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

Orka, Nabil Anan; Muhaimin, Sheikh Samit; Shahi, Md. Nazmush Shakib; Ahmed, Ashik

doi:10.1007/978-3-031-16832-1_5

Cited by 4 publications

(1 citation statement)

References 35 publications

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“…Numerous studies have been carried out to enhance and optimize the stability of systems of single and multiple-area control for area generation. For optimization purposes, numerous algorithms have been studied, such as genetic algorithm (GA) [11], particle swarm optimization (PSO) [12], firefly algorithm (FA) [13], grey wolf optimization (GWO) [14], sine cosine algorithm (SCA) [15,16], fuzzy logic-based approaches [17], cuckoo search algorithm [18], hybrid fuzzy neural network [19], and others that have been modified by Researchers to improve performance. Guha et al studied the use of Grey Wolf optimization in the two-area interconnected system LFC issue; this study demonstrates controller gain optimization using an Integral Time Absolute Error (ITAE)-based objective function, and analyzes the effectiveness of the proposed GWO algorithm with that of the Ensemble of Mutation and Crossover Strategies and Parameters in Differential Evolution (EPSDE), Comprehensive Learning of Particle Swarm Optimization (CLPSO), and other associated techniques [20].…”

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confidence: 99%

Performance Analysis of Automatic Generation Control for a Multi-Area Interconnected System Using Genetic Algorithm and Particle Swarm Optimization Technique

Tabassum,

Jahan,

Goswami

et al. 2024

AJSE

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The primary focus of this paper is to assess an interconnected power system using different optimization techniques. The main purpose is to employ different optimization techniques, including genetic algorithms (GA) and particle swarm optimization (PSO), to systematically enhance the performance of a multi-area or two-area automatic generation control (AGC) system, aiming to optimize the three PID controllers gain values and improve system performance under diverse loading conditions. Two case studies are conducted exploring different loading conditions in the megawatt (MW) range, including increasing load demand and decreasing load demand. The analysis involves four scenarios, covering without any kind of controller, another with solely a proportional integral derivative (PID) controller, a PID controller enhanced through a genetic algorithm (GA), and lastly, a PID controller improved through particle swarm optimization (PSO). The optimization process utilizes the integral time absolute error (ITAE) as the objective function to evaluate the system's performance. The simulation outcomes for ITAE, settling time, overshoot, and undershoot for frequency deviation of area one, area two, and power deviation in the tie-line are compared with previous similar studies to assess the novelty of this work. The article highlights the importance of the multi-area AGC system and the significance of different optimization techniques in improving its performance.

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