Publication Type : Journal Article
Publisher : Elsevier BV
Source : Computers & Electrical Engineering
Url : https://doi.org/10.1016/j.compeleceng.2020.106730
Keywords : Maximum power point tracking, Radial basis function neural network, Photovoltaic array, Partial shading condition, Single-ended primary inductor converter, Brushless DC motor
Campus : Haridwar
School : School of Computing
Year : 2020
Abstract : The integration of artificial intelligence (AI) control techniques for efficient energy extraction will provide the solar energy systems with increased efficiency. Therefore, this manuscript proposes a solar water pumping system topology using a radial basis function neural network (RBFNN) to effectively track the maximum power point (MPP) in a photovoltaic (PV) array fed brushless DC (BLDC) motor drive. The RBFNN maximum power point tracking (MPPT) predicts the duty ratio of a single-ended primary inductor converter (SEPIC) to reach the MPP. The performance of the system under study is compared to trivial MPPT techniques with varying irradiance, temperature and partial shading condition (PSC). The performance in terms of voltage ripple, current ripple, average power loss, MPP settling time, efficiency, torque ripple and stator current total harmonic distortion (THD) is evaluated to show the effectiveness of the proposed MPPT method.
Cite this Research Publication : Surabhi Chandra, Prerna Gaur, Diwaker Pathak, Radial basis function neural network based maximum power point tracking for photovoltaic brushless DC motor connected water pumping system, Computers & Electrical Engineering, Elsevier BV, 2020, https://doi.org/10.1016/j.compeleceng.2020.106730