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[07-08, 2004] 

Journal of Electrical Engineering, Vol 55, 07-08 (2004) 169-179

EXCITATION CONTROL OF A SYNCHRONOUS MACHINE USING POLYNOMIAL NEURAL NETWORKS

Yannis L. Karnavas - Demetrios P. Papadopoulos

   This paper presents a practical design of an intelligent type of controller using polynomial neural network (PNN) concepts for excitation control of a practical power generating system. This type of controller is suitable for real time operation and aims to improve the dynamic characteristics of the generating unit by acting properly on its original excitation system. The modelling of the power system under study consists of a synchronous generator connected via a transformer and a transmission line to an infinite bus. Next, digital simulations of the above system are performed using fuzzy control techniques that are based on previous work. Then, two neural network controllers are designed by adopting the PNN architecture. The first one utilizes a single pi-sigma network (PSN) and the significant advantages over the standard multi layered perceptron (MLP) are discussed. Secondly, an enhanced controller is designed, leading to a ridge polynomial network (RPN) by combining multiple PSNs if needed. Both controllers used can be pre-trained rapidly from the corresponding fuzzy logic controller (FLC) output signal and act as model dynamics capturers. The dynamic performances of the FLC along with those of the two proposed controllers are presented by comparison using the integral square error criterion (ISE). The latter controllers show good convergence properties and accuracy for function approximation. Typical transient responses of the system are shown for comparison in order to demonstrate the effectiveness of the proposed controllers. The computer simulation results obtained demonstrate clearly that the performance of the developed controllers offers competitive damping effects on the generator oscillations, with respect to the associated ones of the FLC, over a wider range of operating conditions, while their hardware implementation is easier and the computational time needed for real- time applications is drastically reduced.

Keywords: Synchronous machine, excitation control, dynamic stability, polynomial neural networks


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