Intelligent controllers for automatic generation control

Abstract

This paper presents a novel approach of artificial intelligence (AI) techniques, viz. fuzzy logic, artificial neural network (ANN) and hybrid fuzzy neural network (HFNN) for the automatic generation control (AGC). The limitations of the conventional controls, viz. proportional, integral and derivative (PID) are slow and lack of efficiency in handling system nonlinearities. The primary purpose of the AGC is to balance the total system generation against system load and losses so that the desired frequency and power interchange with neighboring systems is maintained. Any mismatch between generation and demand causes the system frequency to deviate from scheduled value. Thus high frequency deviation may lead to system collapse. This necessitates an accurate and fast acting controller to maintain constant nominal frequency. The intelligent controllers, viz. fuzzy logic, ANN and hybrid fuzzy neural network approaches are used for automatic generation control for the single area system and two area interconnected power systems. The performance of the intelligent controllers has been compared with the conventional PI and PID controllers for the single area system as well as two-area interconnected power system. The results shows that hybrid fuzzy neural network (HFNN) controller has better dynamic response, i.e. quick in operation, reduced error magnitude and minimized frequency transients.