A Hierarchical Data-Driven Method for Event-Based Load Shedding Against Fault-Induced Delayed Voltage Recovery in Power Systems

Load shedding (LS) is an effective control strategy against voltage instability in power systems. With increasing uncertainties and complexity in modern power grids, there is a pressing need for faster and more accurate control decisions. In this article, a hierarchical data-driven method is proposed for the online prediction of event-based load shedding (ELS) against fault-induced delayed voltage recovery. The ELS problem is hierarchically modeled as a multi-output classification subproblem for identifying the best shedding location and a regression subproblem to predict the minimum shedding amount. To solve the two subproblems, the weighted kernel extreme learning machine is adopted to construct a direct mapping between the system pre-fault operating conditions and the corresponding control variables. The method is tested on the ELS database, which is analytically generated via a novel adaptive sensitivity-based process on the New England 39-bus system. Compared with other methods, the proposed method is very accurate in prediction with excellent control performance, which maintains superior prediction ability under an imbalanced data distribution.

[1]  Zhao Yang Dong,et al.  Real-time prediction of event-driven load shedding for frequency stability enhancement of power systems , 2012 .

[2]  Renke Huang,et al.  Adaptive Power System Emergency Control Using Deep Reinforcement Learning , 2019, IEEE Transactions on Smart Grid.

[3]  Mihai Anitescu,et al.  Graph Convolutional Neural Networks for Optimal Load Shedding under Line Contingency , 2019, 2019 IEEE Power & Energy Society General Meeting (PESGM).

[4]  Henry Wu Optimization and coordination of fault-driven load shedding and trajectory-driven load shedding , 2009 .

[5]  Kit Po Wong,et al.  Preventive Dynamic Security Control of Power Systems Based on Pattern Discovery Technique , 2012, IEEE Transactions on Power Systems.

[6]  Yiqiang Chen,et al.  Weighted extreme learning machine for imbalance learning , 2013, Neurocomputing.

[7]  Luís Torgo,et al.  A Survey of Predictive Modeling on Imbalanced Domains , 2016, ACM Comput. Surv..

[8]  D. Novosel,et al.  See It Fast to Keep Calm: Real-Time Voltage Control Under Stressed Conditions , 2012, IEEE Power and Energy Magazine.

[9]  David J. Hill,et al.  A Data-Based Learning and Control Method for Long-Term Voltage Stability , 2020, IEEE Transactions on Power Systems.

[10]  I. Hiskens,et al.  Power system applications of trajectory sensitivities , 2002, 2002 IEEE Power Engineering Society Winter Meeting. Conference Proceedings (Cat. No.02CH37309).

[11]  Yusheng Xue,et al.  An on-line pre-decision based transient stability control system for the Ertan power system , 2000, PowerCon 2000. 2000 International Conference on Power System Technology. Proceedings (Cat. No.00EX409).

[12]  W. Concepts of Undervoltage Load Shedding for Voltage Stability , 2004 .

[13]  Yuchen Zhang,et al.  A Hybrid Randomized Learning System for Temporal-Adaptive Voltage Stability Assessment of Power Systems , 2020, IEEE Transactions on Industrial Informatics.

[14]  C. Vournas,et al.  Short-term voltage instability: effects on synchronous and induction machines , 2006, IEEE Transactions on Power Systems.

[15]  Ruisheng Diao,et al.  Decision Tree-Based Preventive and Corrective Control Applications for Dynamic Security Enhancement in Power Systems , 2010, IEEE Transactions on Power Systems.

[16]  Chee Kheong Siew,et al.  Extreme learning machine: Theory and applications , 2006, Neurocomputing.

[17]  Luís Torgo,et al.  Utility-Based Regression , 2007, PKDD.

[18]  Yu Wang,et al.  Strategy to minimise the load shedding amount for voltage collapse prevention , 2011 .

[19]  Goran Strbac,et al.  Data-Driven Power System Operation: Exploring the Balance Between Cost and Risk , 2019, IEEE Transactions on Power Systems.

[20]  P. Kundur,et al.  Techniques for Emergency Control of Power Systems and Their Implementation , 1997 .

[21]  Stefan Arnborg,et al.  On undervoltage load shedding in power systems , 1997 .

[22]  Hongming Zhou,et al.  Extreme Learning Machine for Regression and Multiclass Classification , 2012, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[23]  Yutian Liu,et al.  Optimization of the Event-Driven Emergency Load-Shedding Considering Transient Security and Stability Constraints , 2017, IEEE Transactions on Power Systems.

[24]  Muhammad Naveed Aman,et al.  A critical review of the state‐of‐art schemes for under voltage load shedding , 2019, International Transactions on Electrical Energy Systems.

[25]  Yuchen Zhang,et al.  A Hierarchical Self-Adaptive Data-Analytics Method for Real-Time Power System Short-Term Voltage Stability Assessment , 2019, IEEE Transactions on Industrial Informatics.

[26]  Zhe Chen,et al.  A Systematic Approach for Dynamic Security Assessment and the Corresponding Preventive Control Scheme Based on Decision Trees , 2014, IEEE Transactions on Power Systems.

[27]  Yan Xu,et al.  Load shedding and Its strategies against frequency instability in power systems , 2012, 2012 IEEE Power and Energy Society General Meeting.

[28]  D. Chicco,et al.  The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation , 2020, BMC Genomics.

[29]  C.D. Vournas,et al.  Design Strategies for Load-Shedding Schemes Against Voltage Collapse in the Hellenic System , 2008, IEEE Transactions on Power Systems.