Transient voltage stability enhancement using genetic neural proportional integral derivative fine-tuned by fuzzy controller

Abstract

Improving the transient response of power generation systems using the automation control in a precise manner remain challenging. Developing the Automatic Voltage Regulator (AVR) of the synchronous generator with a high potency and prompt response for the stable electric power service is ever-demanding. The proposed techniques for determining the Proportional Integral Derivative (PID) controller parameters of the AVR system such as Real-Coded Genetic Algorithm (RGA) and a Particle Swarm Optimization (PSO) have failed to achieve the desired precision. Enhancing the transient stability responses of synchronous generation using automation control remains the challenging issue. This thesis presents a novel design method for determining the PID controller parameters of an AVR system using combined Genetic Algorithm (GA), Radial-Basis Function Neural Network (RBF-NN) and Sugeno Fuzzy logic approaches for implementation in enhancing the transient stability responses. This new approach renders the design of synchronous generator voltage controller by introducing a complete and modified model of synchronous generator. The problem of obtaining the optimal AVR and PID controller parameters is formulated as an optimization problem and RBF-NN tuned by GA is applied to solve the optimization problem. Meanwhile, RBF-NN is used to enhance the PID parameters obtained from GA to design a fuzzy PID controller of the AVR system for various operating conditions namely Genetic Neural Fuzzy PID (GNFPID). GNFPID is further designed to transfer in Programmable Logic Controllers (PLCs) for implementing the practical AVR system in the experimental model. An inherent control interaction between the excitation current and terminal voltage is revealed. The simulation and experimental results demonstrate the proposed approach has superior features, including easy implementation, stable convergence characteristic, and good computational efficiency. The proposed GA is applied to optimize PID controller parameters. The algorithms for the proposed GA and RBF-NN are coded using MATLAB and executed on a laptop with Intel core (TM) 2 Duo CPU 5550@1.83 GHz with 3GB RAM laptop. This algorithm effectively searches for a high-quality solution to improve the system’s response (~0.005 sec) and transient response of the AVR system for 13.8 kV and 400 V are found to be 0.0025 and 0.001, respectively. Furthermore, the results of the numerical simulation offer a high sensitive response of the novel design compares to the RGA, LQR, PSO and GA. The GNFPID controller configures the control signal based on interaction and thereby reduces the voltage error and the oscillation in the terminal voltage. The GNFPID controller achieves an excellent voltage control performance by testing the proposed fuzzy PID controller on a practical AVR system in synchronous generator with the sizeable improved transient response. The proposed method is indeed more efficient and robust in improving the system’s response and the transient response of an AVR system. It is asserted that this novel approach may be useful for the development of voltage control of power systems in real industrial practices under severe fault.