Recent researches oriented to photovoltaic (PV) systems feature booming interest in current decade. For efficiency improvement, maximum power point tracking (MPPT) of PV array output power is mandatory. Although classical MPPT techniques offer simplified structure and implementation, their performance is degraded when compared with artificial intelligence-based techniques especially during partial shading and rapidly changing environmental conditions. Artificial neural network (ANN) algorithms feature several capabilities such as: (i) off-line training, (ii) nonlinear mapping, (iii) high-speed response, (iv) robust operation, (v) less computational effort and (vi) compact solution for multiple-variable problems. Hence, ANN algorithms have been widely applied as PV MPPT techniques. Among various available ANN-based PV MPPT techniques, very limited references gather those techniques as a survey. Neither classification nor comparisons between those competitors exist. Moreover, no detailed analysis of the system performance under those techniques has been previously discussed. This study presents a detailed survey for ANN based PV MPPT techniques. The authors propose new categorisation for ANN PV MPPT techniques based on controller structure and input variables. In addition, a detailed comparison between those techniques from several points of view, such as ANN structure, experimental verification and transient/steady-state performance is presented. Recent references are taken into consideration for update purpose.
The dependency of photovoltaic (PV) arrays on temperature and irradiance levels shapes their known non linear behavior; hence maximum power point tracking (MPPT) is mandatory. Traditional MPPT techniques, like Perturb and Observe (P&O) and Incremental Conductance (IncCond), offer acceptable performance with a trade-off between accuracy and fast operation. Moreover, moderate operation is remarked at rapidly changing environmental conditions. On the contrary, off-line trained artificial neural network (ANN) is considered as accurate, fast and robust estimation technique. In this paper, a two-stage off-line trained ANN based MPPT technique is proposed where two cascaded ANNs are utilized. The first estimates the temperature and irradiance levels from the array voltage and current signals while the other network determines the optimum peak operating point from the temperature and irradiance, estimated by the first ANN. The proposed technique offers enhanced performance even under rapidly changing environmental conditions, no need for temperature/irradiance measurement, in addition to reduced required training sets because of the presented ANN cascaded structure.
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