Optimal wide-area monitoring and nonlinear adaptive coordinating neurocontrol of a power system with wind power integration and multiple FACTS devices

Wide-area coordinating control is becoming an important issue and a challenging problem in the power industry. This paper proposes a novel optimal wide-area coordinating neurocontrol (WACNC), based on wide-area measurements, for a power system with power system stabilizers, a large wind farm and multiple flexible ac transmission system (FACTS) devices. An optimal wide-area monitor (OWAM), which is a radial basis function neural network (RBFNN), is designed to identify the input-output dynamics of the nonlinear power system. Its parameters are optimized through particle swarm optimization (PSO). Based on the OWAM, the WACNC is then designed by using the dual heuristic programming (DHP) method and RBFNNs, while considering the effect of signal transmission delays. The WACNC operates at a global level to coordinate the actions of local power system controllers. Each local controller communicates with the WACNC, receives remote control signals from the WACNC to enhance its dynamic performance and therefore helps improve system-wide dynamic and transient performance. The proposed control is verified by simulation studies on a multimachine power system.

[1]  Riccardo Poli,et al.  Particle swarm optimization , 1995, Swarm Intelligence.

[2]  L.-A. Dessaint,et al.  Power systems stability enhancement using a wide-area signals based hierarchical controller , 2005, IEEE Transactions on Power Systems.

[3]  Laszlo Gyugyi,et al.  Understanding FACTS: Concepts and Technology of Flexible AC Transmission Systems , 1999 .

[4]  Wei Qiao,et al.  Indirect Adaptive External Neuro-Control for a Series Capacitive Reactance Compensator Based on a Voltage Source PWM Converter in Damping Power Oscillations , 2007, IEEE Transactions on Industrial Electronics.

[5]  Sanjay Ranka,et al.  An effic ient k-means clustering algorithm , 1997 .

[6]  Paul J. Werbos,et al.  Approximate dynamic programming for real-time control and neural modeling , 1992 .

[7]  Balarko Chaudhuri,et al.  Wide-area measurement-based stabilizing control of power system considering signal transmission delay , 2004 .

[8]  B. Chaudhuri,et al.  Wide-area measurement-based stabilizing control of power system considering signal transmission delay , 2004, IEEE Transactions on Power Systems.

[9]  John Moody,et al.  Fast Learning in Networks of Locally-Tuned Processing Units , 1989, Neural Computation.

[10]  S. Hyakin,et al.  Neural Networks: A Comprehensive Foundation , 1994 .

[11]  U.D. Annakkage,et al.  A platform for validation of FACTS models , 2006, IEEE Transactions on Power Delivery.

[12]  Krista Rizman Zalik,et al.  An efficient k 0-means clustering algorithm , 2008 .

[13]  Damir Novosel,et al.  Wide-Area Protection and Emergency Control , 2004, Proceedings of the IEEE.

[14]  P. Kundur,et al.  Power system stability and control , 1994 .