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

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 a...

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Autores principales:	Abu Bakar Waqas, Yasir Saifullah, Muhammad Mansoor Ashraf
Formato:	article
Lenguaje:	EN
Publicado:	IEEE 2021
Materias:	Harmonic estimation power quality particle swarm optimization (PSO) least square (LS) smart grid Production of electric energy or power. Powerplants. Central stations TK1001-1841 Renewable energy sources TJ807-830
Acceso en línea:	https://doaj.org/article/97b411e73bdf4fdc9d743c9b828969f2
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id	oai:doaj.org-article:97b411e73bdf4fdc9d743c9b828969f2
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spelling	oai:doaj.org-article:97b411e73bdf4fdc9d743c9b828969f22021-11-27T00:00:22ZA Hybrid Quantum Inspired Particle Swarm Optimization and Least Square Framework for Real-time Harmonic Estimation2196-542010.35833/MPCE.2019.000098https://doaj.org/article/97b411e73bdf4fdc9d743c9b828969f22021-01-01T00:00:00Zhttps://ieeexplore.ieee.org/document/9127770/https://doaj.org/toc/2196-5420The 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.Abu Bakar WaqasYasir SaifullahMuhammad Mansoor AshrafIEEEarticleHarmonic estimationpower qualityparticle swarm optimization (PSO)least square (LS)smart gridProduction of electric energy or power. Powerplants. Central stationsTK1001-1841Renewable energy sourcesTJ807-830ENJournal of Modern Power Systems and Clean Energy, Vol 9, Iss 6, Pp 1548-1556 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Harmonic estimation power quality particle swarm optimization (PSO) least square (LS) smart grid Production of electric energy or power. Powerplants. Central stations TK1001-1841 Renewable energy sources TJ807-830
spellingShingle	Harmonic estimation power quality particle swarm optimization (PSO) least square (LS) smart grid Production of electric energy or power. Powerplants. Central stations TK1001-1841 Renewable energy sources TJ807-830 Abu Bakar Waqas Yasir Saifullah Muhammad Mansoor Ashraf A Hybrid Quantum Inspired Particle Swarm Optimization and Least Square Framework for Real-time Harmonic Estimation
description	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.
format	article
author	Abu Bakar Waqas Yasir Saifullah Muhammad Mansoor Ashraf
author_facet	Abu Bakar Waqas Yasir Saifullah Muhammad Mansoor Ashraf
author_sort	Abu Bakar Waqas
title	A Hybrid Quantum Inspired Particle Swarm Optimization and Least Square Framework for Real-time Harmonic Estimation
title_short	A Hybrid Quantum Inspired Particle Swarm Optimization and Least Square Framework for Real-time Harmonic Estimation
title_full	A Hybrid Quantum Inspired Particle Swarm Optimization and Least Square Framework for Real-time Harmonic Estimation
title_fullStr	A Hybrid Quantum Inspired Particle Swarm Optimization and Least Square Framework for Real-time Harmonic Estimation
title_full_unstemmed	A Hybrid Quantum Inspired Particle Swarm Optimization and Least Square Framework for Real-time Harmonic Estimation
title_sort	hybrid quantum inspired particle swarm optimization and least square framework for real-time harmonic estimation
publisher	IEEE
publishDate	2021
url	https://doaj.org/article/97b411e73bdf4fdc9d743c9b828969f2
work_keys_str_mv	AT abubakarwaqas ahybridquantuminspiredparticleswarmoptimizationandleastsquareframeworkforrealtimeharmonicestimation AT yasirsaifullah ahybridquantuminspiredparticleswarmoptimizationandleastsquareframeworkforrealtimeharmonicestimation AT muhammadmansoorashraf ahybridquantuminspiredparticleswarmoptimizationandleastsquareframeworkforrealtimeharmonicestimation AT abubakarwaqas hybridquantuminspiredparticleswarmoptimizationandleastsquareframeworkforrealtimeharmonicestimation AT yasirsaifullah hybridquantuminspiredparticleswarmoptimizationandleastsquareframeworkforrealtimeharmonicestimation AT muhammadmansoorashraf hybridquantuminspiredparticleswarmoptimizationandleastsquareframeworkforrealtimeharmonicestimation
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A Hybrid Quantum Inspired Particle Swarm Optimization and Least Square Framework for Real-time Harmonic Estimation

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