AUTHORS: Rati Wongsathan

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ABSTRACT: Maximum power point tracking (MPPT) is a significant technique in order to handle the maximum power utilization of the photovoltaic regulator. By this technique, the operated voltage is perturbed from the closed-loop control algorithm untill reaching the MPP by changing the duty cycle ratio of the DC-DC converter ciucuit. To achieve this power management, this paper integrated offline genetic algorithm (GA) with fuzzy logic (FL) and neuro-fuzzy (NF) to proposed FL controller (FLC-GA) and NF controller (NFC-GA). A MultiObjective Hierarchical GA (MOHGA) is applied to select the control rules silmutaneously to fine the tuned system parameters. Furthermore, the intentional aim of this paper is to find a minimum set of fuzzy rule that can achieve the MPP with acceptable accuracy. Conseqently, the optimized FLC-GA and optimized NFC-GA are alternately proposed to optimize the complexity of the FLC-GA and NFC-GA in order to avoid the overfitting computation which may lead to miss the MPP. The measured voltage and current are directly computed the slope of P-V curve and its change to set as the inputs of the controller while the duty cycle ratio is generated for the controlled output at the given various change weather conditions. Unlike focusing only on the change of the irradiance effect, in this work the panel temperature changing is together considered especially for the case of low irradiance and high temperature. Fast risetime response at the transient state and the stabilzed accuracy at the steady state are used as the controlled performance assesments. In order to prove the effectiveness of the proposed hybrid controllers, the simulations are tested before the practically implementation by using Matlab/Simulink. From the simulation results, the FLC-GA dominantly performs the best stabilized accuracy compared to the optimized NFC-GA, the NFC-GA, the optimized FLC, the conventional FLC, the IC method and the P&O method respectively. In the case of the rise time, the optimized NFC-GA dominantly performs the fastest tracking to the MPP than the NFC-GA, the optimized FLC-GA, the conventional FLC, the FLC-GA, the P&O method, and the IC method respectively. Trading off between the fast time response and stabilized accuracy, the optimized NFC-GA performs most the best.

KEYWORDS: Optimized Fuzzy logic control, Optimized Neuro-fuzzy control, Multi-objective hierarchical genetic algorithm, and MPPT.

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