Benefits of Fuzzy Logic on MPPT and PI Controllers in the Chain of Photovoltaic Control Systems

Abstract

This paper presents a comparative study between two maximum power point tracking (MPPT) algorithms, the incremental conductance algorithm (InC) and the fuzzy logic controller (FLC). The two algorithms were applied to a low photovoltaic power conversion system, and they both use different PI controllers and grid synchronization techniques. Moreover, both InC and FLC methods have Clarke and Park Transformation. To some extent, the incremental conductance and fuzzy logic controller approaches are similar, but their control loops are different. Therefore, the InC has classic Proportional Integrative (PI) controllers with simple phase-locked loops (PLL). At the same time, the FLC works with fuzzy logic PI controllers linked with the Second Order Generalized Integrator (SOGI). The proposed techniques examine the solar energy conversion performance of the photovoltaic (PV) system under possible irradiance changes and constant temperature conditions. Finally, a performance comparison has been made between InC and FLC, which demonstrates the effectiveness of the fuzzy controller over the incremental conductance algorithm. FLC turns to convert photovoltaic power easily, decreasing fluctuations, and it offers a quick response to the variation of solar irradiance (shading effect). The simulation results show a superior performance of the controller with fuzzy logic, which helps the inverter convert over 99% of the power generated by the photovoltaic panels. In comparison, the incremental conductance algorithm converts around 80%.

Application of Fuzzy Control and Neural Network Control in the Commercial Development of Sustainable Energy System

“…For the control and optimization of the photovoltaic system, the fuzzy control algorithm and neural network control algorithm have been widely studied and applied in recent years. Giurgi et al [36] found that the fuzzy control algorithm can effectively improve the power generation efficiency of photovoltaic system under certain lighting and temperature conditions, but in some cases, there were also cases of power output reduction. This is consistent with the conclusion of this study, which shows that the fuzzy control algorithm has a certain universality in different environmental conditions.…”

Optimizing The Maximum Power of Photovoltaic System Using Modified Incremental Conductance Algorithm Operating Under Varying Dynamic Climatic Conditions

“…Once this condition is satisfied, that point is called the maximum power point denoted as (MPP). To extract this MPP out of the module it will be essential to drive the current operating point to operate at the peak level of the MPP of the P-V curve [34], [35].…”

Efficient Photovoltaic Unit for Power Delivering to Stand-Alone Direct Current Buildings Using Artificial Intelligence Approach Based MPP Tracker

“…Based on that, the researchers focused on proposing different algorithms to guarantee accurate and fast tracking to the Maximum Power Point (MPP). Regardless of the type of PV system, whether it is a standalone or grid-connected system, guaranteeing MPP tracking condition maximizes the level of harvested energy from the incident sunlight [10][11][12][13][14][15][16][17][18][19][20][21][22][23][24][25][26][27][28]. The proposed algorithms have different characteristics, including both pros and cons, in terms of response speed, tracking accuracy, and oscillation at steady state conditions.…”

“…Fuzzy Logic Control (FLC) is introduced for MPP tracking function to overcome the mentioned disadvantages of P&O and IC through a quick and stable response using fuzzification, Fuzzy rules, and defuzzification processes [11][12][13][14]. A new global MPP tracker combined FLC and an algorithm of scanning and storing process presented in [15] for stand-alone PV systems that work under partially shaded weather conditions.…”