Highly efficient maximum power point tracking control technique for PV system under dynamic operating conditions

Moosavi, Syed Kumayl Raza; Mansoor, Majad; Zafar, Muhammad Usman; Khan, Noman Mujeeb; Mirza, Adeel Feroz; Akhtar, Naureen

doi:10.1016/j.egyr.2022.10.011

Cited by 10 publications

(10 citation statements)

References 38 publications

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“…Te authors in [7] analysed the performance at diferent irradiation and PSC. Refrences [12,14,15,18,20,33] compared RMSE and, the authors in [20] in terms of MAE. In this paper, three cases, STC, stepchanging irradiation, and PSC, are taken to validate the performance of JFO in terms of maximum power tracking, total execution time, the minimum number of iterations to achieve the GMPP with high tracking efciency, and RMSE for the same set of population and number of iterations.…”

Section: African Vultures Optimization Algorithmmentioning

confidence: 99%

“…Root mean square error (RMSE) is an essential metric for assessing the efectiveness of MPPT methodologies. Te following equation is used for this statistical analysis [33,34].…”

Section: Diferent Algorithms For Tracking Gmppmentioning

confidence: 99%

“…Te authors in [29] used hybrid methods such as BOA-PSO-based algorithm and found that the PSO-BOA algorithm outperforms the PSO and BOA in terms of convergence accuracy, with a tracking accuracy of not less than 99.94%. Te authors in [33] concluded that DGBCO has signifcantly less RMSE value than other algorithms. Te authors in [45] reviewed some algorithms and proposed a SHTS algorithm that was compared with hybrid algorithms in terms of tracking speed and accuracy under partial shading conditions.…”

Section: Introductionmentioning

confidence: 99%

“…Tese are optimization techniques that have been utilized for comparing diferent factors in terms of tracking time [16,21,24,29], tracking efciency [13,14,16,20,22,24,29,31], root mean square error [12,14,15,18,20], and mean absolute error [20]. Some of the new optimization techniques that have been recently introduced in the PV system analysis in 2022 and 2023 are specifed as seagull optimization algorithm [27], African vulture optimization [30], jellyfsh optimization algorithm [32], arithmetic optimization algorithm [21], reptile search algorithm [22], particle swarm optimization with butterfy optimization algorithm [29], DGBCO [33], EVO [34], GRNN-OPA [35], MRO [36], and many others that are utilized in diferent felds of engineering.…”

Section: Introductionmentioning

confidence: 99%

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Comparison of Bioinspired Techniques for Tracking Maximum Power under Variable Environmental Conditions

Yadav,

Singh,

Giri

et al. 2024

International Journal of Intelligent Systems

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This paper presents a comparative analysis of bioinspired algorithms employed on a PV system subject to standard conditions, under step-change of irradiance conditions, and a partial shading condition for tracking the global maximum power point (GMPP). Four performance analysis and comparison techniques are artificial bee colony, particle swarm optimization, genetic algorithm, and a new metaheuristic technique called jellyfish optimization, respectively. These existing algorithms are well-known for tracking the GMPP with high efficiency. This paper compares these algorithms based on extracting GMPP in terms of maximum power from a PV module running at a uniform (STC), nonuniform solar irradiation (under step-change of irradiance), and partial shading conditions (PSCs). For analysis and comparison, two modules are taken: 1Soltech-1STH-215P and SolarWorld Industries GmbH Sunmodule plus SW 245 poly module, which are considered to form a panel by connecting four series modules. Comparison is based on maximum power tracking, total execution time, and minimum number of iterations to achieve the GMPP with high tracking efficiency and minimum error. Minitab software finds the regression equation (objective function) for STC, step-changing irradiation, and PSC. The reliability of the data (P-V curves) was measured in terms of p value, R, R2, and VIF. The R2 value comes out to be near 1, which shows the accuracy of the data. The simulation results prove that the new evolutionary jellyfish optimization technique gives better results in terms of higher tracking efficiency with very less time to obtain GMPP in all environmental conditions, with a higher efficiency of 98 to 99.9% with less time of 0.0386 to 0.1219 sec in comparison to ABC, GA, and PSO. The RMSE value for the proposed method JFO (0.59) is much lower than that of ABC, GA, and PSO.

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Section: African Vultures Optimization Algorithmmentioning

confidence: 99%

“…Root mean square error (RMSE) is an essential metric for assessing the efectiveness of MPPT methodologies. Te following equation is used for this statistical analysis [33,34].…”

Section: Diferent Algorithms For Tracking Gmppmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Comparison of Bioinspired Techniques for Tracking Maximum Power under Variable Environmental Conditions

Yadav,

Singh,

Giri

et al. 2024

International Journal of Intelligent Systems

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show abstract

“…In recent years, a new category of meta-heuristic optimization-based MPPT techniques that effectively solve PSC. The behavior and performance of these techniques depend on many factors, namely the number of iterations, parameter tuning, convergence speed, objective function, population size, and computational time [17]. In [18], a particle swarm optimization (PSO)based MPPT technique shares information through the social iteration of swarm particles.…”

Section: Introductionmentioning

confidence: 99%

Hybrid of Meta-Heuristic Techniques Based on Cuckoo Search and Particle Swarm Optimizations for Solar PV Systems Subjected to Partially Shaded Conditions

Nouh,

Almalih,

Faraj

et al. 2024

jsesd

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Solar energy has a significant role in meeting rising energy demand while reducing environmental impact. Solar radiation and temperature are important factors on which PV energy production depends, but its optimal operation point is influenced by variations in the aforementioned environmental factors. The nonlinear behavior of the solar system and the variable nature of environmental conditions make determining the optimal operation point difficult. To overcome these difficulties, maximum power point tracking (MPPT) finding techniques are used to extract the optimal power from the photovoltaic energy system. The behavior of MPPT varies for different weather conditions, such as partial shading conditions (PSC), and uniform irradiance conditions. Conventional techniques are simple, quick, and efficient for tracing the MPP quickly, but they are limited to uniform weather conditions. In addition, these techniques don't achieve the Global Maxima (GM) and mostly stay stuck at the Local Maxima (LM). The Meta-Heuristic techniques aid in finding the GM, but their primary disadvantage is that they take a longer time to trace the Global Maxima. This study addresses the problem by combining Cuckoo Search (CS) and Particle Swarm Optimization (PSO) algorithms, leading to a hybrid (CSPSO) technique to extract the global maximum (GM). To verify the effectiveness of the suggested technique, its performance is examined under three different irradiance patterns for different PV array configurations (such as 3S and 4S3P) through MATLAB simulation. The outcomes of CSPSO are compared with the prior well-known Meta-Heuristic techniques such as Cuckoo Search (CS), Particle Swarm Optimization (PSO), and Crow Search Algorithm (CSA). The results show the suggested technique excels over other techniques in terms of accuracy, tracking efficiency, and tracking speed. The suggested technique is capable of tracking GMPP with an average efficiency of 99.925% and an average tracking time of 0.13 s in all shading patterns studied.

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Deep reinforcement learning-based robust nonlinear controller for photovoltaic systems

Veisi,

Delavari

2024

Neural Comput & Applic

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Highly efficient maximum power point tracking control technique for PV system under dynamic operating conditions

Cited by 10 publications

References 38 publications

Comparison of Bioinspired Techniques for Tracking Maximum Power under Variable Environmental Conditions

Comparison of Bioinspired Techniques for Tracking Maximum Power under Variable Environmental Conditions

Hybrid of Meta-Heuristic Techniques Based on Cuckoo Search and Particle Swarm Optimizations for Solar PV Systems Subjected to Partially Shaded Conditions

Deep reinforcement learning-based robust nonlinear controller for photovoltaic systems

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