A Hybrid Quantum Inspired Particle Swarm Optimization and Least Square Framework for Real-time Harmonic Estimation  

作　　者：Abu Bakar Waqas Muhammad Mansoor Ashraf Yasir Saifullah 

机构地区：[1]Department of Communication Science and Engineering,School of Information Science and Technology,Fudan University [2]Electrical Engineering Department,University of Engineering and Technology

出　　处：《Journal of Modern Power Systems and Clean Energy》2021年第6期1548-1556,共9页现代电力系统与清洁能源学报（英文）

摘　　要：The power quality is becoming an extensively addressing aspect of the power system because of the sensitive operation of the smart grid, awareness of power quality, and the equipment of modern power systems. In this paper, we have conceived a new hybrid Quantum inspired particle swarm optimization and least square(QPSO-LS) framework for real-time estimation of harmonics presented in time-varying noisy power signals. The technique has strong, robust, and reliable search capability with powerful convergence properties. The proposed approach is applied to various test systems at different signal to noise ratio(SNR) levels in the presence of uniform and Gaussian noise. The results are presented in terms of precision, computation time, and convergence characteristics. The computation time decreases by 3-5 times as compared to the existing algorithms. The technique is further authenticated by estimating harmonics of real-time current or voltage waveforms, obtained from light emitting diode(LED) lamp and axial flux permanent magnet synchronous generator(AFPMSG). The results demonstrate the superiority of QPSO-LS over other methods such as LS-based genetic algorithm(GA), particle swarm optimization(PSO), bacterial foraging optimization(BFO), artificial bee colony(ABC), and biogeography based optimization with recursive LS(BBO-RLS) algorithms, in terms of providing satisfactory solutions with a significant amount of robustness and computation efficiency.

关 键 词：Harmonic estimation power quality particle swarm optimization(PSO) least square(LS) smart grid 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程] TM935[自动化与计算机技术—控制科学与工程]