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Hybrid moth flame optimization mppt algorithm for accurate real-time tracking under partially shaded photovoltaic system

Photovoltaic (PV) module is a packed solar cell, used for generating electricity from the sun’s energy. The application of PV power generation has gained its popularity with its easy implementation and inexhaustible energy resources. Due to the nonlinear characteristic of PV module, a maximum power...

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Main Author:	Tiong, Meng Chung
Format:	Thesis
Language:	English
Published:	2021
Subjects:	T Technology (General) TA Engineering (General). Civil engineering (General) TK Electrical engineering. Electronics Nuclear engineering
Online Access:	http://umpir.ump.edu.my/id/eprint/34676/1/Hybrid%20moth%20flame%20optimization%20mppt%20algorithm%20for%20accurate%20real-time%20tracking.ir.pdf http://umpir.ump.edu.my/id/eprint/34676/
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spelling	my.ump.umpir.346762022-10-14T03:24:39Z http://umpir.ump.edu.my/id/eprint/34676/ Hybrid moth flame optimization mppt algorithm for accurate real-time tracking under partially shaded photovoltaic system Tiong, Meng Chung T Technology (General) TA Engineering (General). Civil engineering (General) TK Electrical engineering. Electronics Nuclear engineering Photovoltaic (PV) module is a packed solar cell, used for generating electricity from the sun’s energy. The application of PV power generation has gained its popularity with its easy implementation and inexhaustible energy resources. Due to the nonlinear characteristic of PV module, a maximum power point tracking (MPPT) is necessary to adjust the operating point based on the maximum power point (MPP) on the current-voltage (I-V) characteristic curve. However, under partial shaded conditions, the PV system is prone to local maxima problem and the challenge for MPPT increases, due to most of the commonly used MPPT algorithms were unable to track for the MPP effectively. To overcome the challenge, soft computing methods have been adapted in MPPT by researchers, with Particle Swarm Optimization (PSO) being the most prominent. However, the high computational power of PSO becomes a disadvantage in real-time and highly dynamic MPPT application. In addition, with the continuous improvement effort and the possibility of a new-comer algorithm can show superior results on the current problem, a new MFO based MPPT algorithm was proposed. In this study, a four-module 980 W solar PV system together with a DC/DC Boost Converter model was developed in MATLAB-Simulink as the MPPT algorithm test platform. Direct control strategy was adapted as the regulator for the DC/DC converter to replace the conventional proportional-integral (PI) controller to eliminate the need to tune the PI controller. Based on the study from MFO, it is found that the MFO was having the capability to perform effective tracking, despite its limitation of premature convergence problem near the MPP. To lift the limitation off, a new hybrid model named Hybrid MFO (HMFO) was proposed based on the combination of feature from MFO and conventional P&O, together with an additional partial shading detection feature. The performance of MFO and HMFO was compared with two well-established MPPT methods, namely Perturb and Observe (P&O) and PSO. To further evaluate the real-time performance of the MPPT algorithms, hardware-in-the-loop (HIL) was utilized to emulate the behavior of the PV system and power converter while a digital signal processor (DSP) was used to implement the MPPT algorithms in study. All four MPPT methods were simulated and real-time evaluated under 10 constant irradiance test cases, 30 dynamic irradiance test cases and 100 partial shaded irradiance test cases. HMFO has shown fast tracking and achieved the highest average efficiency among the soft computing methods under constant irradiance conditions. Under dynamic irradiance condition, HMFO was able to reach the new MPP faster and more effective than both PSO and MFO. Under partial shaded conditions, the HMFO was able to show the highest average tracking efficiency and the fastest convergence time among the soft computing method. The HMFO was able to track for true MPP for about three times more than the P&O under partial shaded conditions and it was able to achieve the average tracking efficiency up to 99.35 %. 2021-04 Thesis NonPeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/34676/1/Hybrid%20moth%20flame%20optimization%20mppt%20algorithm%20for%20accurate%20real-time%20tracking.ir.pdf Tiong, Meng Chung (2021) Hybrid moth flame optimization mppt algorithm for accurate real-time tracking under partially shaded photovoltaic system. PhD thesis, Universiti Malaysia Pahang.
institution	Universiti Malaysia Pahang
building	UMP Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Malaysia Pahang
content_source	UMP Institutional Repository
url_provider	http://umpir.ump.edu.my/
language	English
topic	T Technology (General) TA Engineering (General). Civil engineering (General) TK Electrical engineering. Electronics Nuclear engineering
spellingShingle	T Technology (General) TA Engineering (General). Civil engineering (General) TK Electrical engineering. Electronics Nuclear engineering Tiong, Meng Chung Hybrid moth flame optimization mppt algorithm for accurate real-time tracking under partially shaded photovoltaic system
description	Photovoltaic (PV) module is a packed solar cell, used for generating electricity from the sun’s energy. The application of PV power generation has gained its popularity with its easy implementation and inexhaustible energy resources. Due to the nonlinear characteristic of PV module, a maximum power point tracking (MPPT) is necessary to adjust the operating point based on the maximum power point (MPP) on the current-voltage (I-V) characteristic curve. However, under partial shaded conditions, the PV system is prone to local maxima problem and the challenge for MPPT increases, due to most of the commonly used MPPT algorithms were unable to track for the MPP effectively. To overcome the challenge, soft computing methods have been adapted in MPPT by researchers, with Particle Swarm Optimization (PSO) being the most prominent. However, the high computational power of PSO becomes a disadvantage in real-time and highly dynamic MPPT application. In addition, with the continuous improvement effort and the possibility of a new-comer algorithm can show superior results on the current problem, a new MFO based MPPT algorithm was proposed. In this study, a four-module 980 W solar PV system together with a DC/DC Boost Converter model was developed in MATLAB-Simulink as the MPPT algorithm test platform. Direct control strategy was adapted as the regulator for the DC/DC converter to replace the conventional proportional-integral (PI) controller to eliminate the need to tune the PI controller. Based on the study from MFO, it is found that the MFO was having the capability to perform effective tracking, despite its limitation of premature convergence problem near the MPP. To lift the limitation off, a new hybrid model named Hybrid MFO (HMFO) was proposed based on the combination of feature from MFO and conventional P&O, together with an additional partial shading detection feature. The performance of MFO and HMFO was compared with two well-established MPPT methods, namely Perturb and Observe (P&O) and PSO. To further evaluate the real-time performance of the MPPT algorithms, hardware-in-the-loop (HIL) was utilized to emulate the behavior of the PV system and power converter while a digital signal processor (DSP) was used to implement the MPPT algorithms in study. All four MPPT methods were simulated and real-time evaluated under 10 constant irradiance test cases, 30 dynamic irradiance test cases and 100 partial shaded irradiance test cases. HMFO has shown fast tracking and achieved the highest average efficiency among the soft computing methods under constant irradiance conditions. Under dynamic irradiance condition, HMFO was able to reach the new MPP faster and more effective than both PSO and MFO. Under partial shaded conditions, the HMFO was able to show the highest average tracking efficiency and the fastest convergence time among the soft computing method. The HMFO was able to track for true MPP for about three times more than the P&O under partial shaded conditions and it was able to achieve the average tracking efficiency up to 99.35 %.
format	Thesis
author	Tiong, Meng Chung
author_facet	Tiong, Meng Chung
author_sort	Tiong, Meng Chung
title	Hybrid moth flame optimization mppt algorithm for accurate real-time tracking under partially shaded photovoltaic system
title_short	Hybrid moth flame optimization mppt algorithm for accurate real-time tracking under partially shaded photovoltaic system
title_full	Hybrid moth flame optimization mppt algorithm for accurate real-time tracking under partially shaded photovoltaic system
title_fullStr	Hybrid moth flame optimization mppt algorithm for accurate real-time tracking under partially shaded photovoltaic system
title_full_unstemmed	Hybrid moth flame optimization mppt algorithm for accurate real-time tracking under partially shaded photovoltaic system
title_sort	hybrid moth flame optimization mppt algorithm for accurate real-time tracking under partially shaded photovoltaic system
publishDate	2021
url	http://umpir.ump.edu.my/id/eprint/34676/1/Hybrid%20moth%20flame%20optimization%20mppt%20algorithm%20for%20accurate%20real-time%20tracking.ir.pdf http://umpir.ump.edu.my/id/eprint/34676/
_version_	1748180676801200128
score	13.211869

Hybrid moth flame optimization mppt algorithm for accurate real-time tracking under partially shaded photovoltaic system

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