scholarly journals Power Conversion Performance of a Single-Phase PV Inverter with Fuzzy Logic Algorithm

2021 ◽  
Vol 897 (1) ◽  
pp. 012018
Author(s):  
G I Giurgi ◽  
D Petreus ◽  
D V Giurgi ◽  
L Szolga
Keyword(s):  
Fuzzy Logic ◽  
Power Conversion ◽  
Photovoltaic System ◽  
Phase Locked Loops ◽  
Fuzzy Logic Algorithm ◽  
Point Tracking ◽  
Pv Inverter ◽  
Solar Inverter ◽  
Conversion Performance ◽  
Power Point

Abstract The paper presents a low-power conversion system focusing on implementing new solar inverter control techniques implemented with Fuzzy Logic. The power generated by a solar panel requires robust approaches and efficient methods to be used at its maximum. Therefore, a promising strategy is a Fuzzy Logic based on the Maximum Power Point Tracking (MPPT) algorithm. To gather efficient power conversion, our proposed model uses a control loop composed of Fuzzy Proportional Integrative (PI) regulators, Clarke and Park transform, followed by a synchronization grid mechanism Second-order generalized integrator (SOGI) based phase-locked loops (PLLs). The proposed technique examines photovoltaic system (PV) performance with respect to its non-linearities and eventual shaded conditions that can occur in the PV array. The shading effect is tested by varying the irradiance, which determines the variation of the output current and implicitly of the output power. The simulation results show that the inverter control system is very efficient, generating stable and nearly sinusoidal current and voltage characteristics. Thus, the inverter converts over 99 % of the power generated by PV arrays.

Maximum Power Point Tracker Using Fuzzy Logic Controller with Reduced Rules

2018 ◽  
Vol 9 (3) ◽  
pp. 1381
Author(s):  
Adel Haddouche ◽  
Mohammed Kara ◽  
Lotfi Farah
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Logic Controller ◽  
Good Alternative ◽  
Maximum Power ◽  
Photovoltaic System ◽  
Maximum Power Point ◽  
Practical Applications ◽  
Performance Improvements ◽  
Point Tracking ◽  
Power Point

<p><span lang="EN-US">This paper presents a fuzzy logic controller for maximum power point tracking (MPPT) in photovoltaic system with reduced number of rules instead of conventional 25 rules to make the system lighter which will improve the tracking speed and reduce the static error, engendering a global performance improvements. in this work the proposed system use the power variation and current variation as inputs to simplify the calculation, the introduced controller is connected to a conventional grid and simulated with MATLAB/SIMULINK. The simulation results shows a promising indication to adopt the introduced controller as an a good alternative  to traditional MPPT system for further practical applications</span></p>

scholarly journals Evaluation of Fuzzy Logic Subsets Effects on Maximum Power Point Tracking for Photovoltaic System

2014 ◽  
Vol 2014 ◽  
pp. 1-13 ◽  
Author(s):  
Shahrooz Hajighorbani ◽  
M. A. M. Radzi ◽  
M. Z. A. Ab Kadir ◽  
S. Shafie ◽  
Razieh Khanaki ◽  
...  
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Logic Controller ◽  
Maximum Power Point Tracking ◽  
Maximum Power ◽  
Photovoltaic System ◽  
Maximum Power Point ◽  
Climate Conditions ◽  
Point Tracking ◽  
Power Point Tracking ◽  
Power Point

Photovoltaic system (PV) has nonlinear characteristics which are affected by changing the climate conditions and, in these characteristics, there is an operating point in which the maximum available power of PV is obtained. Fuzzy logic controller (FLC) is the artificial intelligent based maximum power point tracking (MPPT) method for obtaining the maximum power point (MPP). In this method, defining the logical rule and specific range of membership function has the significant effect on achieving the best and desirable results. This paper presents a detailed comparative survey of five general and main fuzzy logic subsets used for FLC technique in DC-DC boost converter. These rules and specific range of membership functions are implemented in the same system and the best fuzzy subset is obtained from the simulation results carried out in MATLAB. The proposed subset is able to track the maximum power point in minimum time with small oscillations and the highest system efficiency (95.7%). This investigation provides valuable results for all users who want to implement the reliable fuzzy logic subset for their works.

scholarly journals Comparative analysis of Maximum Power Point Tracking Algorithms for Photovoltaic Applications

2020 ◽  
Vol 15 ◽  
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Logic Control ◽  
Maximum Power Point Tracking ◽  
Maximum Power ◽  
Photovoltaic System ◽  
Maximum Power Point ◽  
Point Tracking ◽  
Power Point Tracking ◽  
Logic Control ◽  
Power Point

Currently, photovoltaic system has gained importance in electrical power generation system, since it is a clean and renewable energy source. An important characteristic of photovoltaic panels is that the available maximum power is provided only in a single operating point called maximum power point. But the position of maximum power point is not fixed and it moves according to the varying irradiance, varying temperature and the load. This requires a mechanism called maximum power point tracking (MPPT) so that maximum power is obtained effectively. In the literature, many classical methods have been developed and implemented to track the maximum power point. The main objective of this paper is to study and analyze the classical techniques such as Perturb and Observe (P&O), Incremental Conductance (IC) and Fuzzy Logic Control (FLC) algorithms and propose an Adaptive Fuzzy Logic Control (AFLC) for MPPT. The performance of AFLC is compared with the conventional MPPT techniques for varying irradiance. The significance of these methods are studied by implementing the algorithm in MATLAB. The experimental results show the effectiveness and feasibility of the proposed method and the results are verified. The results reveal that the adaptive FLC can quickly track change of MPP for various light intensity and delivers higher power compared to the classical algorithms.

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