Analysis of photovoltaic array maximum power point tracking under uniform environment and partial shading condition: A review

Li, Jianlin; Ma, Suliang; Chen, Mingxuan; Zhang, Baoping; Jia, Bing

doi:10.1016/j.egyr.2022.09.192

Cited by 22 publications

(9 citation statements)

References 61 publications

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“…JFO tracking efciency in all 17 compares the proposed jellyfsh optimization with some existing techniques. Diferent users have used diferent parameters and specifcations to prove that their technique is better than the existing one, so diferent comparison parameters are considered such as tracking time [16], RMSE [14,15,18], efciency [13,17,22,24,27,29,31,[34][35][36], MAE [20], accuracy [41], reduced rise time [21], settling time/ tracking time [21,29,35], and maximum power [28,35,36]. In this paper, maximum power tracked, maximum efciency, total execution time, convergence speed, complexity, parameter, steady-state oscillation, stability, sensitivity, tracking ability, and economy have been compared [10,12,13,23,27]; FPA performs better with a small margin than PSO in terms of maximum power [28].…”

Section: Case Study 2: Solarworld Industries Gmbh Sunmodule Plus Sw 2...mentioning

confidence: 99%

“…ABC has a strong exploration ability to follow MPP, though these approaches are highly prone to exploitation. [37] gives the modeling [38], electrical characteristics, and [39] parameter estimation of photovoltaic strings under PSC and explains the importance of bypass diodes Te authors in [15,19,[40][41][42] has reviewed and compared the performance of some well-known optimization algorithms under partially shaded conditions. Te authors in [13] have reviewed and given the performance comparisons of conventional, artifcial intelligence, and optimization-based MPPT in terms of tracking capability, convergence, implementation, tracking accuracy, tracking speed, efciency, economy, and application and have given advantages and disadvantages of diferent optimization techniques used to track the GMPP.…”

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: Case Study 2: Solarworld Industries Gmbh Sunmodule Plus Sw 2...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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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“…A number of the latest and most significant modern MPPT methods that potentially address some of the problems brought on by conventional MPPT controllers include soft calculation (SC), artificial intelligence (AI), and bio-inspired technology (BT) [14,15]. Sophisticated methods for MPPT possess an outstanding ability for monitoring the MPP despite their high level of complexity.…”

Section: B) Backgroundmentioning

confidence: 99%

“…Power generated by the PV generator is affected by T and I. The practical electrical circuit of the solar cell and the inverted diode is depicted in Fig 2 [14,40].…”

Section: System Understudy and Pv Modelingmentioning

confidence: 99%

A new adaptive MPPT technique using an improved INC algorithm supported by fuzzy self-tuning controller for a grid-linked photovoltaic system

Ibrahim,

Mahmoud,

Alnami

et al. 2023

PLoS ONE

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Solar energy, a prominent renewable resource, relies on photovoltaic systems (PVS) to capture energy efficiently. The challenge lies in maximizing power generation, which fluctuates due to changing environmental conditions like irradiance and temperature. Maximum Power Point Tracking (MPPT) techniques have been developed to optimize PVS output. Among these, the incremental conductance (INC) method is widely recognized. However, adapting INC to varying environmental conditions remains a challenge. This study introduces an innovative approach to adaptive MPPT for grid-connected PVS, enhancing classical INC by integrating a PID controller updated through a fuzzy self-tuning controller (INC-FST). INC-FST dynamically regulates the boost converter signal, connecting the PVS’s DC output to the grid-connected inverter. A comprehensive evaluation, comparing the proposed adaptive MPPT technique (INC-FST) with conventional MPPT methods such as INC, Perturb & Observe (P&O), and INC Fuzzy Logic (INC-FL), was conducted. Metrics assessed include current, voltage, efficiency, power, and DC bus voltage under different climate scenarios. The proposed MPPT-INC-FST algorithm demonstrated superior efficiency, achieving 99.80%, 99.76%, and 99.73% for three distinct climate scenarios. Furthermore, the comparative analysis highlighted its precision in terms of control indices, minimizing overshoot, reducing rise time, and maximizing PVS power output.

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“…In order to discover the maximum power position of a PV panel, artificial intelligence (AI) based methods using genetic algorithm (GA), (ANN), and PSO (particle swarm optimization) are employed as solutions in the MPPT controller. (6,7) Amid this search, a particularly interesting path is emerging: the integration of artificial neural networks into photovoltaic control frameworks. ANNs represent a paradigm shift in the way we think about control mechanisms.…”

Section: Introductionmentioning

confidence: 99%

Improving Photovoltaic System Performance with Artificial Neural Network Control

Benchikh,

Tarik,

Boutahir

et al. 2023

Data and Metadata

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Photovoltaic systems play a pivotal role in renewable energy initiatives. To enhance the efficiency of solar panels amid changing environmental conditions, effective Maximum Power Point Tracking (MPPT) is essential. This study introduces an innovative control approach based on an Artificial Neural Network (ANN) controller tailored for photovoltaic systems. The aim is to elevate the precision and adaptability of MPPT, thereby improving solar energy harvesting. This research integrated an ANN controller into a photovoltaic system in order dynamically optimize the operating point of solar panels in response to environmental changes. The performance of the ANN controller was compared with traditional MPPT approaches using simulation in Simulink/Matlab. The results of the simulation showed that the ANN controller performed better than the traditional MPPT techniques, highlighting the effectiveness of this method for dynamically changing solar panel performance. The ANN particularly demonstrates higher precision and adaptability when environmental conditions vary. The strategy consistently achieves and maintains the maximum power point, enhancing overall energy harvesting efficiency. The integration of an ANN controller marks a significant advance in solar energy control. The study highlights the superiority of the ANN controller through rigorous simulations, demonstrating increased accuracy and adaptability. This approach not only proves effective, but also has the potential to outperform other MPPT strategies in terms of stability and responsiveness.

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Analysis of photovoltaic array maximum power point tracking under uniform environment and partial shading condition: A review

Cited by 22 publications

References 61 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

A new adaptive MPPT technique using an improved INC algorithm supported by fuzzy self-tuning controller for a grid-linked photovoltaic system

Improving Photovoltaic System Performance with Artificial Neural Network Control

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