Abstract
PV systems work under different weather conditions such as uniform and partial shading weather conditions. This causes inconsistent power in PV systems. This paper presents a reconfigurable interconnections approach that uses and compares between two powerful maximum power point tracking (MPPT) techniques of artificial neuro-fuzzy inference system [ANFIS front-end distributive MPPT (DMPPT)] technique and Perturb&Observe distributive MPPT technique. This approach is introduced in order to decrease the partial shading and mismatch effect caused by varying light falling on the PV arrays, which will lead to extract more power from the PV modules. The PV systems were configured as series-connected PV string that uses Perturb&Observe MPPT technique and as a PV series-connected system that uses ANFIS-MPPT technique. The proposed PV systems were tested under uniform and partial shading weather conditions. The results show that MPPT could be tracked accurately with the ANFIS-DMPPT for both cases of uniform irradiance and partial shaded irradiance conditions.
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Farayola, A.M., Hasan, A.N., Ali, A., Twala, B. (2018). Distributive MPPT Approach Using ANFIS and Perturb&Observe Techniques Under Uniform and Partial Shading Conditions. In: Dash, S., Naidu, P., Bayindir, R., Das, S. (eds) Artificial Intelligence and Evolutionary Computations in Engineering Systems. Advances in Intelligent Systems and Computing, vol 668. Springer, Singapore. https://doi.org/10.1007/978-981-10-7868-2_3
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DOI: https://doi.org/10.1007/978-981-10-7868-2_3
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