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MPTC is an important part of the MPPT system used in SPV and wind power generation. Especially in PV, it’s too difficult to implement an efficient MPTC (maximum power tracking controller) due to its nonlinear characteristics and generated power is fully distorted when a sudden change in weather conditions. Whereas MPPT with a conventional controller is limited with accuracy in power tracking, efficiency, response time, design complexity, overshoots. In this paper, an Adaptive Neuro-Fuzzy Inference System-based MPPT is proposed to overcome the difficulties in FLMPPT. The proposed controller is designed for 20 KWp SPV power generation and analysed its performance under three different PV input conditions. In this paper, the proposed controller test results are also compared with FLCMPPT in all three dissimilar conditions (STC, DTC-I &amp; DTC-II). ANFIS-MPPT controller implemented in MATLAB/Simulation 2016 workspace, the proposed new artificial intelligence approach composed with five layers and 25 fuzzy rules, from the test results, the designed controller has better features like efficiency, response time, design complexity and overshoots compare to FLC under different atmospheric circumstances.

Implementation of anfis-mptc for 20 kwp spv power generation and comparison with flmppt under dissimilar conditions