{"title":"基于智能的并网光伏系统最大功率跟踪优化算法综合比较研究","authors":"Marlin S, Sundarsingh Jebaseelan","doi":"10.1016/j.suscom.2023.100946","DOIUrl":null,"url":null,"abstract":"<div><p><span>For maximum power point tracking (MPPT) in the solar Photovolatic (PV) system, the meta-heuristic optimization techniques have been widely applied in the last few decades. This is due to the fact that traditional MPPT methodologies are unable to monitor the global MPP in the face of shifting environmental factors. Hence, it is essential to use an intelligence based controlling algorithm for MPPT controlling. The main purpose of this study is to investigate and assess the effectiveness of three cutting-edge and distinctive </span>optimization algorithms<span> for MPPT controlling, including Mongoose Optimization (MO), Prairie Dog Optimization Algorithm (PDOA), and hybrid PDOA + MO. It also aims to select the most effective and sophisticated optimization algorithm to meet the grid systems' energy requirements. This research's original contribution is the implementation and performance evaluation of three alternative meta-heuristic models for MPPT controlling. The goal of this effort is to maximize the energy yield from photovoltaic systems<span> in order to meet the energy demands of grid systems<span>. Three different controlling strategies, including MO + MPPT, PDOA + MPPT, and MO + PDOA + MPPT, are used in this work to achieve this goal. To evaluate the effectiveness and improved performance outcomes, a number of parameters have been taken into account in this work, including time, error, power, THD, and others. Furthermore, using a comprehensive simulation and comparison study, the outcomes of the MO, PDOA, and hybrid PDOA + MO techniques have also been tested and confirmed in this work. Comparisons are also made between the peak, settling, and increasing times of the present and proposed regulatory models. The results and waveforms generated demonstrate that the hybrid PDOA + MO performs better than the other controlling models in terms of enhanced efficiency of 99.5 %, low rising time of 1.6 s, low peak time of 1.05 s, minimal settling time of 1.24 s, error rate of 0.48, response time of 0.005 s, and tracking time of 0.0019 s</span></span></span></p></div>","PeriodicalId":48686,"journal":{"name":"Sustainable Computing-Informatics & Systems","volume":"41 ","pages":"Article 100946"},"PeriodicalIF":3.8000,"publicationDate":"2023-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A comprehensive comparative study on intelligence based optimization algorithms used for maximum power tracking in grid-PV systems\",\"authors\":\"Marlin S, Sundarsingh Jebaseelan\",\"doi\":\"10.1016/j.suscom.2023.100946\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p><span>For maximum power point tracking (MPPT) in the solar Photovolatic (PV) system, the meta-heuristic optimization techniques have been widely applied in the last few decades. This is due to the fact that traditional MPPT methodologies are unable to monitor the global MPP in the face of shifting environmental factors. Hence, it is essential to use an intelligence based controlling algorithm for MPPT controlling. The main purpose of this study is to investigate and assess the effectiveness of three cutting-edge and distinctive </span>optimization algorithms<span> for MPPT controlling, including Mongoose Optimization (MO), Prairie Dog Optimization Algorithm (PDOA), and hybrid PDOA + MO. It also aims to select the most effective and sophisticated optimization algorithm to meet the grid systems' energy requirements. This research's original contribution is the implementation and performance evaluation of three alternative meta-heuristic models for MPPT controlling. The goal of this effort is to maximize the energy yield from photovoltaic systems<span> in order to meet the energy demands of grid systems<span>. Three different controlling strategies, including MO + MPPT, PDOA + MPPT, and MO + PDOA + MPPT, are used in this work to achieve this goal. To evaluate the effectiveness and improved performance outcomes, a number of parameters have been taken into account in this work, including time, error, power, THD, and others. Furthermore, using a comprehensive simulation and comparison study, the outcomes of the MO, PDOA, and hybrid PDOA + MO techniques have also been tested and confirmed in this work. Comparisons are also made between the peak, settling, and increasing times of the present and proposed regulatory models. The results and waveforms generated demonstrate that the hybrid PDOA + MO performs better than the other controlling models in terms of enhanced efficiency of 99.5 %, low rising time of 1.6 s, low peak time of 1.05 s, minimal settling time of 1.24 s, error rate of 0.48, response time of 0.005 s, and tracking time of 0.0019 s</span></span></span></p></div>\",\"PeriodicalId\":48686,\"journal\":{\"name\":\"Sustainable Computing-Informatics & Systems\",\"volume\":\"41 \",\"pages\":\"Article 100946\"},\"PeriodicalIF\":3.8000,\"publicationDate\":\"2023-11-30\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Sustainable Computing-Informatics & Systems\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2210537923001014\",\"RegionNum\":3,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"COMPUTER SCIENCE, HARDWARE & ARCHITECTURE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Sustainable Computing-Informatics & Systems","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2210537923001014","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, HARDWARE & ARCHITECTURE","Score":null,"Total":0}
A comprehensive comparative study on intelligence based optimization algorithms used for maximum power tracking in grid-PV systems
For maximum power point tracking (MPPT) in the solar Photovolatic (PV) system, the meta-heuristic optimization techniques have been widely applied in the last few decades. This is due to the fact that traditional MPPT methodologies are unable to monitor the global MPP in the face of shifting environmental factors. Hence, it is essential to use an intelligence based controlling algorithm for MPPT controlling. The main purpose of this study is to investigate and assess the effectiveness of three cutting-edge and distinctive optimization algorithms for MPPT controlling, including Mongoose Optimization (MO), Prairie Dog Optimization Algorithm (PDOA), and hybrid PDOA + MO. It also aims to select the most effective and sophisticated optimization algorithm to meet the grid systems' energy requirements. This research's original contribution is the implementation and performance evaluation of three alternative meta-heuristic models for MPPT controlling. The goal of this effort is to maximize the energy yield from photovoltaic systems in order to meet the energy demands of grid systems. Three different controlling strategies, including MO + MPPT, PDOA + MPPT, and MO + PDOA + MPPT, are used in this work to achieve this goal. To evaluate the effectiveness and improved performance outcomes, a number of parameters have been taken into account in this work, including time, error, power, THD, and others. Furthermore, using a comprehensive simulation and comparison study, the outcomes of the MO, PDOA, and hybrid PDOA + MO techniques have also been tested and confirmed in this work. Comparisons are also made between the peak, settling, and increasing times of the present and proposed regulatory models. The results and waveforms generated demonstrate that the hybrid PDOA + MO performs better than the other controlling models in terms of enhanced efficiency of 99.5 %, low rising time of 1.6 s, low peak time of 1.05 s, minimal settling time of 1.24 s, error rate of 0.48, response time of 0.005 s, and tracking time of 0.0019 s
期刊介绍：
Sustainable computing is a rapidly expanding research area spanning the fields of computer science and engineering, electrical engineering as well as other engineering disciplines. The aim of Sustainable Computing: Informatics and Systems (SUSCOM) is to publish the myriad research findings related to energy-aware and thermal-aware management of computing resource. Equally important is a spectrum of related research issues such as applications of computing that can have ecological and societal impacts. SUSCOM publishes original and timely research papers and survey articles in current areas of power, energy, temperature, and environment related research areas of current importance to readers. SUSCOM has an editorial board comprising prominent researchers from around the world and selects competitively evaluated peer-reviewed papers.
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