Mppt Solar Street Light With Iot-enabled Vision-assisted Adaptive Luminous Control

Abstract

Conventional Solar Street Lighting Systems Generally Operate With Fixed Illumination Levels And Non-optimal Power Extraction, Resulting In Inefficient Utilization Of Harvested Solar Energy. This Paper Presents The Modelling, Simulation And Analysis Of A Maximum Power Point Tracking (MPPT) Based Solar Street Lighting System Integrated With An Internet Of Things (IoT)-enabled, Vision-assisted Adaptive Luminous Control Strategy. A Perturb And Observe (P&O) MPPT Algorithm, Implemented On A DsPIC30F2010 Digital Signal Controller, Regulates The Duty Cycle Of A SEPIC DC–DC Converter To Extract Maximum Power From A 50 W Monocrystalline Photovoltaic (PV) Module During The Day. At Night, The Same Converter Is Reconfigured Through A Relay-based Changeover To Drive The LED Load With A Duty Cycle Jointly Determined By The Day's Harvested Energy, Expressed As An Energy Sufficiency Ratio, And Real-time Road Activity Sensed By An ESP32-CAM Vision Module, Ensuring That Continuous 12-hour Illumination Is Guaranteed Irrespective Of Solar Variability While Never Compromising Visibility For Detected Pedestrians Or Vehicles. The Complete System, Including The Single-diode PV Model, The Fourth-order SEPIC Converter, The P&O Control Law And The Two-layer Adaptive-brightness Controller, Is Modelled And Simulated In MATLAB/Simulink. Simulation Results Show Stable Maximum-power-point Operation With An MPPT Tracking Efficiency Of 99.83%, A Cold-start Convergence Time Of Approximately 50 Ms, An Estimated Converter Efficiency Of Approximately 90%, And An Average Night-time Energy Saving Of 58–61% Relative To Fixed Full-brightness Operation. A Day-level Energy Balance Under A 5 Peak-sun-hour Irradiance Profile And A 12 V/18 Ah Battery Confirms A Positive Surplus Of +49.1 Wh Under Full-brightness, Full-night (12 H) Operation And +121.1 Wh Under Adaptive Dimming, Providing Resilience Against Consecutive Overcast Days. The Results Confirm That The Proposed Architecture Provides An Efficient, Low-cost, Scalable And Intelligent Solution For Adaptive Solar Street Lighting Suitable For Rural And Semi-urban Deployment, And Establish A Validated Simulation Baseline For Subsequent Hardware Realization.

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