A Random Forest-Based Approach for Voltage Security Monitoring in a Power System Michael Negnevitsky
Nikita Tomin, Victor Kurbatsky,
Christian Rehtanz,
The School of Engineering and leT University of Tasmania Hobart, Australia
[email protected]
Daniil Panasetsky, Alexey Zhukov
Inst. of Energy Systems, Energy Efficiency and Energy Economics, TU Dortmund University, Dortmund, Germany
[email protected]
Power System Department Melentiev Energy Systems Institute Irkutsk, Russia
[email protected]
Abstract- Voltage collapse is a critical problem that impacts power
system
operational
security.
Timely
and
accurate
assessment of voltage security is necessary to detect alarm states in order to prevent a large-scale blackout. This paper presents an on-line voltage security assessment scheme using periodically updated random forest-based decision trees. We demonstrated the proposed method on the modified 53-bus IEEE power system. Results are presented and discussed.
Index Terms-blackout, voltage instability, security monitoring, machine learning, random forest
I.
INTRODUCTION
The main cause of a great majority of recent large-scale blackouts was voltage instability resulting in a sudden voltage collapse [1], [2]. A typical voltage collapse scenario may begin with a gradual voltage drop in one or several areas due to, for example, high air conditioning load in a hot summer day. This pushes transformer taps towards their maximum. Generators reach their reactive power limits. Then a generator or a transmission line may trip due to a fault. Since reactive power cannot be transmitted over long distances due to high losses, the sudden tripping of the transmission line results in the increased need for the local reactive power (MYAr) support. If this support is not available, the voltage drops rapidly and may cause collapse of the entire system. Depending on the severity of the situation, the time frame of voltage collapse may range from minutes to seconds. Thus, it may leave a sufficient time to apply appropriate emergency control actions, such as blocking the tap changers, shed some load and/or start up some fast units for local reactive power support. If such conditions are identified in time as pre emergency, preventive actions can be taken, and major emergency avoided. To determine voltage security criteria, the machine learning approach seems particularly well suited, as it can systematically screen a large set of events, in order to identity critical operating parameters and to determine security constraints for on-line operation [3]. The main advantage of the machine learning approach is that it uses parallel This work is funded by Russian Science Foundation (project No 14-1900054))
computation and can deliver results fast enough for on-line applications. Traditional approaches, on the other hand, are computationally expensive, which makes it difficult to use them for real-time security analysis of large-scale power networks [3, 4]. The decision tree technique is an effective data mining tool to solve the classification problems in high data dimensions such as voltage security evaluation in large-scale interconnected grids [5]. This paper focuses on voltage collapse problems caused by severe disturbances in the system and presents a decision tree-based scheme for on-line voltage security assessment using real-time measurements. II.
STATE-OF-THE-ART
Automatic emergency control systems are required to prevent cascading emergencies and blackouts. Unfortunately, in many cases, the current generation of these systems is ineffective and unreliable. Recent examples of large-scale blackouts in North America in 2003, Moscow in 2005, Europe in 2003 and 2006, and India in 2012 testity to this. Panasetsky et al [6] analyzed large-scale blackouts that occurred in 1965 -2014 in the power interconnections of different countries. The analysis made it possible to identity the general regularities of their development, which are expressed in some typical phases: pre-emergency state, initiating events, cascading development of emergency, final state and restoration. It was established that the main types of emergency disturbances that occur in the quick phase of development were a voltage collapse and a considerable overload of equipment. The analysis showed that in the phase of initiating events the above-standard disturbances occur. The post-emergency conditions that occur at the end of this phase are off-design for the existing emergency control devices and for the dispatching personnel. Therefore, the existing emergency control systems furnished with the up-to date automation means and the actions of System operator may prove ineffective to prevent the subsequent catastrophic development of the emergency.
The results of the several studies testify to the necessity of the development of next-generation intelligent systems to complement modem emergency control systems, taking into account its "weak points" [7]. Based on the above said, the specific requirements for such intelligent emergency control systems have been developed: the systems should l.
2. 3. 4.
5.
Have a tool for the intelligent monitoring and assessment of the power system operating conditions; Be capable to predict potentially dangerous states of the power system; Be highly resilient and able to coordinate local devices of the emergency control system. Have methods and models providing the protection of an electric power system with a complex structure. Complement the existing ideology of emergency control systems but not contradict it.
is based on the innovative multi-agent system theory application that leads to the achievement of several significant advantages such as control system efficiency enhancement, control system survivability and cyber security. III.
PROPOSED APPROACH
The power system security monitoring concept is based on the classification of a power system state. This paper applies the random forest (RF) learning model for voltage security assessment (Fig. 1). The security alarm system does three things: (1) classify the power system state, to determine how dangerous the state is for the security of the entire system; (2) identify emergency conditions that may lead to large-scale emergencies and blackouts; (3) ranks critical power components, to locate places in a power system where preventive actions should be taken.
''';0;0,
Several studies identified voltage instability and cascading overload are as the major incidents in the progression of blackouts [8]-[10]. The leading idea is that voltage instability following a contingency generally does not develop as fast as the transient one (typically voltage collapse takes several minutes whereas electromechanical loss of synchronism takes only a few seconds); this gives time to detect the potentially critical states after the contingency occurrence and take preventive (corrections) actions, such as blocking the tap changers, shed some load andlor start up some fast units for local reactive power support. If such conditions are identified in time as pre-emergency, preventive actions can be taken, and major emergency avoided. Several approaches have been proposed for voltage security monitoring and assessment. M. La Scala et al [11] proposed a layered feedforward neural network-based method for monitoring on-line voltage security of power systems. Using a dynamic model of the system, voltage stability is measured totally, considering a suitable stability index, and locally, by defming appropriate voltage-margins. H. Mori et al [12] introduced a method for voltage instability monitoring in power systems with hybrid ANNs. A three layered perceptron is used to estimate indices for voltage stability while the Kohonen network is utilized to understand the trajectory of power system states in terms of power system security. H. Khoshkhoo et al [13] proposed a decision tree-based method to assess the small disturbance voltage stability of power systems by only using synchrophasors measured by phasor measurement units (PMUs). The simulation results demonstrate that this method effectively predicts voltage instability during the first two seconds just after the disturbance for the test system. V. Vittal et al [5] presented an online voltage security assessment scheme using PMUs and periodically updated decision trees. The proposed tree-based model is trained off line using the detailed voltage security analysis conducted and updated every hour by including newly predicted system conditions for robustness improvement. Arkhipov et al [14] presented a research of multi-agent voltage and reactive power control system development. The control system architecture
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Random forest ,------------------------,
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:Tree :
TeSIIn
Tree Dataset
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Figure I.
'
5
Random forest structure for voltage security monitoring (adopted from [15]).
A. Random Forest Models The RF model is based on a collection of tree-structured classifiers {h(x, 19k)} where {19k} are independent, identically distributed random vectors, and each tree casts a unit vote for the most popular class at input x [16]. In standard trees, each node is split using the best split among all variables. In RF, each node is split using the best one among a subset of predictors randomly chosen at that node. This somewhat counterintuitive strategy turns out to perform very well compared to many other classifiers, including discriminant analysis, support vector machines and neural networks, and is robust against overfitting. Following [16], every binary decision tree is separately represented by a tree structure T, from an input vector (Xv ... ,Xp) taking its values in (Xl * ... * Xp X) to an output variable y E Y. Every certain node t represents a subset of the space X, with the root node being X itself. Construction of decision trees usually works top-down, by choosing a variable at each step that splits the set of items by the binary (Xm < c). The internal node t divides its subset into test St two subsets1 corresponding to two children nodes tLand tR. For a new instance the predicted value Y is the label of the leaf reached by the instance when it is propagated through the tree. Algorithms for constructing decision trees identify at each =
=
node t the split St s* for which the partition of the Nt node samples into tLand tRmaximizes the decrease. =
(1) for the specified impurity measure i(t) (such as Gini Index, the Shannon entropy). With regard to variable importance in Random Forests, the authors of [16] proposed adding the weighted impurity (loss) decreases p (t) i(st, t) for all nodes t where Xm is used, averaged over all Nt trees in the forest:
where Wi' Wz - weighting factors of system security; LOI the line overload index; VDI - voltage deviation index; nL, nB represent the number of lines, buses respectively. The SI is defined by calculating VDI and LOI as given by following expression, respectively:
if Skm
luk"inl-Iukl I uk"in I
(2) and where p{t) is the proportion Nt of samples reaching t and N VeSt) is a variable which is used in split St. When a training set for the current tree is drawn by sampling with replacement, about one-third of the cases are left out of the sample. This OOB (out-of-bag) data is used as a test set to get an unbiased estimate of the classification error. Therefore, there is no need for cross-validation or a separate test set to get an unbiased estimate of the test set error. B.
The idea of unsupervised random forests is that real data points that are similar to one another will frequently end up in the same terminal node of the tree, which is exacting at measured by the proximity matrix that can be returned. Thus the proximity matrix can be taken as a similarity measure, and clustering or multi-dimensional scaling using this similarity can be applied to divide the original data points into groups for visual exploration. C.
VDlk
Voltage stability assessment method
The selection of a suitable voltage stability assessment method is the first step towards developing an online security index evaluation scheme. The global indicator, an online security index (S1) [17] describing the security level of the complete power system is given by
SI
=
Wi
"'i-l LOI+W, "'i-l VDI nL+nB ",Nl
",Nl
(3)
=
if if
0,
IU�inl
IUkl-luk"inl I uk"in I
IUkl S;
if
IUkl
IU�inl
IU�axl
(5)
IU�inl
where Skm and Sl m represents the MVA flow and MYA limit i of branch k-m; IUk!, IU�axl, IU�inl are the bus voltage magnitude, maximum voltage limit, minimum voltage limit, respectively. TABLE!
Variable importance procedure for crtitical power components ranking
The identification of critical power components for system security under emergency condition provides one important input to this decision-making. It has become more vital in view of the threat of voltage instability leading to voltage collapse. The decision tree techniques, which are the core of the random forests, allow us to rank the importance of variables. Such ranking procedure can be employed as a stand alone procedure combined with different regression models. We have found that the more important attributes produced by the ranking procedure of unsupervised random forests can be useful for locating the critical power components in power systems after a disturbance (Fig. 1). These attributes would typically be system measurements (e.g. voltage magnitudes, power flows, output signals for generators, stator currents). In the emergency state attributes would depend on the disturbance and further deterioration of the system state.
Slim if Skm S; Sl m i >
CLASS LABELS FOR VOLTAGE SECURITY ANALYSIS
N! I
Security index 51
=
0
Class category/Power system state
Normal state
0
