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 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.

Analysis of MPPT Failure and Development of an Augmented Nonlinear Controller for MPPT of Photovoltaic Systems under Partial Shading Conditions

“…voltage) [36]. This voltage-source model is widely used to represent the closed-loop grid-connected inverters owing to its satisfactory balance between accuracy and simplicity, which is confirmed in [30][31][32][33][34][35][36][37][38][39][40]. In the topological structure, Pm is the ANN output, namely, the reference power.…”

“…A common ANN for MPPT [34,35], showed in Figure 4, has three layers: input, hidden and output layers. In this paper, the ANN input can be the PV-array environmental parameters such as irradiance or temperature and the output is the maximum power.…”

Power and Frequency Fluctuations of MPPT Algorithms in Variable Irradiance Environments

“…A faster convergence speed has been observed as a consequence. points in multiple maxima conditions that arises due to variable irradiation, artificially intelligent techniques for MPPT have been proposed in literature [14]. Artificial neural network (ANN) has ability to recognize and estimate unknown parameters.…”

Performance Analysis of Maximum Power Point Tracking Algorithms Under Varying Irradiation

“…Instead of using complicated neural network, a simple and highly efficient single neural control scheme was proposed in (Kofinas et al 2015). The classification of ANN based MPPT techniques were analyzed in (Elobaid et al 2015) and these techniques are dependent on the type of input to the controller. An adaptive FLC was proposed in (Guenounou et al 2014), which is a combination of the two separate rule bases and a gain attached to the MPPT controller, resulting in improved performance.…”