Intelligent PSO-Fuzzy MPPT approach for Stand Alone PV System under Real Outdoor Weather Conditions

Zaghba, Layachi; Khennane, Messaouda; Borni, Abdelhalim; Fezzani, Amor

doi:10.46657/ajresd.2021.3.1.1

Cited by 10 publications

(8 citation statements)

References 11 publications

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“…PSO is employed to determine the optimal gains. When compared to the traditional algorithm, the results demonstrate quicker monitoring of power maxima with minimal oscillation and a shorter reaction time 23 . In 24 , improved MPPT controllers using GA for network-linked solar plants are presented.…”

Section: Introductionmentioning

confidence: 99%

Enhancing grid-connected photovoltaic system performance with novel hybrid MPPT technique in variable atmospheric conditions

Zaghba,

Borni,

Benbitour

et al. 2024

Sci Rep

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This paper proposes an innovative approach to improve the performance of grid-connected photovoltaic (PV) systems operating in environments with variable atmospheric conditions. The dynamic nature of atmospheric parameters poses challenges for traditional control methods, leading to reduced PV system efficiency and reliability. To address this issue, we introduce a novel integration of fuzzy logic and sliding mode control methodologies. Fuzzy logic enables the PV system to effectively handle imprecise and uncertain atmospheric data, allowing for decision-making based on qualitative inputs and expert knowledge. Sliding mode control, known for its robustness against disturbances and uncertainties, ensures stability and responsiveness under varying atmospheric conditions. Through the integration of these methodologies, our proposed approach offers a comprehensive solution to the complexities posed by real-world atmospheric dynamics. We anticipate applications in grid-connected PV systems across various geographical locations and climates. By harnessing the synergistic benefits of fuzzy logic and sliding mode control, this approach promises to significantly enhance the performance and reliability of grid-connected PV systems in the presence of variable atmospheric conditions. On the grid side, both PSO (Particle Swarm Optimization) and GA (Genetic Algorithm) algorithms were employed to tune the current controller of the PI (Proportional-Integral) current controller (inverter control). Simulation results, conducted using MATLAB Simulink, demonstrate the effectiveness of the proposed hybrid MPPT technique in optimizing the performance of the PV system. The technique exhibits superior tracking efficiency, achieving a convergence time of 0.06 s and an efficiency of 99.86%, and less oscillation than the classical methods. The comparison with other MPPT techniques highlights the advantages of the proposed approach, including higher tracking efficiency and faster response times. The simulation outcomes are analyzed and demonstrate the effectiveness of the proposed control strategies on both sides (the PV array and the grid side). Both PSO and GA offer effective methods for tuning the parameters of a PI current controller. According to considered IEEE standards for low-voltage networks, the total current harmonic distortion values (THD) obtained are considerably high (8.33% and 10.63%, using the PSO and GA algorithms, respectively). Comparative analyses with traditional MPPT methods demonstrate the superior performance of the hybrid approach in terms of tracking efficiency, stability, and rapid response to dynamic changes.

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Section: Introductionmentioning

confidence: 99%

Enhancing grid-connected photovoltaic system performance with novel hybrid MPPT technique in variable atmospheric conditions

Zaghba,

Borni,

Benbitour

et al. 2024

Sci Rep

Self Cite

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“…In fuzzy based systems, the fuzzy output sets are aggregated into a single fuzzy set using the fuzzy inference system's aggregation mechanism. It was opined that the consequences of an IF-THEN rule in a Mamdani system can be very different from the consequences of that same rule in a system governed by the most basic principles of logical deductive inference [29]. Mamdani systems have proven to be a very useful tool for function approximation and control.…”

Section: Review Of Related Literaturementioning

confidence: 99%

A Fuzzy Logic-Based Automobile Fault Detection System Using Mamdani Algorithm

E. Kizito,

Ojei,

Okpor

2024

int.jour.sci.res.mana

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Due to advancement and complexity of modern automobiles, fault detection has gone beyond manual or trial by error methods. The fault detection technologies in automotive industry is used to identify any potential or already existing fault in automobiles. Faults in automobiles are usually mechanical or electrical faults that may include airbag control unit, radiator, gearbox, transmission control unit, tyre pressure, brakes, air conditioner, cylinder casket, alternator, hubs malfunctions etc. Each fault has a specific or related sign and symptoms. There are several methods of fault detections in automobiles like the binary logic technique, the fuzzy logic method technique and artificial intelligence technique with different algorithms. In this research work, we employed a fuzzy logic based technique that uses a Mamdani Algorithm which presented a better fault detection mechanism. Mamdani’s algorithm was proposed by Ebrahim Mamdani as a fuzzy inference method which has a rule-bases that are more intuitive and easier to analyse and implement. Mamdani’s algorithm produces fuzzy sets that originate from fuzzy inference system’s output membership function for decision making. This research work is a web-based technology that was implemented using JavaScript, JQuery and SQL server, ASP.Net, Bootstrap 3.5 and CSS. The output of the system showed a greater improvement from other existing methods of fault detections in automobiles.

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“…Several researchers have utilized particle swarm optimization (PSO) with various modifications for maximum power point tracking (MPPT) due to its simple structure. Table 1 provides an overview of previous research endeavors within the realm of maximum power point tracking (MPPT) techniques [28][29][30][31][32][33][34][35][36][37][38][39][40][41].…”

Section: Literature Surveymentioning

confidence: 99%

A Particle Swarm Optimization–Adaptive Weighted Delay Velocity-Based Fast-Converging Maximum Power Point Tracking Algorithm for Solar PV Generation System

Azad,

Tariq,

Sarwar

et al. 2023

Sustainability

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Photovoltaic (PV) arrays have a considerably lower output when exposed to partial shadowing (PS). Whilst adding bypass diodes to the output reduces PS’s impact, this adjustment causes many output power peaks. Because of their tendency to converge to local maxima, traditional algorithms like perturb and observe and hill-climbing should not be used to track the optimal peak. The tracking of the optimal peak is achieved by employing a range of artificial intelligence methodologies, such as utilizing an artificial neural network and implementing control based on fuzzy logic principles. These algorithms perform satisfactorily under PS conditions but their training method necessitates a sizable quantity of data which result in placing an unnecessary demand on CPU memory. In order to achieve maximum power point tracking (MPPT) with fast convergence, minimal power fluctuations, and excellent stability, this paper introduces a novel optimization algorithm named PSO-AWDV (particle swarm optimization–adaptive weighted delay velocity). This algorithm employs a stochastic search approach, which involves the random exploration of the search space, to accomplish these goals. The efficacy of the proposed algorithm is demonstrated by conducting experiments on a series-connected configuration of four modules, under different levels of solar radiation. The algorithm successfully gets rid of the problems brought on by current traditional and AI-based methods. The PSO-AWDV algorithm stands out for its simplicity and reduced computational complexity when compared to traditional PSO and its variant PSO-VC, while excelling in locating the maximum power point (MPP) even in intricate shading scenarios, encompassing partial shading conditions and notable insolation fluctuations. Furthermore, its tracking efficiency surpasses that of both conventional PSO and PSO-VC. To further validate our results, we conducted a real-time hardware-in-the-loop (HIL) emulation, which confirmed the superiority of the PSO-AWDV algorithm over traditional and AI-based methods. Overall, the proposed algorithm offers a practical solution to the challenges of MPPT under PS conditions, with promising outcomes for real-world PV applications.

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Intelligent PSO-Fuzzy MPPT approach for Stand Alone PV System under Real Outdoor Weather Conditions

Cited by 10 publications

References 11 publications

Enhancing grid-connected photovoltaic system performance with novel hybrid MPPT technique in variable atmospheric conditions

Enhancing grid-connected photovoltaic system performance with novel hybrid MPPT technique in variable atmospheric conditions

A Fuzzy Logic-Based Automobile Fault Detection System Using Mamdani Algorithm

A Particle Swarm Optimization–Adaptive Weighted Delay Velocity-Based Fast-Converging Maximum Power Point Tracking Algorithm for Solar PV Generation System

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