Robust Online Dynamic Security Assessment Using Adaptive Ensemble Decision-Tree Learning

He, Miao; Zhang, Junshan; Vittal, Vijay

doi:10.1109/tpwrs.2013.2266617

Cited by 177 publications

(84 citation statements)

References 25 publications

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“…The measurements collected from a wide area of power system using phasor measurement unit (PMU) synchronized by the global positioning system (GPS) open up the possibilities to use data mining as predictive tools to provide transient stability information and support close-to-real-time decision making. Different classification techniques such as decision tree (DT) [1]- [7], ensemble decision tree (EDT) [8], [9], support vector machine (SVM) [10]- [12] and artificial neural network (ANN) [13]- [15] have been applied. Typically, the prediction focuses on whether the system remains stable or goes unstable after the clearance of transient disturbance.…”

Section: Introductionmentioning

confidence: 99%

Online Identification of Power System Dynamic Signature Using PMU Measurements and Data Mining

Guo

Milanović

2016

IEEE Trans. Power Syst.

136

135

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This paper proposes a two-stage methodology for online identification of power system dynamic signature using phasor measurement unit (PMU) measurements and data mining. Only transient stability status is usually predicted in the literature to assist with corrective control, without the dynamic behavior of generators in the event of instability. This paper uses traditional binary classification to identify transient stability in the first stage, and then develops a novel methodology to predict the nature of unstable dynamic behavior in the second stage. The method firstly applies hierarchical clustering to define patterns of unstable dynamic be- havior of generators, and then applies different multiclass classification techniques, including decision tree, ensemble decision tree and multiclass support vector machine to identify characterized unstable responses. The proposed methodology is demonstrated on a multi-area transmission test system. High prediction accuracy at both stages of identification is demonstrated.Index Terms-Corrective control, data mining, decision tree, ensemble, hierarchical clustering, phasor measurement units, power system dynamic signature, support vector machine, transient stability.Tingyan Guo (S'11) received the B.Eng degree in electrical and electronic engineering from the University of Manchester, Manchester, U.K., in 2011. She is currently working towards the Ph.D. degree at the same institution. Jovica V. Milanović (M'95-SM'98-F'10) received the Dipl.Ing. and M.Sc. degrees from

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Section: Introductionmentioning

confidence: 99%

Online Identification of Power System Dynamic Signature Using PMU Measurements and Data Mining

Guo

Milanović

2016

IEEE Trans. Power Syst.

136

135

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“…Another reason for lack of progress in this direction was due to the fact that analytical energy functions for multi-machine power system models with detailed device models cannot be derived [13]. Further, the assessment accuracy obtained using the direct method of stability assessment using simplified power system models along with simple methods of critical energy computation (Potential Energy Boundary Surface (PEBS) [14]) is insufficient for DSA The applicability of machine learning for transient stability assessment has been investigated in literature for DSA and for post disturbance transient stability assessment and has shown promising results in recent literature [15][16][17][18][19][20][21]. The approaches presented in [15][16][17][18][19][20] use databases generated off-line aiming to cover all possible operating conditions and topology changes.…”

Section: Motivationmentioning

confidence: 99%

“…On the other hand, producing a single learned network to address the entire set of operating conditions of a power system is most unlikely to succeed. The recently proposed approach in [21] recognizes the significance of frequently updating the classification model and has made provisions to revise the decision tree in use.…”

Section: Motivationmentioning

confidence: 99%

“…However, it is challenging to determine if an initially trained network is valid to assess the stability of a particular system state once the system state is evolved to a different operating state or a different network topology. The recently proposed approach in [21] recognizes significance of frequently updating the classification model and has made provisions to revise the decision tree in use.…”

Section: Machine Learning Techniquesmentioning

confidence: 99%

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Application of energy-based power system features for dynamic security assessment

Geeganage

Annakkage

Weekes

et al. 2015

2015 IEEE Power &Amp; Energy Society General Meeting

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To date, the potential of on-line Dynamic Security Assessment (DSA)-to monitor, alert, and enhance system security-is constrained by the longer computational cycle time. Traditional techniques requiring extensive numerical computations makes it challenging to complete the assessment within an acceptable time. Longer computational cycles produce obsolete security assessment results as the system operating point evolves continuously. This thesis presents a DSA algorithm, based on Transient Energy Function (TEF) method and machine learning, to enable frequent computational cycles in on-line DSA of power systems.The use of selected terms of the TEF as pre-processed input features for machine learning demonstrated the ability to successfully train a contingency-independent classifier that is capable of classifying stable and unstable operating points. The network is trained for current system topology and loading conditions. The classifier can be trained using a small dataset when the TEF terms are used as input features. The prediction accuracy of the proposed scheme was tested under the balanced and unbalanced faults with the presence of voltage sensitive and dynamic loads for different operating points. The test results demonstrate the potential of using the proposed technique for power system on-line DSA. Power system devices such as HVDC and FACTS can be included in the algorithm by incorporating the effective terms of a corresponding TEF.An on-line DSA system requires the integration of several functional components.Some of these components already exist and the others are newly introduced to the i power system. The practicality of the proposed technique in terms of a) critical data communications aspects b) computational hardware requirements; and c) capabilities and limitations of the tools in use was tested using an implementation of an on-line DSA system. The test power system model was simulated using a real-time digital simulator. The other functional units were distributed over the Local Area Network (LAN). The implementation indicated that an acceptable computational cycle time can be achieved using the proposed method.In addition, the work carried out during this thesis has produced two tools that can be used for a) web-based automated data generation for power system studies; and b) testing of on-line DSA algorithms.ii

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“…Power systems' operators are required to continuously monitor the security of power systems for a probable set of contingency and be prepared to take appropriate preventive and emergency control measures if need arises. There are several established methods and software tools to evaluate transient security of power system for a probable set of contingency [1][2][3][4][5]. During normal operation if an operating state is found to be insecure, preventive control is initiated to bring the system back to normal operating state.…”

Section: Introductionmentioning

confidence: 99%

Preventive and Emergency Control of Power System for Transient Stability Enhancement

Siddiqui¹,

Verma²,

Niazi³

et al. 2015

Journal of Electrical Engineering and Technology

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-This paper presents preventive and emergency control measures for on line transient stability (security) enhancement. For insecure operating state, generation rescheduling based on a real power generation shift factor (RPGSF) is proposed as a preventive control measure to bring the system back to secure operating state. For emergency operating state, two emergency control strategies namely generator shedding and load shedding have been developed. The proposed emergency control strategies are based on voltage magnitudes and rotor trajectories data available through Phasor Measurement Units (PMUs) installed in the systems. The effectiveness of the proposed approach has been investigated on IEEE-39 bus test system under different contingency and fault conditions and application results are presented.

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Robust Online Dynamic Security Assessment Using Adaptive Ensemble Decision-Tree Learning

Cited by 177 publications

References 25 publications

Online Identification of Power System Dynamic Signature Using PMU Measurements and Data Mining

Online Identification of Power System Dynamic Signature Using PMU Measurements and Data Mining

Application of energy-based power system features for dynamic security assessment

Preventive and Emergency Control of Power System for Transient Stability Enhancement

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