An Enhanced Gradient Based Optimized Controller for Load Frequency Control of a Two Area Automatic Generation Control System

TL;DR

This paper introduces an Enhanced Gradient-Based Optimizer (EGBO) for optimizing load frequency control in a two-area power system, demonstrating superior performance over several existing algorithms in terms of resilience, precision, and latency.

Contribution

The paper presents a novel EGBO algorithm specifically designed for LFC parameter optimization, outperforming other popular algorithms in accuracy and efficiency.

Findings

EGBO outperforms GBO, ChOA, SCA, GWO, and PSO in tests.

EGBO shows higher resilience and precision.

Statistical analysis confirms EGBO's superiority.

Abstract

This work proposes the adoption of Enhanced Gradient-Based Optimizer (EGBO) as a new approach to the Load Frequency Control (LFC) problem in a two-area interconnected power system. The importance of determining the optimal parameters for the controllers for the LFC problem cannot be overstated, and the fact that estimating these parameters require complex and nonlinear computations makes the optimization procedure even more unique and challenging. Consequently, application of an efficient optimization algorithm to successfully attain optimal controller parameters is critical. To accomplish this task, the proposed EGBO algorithm is compared to the fundamental Gradient-Based Optimizer (GBO), Chimp Optimization Algorithm (ChOA), Sine Cosine Algorithm (SCA), Grey Wolf Optimization (GWO), and Particle Swarm Optimization (PSO) for optimizing an Integral-Time-multiplied-Absolute-Error (ITAE) based objective function. The relevant findings show that the EGBO algorithm is competitively superior in terms of resilience, precision, and latency when compared to other optimization methods. Lastly, the statistical comparison further strengthens the outcome of the study.