This paper introduces non-electrical input based artificial neural network (ANN) maximum power point tracking (MPPT) technique to the solar powered water pumping system using brushless DC (BLDC) motor. The objective is to model a step size independent MPPT using neural network for water pumping application. A DC-DC boost converter is being utilized which is driven by ANN based MPPT to extract maximum power out of solar photovoltaic (SPV) array. And also responsible for soft starting of BLDC motor. Pulse width modulated (PWM) control of the voltage source inverter (VSI) using DC link voltage controller is used to control the speed of the BLDC motor. PWM signal is generated using the inbuilt encoder to perform the electronic commutation by hall signal sensing. Performance analysis of a BLDC motor driving pump system is carried out under the MATLAB/Simulink environment. And efficiency of the overall system is calculated under various irradiance conditions.
In this paper, a non-electrical input-based ANN MPPT is introduced for solar power water pumping system using BLDC motor. The objective was to introduce a step size independent MPPT technique and optimal modeling of the system. The outcomes have demonstrated that usage of ANN-based MPPT is one of conceivable option design step size independent operation of PV array driving water pumping system using BLDC motor. It has been observed that the system has excellent transient and steady-state performance over a wide range of irradiance. Results have proven the optimal performance of the system with the highest efficiency of 81.55% and maintain a continuous flow of water even at the lowest irradiance with an efficiency of 69.03%. Soft starting of BLDC motor is also achieved using a proposed method which is desirable for smooth operation of the motor pump set.
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