A High Gain DC-DC Converter with Grey Wolf Optimizer Based MPPT Algorithm for PV Fed BLDC Motor Drive

Photovoltaic (PV) water pumping systems are becoming popular these days. In PV water pumping, the role of the converter is most important, especially in the renewable energy-based PV systems case. This study focuses on one such application. In this proposed work, direct current (DC) based intermediate DC-DC power converter, i.e., a modified LUO (M-LUO) converter is used to extricate the availability of power in the high range from the PV array. The M-LUO converter is controlled efficiently by utilizing the Grey Wolf Optimizer (GWO)-based maximum power point tracking algorithm, which aids the smooth starting of a brushless DC (BLDC) motor. The voltage source inverter’s (VSI) fundamental switching frequency is achieved in the BLDC motor by electronic commutation. Hence, the occurrence of VSI losses due to a high switching frequency is eliminated. The GWO optimized algorithm is compared with the perturb and observe (P&O) and fuzzy logic based maximum power point tracking (MPPT) algorithms. However, by sensing the position of the rotor and comparing the reference speed with the actual speed, the speed of the BLDC motor is controlled by the proportional-integral (PI) controller. The recent advancement in motor drives based on distributed sources generates more demand for highly efficient permanent magnet (PM) motor drives, and this was the beginning of interest in BLDC motors. Thus, in this paper, the design of a high-gain boost converter optimized by a GWO algorithm is proposed to drive the BLDC-based pumping motor. The proposed work is simulated in MATLAB-SIMULINK, and the experimental results are verified using the dsPIC30F2010 controller

Optimally Tuned Interleaved Luo Converter for PV Array Fed BLDC Motor Driven Centrifugal Pumps Using Whale Optimization Algorithm—A Resilient Solution for Powering Agricultural Loads

The use of brushless direct current (BLDC) motors are gaining much prominence in water pumping systems (WPS), especially for agricultural purposes. In most cases, the BLDC based WPS is powered using electricity from the grid, which is vulnerable to disruptive events causing a resilience problem. However, to avoid the resilience issue, grid-interactive solar photovoltaics (PV) are being used, and this is due to the increased penetration of distributed generation sources into the grid. In these systems, based on the inherent nature of solar PV, power converters are preferred, and as a result, problems like switching losses and maintaining steady-state voltages are commonly seen. In this paper, a framework of PV powered WPS with scope for optimizing controller parameters is proposed to avoid the above-raised issues. Based on the proposed framework, the overall structure of the PV powered WPS is modeled, designed, and analyzed. In the proposed system, the power output from solar PV is fed to the BLDC motor and the grid. If any problem arises in obtaining the power from solar PV, grid-interaction helps to run the motor at required speeds making the WPS resilient to unexpected disruptions and vice versa. For retrieving the generated power from PV array, a positive interleaved Luo converter (I-Luo) is used, which boosts the output with minimum switching losses. To maintain the steady-state voltage at the output of the I-Luo converter, a proportional-integral (PI) controller whose parameters are tuned by whale optimization algorithm (WOA) is used. This voltage is fetched to the BLDC motor via a 3-phase (3-Ф) inverter and then to the grid via a single-phase (1-Ф) inverter. The overall system is simulated and experimentally validated, with a detailed analysis of the observed results. The results include the various performance characteristics of the solar PV, converter, and BLDC motor. Besides, by using the field-programmable gate array (FPGA) based SPARTAN6E controller, the performance of the I-Luo is examined experimentally