Curve fitting polynomial technique compared to ANFIS technique for maximum power point tracking

Title: Curve fitting polynomial technique compared to ANFIS technique for maximum power point tracking
Creator: Farayola, Adedayo M., Hasan, Ali N., Ali, Ahmad
Subject: ANFIS, Artificial Intelligence (AI), Curve fitting polynomials
Date: 2017
Type: Conference proceedings
Identifier: http://hdl.handle.net/10210/232998
Identifier: uj:23778
Identifier: Citation: Farayola, A.M., Hasan, A.N. & Ali, A. 2017. Curve fitting polynomial technique compared to ANFIS technique for maximum power point tracking. The 8th International Renewable Energy Congress (IREC 2017).
Description: Abstract: In this paper, an approach of designing a fast tracking MPPT is introduced using a predicted sixth order polynomial curve fitting MPPT technique. The results are compared with the lower order polynomials curve fitting MPPT and also compared with the Artificial Neuro-Fuzzy Inference System (ANFIS) results. The polynomials were generated from an offline solar data. This work was done to validate the effect of using a higher order polynomials under various weather conditions using modified CUK DC-DC converter. Findings suggest that using the 6th order polynomial curve fitting and the ANFIS techniques could track the highest maximum power point than the lower order curve techniques.
Language: English
Rights: ©2017, authors
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