to optimize, evaluated and operate using the same software and hardware which is an important feature of virtual testbeds. These modelling concepts are highly relevant for the future algorithms that will support the risk monitoring dashboard.


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Figure V3.2 Virtual testbed [20]


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References

[1] "International Standard ISO 31000 Risk management - principles and guidelines," 2009.

[2] M. Istad, E. Solvang, A. Verdal and M. Renstrom, "Status and further work - Results from WP1 in the SAMBA-project," Statnett, Oslo, 2016.

[3] M. Istad, J. Foros, E. Tveten, M. Kolstad, E. Solvang, C. Langeland, E. Viberg, S. Hagner and J.-L. Coullon, "Use case collection," 2017.

[4] M. Ukkelberg, "Utilisation of Machine Learning in Power Transformer Asset Management," 2018. [Online]. Available: https://brage.bibsys.no/xmlui/handle/11250/2559715.

[5] M. Istad, "SINTEFblogg," [Online]. Available: https://blogg.sintef.no/sintefenergy-nb/elkraftkomponenter/sommerforskere-pa-tur/.

[6] "ISO Guide 73 - Risk management vocabulary," 2009.

[7] "http://www.nist.gov/itl/bigdata/bigdatainfo.cfm," NIST BIG DATA INTEROPERABILITY FRAMEWORK: VOLUME 1, DEFINITIONS. [Online].

[8] "Team Data Science Process lifecycle," Microsoft, [Online]. Available: https://docs.microsoft.com/ en-us/azure/machine-learning/data-science-process-overview. [Accessed 27 June 2017].

[9] K. Murphy, Machine learning - A probablistic perspective, Massachusetts Institute of Technology, 2012.

[10] ABB, "http://www.abb.com/cawp/seitp202/e085d7d2e9a8eda6c1257f18002eb4d8.aspx," [Online].

[11] ABB, "http://www02.abb.com/global/noabb/noabb069.nsf!OpenDatabase&db=/global/noabb/ noabb071.nsf&v=3195A&e=no&url=/global/seitp/seitp202.nsf/0/14FC99CC8C51C456C12580F0003DC1A6!OpenDocument," [Online].

[12] "Asset Health Centre," [Online]. Available: http://new.abb.com/enterprise-software/asset-optimization-management/asset-health-center.

[13] Q. Qui, J. Fleeman, D. Ball, G. Rackliffe, J. Hou and L. Cheim, "Managing critical transmission infrastructure with advanced analytics and smart sensors," in IEEE Power & Energy Society General Meeting, Vancouver, BC, 2013.

[14] J. Coullon and N. Rego, "Flexible analytics for management of grid assets," in Saudi Arabia Smart Grid Conference (SASG), Jeddah, 2014.

[15] Transgrid, "https://www.transgrid.com.au/news-views/blog/default.aspx?Paged=TRUE&p_PublishedDate=20151216%2022%3A56%3A00&p_ID=73&PageFirstRow=15051&&View=%7BD6D3D2DE-448C-4FC9-9BD0-E77FB0BBAEB4%7D," [Online].

[16] M. Vermeer, J. Wetzer, P. v. d. Wielen, E. d. Haan and E. d. Meulemeester, "Asset-management decision-support modeling, using a health and risk model," in IEEE PowerTech , Eindhoven, 2015.


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[17] L. Hua, Z. JiRan and T. Haiguo, "Risk assessment for distribution systems based on key performance indicator," in IEEE 3rd Information Technology and Mechatronics Engineering Conference (ITOEC) , Chongqing, 2017.

[18] M. Anzola and C. Vila, "THE RELEVANCE OF QUALITY DATA MANAGEMENT FOR CONDITION BASED RISK MANAGEMENT," in CIRED Workshop, Helsinki, 2016.

[19] CEN/CENELEC/ETSI, Smart Grids Coordination Group Reference Architecture for the Smart Grid, 2012.

[20] M. Schluse and J. Rossmann, "From simulation to experimental digital twins - Simultaion-based development and operation of complex technical systems," in IEEE International Symposium on Systems Engineering (ISSE), Edinburgh, 2016.

[21] "PAS 55-1 Asset management Part 1: Specification for the optimized management of physical assets," 2008.

[22] S. R. Mohapatra and S. Mukhopadhyay, "Risk and asset management of transmission system in a reformed power sector," in IEEE Power India Conference , New Delhi, 2006.


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samba wp 6 report - statnett · this report is a result from wp6 "risk monitoring centre"...

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