Abstract
In this research paper, a hybrid Artificial Neural Network (ANN)-Fuzzy Logic Control (FLC) tuned Flower Pollination Algorithm (FPA) as a Maximum Power Point Tracker (MPPT) is employed to amend root mean square error (RMSE) of photovoltaic (PV) modeling. Moreover, Gaussian membership functions have been considered for fuzzy controller design. This paper interprets the Luo converter occupied brushless DC motor (BLDC)-directed PV water pump application. Experimental responses certify the effectiveness of the suggested motor-pump system supporting diverse operating states. The Luo converter, a newly developed DC-DC converter, has high power density, better voltage gain transfer and superior output waveform and can track optimal power from PV modules. For BLDC speed control there is no extra circuitry, and phase current sensors are enforced for this scheme. The most recent attempt using adaptive neuro-fuzzy inference system (ANFIS)-FPA-operated BLDC directed PV pump with advanced Luo converter, has not been formerly conferred.
Highlights
As conventional energy sources are depleting day by day, the demand forrenewable energy sources is raising [1,2,3]
Performance justification of the brushless DC (BLDC)-driven PV pumping-employed Luo converter has been done through the dSPACE controller
Electronic commutation/controlling BLDC has been executed by obtaining hall pulses from the input/output generated pulses are outturned to the inverter
Summary
As conventional energy sources are depleting day by day, the demand forrenewable energy sources is raising [1,2,3]. The classical particle swarm optimization PSO technique has randomness in acceleration value with high regulation parameters as major problems Variance of this algorithm is capitulated when randomness is miniaturized. Ram et al [22] has discussed the FPA algorithm for PV MPPT under dynamic operating conditions This algorithm provides single-stage global searching, simpler coding, and lower tuned specification requirements with low cost implementation and has fast response compared to P&O and PSO techniques under dynamic weather conditions. Compared to the FPA algorithm, the merits of the hybrid ANFIS-Flower Pollination Algorithm (FPA) are simple implementation, high convergence speed with tune parameters and easier code compilation. The most recent attempt, using adaptive neuro-fuzzy inference system (ANFIS)-FPA-operated BLDC directed PV pump with advanced Luo. Energies 2018, 11, x FOR PEER REVIEW converter, been formerly conferred has and not examined using dSPACE (DS1104).
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