Optimal controller for mitigation of harmonics in hybrid shunt active power filter connected distribution system: An EGOANN technique

• Published in: Journal of renewable and sustainable energy Vol. 11; no. 2
• Main Authors: Arunsankar, Ganesan, Srinath, Subbaraman
• Format: Journal Article
• Language: English
• Published: Melville American Institute of Physics 01.03.2019
• Subjects: Algorithms; Computer simulation; Electric potential; Electric power distribution; Electronic equipment; Harmonic distortion; Hybrid systems; Parameters
• ISSN: 1941-7012; 1941-7012
• DOI: 10.1063/1.5085028

One of the most important concerns in power system networks is harmonics, which is mainly developed with the huge usage of nonlinear loads and power electronic equipment. This harmonics leads to damage in the distribution system. This paper proposes an efficient hybrid approach with SHAPF for harmonic mitigation to maintain the PQ in the distribution system. An efficient hybrid approach is the joined execution of an EGOA and ANN named as EGOANN. The main aim of the proposed strategy is to improve the quality of power by mitigating the harmonics subject to the minimum THD. In the proposed approach, EGOA optimizes the dataset of fundamental and harmonic loop parameters such as Terminal voltage and DC voltage present in the SHAPF based on the load change and parameter variation of the system. In light of the accomplished dataset, ANN accurately predicts the parameters and produces optimized control signals. Finally, the proposed model is executed in the MATLAB/Simulink working platform, also includes the simulation results of a few test cases to maintain the PQ, and illustrates the superior performance of the proposed design philosophy. The simulation result shows that the harmonic distortion can be reduced to an acceptable level in response to the load change or parameter variation of the power system by the proposed EGOANN hybrid technique. Also, the proposed technique is more effective with less computation and reduces the complexity of the algorithm.