Novel intelligent MPP tracker and sliding mode control for decentralized street lighting systems using photovoltaic energy
This paper proposes a solution for powering an unlimited number of streetlight poles systems. Firstly, an intelligent MPPT battery charger incorporates battery state of charge monitoring and an Artificial Neural Network algorithm is incorporated to ensure optimal system performance. Secondly, an eff...
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2025-12-01
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| Series: | International Journal of Sustainable Engineering |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/19397038.2025.2453934 |
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| author | Hussain Attia Ali Al-Ataby Maen Takruri Amjad Omar |
| author_facet | Hussain Attia Ali Al-Ataby Maen Takruri Amjad Omar |
| author_sort | Hussain Attia |
| collection | DOAJ |
| description | This paper proposes a solution for powering an unlimited number of streetlight poles systems. Firstly, an intelligent MPPT battery charger incorporates battery state of charge monitoring and an Artificial Neural Network algorithm is incorporated to ensure optimal system performance. Secondly, an effective DC driver is proposed using a buck converter supported by a Sliding Mode Controller to achieve a smooth response under different loading conditions. Unlike traditional decentralised street lighting solutions, which focus mainly on LED dimming based on motion detection or basic MPPT algorithms, this paper proposes a hybrid approach that integrates an intelligent ANN-based MPPT battery charger with a robust SMC for load driving. The collected testing data are analysed to show effective MPPT battery charger and load driver using MATLAB/Simulink software. Simulation results demonstrate high performance of the proposed system, with accurate MPP location tracking by the ANN algorithm, achieving a Mean Square Error of 7.9467 × 10−5 and battery charging up to 80 % of SOC. The results also confirm an accurate controlling response of load driver with minimal voltage fluctuation of 0.25 mV and a low overshoot of 50 mV. The approach proposed enhances energy efficiency, making it ideal for large-scale, sustainable urban infrastructure. |
| format | Article |
| id | doaj-art-8f8803202cf244d5be61e34f62fa9d63 |
| institution | Kabale University |
| issn | 1939-7038 1939-7046 |
| language | English |
| publishDate | 2025-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | International Journal of Sustainable Engineering |
| spelling | doaj-art-8f8803202cf244d5be61e34f62fa9d632025-02-02T16:00:01ZengTaylor & Francis GroupInternational Journal of Sustainable Engineering1939-70381939-70462025-12-0118110.1080/19397038.2025.2453934Novel intelligent MPP tracker and sliding mode control for decentralized street lighting systems using photovoltaic energyHussain Attia0Ali Al-Ataby1Maen Takruri2Amjad Omar3Department of Electrical and Electronics Engineering, School of Engineering, American University of Ras Al Khaimah, Ras Al Khaimah, UAEDepartment of Electrical and Electronics Engineering, School of Engineering, American University of Ras Al Khaimah, Ras Al Khaimah, UAEElectrical Engineering Department, American University of the Middle East, Kuwait, KuwaitDepartment of Electrical and Communication Engineering, United Arab Emirates University, Al Ain, United Arab EmiratesThis paper proposes a solution for powering an unlimited number of streetlight poles systems. Firstly, an intelligent MPPT battery charger incorporates battery state of charge monitoring and an Artificial Neural Network algorithm is incorporated to ensure optimal system performance. Secondly, an effective DC driver is proposed using a buck converter supported by a Sliding Mode Controller to achieve a smooth response under different loading conditions. Unlike traditional decentralised street lighting solutions, which focus mainly on LED dimming based on motion detection or basic MPPT algorithms, this paper proposes a hybrid approach that integrates an intelligent ANN-based MPPT battery charger with a robust SMC for load driving. The collected testing data are analysed to show effective MPPT battery charger and load driver using MATLAB/Simulink software. Simulation results demonstrate high performance of the proposed system, with accurate MPP location tracking by the ANN algorithm, achieving a Mean Square Error of 7.9467 × 10−5 and battery charging up to 80 % of SOC. The results also confirm an accurate controlling response of load driver with minimal voltage fluctuation of 0.25 mV and a low overshoot of 50 mV. The approach proposed enhances energy efficiency, making it ideal for large-scale, sustainable urban infrastructure.https://www.tandfonline.com/doi/10.1080/19397038.2025.2453934Artificial neural network algorithmMPPT battery chargersliding mode controllerstreet light dimmingmotion sensor |
| spellingShingle | Hussain Attia Ali Al-Ataby Maen Takruri Amjad Omar Novel intelligent MPP tracker and sliding mode control for decentralized street lighting systems using photovoltaic energy International Journal of Sustainable Engineering Artificial neural network algorithm MPPT battery charger sliding mode controller street light dimming motion sensor |
| title | Novel intelligent MPP tracker and sliding mode control for decentralized street lighting systems using photovoltaic energy |
| title_full | Novel intelligent MPP tracker and sliding mode control for decentralized street lighting systems using photovoltaic energy |
| title_fullStr | Novel intelligent MPP tracker and sliding mode control for decentralized street lighting systems using photovoltaic energy |
| title_full_unstemmed | Novel intelligent MPP tracker and sliding mode control for decentralized street lighting systems using photovoltaic energy |
| title_short | Novel intelligent MPP tracker and sliding mode control for decentralized street lighting systems using photovoltaic energy |
| title_sort | novel intelligent mpp tracker and sliding mode control for decentralized street lighting systems using photovoltaic energy |
| topic | Artificial neural network algorithm MPPT battery charger sliding mode controller street light dimming motion sensor |
| url | https://www.tandfonline.com/doi/10.1080/19397038.2025.2453934 |
| work_keys_str_mv | AT hussainattia novelintelligentmpptrackerandslidingmodecontrolfordecentralizedstreetlightingsystemsusingphotovoltaicenergy AT alialataby novelintelligentmpptrackerandslidingmodecontrolfordecentralizedstreetlightingsystemsusingphotovoltaicenergy AT maentakruri novelintelligentmpptrackerandslidingmodecontrolfordecentralizedstreetlightingsystemsusingphotovoltaicenergy AT amjadomar novelintelligentmpptrackerandslidingmodecontrolfordecentralizedstreetlightingsystemsusingphotovoltaicenergy |