Title: Adaptive-fuzzy detection and assessment of partial shading in solar panels feeding induction motor drives
Authors: Mohamed A. Awadallah; Fawzan Salem
Addresses: Department of Electrical Power and Machines, University of Zagazig, Zagazig 44111, Egypt ' Power Electronics and Energy Conversion Department, Electronics Research Institute (ERI), Cairo 12622, Egypt
Abstract: The paper presents a methodology based on adaptive neuro-fuzzy inference systems (ANFIS) for the detection and assessment of partial shading conditions in solar photovoltaic (PV) panels. The impact of partial shading on the operation of the induction motor drive load is also studied. The solar panel is modelled under normal and partial shading conditions for performance comparison. Three ANFIS agents are designed, trained, and tested for full identification of the partial shading condition. One ANFIS agent detects the initiation of partial shading, another agent determines the shading factor, and a third agent infers the number of shaded modules of the panel. Results show excellent performance of ANFIS on the detection and assessment of partial shading. Impact on the induction motor drive operation is found insignificant.
Keywords: solar panels; PV panels; partial shade; adaptive neuro-fuzzy inference systems; ANFIS; induction motor drives; solar energy; solar power; neural networks; fuzzy logic; modelling; shading impact.
International Journal of Industrial Electronics and Drives, 2015 Vol.2 No.3, pp.151 - 162
Available online: 27 Oct 2015 *Full-text access for editors Access for subscribers Purchase this article Comment on this article