Adaptive linear neuron control of three-phase shunt active power filter with anti-windup PI controller optimized by particle swarm optimization. (December 2021)

• Record Type: Journal Article
• Authors: Tamer, Abdedjebbar; Zellouma, Laid; Benchouia, Mohamed Toufik; Krama, Abdelbasset
• Is Part Of: Computers & electrical engineering. Volume 96:Part A(2021)
• Publication Date: 2021-12
• Subjects: Total harmonic distortion (THD) -- Anti-windup PI controller -- Adaptive linear neuron (ADALINE) -- Particle swarm optimization (PSO) -- Shunt active power filter (SAPF); Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2021.107471
• Languages: English
• ISSNs: 0045-7906

Abstract

Highlights: Particle swarm optimization (PSO) is used to perform offline tuning of the anti-windup PI controller parameters of shunt active power filter. DC bus's voltage error and the total harmonic distortion of the source current are considered in the fitness function minimization. Adaptive Linear Neuron (ADALINE) neural network used to identify the current harmonics. The system simulated with MATLAB/Simulink and validated experimentally using DS1104 platform. Simulation and experimental results prove the effectiveness of the controller in terms of performance, efficiency, and power quality enhancement. Abstract: In the present paper, particle swarm optimization technique (PSO) is used to tune anti-windup PI controller gains for DC bus voltage control of the shunt active power filter by offline simulation. The main advantage of the PSO tuning algorithm is to accurately estimate PI controller parameters and overcome the complexity of analytical calculation approaches as well as classical tuning methods. The sum of the total harmonic distortion of source currents and the DC bus's voltage error are used as a fitness function. The reference current is obtained using an artificial neural network named adaptive linear neuron (ADALINE). Three ADALINEs are trained online using the least mean square algorithm (LMS). The proposed system is designed by MATLAB/Simulink and validated experimentally through the dSPACE DS1104 platform. The analysis is done for various conditions, and the obtained results demonstrate the effectiveness of the controller in terms of performance, efficiency, and power quality enhancement. Graphical abstract: Image, graphical abstract