Hybrid Transient Energy Function-Based Real-Time Optimal Wide-Area Damping Controller

This paper presents a real-time wide-area damping controller design based on a hybrid intelligent and direct method to improve the power system transient stability. The algorithm applied as a nonlinear optimal wide-area damping controller monitors the oscillations in the system and optimally augments the local excitation system of the synchronous generators. First, energy functions and Prony analysis techniques are used to identify these local or interarea oscillations and develop stability or damping performance index at a given time. Second, artificial neural networks are deployed to learn the dynamics of the system and energy functions based on supervised learning to construct an optimal control design. Then, using online reinforcement learning the quadratic objective function based on the stability index is estimated and optimized forward-in-time. Results on the IEEE 68-bus in Power System Toolbox and HYPERSIM real-time simulator show better transient and damping response when compared to conventional schemes and local power system stabilizers.

[1]  Sukumar Kamalasadan,et al.  Design and Real-Time Implementation of Optimal Power System Wide-Area System-Centric Controller Based on Temporal Difference Learning , 2014, IEEE Transactions on Industry Applications.

[2]  Frank L. Lewis,et al.  Adaptive optimal control for continuous-time linear systems based on policy iteration , 2009, Autom..

[3]  Ahmad Afshar,et al.  Wide-area measurement-based fault tolerant control of power system during sensor failure , 2016 .

[4]  Visakan Kadirkamanathan,et al.  Functional Adaptive Control: An Intelligent Systems Approach , 2012 .

[5]  L.-A. Dessaint,et al.  Dynamic equivalent modeling of large power systems using structure preservation technique , 2006, IEEE Transactions on Power Systems.

[6]  Innocent Kamwa,et al.  Wide-area measurement based stabilizing control of large power systems-a decentralized/hierarchical approach , 2001 .

[7]  A.R. Messina,et al.  Online assessment and control of transient oscillations damping , 2004, IEEE Transactions on Power Systems.

[8]  Atena Darvishi,et al.  Threshold-Based Monitoring of Multiple Outages With PMU Measurements of Area Angle , 2016, IEEE Transactions on Power Systems.

[9]  Janusz Bialek,et al.  Decentralized stability-enhancing control of synchronous generator , 2000 .

[10]  Ali Heydari,et al.  Global optimality of approximate dynamic programming and its use in non-convex function minimization , 2014, Appl. Soft Comput..

[11]  Jianhua Zhang,et al.  Distributed Optimization Algorithms for Wide-Area Oscillation Monitoring in Power Systems Using Interregional PMU-PDC Architectures , 2015, IEEE Transactions on Smart Grid.

[12]  Felix F. Wu,et al.  A BCU method for direct analysis of power system transient stability , 1994 .

[13]  P. Kundur,et al.  Definition and classification of power system stability IEEE/CIGRE joint task force on stability terms and definitions , 2004, IEEE Transactions on Power Systems.

[14]  John N. Tsitsiklis,et al.  Neuro-dynamic programming: an overview , 1995, Proceedings of 1995 34th IEEE Conference on Decision and Control.

[15]  R. Grondin,et al.  Time-varying contingency screening for dynamic security assessment using intelligent-systems techniques , 2001, PICA 2001. Innovative Computing for Power - Electric Energy Meets the Market. 22nd IEEE Power Engineering Society. International Conference on Power Industry Computer Applications (Cat. No.01CH37195).

[16]  Adeniyi A. Babalola,et al.  Reinforcement learning approach for congestion management and cascading failure prevention with experimental application , 2016 .

[17]  Sukumar Kamalasadan,et al.  Hybrid energy function based real-time optimal wide-area transient stability controller for power system stability , 2015, 2015 IEEE Industry Applications Society Annual Meeting.

[18]  Janath Geeganage,et al.  Application of Energy-Based Power System Features for Dynamic Security Assessment , 2015, IEEE Transactions on Power Systems.

[19]  Christian Dufour,et al.  On the Use of Real-Time Simulation Technology in Smart Grid Research and Development , 2013, IEEE Transactions on Industry Applications.

[20]  Ramtin Hadidi,et al.  Reinforcement Learning Based Real-Time Wide-Area Stabilizing Control Agents to Enhance Power System Stability , 2013, IEEE Transactions on Smart Grid.

[21]  R.G. Harley,et al.  Optimal Wide Area Controller and State Predictor for a Power System , 2007, IEEE Transactions on Power Systems.

[22]  Joe H. Chow,et al.  Time scale modeling of sparse dynamic networks , 1985 .

[23]  R.C. Schaefer,et al.  Understanding Power-System Stability , 2005, IEEE Transactions on Industry Applications.

[24]  Jinyu Wen,et al.  Wide-Area Damping Controller for Power System Interarea Oscillations: A Networked Predictive Control Approach , 2015, IEEE Transactions on Control Systems Technology.

[25]  N. Amjady,et al.  Transient Stability Prediction by a Hybrid Intelligent System , 2007, IEEE Transactions on Power Systems.

[26]  Sukumar Kamalasadan,et al.  Energy Function Inspired Value Priority Based Global Wide-Area Control of Power Grid , 2018, IEEE Transactions on Smart Grid.

[27]  S.D.J. McArthur,et al.  Multi-Agent Systems for Power Engineering Applications—Part I: Concepts, Approaches, and Technical Challenges , 2007, IEEE Transactions on Power Systems.

[28]  R. G. Harley,et al.  Virtual generators: Simplified online power system representations for wide-area damping control , 2012, 2012 IEEE Power and Energy Society General Meeting.

[29]  Kaushik Das,et al.  Power grid transient stability prediction using wide area synchrophasor measurements , 2012, 2012 3rd IEEE PES Innovative Smart Grid Technologies Europe (ISGT Europe).

[30]  Ganesh Kumar Venayagamoorthy,et al.  Power System Control With an Embedded Neural Network in Hybrid System Modeling , 2008, IEEE Transactions on Industry Applications.