Treffer: 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 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. [ABSTRACT FROM AUTHOR]

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