Comparative study between HDP and PSS on DFIG damping control

In this paper, heuristic dynamic programming (HDP) based supplementary control is compared with the power system stabilizer (PSS) based supplementary control for doubly-fed induction generators (DFIG), namely, the active power damping control. The traditional design of PSS control on DFIG damping is based on eigenvalue analysis. For such analysis, disturbances are considered sufficiently small to permit the nonlinear model representing the power system to be linearized and expressed in state space form. When the disturbance or the system configuration changes, this kind of design is easy to become entrapped in local minimal and the robust ability of the controller is not guaranteed. On the other side, the HDP based supplementary damping controller analyzed in this paper is “model-free” with on-line learning capability: once a system state is observed, an action will be subsequently produced based on the performance index function. The obtained results by such a HDP supplementary controller on a benchmark power system are compared with the traditional PSS controller, demonstrating the improved control performance and robustness.

[1]  Haibo He,et al.  Optimized Control of DFIG-Based Wind Generation Using Sensitivity Analysis and Particle Swarm Optimization , 2013, IEEE Transactions on Smart Grid.

[2]  Ganesh K. Venayagamoorthy,et al.  Dynamic, Stochastic, Computational, and Scalable Technologies for Smart Grids , 2011, IEEE Computational Intelligence Magazine.

[3]  Babu Narayanan,et al.  POWER SYSTEM STABILITY AND CONTROL , 2015 .

[4]  C. Y. Chung,et al.  Coordinated Damping Control Design for DFIG-Based Wind Generation Considering Power Output Variation , 2012, IEEE Transactions on Power Systems.

[5]  Haibo He,et al.  Adaptive control for an HVDC transmission link with FACTS and a wind farm , 2013, 2013 IEEE PES Innovative Smart Grid Technologies Conference (ISGT).

[6]  Chao Lu,et al.  Direct Heuristic Dynamic Programming for Damping Oscillations in a Large Power System , 2008, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[7]  Niraj Kshatriya,et al.  Optimized Partial Eigenstructure Assignment-Based Design of a Combined PSS and Active Damping Controller for a DFIG , 2010, IEEE Transactions on Power Systems.

[8]  Haibo He,et al.  A three-network architecture for on-line learning and optimization based on adaptive dynamic programming , 2012, Neurocomputing.

[9]  Warren B. Powell,et al.  Handbook of Learning and Approximate Dynamic Programming , 2006, IEEE Transactions on Automatic Control.

[10]  M. Ghandhari,et al.  Using power system stabilisers in doubly fed induction generators , 2008, 2008 Australasian Universities Power Engineering Conference.

[11]  Jennie Si,et al.  Online learning control by association and reinforcement. , 2001, IEEE transactions on neural networks.

[12]  Kit Po Wong,et al.  Oscillatory Stability and Eigenvalue Sensitivity Analysis of A DFIG Wind Turbine System , 2011, IEEE Transactions on Energy Conversion.

[13]  Raja Ayyanar,et al.  Supplementary control for damping power oscillations due to increased penetration of doubly fed induction generators in large power systems , 2011, 2011 IEEE/PES Power Systems Conference and Exposition.

[14]  Frede Blaabjerg,et al.  Control of Variable Speed Wind Turbines with Doubly-Fed Induction Generators , 2004 .

[15]  Om P. Malik,et al.  Application of neural adaptive power system stabilizer in a multi-machine power system , 1999 .

[16]  O.P. Malik,et al.  Design of an Adaptive PSS Based on Recurrent Adaptive Control Theory , 2009, IEEE Transactions on Energy Conversion.

[17]  Haibo He.  Self-Adaptive Systems for Machine Intelligence , 2011 .

[18]  J. A. Pecas Lopes,et al.  Robust tuning of power system stabilisers to install in wind energy conversion systems , 2009 .

[19]  Xiao-Ping Zhang,et al.  Decentralized Nonlinear Control of Wind Turbine With Doubly Fed Induction Generator , 2008, IEEE Transactions on Power Systems.

[20]  Haibo He.  Self-Adaptive Systems for Machine Intelligence: He/Machine Intelligence , 2011 .

[21]  O. Anaya-Lara,et al.  A power system stabilizer for DFIG-based wind generation , 2006, IEEE Transactions on Power Systems.

[22]  Frank L. Lewis,et al.  Learning and Optimization in Hierarchical Adaptive Critic Design , 2013 .

[23]  Paul J. Werbos,et al.  Computational Intelligence for the Smart Grid-History, Challenges, and Opportunities , 2011, IEEE Computational Intelligence Magazine.

[24]  Haibo He,et al.  Reactive power control of grid-connected wind farm based on adaptive dynamic programming , 2014, Neurocomputing.