Neural-network-based adaptive UPFC for improving transient stability performance of power system.
This paper uses the recently proposed H(infinity)-learning method, for updating the parameter of the radial basis function neural network (RBFNN) used as a control scheme for the unified power flow controller (UPFC) to improve the transient stability performance of a multimachine power system. The RBFNN uses a single neuron architecture whose input is proportional to the difference in error and the updating of its parameters is carried via a proportional value of the error. Also, the coefficients of the difference of error, error, and auxiliary signal used for improving damping performance are depicted by a genetic algorithm. The performance of the newly designed controller is evaluated in a four-machine power system subjected to different types of disturbances. The newly designed single-neuron RBFNN-based UPFC exhibits better damping performance compared to the conventional PID as well as the extended Kalman filter (EKF) updating-based RBFNN scheme, making the unstable cases stable. Its simple architecture reduces the computational burden, thereby making it attractive for real-time implementation. Also, all the machines are being equipped with the conventional power system stabilizer (PSS) to study the coordinated effect of UPFC and PSS in the system.
Department of Electrical Engineering, Indian Institute of Technology, Delhi 110016, India. sukumar@ee.iitd.ac.in
Sukumar
S
Mishra
D000465:Algorithms; D003198:Computer Simulation; D004867:Equipment Design; D019544:Equipment Failure Analysis; D005246:Feedback; D008962:Models, Theoretical; D016571:Neural Networks, Computer; D011210:Power Plants / Q000295:instrumentation* / Q000379:methods*