ANN-Tuned PID Controller for LFC Investigation in Two-Area Interconnected System

Abstract

The adaptive ANN (artificial neural network)-tuned PID (proportional-integral-derivative) controller is proposed here for LFC (load frequency control) investigation in a two-area interconnected system. This will make frequency regulation much simpler. This controller was developed specifically to aid in the process of frequency regulation. The interconnected two-area thermal-gas turbine power system (area 1), as well as the thermal-hydro power system (area 2), are taken into consideration as part of the LFC analysis process. To determine the final values for the PID controller parameters, two distinct strategies were utilized. In the first scenario, a relatively new optimization method known as the “opposition-learning-based volleyball premier league (OVPL) algorithm” is used to fine-tune the PID controller parameters. This method was developed by the opposition learning-based volleyball premier league (OVPL). In the second possible scenario, an artificial neural network is used to make adjustments to the parameters of the PID controller. The dynamic behavior of the two categories of the system is analyzed by using an OVPL-tuned PID controller as well as an ANN-tuned PID controller. It is discovered that the ANN-tuned PID controller demonstrates superior performance in comparison to the OVPL-tuned PID controller. After examining the similarities and differences between the two controllers, we came to this conclusion. The system also uses step-change in load demand (SLD) as a way to test how stable the suggested ANN-tuned PID controller is.