Identification of Partial Shading Conditions for Photovoltaic Strings

Bi, Ziqiang; Ma, Jing; Wang, Kangshi; Man, Ka Lok; Smith, Jeremy S.; Yue, Yong

doi:10.1109/access.2020.2988017

Cited by 21 publications

(11 citation statements)

References 26 publications

(59 reference statements)

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“…Using (3), (4), (5), (8) and (130), the parameters of the SDM can be extracted numerically. The method relies on using equations presented in [50], which correlate the maximum power point with the SDM parameters using the Lambert W-function and properties of the ideal model to extract the five model parameters as follows:…”

Section: Methods Sixmentioning

confidence: 99%

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Performance Evaluation of Analytical Methods for Parameters Extraction of Photovoltaic Generators

Anani

Ibrahim

2020

Energies

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This paper presents a succinct exploration of several analytical methods for extracting the parameters of the single-diode model (SDM) of a photovoltaic (PV) module under standard test conditions (STC). The paper investigates six methods and presents the detailed mathematical analysis leading to the development of each method. To evaluate the performance of these methods, MATLAB-based software has been devised and deployed to generate the results of each method when used to extract the SDM parameters of various PV test modules of different PV technologies. Similar software has also been developed to extract the same parameters using well-established numerical and iterative techniques. A comparison is subsequently made between the synthesized results and those obtained using numerical and iterative methods. The comparison indicates that although analytical methods may involve a significant amount of approximations, their accuracy can be comparable to that of their numerical and iterative counterparts, with the added advantage of a significant reduction in computational complexity, and without the initialization and convergence difficulties, which are normally associated with numerical methods.

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Section: Methods Sixmentioning

confidence: 99%

“…The design of an MPPT system is made more difficult due to the effects of PS (partial shading) [7]. Partial shading can lead to significant reduction in the energy yield of a PV system and its reliability [8,9]. To mitigate the adverse consequences of PS, bypass diodes are normally deployed in PV modules [10] and arrays [11].…”

Section: Introductionmentioning

confidence: 99%

Performance Evaluation of Analytical Methods for Parameters Extraction of Photovoltaic Generators

Anani

Ibrahim

2020

Energies

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“…This process is referred to complete local optimization [88]. Other emerging algorithms are evolutionary algorithm (EA) [89] and TS [90]. EA is a generic population-based metaheuristic algorithm based on biological evolution which includes reproduction, mutation, recombination and selection.…”

Section: K Development Of New Ai-based Mppt and Other Emerging Metahmentioning

confidence: 99%

Artificial Intelligence Based MPPT Techniques for Solar Power System: A review

Yap

Sarimuthu

Lim

2020

Journal of Modern Power Systems and Clean Energy

181

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In the last decade, artificial intelligence (AI) techniques have been extensively used for maximum power point tracking (MPPT) in the solar power system. This is because conventional MPPT techniques are incapable of tracking the global maximum power point (GMPP) under partial shading condition (PSC). The output curve of the power versus voltage for a solar panel has only one GMPP and multiple local maximum power points (MPPs). The integration of AI in MPPT is crucial to guarantee the tracking of GMPP while increasing the overall efficiency and performance of MPPT. The selection of AI-based MPPT techniques is complicated because each technique has its own merits and demerits. In general, all of the AI-based MPPT techniques exhibit fast convergence speed, less steady-state oscillation and high efficiency, compared with the conventional MPPT techniques. However, the AI-based MPPT techniques are computationally intensive and costly to realize. Overall, the hybrid MPPT is favorable in terms of the balance between performance and complexity, and it combines the advantages of conventional and AI-based MPPT techniques. In this paper, a detailed comparison of classification and performance between 6 major AI-based MPPT techniques have been made based on the review and MATLAB/Simulink simulation results. The merits, open issues and technical implementations of AI-based MPPT techniques are evaluated. We intend to provide new insights into the choice of optimal AI-based MPPT techniques. Index Terms-Maximum power point tracking (MPPT), artificial intelligence (AI), fuzzy logic control (FLC), artificial neural network (ANN), genetic algorithm (GA), swarm intelligence (SI), machine learning (ML).

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“…One such challenge is the global maximum power point tracking (GMPPT) under partial shading conditions. Partial shading refers to a condition when a PV system sees non-uniform irradiance and consequently non-uniform temperature conditions, either within the module or across different modules in an electrically connected PV array [1]. Partial shading not only reduces the output of the PV systems but if long operations are continued under this condition, without bypass diode protection, it can lead to hotspot induced breakdown of systems.…”

Section: Introductionmentioning

confidence: 99%

Optimized Global Maximum Power Point Tracking of Photovoltaic Systems Based on Rectangular Power Comparison

Bharadwaj

2021

IEEE Access

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Global maximum power point tracking (GMPPT) refers to the extraction of the maximum power from photovoltaic (PV) modules in real time under changing ambient conditions. Due to the installation of PV systems in densely built-up areas, partial shading scenarios are commonplace. Commercially established GMPPTs suffer from low tracking speeds and inefficiency. A novel GMPPT algorithm is proposed here based on the rectangular power comparison (RPC), which exploits the fundamental relationship between the shading factor, the bypass diode voltage and the global maximum power point. The entire theoretical formulation of RPC is presented systematically for the first time. This method boasts of increased conversion speeds owing to the precomputation of the module voltage versus the shading factor correlations using the regression of diode model from the experimentally obtained bypass diode characteristics. The proposed method is simple to implement with the computational complexity of order n, which represents the number of uniquely shaded PV modules in a series string. The proposed approach addresses the much-needed intersection problem between the distributed and centralized PV systems and therefore targets PV strings which are most common in residential and small to medium scale commercial PV installations world over. The proposed approach is validated with the in-house developed prototype hardware set-up and software control implementation giving a 99% tracking efficiency with a recorded tracking time of 10 ms. The experimental results show 50 times improvement in speed and 95% increase in power gain as compared to the other popular existing methods namely scanning based GMPPT and local MPPT methods respectively, with negligible computational burden and less than 0.5% added cost to the conventional PV energy conversion system. INDEX TERMS Efficiency optimization, Global maximum power point tracking, Partial shading, Photovoltaic module measurements and Solar energy conversion.

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Identification of Partial Shading Conditions for Photovoltaic Strings

Cited by 21 publications

References 26 publications

Performance Evaluation of Analytical Methods for Parameters Extraction of Photovoltaic Generators

Performance Evaluation of Analytical Methods for Parameters Extraction of Photovoltaic Generators

Artificial Intelligence Based MPPT Techniques for Solar Power System: A review

Optimized Global Maximum Power Point Tracking of Photovoltaic Systems Based on Rectangular Power Comparison

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