K-Nearest Neighbors hybrid method for maximum power point tracking under partial shading for photovoltaic power systems
Partial shading conditions (PSC) can significantly reduce the performance of solar photovoltaic (PV) systems. Consequently, to enhance maximum power point tracking (MPPT) under such conditions, the implementation of advanced algorithms is imperative for effectively addressing the challenges posed by...
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Elsevier
2025-09-01
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| Series: | Results in Engineering |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2590123025027616 |
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| author | Djamel Guessoum Maen Takrouri Mohammad Rabih Maissa Farhat Sufian A. Badawi |
| author_facet | Djamel Guessoum Maen Takrouri Mohammad Rabih Maissa Farhat Sufian A. Badawi |
| author_sort | Djamel Guessoum |
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| description | Partial shading conditions (PSC) can significantly reduce the performance of solar photovoltaic (PV) systems. Consequently, to enhance maximum power point tracking (MPPT) under such conditions, the implementation of advanced algorithms is imperative for effectively addressing the challenges posed by multiple local power peaks. This paper presents a hybrid approach that integrates the K-Nearest Neighbors (KNN) machine learning algorithm with an enhanced local search for optimizing the duty cycle D. This method is inspired by the Perturb & Observe (P&O) algorithm. The KNN algorithm serves as an initial step to identify the irradiance levels around the actual solar irradiation, along with their corresponding maximum power (Pmax) and maximum voltage (Vmpp). Using these values in conjunction with the load, a set of duty cycles (Dth.) is calculated, creating a limited search space for the optimal duty cycles. The collected dataset inputs are the non-uniform solar irradiance levels ranging from 100 W/m² to 1000 W/m², incrementing by 50 W/m². It includes all possible combinations of irradiation conditions, with the outputs being the Pmax and the corresponding Vmpp. The proposed method has surpassed various optimization techniques for MPPT, including particle swarm optimization (PSO) and Cuckoo search (CS). It achieved an impressive efficiency of 98.227%, enhanced stability around the maximum power point (MPP), and a fast tracking speed of 3.9635 ms. Furthermore, this method has been used to initialize PSO, CS, and P&O algorithms, significantly improving their overall performance under PSC. Moreover, the KNN method proved to be highly adaptable to variations in PSC and rapid changes in solar irradiation. |
| format | Article |
| id | doaj-art-d26bf6ac38fd45b5ae7d7e3e568d0a1f |
| institution | Kabale University |
| issn | 2590-1230 |
| language | English |
| publishDate | 2025-09-01 |
| publisher | Elsevier |
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| series | Results in Engineering |
| spelling | doaj-art-d26bf6ac38fd45b5ae7d7e3e568d0a1f2025-08-20T03:36:40ZengElsevierResults in Engineering2590-12302025-09-012710669410.1016/j.rineng.2025.106694K-Nearest Neighbors hybrid method for maximum power point tracking under partial shading for photovoltaic power systemsDjamel Guessoum0Maen Takrouri1Mohammad Rabih2Maissa Farhat3Sufian A. Badawi4Djamel Guessoum, Center of Information, Communication and Networking Education and Innovation (ICONET), American University of Ras Al Khaimah, Ras Al Khaimah, United Arab Emirates; Corresponding author.Maen Takruri, College of Engineering and Technology, American University of the Middle East, Egaila 54200, KuwaitMohammad Rabih, Department of Electrical Engineering, American University of Sharjah, United Arab EmiratesMaissa Farhat, Department of Electrical, and Electronics Engineering, American University of Ras Al Khaimah, United Arab EmiratesSufian A. Badawi, Department of Computer Science, Faculty of Information Technology, Applied Science Private University, P.O.Box 541350, Amman 11937, JordanPartial shading conditions (PSC) can significantly reduce the performance of solar photovoltaic (PV) systems. Consequently, to enhance maximum power point tracking (MPPT) under such conditions, the implementation of advanced algorithms is imperative for effectively addressing the challenges posed by multiple local power peaks. This paper presents a hybrid approach that integrates the K-Nearest Neighbors (KNN) machine learning algorithm with an enhanced local search for optimizing the duty cycle D. This method is inspired by the Perturb & Observe (P&O) algorithm. The KNN algorithm serves as an initial step to identify the irradiance levels around the actual solar irradiation, along with their corresponding maximum power (Pmax) and maximum voltage (Vmpp). Using these values in conjunction with the load, a set of duty cycles (Dth.) is calculated, creating a limited search space for the optimal duty cycles. The collected dataset inputs are the non-uniform solar irradiance levels ranging from 100 W/m² to 1000 W/m², incrementing by 50 W/m². It includes all possible combinations of irradiation conditions, with the outputs being the Pmax and the corresponding Vmpp. The proposed method has surpassed various optimization techniques for MPPT, including particle swarm optimization (PSO) and Cuckoo search (CS). It achieved an impressive efficiency of 98.227%, enhanced stability around the maximum power point (MPP), and a fast tracking speed of 3.9635 ms. Furthermore, this method has been used to initialize PSO, CS, and P&O algorithms, significantly improving their overall performance under PSC. Moreover, the KNN method proved to be highly adaptable to variations in PSC and rapid changes in solar irradiation.http://www.sciencedirect.com/science/article/pii/S2590123025027616KNNPhotovoltaic power systemMPPTDC/DC converterPartial shading |
| spellingShingle | Djamel Guessoum Maen Takrouri Mohammad Rabih Maissa Farhat Sufian A. Badawi K-Nearest Neighbors hybrid method for maximum power point tracking under partial shading for photovoltaic power systems Results in Engineering KNN Photovoltaic power system MPPT DC/DC converter Partial shading |
| title | K-Nearest Neighbors hybrid method for maximum power point tracking under partial shading for photovoltaic power systems |
| title_full | K-Nearest Neighbors hybrid method for maximum power point tracking under partial shading for photovoltaic power systems |
| title_fullStr | K-Nearest Neighbors hybrid method for maximum power point tracking under partial shading for photovoltaic power systems |
| title_full_unstemmed | K-Nearest Neighbors hybrid method for maximum power point tracking under partial shading for photovoltaic power systems |
| title_short | K-Nearest Neighbors hybrid method for maximum power point tracking under partial shading for photovoltaic power systems |
| title_sort | k nearest neighbors hybrid method for maximum power point tracking under partial shading for photovoltaic power systems |
| topic | KNN Photovoltaic power system MPPT DC/DC converter Partial shading |
| url | http://www.sciencedirect.com/science/article/pii/S2590123025027616 |
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