Adaptive Artificial intelligence based fuzzy logic MPPTcontrol for stande-alone photovoltaic system under different atmospheric conditions

For maximum utilization of solar energy, photovoltaic (PV) power systems should be operated at the maximum power point (MPP) which can be achieved using maximum power point tracking (MPPT) methods. However, the occurrence of multi-peak on P-V curve of a PV array due to the changing environmental conditions such as being partially shaded increases the complexity of the tracking process. The global MPP cannot always be achieved by the conventional MPPT methods. Therefore a novel MPPT method for PV systems using flower pollination (FP) algorithm is proposed in this paper and the Levy flight is used to improve the convergence of FP algorithm. MPPT model of the PV system is established in MATLAB to verify the effectiveness of the proposed method, and the proposed method is compared with two well established MPPT methods. The simulation results indicate that the proposed MPPT method can quickly track the changes in external environment and effectively handle the partially shaded condition.

In photovoltaic (PV) systems, extracting maximum power under varying environmental conditions remains a significant challenge, particularly when partial shading occurs. The classical Maximum Power Point Tracking (MPPT) methods often fail to distinguish the global peak from multiple local peaks introduced by shaded PV arrays. In this paper, a simple yet powerful MPPT technique based on the Rao-3 optimization algorithm is proposed. The proposed approach dynamically navigates the complex power-voltage characteristics of partially shaded PV arrays to accurately locate the global maximum power point (GMPP) without relying on extensive control parameters or derivative information. The performance of the proposed algorithm is systematically compared with Particle Swarm Optimization (PSO) and the classical Perturb and Observe (P&O) method, and Fuzzy Logic Controller (FLC). Simulation results demonstrate that while P&O exhibits fast tracking but often gets trapped in local maxima, FLC provides better adaptability but lacks precision in rapidly changing irradiance. PSO improves global tracking but suffers from high computational complexity, less tracking efficiency, and slower convergence. Simulation results demonstrate that the Rao-3 based MPPT significantly outperforms conventional methods in terms of tracking speed, efficiency, and stability under diverse shading patterns. The findings suggest that the proposed technique offers a reliable and computationally efficient solution for enhancing the energy harvest of PV systems in real-world conditions.

This study offers a thorough examination of a photovoltaic (PV) system using a variety of maximum power point tracking (MPPT) methods, including fuzzy logic control (FLC), adaptive neuro-fuzzy inference systems (ANFIS), perturb and observe (P&O), and artificial neural networks (ANN). Optimizing power extraction from PV systems under various environmental circumstances, including temperature variations and irradiance, is the main goal of these MPPT algorithms. Despite its widespread use and affordability, the P&O algorithm may have performance issues in dynamic circumstances. By using fuzzy logic to adjust to non-linear changes in environmental conditions, FLC improves P&O and offers more dependable and seamless operation. Although they demand a large amount of data and processing power, ANN-based MPPT approaches provide sophisticated capabilities by predicting optimal operating points by learning from historical system actions. By fusing fuzzy logic and neural networks, ANFIS offers a reliable solution that can more accurately adjust in real time to changing circumstances. These algorithms' incorporation into a PV system allows for more flexible and effective power management, guaranteeing peak performance in a range of climatic conditions. By combining many MPPT techniques, hybrid approaches can further reduce the drawbacks of individual approaches and improve the overall dependability and efficiency of PV systems.

In the several last years, numerous Maximum Power Point Tracking (MPPT) methods for photovoltaic (PV) systems have been proposed. An MPPT strategy is necessary to ensure the maximum power efficiency provided to the load from a PV module that is subject to external environmental perturbations such as radiance, temperature and partial shading. In this paper, a new MPPT technique is presented. Our approach has the novelty that it is a MPPT algorithm with a dynamic hysteresis model incorporated. One of the most cited Maximum Power Point Tracking methods is the Perturb and Observer algorithm since it is easily implemented. A comparison between the approach presented in this paper and the known Perturb and Observer method is evaluated. Moreover, a new PV-system platform was properly designed by employing low cost electronics, which may serve as an academical platform for further research and developments. This platform is used to show that the proposed algorithm is more efficient than the standard Perturb and Observer method.

Supervisor control and optimization of multi-sources pumping system with battery storage

This paper reviews the existing models, connection schemes and maximum power point tracking (MPPT) methods of PV arrays. The partial shading causes significant power losses. It is very difficult to maintain uniform irradiance over the entire PV array, especially for rooftop or building-integrated PV systems. The aspects such as connection scheme of PV array, shading pattern and maximum power point tracking (MPPT) techniques, etc., decide the power generated by the PV array. The conventional MPPT methods are only capable of reaching maximum power point (MPP) in identical insolation situations. Hence, conventional MPP tracking becomes inept due to the existence of several peaks in the P-V characteristics. There are different global maximum power point (GMPP) tracking techniques available in the literature that operate in partial shading scenarios. The reconfiguration of the PV array reduces the number of multiple peaks. This reduction in several peaks makes conventional MPP tracking techniques competent even under partial shading conditions with low implementation complexity and higher tracking speed. This paper can assist scientists in choosing a precise objective-based PV module model, connection schemes and MPP tracking methods out of numerous schemes available in the literature.

Improved particle swarm optimization for maximum power point tracking in photovoltaic module arrays

This paper presents an improved reinitialisation condition for time invariant maximum power point tracking (MPPT) methods used in photovoltaic (PV) systems experiencing partial shading conditions (PSC). Time invariant (MPPT) methods, such as Particle Swarm Optimisation (PSO), overcome the limitations of existing MPPT by tracking the global maximum power point (GMPP) of a PV system operating under PSC. However, due to the time invariant structure of these MPPT methods, they also require a reinitialisation condition to be defined for when a change in irradiance or temperature occurs. Testing was performed using simulations of a model built in Matlab/ Simulink, where the performance of existing and developed conditions was evaluated using test cases with changes in solar irradiance. Limitations of existing conditions were identified and a more robust reinitialisation condition developed. The developed reinitialisation condition used sentry particles to monitor the PV voltage range for changes in the measured power of any sentry. The developed condition had a 96 % rate of successful detection, as compared to as low as 68 % successful detection for existing methods, demonstrating improved performance and robustness.

Simulation and experimental design of adaptive-based maximum power point tracking methods for photovoltaic systems

태양전지의 출력 특성은 비선형이고 온도와 일사량에 많은 영향을 받는다. 최대전력점 추종 제어는 태양광 발전의 출력을 최대로 하기 위해 사용되는 제어기법이다. 태양광발전 시스템의 출력 및 효율을 증가시키기 위해 더욱 우수한 최대전력점 추종 제어기법이 필요하다. 본 논문에서는 태양광 발전 시스템의 효율을 개선하기 위해 일사량을 고려한 새로운 최대전력점 추종 제어 알고리즘을 제시한다. 제시한 알고리즘은 종래의 P&O방법과 CV 방법을 혼합한 것이며, PSIM 시뮬레이터를 통하여 종래의 MPPT 알고리즘과 다양한 일사량 조건에서 성능시험을 비교하였다.<BR> 제시한 알고리즘은 종래의 알고리즘에 비해 출력의 자려진동없이 다양한 일사량에서 우수한 성능을 나타냈다. 이로서 본 논문에서 제시한 HB 방법의 최대전력점 추종 제어 알고리즘의 타당성을 입증하였다.

The power-voltage characteristic of the photovoltaic (PV) array shows multiple maximum power points (MPP) under partially shaded conditions. This makes the conventional maximum power point tracking (MPPT) methods ineffective due to their inability to discriminate between the local and global MPP. This paper proposes a new MPPT method based on tabu search (TSMPPT) for PV systems under partially shaded conditions. It combines three different search strategies, i.e. diversification search, local search and intensification search, to avoid the risk of trapping into the local MPP. Simulation results show that the proposed method could accurately track the global MPP with low complexity. (4 pages)

To be the photovoltaic arrays capable to transport the maximum power obtainable, it must be combined with Maximum Power Point Tracking (MPPT). To succeed, we will improve a Maximum Power Point Tracking (MPPT) method, based on intelligent technique such as Fuzzy Logic Controller (FLC), compared with a conventional method which is P&O. This improved method applied to a stand-alone photovoltaic system under loading conditions and atmospheric data (temperature and irradiance). The goal of this controller is to prove that the FLC is the best technique of MPPT to extract the maximum power of photovoltaic modules. The main objective of this work is the implementation on a "Cyclone II" circuit using "EP2C35F672C6" Development Board reconfigurable Field-Programmable Gate Array (FPGA) with Hardware Description Language (VHDL). Thus, we show the advantages of using the FPGA circuits, which are their short progress time, their low price and their suppleness of process. The close optimum design of this technique is based on the best choice membership functions and control rules for two-input, one-output digital Fuzzy Logic Controller (FLC) based on "Mamdani" mechanism. The simulation and implementation results obtained respectively with waveform under Quartus software and on Cyclone II, both show a satisfactory performance and confirm the good tracking efficacy and speedy response to various atmospheric conditions.

Partial shading conditions (PSCs) in Photovoltaic (PV) system is an inevasible situation which curtails the PV array output by exhibiting multiple peaks in its Power-Voltage (P-V) curve. The multiple peaks consist of a single global maximum power point (GMPP) and many local maximum power points (LMPP). The presence of multiple peaks makes tracking of maximum power point more difficult and demands an efficient controller to track the global peak of the P-V curve. In the present work, a novel intelligent asymmetrical Fuzzy Logic Control (AFLC) based maximum power point tracking (MPPT) algorithm has been proposed for tracking GMPP. The fuzzy membership functions of the proposed algorithm have been optimized using a heuristic approach. The algorithm has been designed, developed and analyzed using MATLAB/Simulink. Furthermore, to establish the superiority of proposed AFLC algorithm, it has been compared with conventional perturb & observe (P&O) algorithm and intelligent Fuzzy Logic Control (FLC) based algorithm for GMPP tracking and shading losses under standard test condition (STC) and partially shaded conditions.

This paper presents a control for a three-phase an eleven-level cascaded H-bridge inverter for photovoltaic (PV) system. The maximum power point tracking (MPPT) is capable of extracting maximum power from the PV array connected to each DC link voltage level. The MPPT algorithm is solved by fuzzy logic controller. Space vector pulse width modulation (SVPWM) algorithm uses a simple mapping to generate gate signals for the inverter. This is done to achieve high dynamic performance with low total harmonic distortion (THD). The validity of the system is verified through MATLAB/Simulink and results are compared with three-phase three-level, five-level, seven-level and nine-level cascaded H-bridge inverter for PV system in terms of THD. Finally, simulation results are presented to verify the effectiveness and accuracy of the proposed system.

In order to use photovoltaic cell effectively and improve its photoelectric conversion efficiency, the maximum power point of photovoltaic generation system should be tracked rapidly and stably. In this paper after comparison and analysis common methods used in controller of photovoltaic systems such as Fuzzy and P&O, proposed an approach combined from FLC and particle swarm optimization algorithm (PSO) as an appropriate method to achieve maximum power point tracking (MPPT). Indeed Fuzzy logic control can cope with photovoltaic system using heuristic knowledge rules, but tuning the control parameters is not straightforward. PSO performs an on-line haphazard global search for input and output scaling factors of a PD-type fuzzy controller. The objective function of the PSO algorithm has been defined to minimize slope of P-V curve. The simulation results in SIMULINK of MATLAB indicate that proposed method can effectively eliminate the power oscillation around MPP and raise stability and reach steady state of the system.