Studying and analyzing the performance of photovoltaic system by using fuzzy logic controller

Abstract

The main objective of this paper is to implement a circuit-based simulation model of a photovoltaic (PV) cell in order to investigate the electrical behavior of the practical cell with respect to some changes in weather parameters such as irradiation and temperature. The research focuses on using a simulation model to achieve the maximum power of solar energy by using the maximum power point tracking (MPPT) controller. The circuit simulation model consists mainly of three subsystems: PV cells: DC/DC converter; and MPPT controller-based logic fuzzy control. Dynamic analysis of the system is carried out and the results are recorded. The maximum power control function is achieved with the appropriate power control of the power inverter. Fuzzy logic controller has been used to perform MPPT functions to get maximum power from the PV panel. The proposed circuit was implemented in MATLAB/Simulink and the results show that the output sequence is non-linear and almost constant current to the open circuit voltage and the power has maximum motion to voltage for certain environmental conditions.

Optimized ANN-fuzzy MPPT controller for a stand-alone PV system under fast-changing atmospheric conditions

“…One of the most successful MPPT optimization strategies is FL. Numerous studies, including [31]- [42], have used FL to make the PV work around the MPP [36]. As shown above, climate is the primary effect on the non-linear features of the I-V and P-V characteristics.…”

Modernisation of DC-DC converter topologies for solar energy harvesting applications: A review

“…Solar tracking is the traditional way that is utilized to get the most out of the energy that is collected [156] [157]. One other approach that may be taken to get maximum power is called MPPT [6]- [8], [11], [12], [15], [19], [27], [28], [35], [41], [42], [48], [51], [52], [57], [62], [64]- [66], [69]- [71], [74], [77], [80], [82], [83], [85], [89], [90], [93], [95], [96], [98], [106], [108], [112], [113], [158]- [245]. The MPP can be detected with the use of an MPPT algorithm that is placed on the microcontroller [246].…”

“…Numerous methods are utilized on a regular basis to accomplish the task of tracking maximum power point in PV systems [185]. These methods include hill-climbing, perturbation and observation (P&O), look-up table, incremental conductance (Inc-Cond), linearization-based, DC-link capacitor droop control, curve fitting, extremum seeking control, feedback voltage or current, feedback of power variation with voltage, feedback of power variation with current, firefly algorithm, fractional open-circuit voltage (FOCV), fractional short-circuit current (FSCI), artificial neural network (ANN), fuzzy logic, genetic algorithm, particle swarm optimization (PSO), ant colony based optimization, one cycle control (OCC), ripple correlation control (RCC), parasitic capacitance, and slidingmode [6]- [8], [11], [12], [15], [19], [27], [28], [35], [41], [42], [48], [51], [52], [57], [62], [64]- [66], [69]- [71], [74], [77], [80], [82], [83], [85], [89], [90], [93], [95], [96], [98], [106], [108], [112], [113], [158]- [245].…”

Particle swarm optimization for enhanced maximum power point tracking: design and implementation in Proteus

“…Moreover, the intricate non-linear behavior of PV panels, coupled with their strong reliance on external weather conditions and load profiles, presents a formidable challenge in maximizing PV energy generation. One strategy proposed to alleviate the problem in existing methods and to address this problem of operating PV panels at their maximum power point (MPP) [3]. Consequently, several algorithms for maximum power point tracking (MPPT) have been introduced.…”