Parallel fault detection algorithm for grid-connected photovoltaic plants

Dhimish, Mahmoud; Holmes, Violeta; Dales, Mark

doi:10.1016/j.renene.2017.05.084

Cited by 67 publications

(26 citation statements)

References 31 publications

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“…[85][86][87][88][89][90][91][92] When the suspicion on the wrong component is detected, the concrete strings are then evaluated using other methods. Usual configuration of large systems consists of string inverters with the middle power (about 5-10 kW) because of the problems with local shading caused by clouds movements.…”

Section: Proposed Topologymentioning

confidence: 99%

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Fault diagnosis of Photovoltaic Modules

Haque

Bharath

Khan

et al. 2019

Energy Science & Engineering

104

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Fault diagnosis and condition monitoring are important to increase the efficiency and reliability of photovoltaic modules. This paper reviews the challenges and limitations associated with fault diagnosis of solar modules. A thorough analysis of various faults responsible for failure of solar modules has been discussed. After reviewing relevant work, a monitoring tool is designed using thermography and artificial intelligent systems that allows the detection of various types of faults in PV modules and at the same time the designed tool aims to filter the nonsignificant anomalies. A neural network (NN) classifier is applied to the transfer characteristics (I‐V data) of the faulty PV module for the diagnosis which adapts multilayer perceptron (MLP) networks to identify the type and location of occurring faults. The Discrete wavelet transform (DWT) based signal processing technique is utilized in the feature extraction process to reduce the NN input size. The developed detection algorithm is adapted for 24/7 automated surveillance. For a given fault condition, the average fault detection time is observed to be <9 seconds, which is lower than the previous work done. The developed algorithm achieved 100% accuracy when tested on a predetermined fault data set.

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Section: Proposed Topologymentioning

confidence: 99%

“…The problem of data evaluation is out of range of this work and is described in many publications. [85][86][87][88][89][90][91][92] When the suspicion on the wrong component is detected, the concrete strings are then evaluated using other methods.…”

Section: Proposed Topologymentioning

confidence: 99%

Fault diagnosis of Photovoltaic Modules

Haque

Bharath

Khan

et al. 2019

Energy Science & Engineering

104

View full text Add to dashboard Cite

show abstract

“…Various types of faults associated with different PV array configurations are listed in Table . The tick and cross marks indicate, respectively, the presence and absence of particular fault in specific PV array configuration.…”

Section: Comparison Of Different Pv Array Configurations On the Basismentioning

confidence: 99%

A detailed comparative analysis of different photovoltaic array configurations under partial shading conditions

Jha

Triar

2019

Int Trans Electr Energ Syst

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Summary The objective of this paper is to compare the performances of different photovoltaic (PV) array configurations (series, parallel, series‐parallel, total‐cross‐tied, bridge‐linked, and honey comb) and proposed hybrid configurations (series parallel–total cross tied, bridge linked–total cross tied, honey comb–total cross tied, and bridge linked–honey comb) under artificial and realistic partial shading conditions. The performances of all these configurations have been compared on the basis of their maximum powers, fill factors, thermal voltages, and relative power losses. Different PV array configurations have also been compared on the basis of other significant factors (faults, limitations of specific parameters of the PV system, and cost). The single‐diode model of PV module is considered in this paper for modelling of PV module. The obtained results of different PV array configurations give beneficial knowledge regarding the performances of these configurations for several shading patterns and assist to choose the most satisfactory configuration for a particular shading pattern.

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“…For faults occurring in the grid connected PV systems, a new parallel algorithm is introduced to detect the faults on the DC and AC sides utilizing data measurements evaluated with t-statistical analysis methods [39]. Different scenarios are deployed and simulated using virtual instruments-based LabVIEW software.…”

Section: Hybrid Computational Techniquesmentioning

confidence: 99%

Review on Methods of Fault Diagnosis in Photovoltaic System Applications

Syafaruddin¹,

Zinger²

2019

JESTR

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Recently, detection and identification of faults in photovoltaic (PV) system applications have been attracting researchers worldwide. Some of them have investigated the causes of potential faults, have worked on how to monitor and identify the faults through fault diagnosis, and have elaborated on how to develop problem-solving methods for the early detection of faults in PV system operations. This paper attempts to provide information comprehensively from the previous research. It summarizes types of faults, methods used in early fault identification and monitoring, and PV fault prevention and protection systems. The paper also investigates future trends in PV system fault diagnosis which include real-time implementation. The paper is expected to bring new perspective about the literature associate with fault studies in PV systems. This survey will be beneficial for the future discussion on how to provide comprehensive solutions for PV system fault problems. It will help avoid research repetition on similar topics and focus on the improvement and performance development of PV fault diagnosis methods.

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Parallel fault detection algorithm for grid-connected photovoltaic plants

Cited by 67 publications

References 31 publications

Fault diagnosis of Photovoltaic Modules

Fault diagnosis of Photovoltaic Modules

A detailed comparative analysis of different photovoltaic array configurations under partial shading conditions

Review on Methods of Fault Diagnosis in Photovoltaic System Applications

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