A Photovoltaic Array Fault Diagnosis Method Considering the Photovoltaic Output Deviation Characteristics

Zhao, Jian; Sun, Qi; Zhou, Ning; Líu, Hao; Wang, Haizheng

doi:10.1155/2020/2176971

Cited by 9 publications

(5 citation statements)

References 33 publications

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“…One similar work (Chine, W.; Mellit, A et al 2016) by Zhao et al [27] is also inspired with such type of approach. Zhao et al [28] proposed that the FCM algorithm effectively defines various forms of fault distribution characteristics, decides how a fugitive Membership algorithm identifies the PV array operating parameters, and the benefit of the proposed approach is that the fault data from ordinary working data may be defined without prior knowledge.…”

Section: Related Workmentioning

confidence: 99%

“…FD MAX = p theoritical/p measured × MPPT tolerance rate (27) This was because the fault algorithm decided whether the MPPT device had a flaw or whether the whole PV device was entirely different from the PV array when the PR exceeded 50. To diagnose which form of defect in the PR area had occurred, the voltage ratio value was taken into account.…”

Section: Fault Detection Using Designed Type 2 Fuzzy Logic Systemmentioning

confidence: 99%

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Intelligent Detection of the PV Faults Based on Artificial Neural Network and Type 2 Fuzzy Systems

Janarthanan¹,

Maheshwari

Shukla

et al. 2021

Energies

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The real-time application research on the Fuzzy Logic Systems (FLSs) and Artificial Neural Networks (ANN) is vast and, in this paper, a technique for a photovoltaic failure analysis using the type 2 FLS and ANN is proposed. The method is proposed to build T2 FLS with a guaranteed value equal to or lower than T2 and ANN. Several explanations are conducted to illustrate the effectiveness of the methodologies. It is found that both the type 2 Fuzzy and ANN can be configured for productive actions in applications for a PV fault analysis, and choice is typically applied. The methods discussed in this paper lay the groundwork for developing FLSs and ANNs with durable characteristics that will be extremely useful in many functional applications. The result demonstrates that specific fault categories can be detected using the fault identification method, such as damaged PV modules and partial PV unit shades. The average detection performance is similar in both ANN and fuzzy techniques. In comparison, both systems evaluated show approximately the same performance during experiments. The architecture of the type 2 fuzzy logic system and ANN with radial basic function, including the roles of the output port and the rules for identifying the type of defect in the PV structure is slightly different.

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Section: Related Workmentioning

confidence: 99%

Section: Fault Detection Using Designed Type 2 Fuzzy Logic Systemmentioning

confidence: 99%

Intelligent Detection of the PV Faults Based on Artificial Neural Network and Type 2 Fuzzy Systems

Janarthanan¹,

Maheshwari

Shukla

et al. 2021

Energies

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show abstract

“…This defect becomes a load, so the current additionally concentrates on the current space, inflicting a huge dissipation of power within the broken cell, hence, making a hotspot [7]. As a result, the output power decreases, thereby decreasing the potency of the PV module [5]. For the purposes of assessing the performance of solar PV modules and obtaining parameters such as maximum power output and conversion efficiency, current-voltage (I-V) characterization is used.…”

Section: Introductionmentioning

confidence: 99%

Photovoltaic Modules Diagnosis Using Computer Vision

Dhamale¹,

Nakashe²,

Selvakumaran³

et al. 2023

Artificial Intelligence and Communication Technologies

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As environmental awareness has been culminating for the past few decades, people have been switching towards renewable sources of energy such as solar energy, wind energy, and hydropower. Among these, solar energy seems to be largely used due to the availability of solar energy in abundance, which in turn, has led to a mass increase in the number of solar power plants. Nevertheless, these solar panels are left unattended due to difficulty in the maintenance of solar power plants in large areas and the manual work required. It is important to find and replace these defective panels in time before any severe event occurs. Detecting hotspots, cracking and various other malfunctions in the photovoltaic cell can lead to an increase in the life of the solar panels by 5-10 years. In this paper, we propose a compact intelligent photovoltaic module diagnosis system based on a Deep Learning model that uses multilayer perceptron (MLP) to accurately identify and classify the faults in the photovoltaic module. We have achieved this by tweaking the VGG16 convolutional network architecture to detect 3 kinds of malfunctions – Hotspots, Cracking, Diode, and No-Anomaly for panels that are in good condition by using 2310 images for training, 665 for validation, and, 665 for testing. By using accuracy-score from sklearn.metrics, the model’s accuracy was estimated to be above 90 percent for classifying photovoltaic modules.

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“…Because of the large number and almost the same output characteristics of PV arrays, it is difficult to analyze the performance difference between PV arrays effectively. Moreover, PV power generation has obvious random fluctuating characteristics [17][18][19][20], which lead to the uncertainty of PV array performance evaluations, so it is of great practical significance to accurately calculate the stability of PV arrays. More and more attention has been paid to stability research.…”

Section: Introductionmentioning

confidence: 99%

Performance evaluation and state recognition method based on non‐parametric estimation algorithm for photovoltaic arrays

Sun

et al. 2023

IET Renewable Power Gen

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With the rapid development of photovoltaic (PV) power generation, state recognition of PV arrays is of great significance to its intelligent operation and maintenance. But the random fluctuation of PV power output leads to the obvious uncertainty for performance calculation of PV arrays; this paper proposes an evaluation method of PV arrays. Firstly, the performance calculation method and its distribution model were proposed by the kernel density estimation (KDE) method. Secondly, the probability density distribution (PDD) of different indicators was analyzed to determine the suitable observation indicator so as to introduce the stability indicator, from which the benchmark array was selected. Finally, the state recognition of PV arrays was achieved. The main conclusions and innovations of this paper are the stability evaluation method of PV arrays based on the non‐parameter estimation method and the effective operating state recognition method for PV arrays. The proposed method was proven to be more reliable by comparing it to the power generation ranking method (PGRM). The effectiveness was verified by comparing the statistical and real‐time distribution of the performance ratio, and the excess rate of different confidence levels and Euclidean distance were introduced to demonstrate the accuracy of the proposed state recognition method.

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A Photovoltaic Array Fault Diagnosis Method Considering the Photovoltaic Output Deviation Characteristics

Cited by 9 publications

References 33 publications

Intelligent Detection of the PV Faults Based on Artificial Neural Network and Type 2 Fuzzy Systems

Intelligent Detection of the PV Faults Based on Artificial Neural Network and Type 2 Fuzzy Systems

Photovoltaic Modules Diagnosis Using Computer Vision

Performance evaluation and state recognition method based on non‐parametric estimation algorithm for photovoltaic arrays

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