decision tree assisted controlled isla
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Decision Tree Assisted Controlled IslandingndingTRANSCRIPT
Decision Tree Assisted Controlled Islanding
Pohon keputusan untuk membantu kontrol Islanding
Abstrak - pengontrolan islanding mengacu pada kontrol pemisahan dari sistem
tenaga interkoneksi ke daerah elektrik yang terisolasi. Tujuan dari makalah ini
adalah untuk mengembangkan islanding yang dikendalikan secara adaptif sebagai
komponen sistem strategi kontrol daya darurat. Ada dua aspek utama
pengontrolan islanding : 1) di mana island dan 2) kapan menjadi island?. Dibantu
oleh pendekatan keputusan pohon (DT), makalah ini berusaha untuk mengatasi
aspek "ketika menjadi island". Sebuah alat berbasis pohon keputusan diusulkan
untuk mengenali kondisi yang ada dalam sistem yang menjamin kontrol islanding.
Sebuah sistem 29-generator, 179-bus digunakan untuk mendemonstrasikan tool
ini. Data simulasi digunakan untuk melatih DTs, dan kinerja online DTs
kemudian dievaluasi sebagai bagian dari strategi kontrol islanding.
Indeks istilah - Pengendalian islanding, pohon keputusan, pembelajaran mesin,
stabilitas sistem daya R-Rdot relay, self healing, sistem perlindungan khusus.
I. PENDAHULUAN
Ketersediaan biaya murah dan keandalan listrik dicapai melalui modem
sistem tenaga interkoneksi. Misalnya kegagalan Cascading terjadi karena
serangkaian peristiwa probabilitas yang rendah. Beberapa relay pelindung
konvensional yang dirancang untuk melindungi peralatan lokal dari kerusakan
selama gangguan tetapi tidak fokus pada menjaga kelangsungan seluruh sistem
pelayanan. Islanding tidak terkontrol mengacu pada proses dimana sistem listrik
interkoneksi dibagi ke dalam island - island yang tidak direncanakan sebagai
akibat dari gangguan yang parah [1]. Pemulihan sistem tidak dapat dijamin oleh
pembentukan island - island tersebut. Hal ini diinginkan untuk mengontrol
pembentukan island-island ini, dengan memuat dampak gangguan pada wilayah
tertentu dari jaringan sedini mungkin Pengendalian islanding disajikan dalam [2],
sebagai strategi untuk memisahkan gangguan besat sistem daya dalam self-
healing island yang ditandai karekteristik dengan minimal ketidakseimbangan
beban generator dan generator slowly-coherent. Dalam strategi ini, khusus R-Rdot
relay [3] yang terletak di saluran minimum cut-set antara island’s yang
direncanakan dan dilengkapi untuk merespon variasi lokal di saluran reisitansi
semu. Relay utama R-Rdot, ketika dilengkapi, merespon gangguan pengaturan
resistensi saluran semu, dan dengan demikian dapat menciptakan Islands yang
direncanakan oleh cross tripping saluran kritis lainnya. Pengendalian islanding
seperti yang dijelaskan dalam [2] memiliki dua aspek utama yang akan diteliti: 1)
di mana island dan 2) kapan menjadi island?. Pendekatan [2] dikembangkan lebih
lanjut dalam makalah ini dengan mengusulkan pohon keputusan (DT) alat untuk
membantu kontrol islanding yang berusaha untuk mengatasi "kapan" aspek
pengontrolan islanding. Pengontrolan islanding akan dimulai ketika hanya
keputusan ketidakstabilan global didukung oleh indikator stabilitas sistem lokal.
A wide-area measurement system (WAMS) [1] can lead to increased situational
awareness, and improved observability and controllability. In the context of
WAMS, it is useful to mention the Emergency SIME method [4] that starts acting
after the disturbance inception, to prevent loss of synchronism. The system status
is 'predicted' by decomposing the multi-machine dynamics into candidate single-
machine-equivalents and further analyzing their power angle curves. Taking a
machine learning approach, this paper proposes an online transient stability as-
sessment (TSA) tool to detect when controlled islanding is warranted in a
disturbed power system. This tool can arm/inhibit strategically located
R-Rdot/out-of-step relays' response to the local dynamics. The goal of such
controlled islanding is to obtain self-sustaining islands that have potential for
swift recovery. This strategy is part of the last line of defense against a potential
system blackout. Controlled islanding contains the impact of the disturbance
within the islands created. Subsequent restorative control actions (generator run-
back in generation rich islands and load shedding in load rich islands) are required
to improve frequency and voltage stability. Once all the islands have recovered
from the initial disturbance, the tie-lines can be brought back into service in a
controlled manner, leading to total system recovery. In effect, the proposed
approach to intelligent Islanding is a progression from WAMS to WACS (wide
area control system, [5]).
Sebuah sistem pengukuran wilayah yang luas (WAMS) [1] dapat menyebabkan peningkatan kesadaran situasional, dan meningkatkan cara pengamatan dan cara pengendalian pengendalian. Dalam konteks WAMS, hal ini berguna untuk menyebutkan metode SIME Darurat [4] yang mulai bertindak setelah awal gangguan, untuk mencegah hilangnya sinkron. Status sistem 'diprediksi' dengan menguraikan dinamika multi-mesin ke calon tunggal-mesin-setara dan selanjutnya melakukan analisis kurva sudut daya mereka.
The paper is divided into eight sections. Section II introduces controlled
islanding as an emergency control strategy for severely disturbed power systems.
Some of the previous research on the application of DTs in power systems is
introduced in Section III Sections IV and V examine the preliminary issues
associated with the application of DTs to predict synchronous instability in a
system. The islanding tool is demonstrated on a large power system in Sections
VI (offline training) and VII (online performance). The intention is to provide a
proof of concept only. Actual implementation of this idea on realistic systems is
expected to raise other Issues such as communication delays and measurement
uncertainty that are beyond the scope of this paper. Section VIII provides the
conclusions.
II. CONTROLLED ISLANDING USING DECISION TREES
Angular stability is a classic power system stability problem, and there exist many
effective techniques to assess and insure it. Decentralized control responding to
local variations of system parameters is a first choice for power system protection
engineers, for reasons of low associated costs and simplicity of implementation.
Additional reliability requirements call for mul-
Uncontrolled islanding is a spontaneous phenomenon and typically starts from
the electrical centers in a power system [7]. One of the primary objectives of
controlled islanding is to block some electrical centers and initiate islanding at
alternative locations. The alternative locations correspond to the minimum cut-set tie
lines between the desired islands for a particular scenario. In this paper, slow
coherency between generators is used as an aggregation criterion, (along with
minimum load-generation unbalance) to form all the possible planned islands.
Slowly coherent machine groups are characterized by their tight coupling, while the
interconnections between such groups are relatively weaker [2]. If the weaker
connections are opened before the perturbation can travel from one part of the
system to another, the disturbance may be effectively contained within a limited
region.
A critical component of any special protection system for adaptive controlled
islanding is an online transient stability assessment tool. In this context, it is
proposed to evaluate the power system online to predict system separation with the
help of machine angle data (Fig. 1). DTs for the system are generated in the form of
offline precalculated logical paths. These DTs serve as a statistical alternative to the
deterministic system models, If a large number of contingencies are analyzed, and
DTs generated offline, these trees can be quickly accessed in real time for
prediction. With the help of sensory data, the condition of an evolving system can
then be traced on the branches of a DT and rapid response, if warranted, may be
initiated [8],
HI. DECISION TREES BASED SECURITY ASSESSMENT
In this section, the application of DTs to system security assessment is reviewed.
Production grade artificial intelligence tools have not been applied to the field of
online TS A [9]. However, considerable research has been done in applying machine
learning based classifiers to stability assessment in power systems [10]-[15]. The
application of DTs to stability assessment in power systems was given in [11], in
which a set of decision rules was built offline for transient stability assessment. The
application for an inductive inference method to obtain the set of decision rules for a
system is given in [11], [16] and an analytical basis for splitting the nodes of a tree,
information measures and attribute selection during training are discussed.
Initially, DTs were suggested for preventive transient stability assessment. In other
words, given the steady state operating point of the system, the critical clearing
time for any fault in the system was to be determined. In [13], a detailed case study
on the French EHV system was presented in which randomized training database
generation was introduced to stress the need for richness of the training samples,
Pre-contingency parameters were employed as attributes, in this study to determine
the transient behavior of the system. In [17] and [18], DTs were used for transient
stability prediction. Training samples were derived from simulations of disturbed
power system. Rotor angles and velocities, obtained from a time window
immediately after a fault was cleared, were used to predict the angular stability of
the generator. This paper adopts a similar approach of using generator angles and
velocities as predictors. However, [17] did not examine the impact of the spacing of
the training data points within a simulation on its prediction capability. DTs were
also used to predict the loss of synchronism in the Pacific AC Intertie (PACI), given
the variation of the apparent line resistance in the center of the line [19]. DTs have
also been proposed to set the decision boundaries in the R-Rdot space for loss of
synchronism on the PACI [20].
IV. DECISION TREES—APPLICATION ISSUES
A complete description of the theory of DTs appears in [16], [21 ]-[24], The goal in
any supervised machine learning problem is the following: Given a set of learning
examples consisting of input-output pairs, is there an underlying cause-effect rela-
tionship? Can this relationship be used further to predict the output(s) in the
presence of unseen inputs? The inputs to the problem are called predictors, while
the output in the case of a DT classifier is symbolic. The DTs used in this study are
applied as a special protection system for dynamic security. Angular instability
manifests itself in the electromechanical states of the system. It was also found from
simulations that the transient stability behavior of a power system was best
characterized by generator angles and velocities. Generator angles and velocities
were also found to be sufficient to understand the degree to which a stable machine
is disturbed. Therefore, the DT predictors were selected to be the generator angles
and rotor velocities of a severely disturbed power system, and the output is the de-
cision whether the system is potentially stable or unstable.
A DT is a classifier in high dimensions. Each internal node in the tree tests the value
of a predictor while each branch of the tree represents the outcome of a test. The
terminating nodes, also referred to as leaf nodes, represent a classification. The
number of predictors, used in the classification problem, indicates the dimension of
the problem. Associated with each decision (leaf) of the tree is the confidence of the
decision. This is simply a measure of the ratio of the particular class to all the classes
present in the dataset for that node. The main strength of a DT is its readability, as
shown in Fig. 2. Such a tree may be interpreted as a series of if-then statements that
use the values of the predictors to arrive at a classification. This indicates the
relevant predictors for a particular class of the output. To classify the training data
(into potentially stable and unstable cases), it is necessary to apply a sequence of
rules to split the dataset. One splitting
criterion, referred to as the chi-square automatic interaction detector (CHAID)
algorithm [25], utilizes the chi-square test to evaluate all possible ways of splitting
a dataset. An alternative tree building algorithm is the CART algorithm
(classification and regression trees) [26].
In the application of the DT to predict the final state of an evolving power system
in real time, the measures of success are slightly different. It is expected that a DT
will ultimately recognize (classify) an unstable generator swing curve as unstable.
How soon it performs the classification, is a measure of its success. It is stressed here
that the DT by itself does not physically island the system. Strategically located R-
Rdot/out-of-step relays are armed by the DT, to trip independently in response to
local sensory signals. The relay locations correspond to the weak links connecting
groups of coherent machines. A disturbance takes time to reach these cut-set lines,
and the DT must arm/disarm the relay within this time window. A late recognition
of potentially unstable swing curves is referred to as & false dismissal. A false alarm
means that a stable swing curve is mis-classified as unstable. An exacerbation of
the critical state of a dynamically evolving system can occur only if a false alarm
of the DT is followed by a false tripping of the corresponding R-Rdot/out-of-step
relay,
V. DECISION TREES—TRANSIENT INSTABILITY PREDICTION
In experiments on a sample six-machine system, key issues about the training of
DTs were determined. The objective of these experiments was to examine the
importance of the composition of the training data on the online performance of the
DTs. The absolute angles of all six machines and their angular velocities were used
as predictors. The transient stability simulations on the system were carried out
using the PowerTech software TSAT 5.0 [27]. All the DTs in this paper were built
using the Enterprise Miner 5.2 module contained in the statistical analysis software,
statistical analysis system (SAS) [28], The splitting criterion used by the SAS
Enterprise Miner is the chi-square test. Three-phase bolted faults (of different clearing
times) were simulated on all the buses of the power system. Once a simulation was
complete, it was classified as stable or unstable. From each simulation, n data points
are selected for training. The distribution of the data points within the time window of
simulation was found to be critical, affecting the predictability of the DT. Two
options were considered for the distribution of the points—exponential and equal
spacing. In exponential spacing, the points
are exponentially spaced, with most of the points located at the start of the window
(Fig. 3). Thus, majority of the training data consists of rotor angles and velocities of
small magnitude. The alternative is to space the points equally within the simulation
time window.
Some conclusions were drawn from the tree building exercises using a sample six-
machine test system. When the training data were obtained by simulating three-phase
faults uniformly on all the buses of the system, the DTs obtained can be considered
unbiased with respect to any particular bus/region of the system. Such a globally
trained DT, while a good indicator of the overall stability of the complete system did
not respond satisfactorily to events in a sensitive portion of the system. DTs focused
on a particular area/bus or kind of event in the power system would better
complement the response of such a globally trained unbiased tree. Further, it was
confirmed that the more the training data, the higher is the general confidence level
of the tree. Enriching the training data by including disturbed (yet stable) machine
angles also decreased the occurrence of false alarms. The distribution of the training
data within the simulation time window also has a significant effect on the response
of the tree. A DT trained on data that is exponentially chosen towards the start of the
simulation window gives an earlier prediction than a tree trained on data spaced
equally within the simulation time window. This is because the trained tree classifies
an evolving power system, while its individual machine angles are small in
magnitude. Finally, the criteria for controlled islanding must be defined clearly.
Controlled islanding is not proposed as the answer to all instability problems in the
system. For instance, controlled islanding need not be initiated for plant mode
instability. From numerous simulations on the test beds, it was observed that slow
coherent generators tended to separate from the rest of the system in well defined and
consistent groups. Each critical group of machines separating from the system cor-
responds to a particular mode of instability. The DT must be trained accordingly to
recognize only those modes of instabilities that are likely to result in uncontrolled
system separation.
VL DECISION TREE TRAINING DEMONSTRATION
A 29-machine, 179-bus test system is employed to demonstrate the DT based
controlled islanding strategy. There are two aspects of the illustration.
• Offline processing of simulation data for training the DT.
• Online performance of the trained DT.
The test bed system, shown in Fig. 4, consists of 29 machines modeled in detail
along with their turbines and exciters, 179 buses and three HVDC lines, modeled as
load injections at the appropriate buses. The system is loosely based and inspired by
the WECC system in the western United States. Possible island boundaries (shown
in Fig, 4) were calculated using slow coherency information along with the
minimum load generation unbalance obtained from the base case. Some of the
relevant buses and test cases are indicated in Fig. 4, for clarity of the
demonstration. The objective is to create DTs for this system, which can detect an
impending loss of synchronism among generators with the potential to cause
systemwide failure.
The 179-bus system at base case loading was subjected to three phase bolted
faults at all 179 buses (one at a time), with the fault clearing time ranging from
0.05 to 0.29 s (total 4550 simulations). The disturbed system was simulated for 5
s, at the end of which angular stability in the system was investigated. Conventional
protection was not represented within the simulations, and the possibility of outages
dependent on initiating disturbances was not examined. The simulation was labeled
as stable if all the machines remain in synchronism (3986 simulations). The 3986
stable simulations were further examined and classified into "disturbed stable"
simulations and "extremely stable" simulations. The definition of "extremely stable"
is a case in which at least 80% of the generators remain (qualitatively) relatively
undisturbed. This is a fuzzy definition based on numerous tests using the test
system. A relatively
undisturbed dynamic response of a generator rotor angle is of the order of
approximately 5° or less. The term "disturbed stable" refers qualitatively to the
stable cases that do not qualify as "extremely stable". There are 2743 "extremely
stable" simulations. In the remaining 1243 "disturbed" simulations, the majority of
the machines exhibit oscillations, while remaining in synchronism. The objective is
to train the tree on a wide variety of stable simulations. Fig. 5 contrasts "disturbed
stable" and "extremely stable" cases.
If isolated groups of machines exhibited loss of synchronism (i.e., plant mode
instability), the simulation was discarded (475 simulations). If the system
spontaneously separated into clearly distinguishable groups of generators which
have previously been identified as coherent, it was labeled as unstable, i.e., fit for
islanding (89 simulations). All training data points obtained from these 89
simulations were labeled as unstable when a global DT was being trained The 89
unstable
simulations were further examined to see which islands were separating. Accord-
ingly, they were labeled with respect to the island separating from the system. It was
seen that in 42 (out of 89) simulations, the system separated into three islands
(East, South, North). In 44 (out of 89) simulations, only the East island separated
from the rest of the system. The South island was observed to separate in only
simulation, while in the remaining two simulations, the North island separates. The
structure of the training data set is given in Fig. 6. When a DT for a particular
island was being trained, the unstable training data were obtained from
simulations where the particular island was observed to separate. For instance,
when a DT for the South island was being trained, data points labeled unstable are
obtained from 43 simulations (42 4-1).
During each simulation, the absolute rotor angles for the 29 machines present in the
system were collected. The rotor angle
velocity for each machine was then approximated using a backward difference
approximation
where UJ is rotor angle velocity of the generator, 6 is absolute angle of the
generator, and At is sampling time interval. The sampling interval was chosen to be
one cycle (i.e., 1/60 s in a 60-Hz system). The larger the sampling time interval, At,
the greater will be the error in approximating the rotor angle velocity of the
generator. The absolute rotor angles along with the velocities of the machines were
used as predictors of a "target" variable, which can hold binary values of stable and
unstable. Thus, there were 58 predictors available to classify one target variable.
While this seemingly increases the dimension of the classification problem to 58, it
is advisable to supply the tree with a maximum number of predictors during the
training process. A trained DT relies on a relatively small number of predictors to
accomplish the task of classification effectively. Hence, the DT achieves economy
of measurement in the sense that it filters out the irrelevant predictors
(measurements).
Table I summarizes the ten DTs trained for this network, There were three DTs
trained for each island {North, South, East), in addition to a global DT, that predicts
the formation of any/all of the three islands as a result the disturbance. The
training data composition for each tree is given by the ratio of stable data points to
unstable ones. Reducing the amount of unstable training data results in delayed
prediction of truly unstable contingencies. The stable points in the training data for
all trees, except E9(Lsld2, N9(Lsld2, and S90_sld2 were derived from the
'disturbed stable' simulations. For E90_sld2, N9G_s1d2, and S90_sld2 trees, the
stable data points are obtained from both "extremely stable" and "disturbed
stable" simulations. Each DT was thus trained with a different composition of data,
to compare their classification performance. It was observed that the DTs relied on
angular velocities more than the absolute angles, to accomplish early prediction of
the system tendency to spontaneous islanding. The predictors used by the trained
tree to classify new observations are also tabulated in Table I.
VII. ONLINE PERFORMANCE AND ROBUSTNESS
In this section, the online performance of DTs for the 29 generator test system is
presented. The test system was subjected to various contingencies, and the ability
of the DTs to predict the need for controlled islanding with accuracy (minimum fa he
alarms) and foresight (minimum false dismissals) was examined. The DTs were
trained from simulations carried out on the test system at base case loading. Further,
the pre-fauk s; topology was not changed during the training period. To determine
the robustness of the trained DT, the test system wa jected to a wide variety of
disturbances, not "seen" by the DT during training. When the DT based islanding
warning mechanism declared a particular contingency7 unstable and in need for
"islanding in a particular configuration", the R-Rdot/out-of-step relays on relevant tie
lines were armed. Local variations of the apparent line resistance caused the R-
Rdot/out-of-step relays to trip, creating the desired islands. The relevant tie lines to
create the desired islands (and hence the locations of the relays) are lines 99-84 and
7-28 for the East island, lines 99-84, 119-132 and 119-134 for the North island, and
lines 119-132, 119-134 and 7-28 for the South island. The setting of the R-Rdot/out-
of-step relays is not the focus of this paper; hence the physical tripping of tie lines to
create the islands was done arbitrarily. Conventional protection was not considered
in the simulations, and outages dependent on an initiating event were not studied.
Additionally, transfer tripping was been modeled, and all the tie-line tripping was
done in response to variations in the apparent line resistance.
A wide variety of contingencies (not seen by the DT during its training) was
simulated on the indicated test system. Two cases are selected as illustrations of the
robustness of the DT.
• Case I: Single circuit from bus 7 to bus 28 tripped.
• Case II: HVDC tie line from bus 60 to bus 71 tripped. Case I: In this case, the
test system at base case loading
was subjected to a line tripping between bus 7 and bus 28. The system was simulated,
and it was observed that this event caused global instability and the East island starts
to separate from the system after 2.2 s. The ability of the DTs to predict this sepa-
ration was investigated. From Table I it can be seen that three DTs focus on
predicting the separation of the East island—E50, E90, and E90_sld2. In addition,
A50 focuses on the potential separation of the system into any islanding
configuration. For this event all the four abovementioned trees predicted islanding by
giving a trip = 1 signal. The remaining trees expectedly gave a trip = 0 signal for
this event. In Fig. 7, the response of four DTs is shown. Further information about
the response of these DTs can be obtained from their confidence levels. Every
tirne the DT is given inputs, it gives a decision with a corresponding confidence
level The earliest islanding warning (at 0.4 s) came from E9G_sld2 tree and it
remained high for the whole duration of the simulation (i.e. up to 2.29 s) with a
confidence of 98.7% throughout. The latest warning came from the tree A50 at 0.66
s with a confidence of 53.5%. At 0.66 s, all four DTs gave a trip = 1 decision, and
the decision to arm the R-Rdot/out-of-step relays was taken at this juncture.
An interesting feature of the tree A50 was that although it gave a trip = 1 signal
at 0.66 s, the confidence of 'trip = 0' decision fell from 95.5% to 58.5% at 0.51 s. It
may be concluded that the tree A50 recognizes the formation of the East island with
low confidence. Tree E50 gave a trip = 1 decision at 0.5 s with 97.6%. The false
dismissal by tree E50 from 1.88 s to 2.20 s, was given with a confidence of 75%.
Tree E90 gave a trip = 1 decision at 0.41 s with 88.6%, which improved to 99.5% at
0.51 s. The false dismissal by this tree from 1.86 to 2.20 s was given with a
confidence of 52.9%.
From the above discussion, it is clear that the confidence of a particular decision
must also be monitored to interpret the decision correctly. A localized DT must give
its decisions with high confidence, to be acknowledged by the islanding warning
mechanism. A global DT, on the contrary, may show persistent low confidence
while recognizing localized events.
The R-Rdot/out-of-step relays placed on the relevant cut-set lines (line 99-84 in this
case) were armed at 0.66 s. The remaining R-Rdot/out-of-step relays on other lines
were disarmed to prevent the formation of the North and South islands. It was
observed that there is a clear change in line resistance between 1.5 and 2 s. The R-
Rdot/out-of-step relay is expected to interpret this change as an unstable swing in
line resistance, and accordingly, the line 99-84 was tripped at 1.8 s. This results in the
formation of East island, electrically isolated from the rest of the system. The
intention of islanding is to confine the disturbance to a particular island, as well as to
prevent the spread of the disturbances. From Fig. 8, it can be observed that the
generators 70, 43, and 118 (all outside the East island) remain in synchronism as a
result of the formation of East island. A significant improvement in the voltage profile
was observed on bus 39 (Fig. 9). Bus 39 is located in the southwest region of test
system (see Fig. 4). The thicker lines correspond to the voltage of the bus, when the
tie-lines were tripped to form the islands. The thinner lines correspond to the voltage
of the bus if controlled islanding had not been initiated.
Case II: In this case an HVDC de-line was tripped. One HVDC tie line in the test
system is modeled as load injections at the appropriate buses i.e. negative load at
Bus 60 and corresponding positive load at Bus 71. The total dc power flow (negative
load at Bus 60) is 2771 MW. The effect of tripping this line is to create a severe
contingency situation that can potentially lead to spontaneous loss of synchronism
between the machines in the system. It was observed that all the machines in the
system start to lose synchronism 1.5 s, after the HVDC tripping. From Fig. 10, the
earliest islanding warning (trip = 1) was obtained from E90_sld2 (at 0.699 s)
followed by N90_sld2 (at 0.719 s) and S90_sld2 (at 0.759 s). A50, which was
trained to predict any kind of islanding in the system, gave a trip = 1 decision at
1.23 s. The confidence level of E90_sld2 fell from 99.5% to 98.4% when it gave
its trip = 1 decision at 0.699 s. The confidence level of N90_sld2 fell from 100% to
98% when it gave a trip = 1 decision at 0.719 s. The confidence level of S90_sld2
fell from 99.8% to 99.6% when it gave a trip = 1
decision at 0.75 s. The confidence of A50 for a trip — 1 decision at 1.23 s was
97.7%. In general, all the DTs had a high confidence level for whatever decision
they gave, for this contingency. The lowest confidence level was shown by the E50
tree (99.3% to 92% when it changed decision from trip = 0 to trip = 1 at 1.159 s),
and E90 tree (98.8% to 92.8% when it changed decision from trip = 0 to trip = 1 at
1.21 s). Once the islanding warning was obtained from all the DTs, islanding was
initiated by arming the R-Rdot/out-of-step relays placed at the strategic tie lines:
99-84, 7-28,119-132, and 119-134. The first major disturbances were seen on line
99-84 at 1.5 s, followed by lines 119-132 and 119-134 at 2 s. Line 99-84 was
tripped at 1.5 s and lines 119-132 and 119-134 were tripped at 2.0 s. Line 7-28 was
tripped at 2.5 s.
As a result of the controlled islanding the effect of the disturbance was restricted, and
distant generators and buses were less affected. The effect of controlled islanding
may be observed from the voltage magnitude profile of bus 156 (located in the East
island) in Fig. 11. The thicker lines correspond to the voltage of the bus, when
controlled islanding was initiated. The thinner lines correspond to the voltage of the
bus in the absence of controlled islanding. Without the tripping of line 99-84 and 7-
28 to form the East island, the voltage on Bus 156 would have sagged to 0.5 p.u. after
1.8 s as a result of the HVDC tripping. In Fig. 12, it is evident that machines 148,40
and 144 located in the South island remain in synchronism as a result of controlled
islanding. The tripping of the HVDC line was, thus, prevented from affecting the
synchronous stability of generators located in the East and South islands. An
important point to be noted here is that due to the HVDC tripping, 2700 MW of
incoming power to the South island was disrupted. Therefore, South
island is expected to be load rich, and further load-shedding programs, beyond the
scope of this paper, must be initiated [2],
Vm. CONCLUSIONS
The contribution of this paper is the application of a tool to detect cascading
degradation of power system security at an early stage using machine learning via
DTs. The training process to obtain various kinds of DTs for a large interconnected
power system has been demonstrated. A 29-generator, 179-bus test system was used
to simulate various kinds of contingencies that lead to stable and unstable scenarios.
The training data extracted from each simulation were exponentially distributed in
time within the time horizon of the simulation. While each DT was supplied with the
rotor angles and velocities of all the 29 generators in the system as predictors, it was
found that the final trained DT was able to filter out many predictors as irrelevant to
its task of classification. Thus, a trained DT was able to discriminate between
relevant and irrelevant measurements. The DT relied primarily on rotor angle
velocities rather than the absolute rotor angles, for the task of early prediction of
impending instability. The simulation data were used to train ten differem kinds of
DTs offline. Some of the DTs were trained to predict pessimistically, by training
with "extremely stable*' simulation data. The result is early detection of truly unstable
contingencies and increased possibility of false alarms. Other DTs. in contrast, were
trained to be cautious and hence, their decisions were more "reliable". A combination
of a cautious global DT and a 'pessimistic* localized DT was used to rninimize the
possibility of false alarms as well as effectuate early and accurate islanding
decisions. Such a controlled islanding algorithm appears to exhibit robustness [29] in
the sense that all contingencies, seen and unseen during training were correctly
classified. The DTs developed in this paper, were trained on simulations on a
particular pre-fault operating point. As the operating point changes, a new set of DTs
may be required for robust security assessment. The rate of "refreshing" DTs would
ultimately depend on the sensitivity of the DTs to changes in operating conditions.
This kind of a special protection system is effective in particular interconnected
systems that show consistent slow coherency between groups of machines. The
islands formed as a result of this mechanism and approach, are expected to be self-
healing and healthy. This is desirable in the face of an alternative scenario of large
scale failure. The DT-based islanding tool, if implemented, would rely heavily on
wide-area phasor measurements, A key feature of the DT training process is the
resultant selectivity and discrimination between relevant and irrelevant
measurements. Thus, only important generator angles and rotor velocities are
required by the islanding tool to predict the need for controlled islanding. The
readability of DTs sheds more light on the contribution of different machines to
spontaneous system-wide separation. Discrimination between a global DT and a
localized DT is also especially relevant in the context of implementing this idea in a
large power system.