Improving maximum power point tracking efficiency in solar photovoltaic systems using super-twisting algorithm and grey wolf optimizer

IF 2.6 4区工程技术 Q3 ENERGY & FUELS

IET Renewable Power Generation Pub Date : 2024-10-19 DOI:10.1049/rpg2.13138

Nassir Deghfel, Abd Essalam Badoud, Ahmad Aziz Al-Ahmadi, Mohit Bajaj, Ievgen Zaitsev, Sherif S. M. Ghoneim

{"title":"Improving maximum power point tracking efficiency in solar photovoltaic systems using super-twisting algorithm and grey wolf optimizer","authors":"Nassir Deghfel, Abd Essalam Badoud, Ahmad Aziz Al-Ahmadi, Mohit Bajaj, Ievgen Zaitsev, Sherif S. M. Ghoneim","doi":"10.1049/rpg2.13138","DOIUrl":null,"url":null,"abstract":"<p>This study presents a new Maximum Power Point Tracking (MPPT) approach for solar photovoltaic (PV) systems, combining the Super-Twisting Algorithm (STA) and Grey Wolf Optimizer (GWO). The STA-GWO-MPPT method improves efficiency in dynamic conditions by using STA for control and GWO for parameter optimization, enhancing stability and robustness. Performance evaluation is conducted through MATLAB/Simulink simulations and experimental validation on a small-scale test bench. Various quantitative metrics, including rise time, settling time, power production, efficiency, root mean square error (RMSE), and standard deviation (STD), are employed for assessment. Results indicate significantly faster convergence speeds for the proposed method compared to conventional MPPT techniques. Specifically, the rise time for the proposed method is 0.0129 seconds, outperforming Fuzzy Logic Control (FLC) (0.2638 seconds) and Grey Wolf Optimizer with Sliding Mode Control (GWO-SMC) (0.0181 seconds). Additionally, the proposed method exhibits superior tracking efficiency, with an average efficiency of 99.33%, surpassing FLC (96.93%) and GWO-SMC (99.19%). Moreover, it reduces power fluctuations, with an RMSE of 7.819% and STD of 6.547%, compared to FLC (RMSE: 13.471%, STD: 4.519%) and GWO-SMC (RMSE: 8.507%, STD: 6.108%). Overall, this study contributes valuable insights into enhancing MPPT efficiency in solar PV systems, with implications for both research and practical applications.</p>","PeriodicalId":55000,"journal":{"name":"IET Renewable Power Generation","volume":"18 15","pages":"3329-3354"},"PeriodicalIF":2.6000,"publicationDate":"2024-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/rpg2.13138","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IET Renewable Power Generation","FirstCategoryId":"5","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1049/rpg2.13138","RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"ENERGY & FUELS","Score":null,"Total":0}

引用次数: 0

Abstract

This study presents a new Maximum Power Point Tracking (MPPT) approach for solar photovoltaic (PV) systems, combining the Super-Twisting Algorithm (STA) and Grey Wolf Optimizer (GWO). The STA-GWO-MPPT method improves efficiency in dynamic conditions by using STA for control and GWO for parameter optimization, enhancing stability and robustness. Performance evaluation is conducted through MATLAB/Simulink simulations and experimental validation on a small-scale test bench. Various quantitative metrics, including rise time, settling time, power production, efficiency, root mean square error (RMSE), and standard deviation (STD), are employed for assessment. Results indicate significantly faster convergence speeds for the proposed method compared to conventional MPPT techniques. Specifically, the rise time for the proposed method is 0.0129 seconds, outperforming Fuzzy Logic Control (FLC) (0.2638 seconds) and Grey Wolf Optimizer with Sliding Mode Control (GWO-SMC) (0.0181 seconds). Additionally, the proposed method exhibits superior tracking efficiency, with an average efficiency of 99.33%, surpassing FLC (96.93%) and GWO-SMC (99.19%). Moreover, it reduces power fluctuations, with an RMSE of 7.819% and STD of 6.547%, compared to FLC (RMSE: 13.471%, STD: 4.519%) and GWO-SMC (RMSE: 8.507%, STD: 6.108%). Overall, this study contributes valuable insights into enhancing MPPT efficiency in solar PV systems, with implications for both research and practical applications.

查看原文本刊更多论文

利用超级扭曲算法和灰狼优化器提高太阳能光伏系统的最大功率点跟踪效率

本研究提出了一种新的太阳能光伏（PV）系统最大功率点跟踪（MPPT）方法，该方法结合了超级扭曲算法（STA）和灰狼优化器（GWO）。STA-GWO-MPPT 方法通过使用 STA 进行控制和 GWO 进行参数优化，提高了动态条件下的效率，增强了稳定性和鲁棒性。性能评估是通过 MATLAB/Simulink 仿真和小型试验台的实验验证进行的。评估采用了各种量化指标，包括上升时间、稳定时间、发电量、效率、均方根误差 (RMSE) 和标准偏差 (STD)。结果表明，与传统的 MPPT 技术相比，拟议方法的收敛速度明显更快。具体地说，所提方法的上升时间为 0.0129 秒，优于模糊逻辑控制（FLC）（0.2638 秒）和带滑动模式控制的灰狼优化器（GWO-SMC）（0.0181 秒）。此外，所提出的方法还具有卓越的跟踪效率，平均效率高达 99.33%，超过了 FLC（96.93%）和 GWO-SMC（99.19%）。此外，与 FLC（RMSE：13.471%，STD：4.519%）和 GWO-SMC（RMSE：8.507%，STD：6.108%）相比，它减少了功率波动，RMSE 为 7.819%，STD 为 6.547%。总之，这项研究为提高太阳能光伏系统的 MPPT 效率提供了宝贵的见解，对研究和实际应用都具有重要意义。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

IET Renewable Power Generation 工程技术-工程：电子与电气

CiteScore

6.80

自引率

11.50%

发文量

268

审稿时长

6.6 months

期刊介绍： IET Renewable Power Generation (RPG) brings together the topics of renewable energy technology, power generation and systems integration, with techno-economic issues. All renewable energy generation technologies are within the scope of the journal. Specific technology areas covered by the journal include: Wind power technology and systems Photovoltaics Solar thermal power generation Geothermal energy Fuel cells Wave power Marine current energy Biomass conversion and power generation What differentiates RPG from technology specific journals is a concern with power generation and how the characteristics of the different renewable sources affect electrical power conversion, including power electronic design, integration in to power systems, and techno-economic issues. Other technologies that have a direct role in sustainable power generation such as fuel cells and energy storage are also covered, as are system control approaches such as demand side management, which facilitate the integration of renewable sources into power systems, both large and small. The journal provides a forum for the presentation of new research, development and applications of renewable power generation. Demonstrations and experimentally based research are particularly valued, and modelling studies should as far as possible be validated so as to give confidence that the models are representative of real-world behavior. Research that explores issues where the characteristics of the renewable energy source and their control impact on the power conversion is welcome. Papers covering the wider areas of power system control and operation, including scheduling and protection that are central to the challenge of renewable power integration are particularly encouraged. The journal is technology focused covering design, demonstration, modelling and analysis, but papers covering techno-economic issues are also of interest. Papers presenting new modelling and theory are welcome but this must be relevant to real power systems and power generation. Most papers are expected to include significant novelty of approach or application that has general applicability, and where appropriate include experimental results. Critical reviews of relevant topics are also invited and these would be expected to be comprehensive and fully referenced. Current Special Issue. Call for papers: Power Quality and Protection in Renewable Energy Systems and Microgrids - https://digital-library.theiet.org/files/IET_RPG_CFP_PQPRESM.pdf Energy and Rail/Road Transportation Integrated Development - https://digital-library.theiet.org/files/IET_RPG_CFP_ERTID.pdf