Domination of Meta-Heuristic Algorithms Over Nature Algorithms in Automatic Load Frequency Management

This paper depicts a completely unique scheme to develop a much better automatic load frequency (ALF) management for an interconnected power facility. In an interconnected power system, tiny load disturbance in any of the area results in frequency and tie line power fluctuation in each and every area. Due to an increase in complexity of an electrical power system, there is a requirement to reinforce and develop new control tactics. Though most of the acceptance literature does not base on the attempt that power system performance does not solely depend on the control structure, however conjointly depends on well-tuned controllers. For this purpose, a domination of metaheuristic algorithms over nature algorithms has provided insight that every of them has their own fruitful characteristics to find solution if the application is used for particular purpose. The conventional two area interconnected thermal power system has been considered with nonlinearities. For optimal control, meta heuristic algorithms i.e fruit fly optimization algorithm (FFA) and Backtracking Search Optimization Algorithms (BSA) were employed for tuning control parameters under load perturbation and sensitivity analysis. Further, the results were validated in response of change in the frequency and tie line variables obtained using objective functions based on Integrated Time multiplied Absolute Error (ITAE) and compared with Particle Swarm Optimization (PSO), Genetic Algorithm (GA) and Evolutionary Learning, Design and Search Algorithms. IndexTerms Automatic Load Frequency (ALF), Automatic Generation Control (AGC), Fruit Fly Optimization Algorithm (FFA), Backtracking Search Optimization Algorithm (BSA), Particle Swarm Optimization Algorithm (PSO), Genetic Algorithm (GA), Area Control Error (ACE), Integrated Time multiplied Absolute Error (ITAE)