An optimal artificial neural network controller for load frequency control of a four-area interconnected power system

Abstract

In this paper, an optimal artificial neural network (ANN) controller for load frequency control (LFC) of a four-area interconnected power system with non-linearity is presented. A feed forward neural network with multi-layers and Bayesian regularization backpropagation (BRB) training function is used. This controller is designed on the basis of optimal control theory to overcome the problem of load frequency control as load changes in the power system. The system comprised of transfer function models of twothermal units, one nuclear unit and one hydro unit. The controller model is developed by considering generation rate constraint (GRC) of different units as a non-linearity. The typical system parameters obtained from IEEE press power engineering series and EPRI books. The robustness, effectiveness, and performance of the proposed optimal ANN controller for a step load change and random load change in the system is simulated through using MATLAB-Simulink. The time response characteristics are compared with that obtained from the proportional, integral and derivative (PID) controller and non-linear autoregressive-moving average (NARMA-L2) controller. The results show that the algorithm developed for proposed controller has a superiority in accuracy as compared to other two controllers.

Prediction of dementia using machine learning model and performance improvement with cuckoo algorithm

“…This controller is designed by optimal control theory to defeat the issue of load frequency control. A feed-forward neural network with multi-layers and Bayesian regularization backpropagation training function is utilized [30]. The literature shows that various machine learning methods are used for the prediction and classification of the disease but no performance improvement has been done.…”