ANN-BASED SYNCHRONOUS GENERATOR EXCITATION FOR TRANSIENT STABILITY ENHANCEMENT AND VOLTAGE REGULATION

Abstract

Control of the synchronous generator, also referred to as an alternator, has always remained very significant in power system operation and control. Alternator output is proportional to load angle, but as the parameter is moved up, the power system security approaches the extreme limit. Hence, generators are operated well below their steady state stability limit for the secure operation of a power system. This raises demand for efficient and fast controllers. Artificial intelligence, specifically artificial neural network (ANN), is emerging very rapidly and has become an efficient tool for operation and control of power systems. ANN requires considerable time to tune weights, but it is fast and accurate once tuned properly. Previously, ANNs have been trained with high-dimensional input space or have been trained online. Hence, either one requires considerable time to yield the control signal or is a bit risky technique to apply in interconnected power systems. In this study, a multilayer perceptron (MLP) ANN is proposed to control generator excitation trained with low-dimensional input space. Moreover, MLP has been trained offline to avert the risk potential of online training. The results illustrate preeminence of the proposed neurocontroller-based excitation system over the conventional controllers-based excitation system.