Investigation of the Partial Shading Effect of Photovoltaic Panels and Optimization of Their Performance Based on High-Efficiency FLC Algorithm

Abstract

The present work proposes an enhanced method of investigation and optimization photovoltaic (PV) modules by approaching and using MPPT (Maximum Power Point Tracking) technique to improve their output power. The performance of the PV panels is strongly influenced by the operating conditions, especially regarding the solar irradiance, temperature, configuration, and the shading (due to a passing cloud or neighboring buildings); all these cause, both on energy conversion loss, and further on non-linearity of the I-V characteristics. From this reason, the present study could have a high relevance based on the improvement of the performances (including the efficiency) of the shaded photovoltaic panels and would quantify the impact of a complex approach represented by numerical modeling and experimental validation. For a better understanding of these issues determined by partial shading, and improvement of MPP tracking, it is required to study the behavior of individual panels. For the best accuracy of the implemented models a comparative analysis and optimized method of the PV modules was considered based on: (1) the influence of temperature and solar irradiance and behavior of the PV modules in partial shading conditions; (2) a comparison between the optimized output power of four algorithms (FLC—Fuzzy Logic Controller, P&O—Perturb and Observe, IncCond—Incremental Conductance and RC Ripple Correlation) and the selection of the best one (FLC); (3) discussion of customized/improved fuzzy logic controller (FLC) algorithm on five operation points introduced in order to increase PV module efficiency under fluctuating weather conditions and rapidly changing uncertainties. Furthermore, the FLC provides a set of rules useful for predicting the current-voltage behavior and the maximum power points of shaded photovoltaic modules. This FLC algorithm was implemented in a specialized software, namely MATLAB/Simulink. The authors highlighted the development and implementation of a numerical simulation model for an advanced PV module to determine its behavior under different operating conditions and improve its performance. The essence of the authors’ research and the motivation of this work is described. The authors were able to stabilize and improve the output performance of the PV module. The results concerning the shading effect as well as the shading patterns were developed, demonstrated, and experimentally validated. These results could be applied for the actual photovoltaic installations, respectively complex stand-alone or grid-connected photovoltaic systems.

A Comprehensive Review of Maximum Power Point Tracking (MPPT) Techniques Used in Solar PV Systems

“…The fuzzy logic controller's linguistic output variable is transformed into a numerical variable at the defuzzification stage, resulting in an analog signal that drives the power converter to the MPP. Under varying climatic circumstances, the MPPT fuzzy logic controller performs admirably [81]. However, its success is contingent on selecting the appropriate error computation and formulating the rule base table [32,50,82].…”

Optimizing Large-Scale PV Systems with Machine Learning: A Neuro-Fuzzy MPPT Control for PSCs with Uncertainties

“…Other researchers have also employed population-based global optimization methods which have been combined with deep neural networks to enhance their global exploration capabilities and reduce their computational complexity [16][17][18] such as the firefly algorithm (FA) [19], the artificial bee colony algorithm (ABC) [20], and the genetic algorithm (GA) [21] to achieve maximum power, but these algorithms suffer from convergence speed, require lot of tuning parameters (population size, crossover probability, and mutation rate), and are sentinel to noise. Moreover, all these PSO, GA, FA, and ABC population-based algorithms are not efficient when it comes to the control problem, because of their inability to handle uncertainty and nonlinear systems efficiently and are less flexible to changing environment conditions.…”

“…A controller design based on the nonlinear robust terminal sliding mode (TSM) is proposed for tracking the MPPT of PV arrays under PSCs using a non-inverting buck-boost converter. In this controller, the error, e1, is defined as the discrepancy between the desired output voltage of the PV array and the actual one and is given in Equation (17). The controller uses this error to adjust the reference voltage generated by the MLNFN algorithm and to extract the maximum power from the PV array,…”

Analysing the Effects of Thin Object Shading on PV Sources: A Dual Approach Combining Outdoor and Laboratory Solar Simulator Experimentation

“…As elucidated by [6], this constitutes just one aspect of partial shading. Moreover, researchers such as [7,8] employ simulation tools to replicate PV shading, affirming the effectiveness of innovative algorithms and electrical circuit devices similar to those outlined in [9,10].…”