A novel global MPP tracking scheme based on shading pattern identification using artificial neural networks for photovoltaic power generation during partial shaded condition

Abstract

Efficiency of the photovoltaic (PV) power generating system is affected during partial shaded condition (PSC). The power-voltage characteristic of PV system exhibits multiple peaks during PSC. It is the task of maximum power point tracking (MPPT) controller to track global maximum power point (GMPP). Conventional MPPT schemes stop at first peak and fail to accomplish GMPP during PSC. Metaheuristic algorithms developed to track GMPP are complex, costly and require much time to track GMPP. Hence, this study put forwards a novel GMPPT scheme for effective tracking based on shading pattern identification using artificial neural network (ANN). In this scheme, ANN is used to estimate the shading pattern on PV panels and a two-dimensional lookup table supplies the MPP voltage corresponding to the shading pattern. By maintaining this voltage across PV panel, maximum power is extracted. The proposed scheme is compared with the existing artificial bee colony and particle swarm optimisation algorithms under different shading configurations to verify their performance under PSC. It is observed that the proposed scheme extracts maximum power effectively under various partial shading conditions. The proposed scheme is implemented in field-programmable gate array (FPGA) controller and the experimental results prove effectiveness of the proposed scheme.

Framework of maximum power point tracking for solar PV panel using WSPS technique

“…In dynamic condition (5) where P(t) is present solar PV power, and P MPP Rating is rating of the solar PV array. From (2) and (3), I PV is derived as…”

“…A literature survey on MPPT illustrates that initially 'fractional open circuit voltage [4]', 'fractional short circuit current', lookup table-based MPPT [5] [6], hill climbing, Incremental Connection (InC) [7] etc. These techniques are capable to track the MPP in every type of dynamic condition.…”

“…A literature survey on MPPT illustrates that initially ‘fractional open circuit voltage [4]’, ‘fractional short circuit current’, lookup table‐based MPPT [5] techniques have used. With time, the variable nature of open‐circuit voltage and short circuit current of PV array are the key drawbacks for ‘fractional open circuit voltage’, and ‘fractional short circuit current’ based MPPT techniques.…”

A Hybrid Evolutionary-Based MPPT for Photovoltaic Systems Under Partial Shading Conditions

“…In order to improve the above-mentioned drawbacks, several methods in the family of soft computing (SC) techniques such as artificial intelligence approach and evolutional algorithms have been attracting considerable interests recently. In the field of artificial intelligence techniques, fuzzy logic controllers (FLC) [17], [18], artificial neural network (NN) [19], [20] methods have shown effective solution in dealing with the nonlinear characteristics of the current-voltage (I-V) curve, in particular, under PSCs. However, they require extensive computation.…”

An Improved Bat Algorithm for More Efficient and Faster Maximum Power Point Tracking for a Photovoltaic System Under Partial Shading Conditions

“…AI-based MPPT is a promising approach to achieve GMPP for P-VCC with multiple peaks. AI-based MPPT can be categorized into fuzzy logic (FL) [25]- [27], artificial neural network (ANN) [28]- [30], and meta-heuristic approaches (MHAs) [31]- [33]. In [34], the FL method combined with InC is employed to determine the duty cycle based on the rule base and the input variables.…”