Genetic Algorithm Based Optimal Load Frequency Control in Two-Area Interconected Power Systems

Abstract

Load frequency control in power systems introduces as one of the most important items in order to supply reliable electric power with good quality. The goals of the Load Frequency Control (LFC) are to maintain zero steady state errors in a two area interconnected power system. To achieve this goal a fast controller with having no steady-state error will be necessary to be included in power systems. In this paper a new genetic algorithm based method is presented to obtain optimal gains of this controller included in two-area interconnected power system. Simulation results in comparison with correspondence methods confirm the efficiency of proposed method through fastdamping steady-state deviations in power and frequency with presence of step load disturbance.

Robust Load Frequency Control of Hybrid Solar Power Systems Using Optimization Techniques

“…New generations are produced iteratively with the repetition of these three steps. The process continues until the stopping criteria is reached like the maximum number of iterations is achieved or no improvements (Milani and Mozafari, 2009) (Chang et al, 1998;Rerkpreedapong et al, 2003;Das et al, 2010;Mallesham et al, 2012;Konar et al, 2014;Regad et al, 2019;Hemeida et al, 2020;Sidi Brahim et al, 2021). This technique is adopted to find the optimal values of the PID controller parameters.…”

“…Similarly, fuzzy logic requires hard work to get the influential signal response from the power system (Abd-Elazim and Ali, 2018). Optimization techniques such as genetic algorithm (GA) (Milani and Mozafari, 2009) (Chang et al, 1998;Rerkpreedapong et al, 2003;Das et al, 2010;Mallesham et al, 2012;Konar et al, 2014;Regad et al, 2019;Hemeida et al, 2020;Sidi Brahim et al, 2021), particle swarm optimization (PSO) (Pain and Acharjee, 2014;Abd-Elazim and Ali, 2018) (Gözde et al, 2008;Selvakumaran et al, 2012;Modi et al, 2013;Rao and Rama Krishna Reddy, 2015;Shankar et al, 2015;Hazlee Azil et al, 2016;Jeyalakshmi and Subburaj, 2016;Singh and Ramesh, 2019;Hemeida et al, 2020;Veerasamy et al, 2020;Safari et al, 2021) (Sidi Brahim et al, 2021 (Mallesham et al, 2012)- (Kumari and Jha, 2014), firefly algorithm (FA) (Naidu et al, 2013;Shakarami et al, 2013;Padhan et al, 2014;Chandra Sekhar et al, 2016;Abd-Elazim and Ali, 2018;Boddepalli and Navuri, 2018;Gupta et al, 2021), and artificial bee colony (Ghasemi and Shayeghi, 2011;Rathor et al, 2011;Gozde et al, 2012;Naidu et al, 2014;Elsisi et al, 2015;Kouba et...…”

Whale Optimization Based Automatic Generation Control for Two Area Interconnected Thermal Power System

“…Hence, these methods are not suitable for optimal control. On the other hand, modern optimizations techniques like Genetic Algorithm (GA) [9], Grey Wolf Optimization (GWO), Particle Swarm Optimization (PSO) [7], [8], [10], Bacterial Foraging Optimization Algorithm (BFOA) [11], Differential Evolution (DE) [12], Moth flame Optimization (MFO) [13] , and Cuckoo Search (CS) [14], etc have been proved better for the design of controller and obtaining optimal parameters for AGC with respect to each other. However, due to the complexity and for different interconnections and systems, authors are looking forward to different and more optimize algorithms that can perform better over existing algorithms.…”

A Fuzzy Logic Approach to Non-Linearity Problem of Load Frequency Controller

“…At present, human operators at the load dispatch centers carries out the load scheduling with the help of Supervisory Control and Data Acquisition (SCADA). The conventional controls for LFC based on optimal control have been proposed by [6,7]. These conventional controls become impotent when the size and parameters of the power system increases.…”