PROJECT TITLE :

Artificial neural network-based photovoltaic maximum power point tracking techniques: a survey

ABSTRACT:

Recent researches oriented to photovoltaic (PV) systems feature booming interest in current decade. For potency improvement, most power purpose tracking (MPPT) of PV array output power is necessary. Although classical MPPT techniques provide simplified structure and implementation, their performance is degraded when put next with artificial intelligence-based mostly techniques especially throughout partial shading and rapidly changing environmental conditions. Artificial neural network (ANN) algorithms feature many capabilities like: (i) off-line training, (ii) nonlinear mapping, (iii) high-speed response, (iv) sturdy operation, (v) less computational effort and (vi) compact solution for multiple-variable issues. Hence, ANN algorithms are widely applied as PV MPPT techniques. Among various on the market ANN-primarily based PV MPPT techniques, very restricted references gather those techniques as a survey. Neither classification nor comparisons between those competitors exist. Moreover, no detailed analysis of the system performance underneath those techniques has been previously discussed. This study presents a close survey for ANN based PV MPPT techniques. The authors propose new categorisation for ANN PV MPPT techniques primarily based on controller structure and input variables. Yet, a detailed comparison between those techniques from several points of view, like ANN structure, experimental verification and transient/steady-state performance is presented. Recent references are taken into consideration for update purpose.


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