Control and Identification of Single Machine Infinite Bus with Neural Network for System Stability

Abstract

This paper deals with implementing artificial neural networks for the identification and control Investigating the stability and stabilization of a single machine connected to an infinite bus through a transmission line (SMIB) system. Artificial Neural Network (ANN) employs a multi-layer feedforward network trained using the Backpropagation (BP) algorithm by simulations using MATLAB/Simulink. Weight coefficients of the ANN are determined using the LevenbergMarquardt algorithm. The proposed approach uses two types of neural networks: neural controller and neural identification, neural network control is a single device on an infinite bus instead of the PID-PSS controller, to improve the performance of the SMIB system, and neural identification to emulate the characteristics of the single machine infinite bus (SMIB) system These neural networks model system dynamics and nonlinear for selection and control purposes. The primary objective is to develop a neuronal identification model that accurately equals the characteristics of the single machine infinite bus (SMIB) system and a neuro-controller is implemented to replace traditional controllers such as Power System Stabilizers (PSS) and Automatic Voltage Regulators (AVR). Simulations are performed to examine the system under various conditions, evaluating rotor speed deviation, stator voltage, and rotor angle delta.