[This article belongs to Volume - 55, Issue - 01, 2023]
Gongcheng Kexue Yu Jishu/Advanced Engineering Science
Journal ID : AES-30-01-2023-037

1Madhavi Dasari, 2V.S.Bharath, 3Madhu Palati, 4D M Ganapathi

Abstract :

It has been observed that harmonics play a major role in reducing the quality of power in industrial and utility power systems. These harmonics are activated by the growing use of nonlinear loads connected to the power system. Passive and active filters (shunt and series filter) were traditionally used to generate the power efficiency. However, they suffered from resonance problems, fixed compensation and other PQ problems. To minimise these problems, we therefore go for controllers such as PID, Fuzzy and Neuro Fuzzy controllers. In order to implement the proposed control technique, and is tested using various voltage conditions, such as sag swell and unbalanced. By comparing research with traditional control methods, the efficacy of the proposed neuro fuzzy-based SVPWM controller is analysed. The main objective of this paper is to reduce the total harmonic distortion and remove power quality issues and maintain stability in load voltage by using three methods and is compared in three cases. They are the PWM based PID and fuzzy logic controller which are compared with SVPWM based Neuro fuzzy controller. One of the new metaheuristic algorithms is the Modified Firefly Algorithm (MFA) is used for the optimization problems to yield better PID gain parameters. In this algorithm, randomly generated solutions will be considered as fireflies and the brightness is assigned depending on their performance on the objective function. The basic rule used to construct the algorithm is, a firefly will be attracted to a brighter firefly and if there is no brighter firefly it moves randomly generating random directions in order to determine the best direction in which the brightness increases. If such a direction is not generated, it will remain in its current position. The aim of attractiveness is modified in such a way that the effect of the objective function is modified which helps in finding the best solution with smaller CPU time and is implemented in MATLAB/simulation.