A Method to Detect Photovoltaic Array Faults and Partial Shading in PV Systems

Hariharan, R.; Chakkarapani, M.; Nagamani, C.

doi:10.1109/jphotov.2016.2581478

Cited by 155 publications

(60 citation statements)

References 16 publications

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“…The actual operation of the PV power station is affected by multiple external factors such as solar radiation intensity, temperature, humidity, dust, hail, and snow constituting a harsh (and highly fault-prone) environment [23]. The PV array is an integral part of the PV system; its cost can account for about 40% of the power system as a whole.…”

Section: Generation Mechanism Of Typical Faults In Pv Arraysmentioning

confidence: 99%

A New PV Array Fault Diagnosis Method Using Fuzzy C-Mean Clustering and Fuzzy Membership Algorithm

Zhao

Shao

et al. 2018

Energies

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Photovoltaic (PV) power station faults in the natural environment mainly occur in the PV array, and the accurate fault diagnosis is of particular significance for the safe and efficient PV power plant operation. The PV array's electrical behavior characteristics under fault conditions is analyzed in this paper, and a novel PV array fault diagnosis method is proposed based on fuzzy C-mean (FCM) and fuzzy membership algorithms. Firstly, clustering analysis of PV array fault samples is conducted using the FCM algorithm, indicating that there is a fixed relationship between the distribution characteristics of cluster centers and the different fault, then the fault samples are classified effectively. The membership degrees of all fault data and cluster centers are then determined by the fuzzy membership algorithm for the final fault diagnosis. Simulation analysis indicated that the diagnostic accuracy of the proposed method was 96%. Field experiments further verified the correctness and effectiveness of the proposed method. In this paper, various types of fault distribution features are effectively identified by the FCM algorithm, whether the PV array operation parameters belong to the fault category is determined by fuzzy membership algorithm, and the advantage of the proposed method is it can classify the fault data from normal operating data without foreknowledge.

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Section: Generation Mechanism Of Typical Faults In Pv Arraysmentioning

confidence: 99%

A New PV Array Fault Diagnosis Method Using Fuzzy C-Mean Clustering and Fuzzy Membership Algorithm

Zhao

Shao

et al. 2018

Energies

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“…The above-mentioned methods are for the photovoltaic modules, of which each photovoltaic module needs to be monitored, leading to high costs. In [9][10][11][12][13], the fault diagnosis methods of the photovoltaic arrays were introduced. The fault detection is based on the comparison between the measured and model prediction results of the power production [9].…”

Section: Analysis Of Internal Parameters Of Photovoltaic Array Equivamentioning

confidence: 99%

“…The methods in these works can only identify whether there is a fault or not, but the fault type is unknown. In other research [12], a method is proposed to detect faults and partial shading under all of the irradiation conditions using the measured values of array voltage, array current, and irradiance, but the diagnosis of fault type is not comprehensive enough.…”

Section: Analysis Of Internal Parameters Of Photovoltaic Array Equivamentioning

confidence: 99%

Fault Diagnosis Model of Photovoltaic Array Based on Least Squares Support Vector Machine in Bayesian Framework

Sun

Fan

et al. 2017

Applied Sciences

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Abstract:With the rapid development of the photovoltaic industry, fault monitoring is becoming an important issue in maintaining the safe and stable operation of a solar power station. In order to diagnose the fault types of photovoltaic array, a fault diagnosis method that is based on the Least Squares Support Vector Machine (LSSVM) in the Bayesian framework is put forward. First, based on the elaborate analysis of the change rules of the output electrical parameters and the equivalent circuit internal parameters of photovoltaic array in different fault states, the input variables of the photovoltaic array fault diagnosis model are determined. Second, through the LSSVM algorithm in the Bayesian framework, the fault diagnosis model based on the output electrical parameters and the equivalent circuit internal parameters of the photovoltaic array is built, which can effectively detect the photovoltaic array faults of short circuit, open circuit, and abnormal aging. Then, the simulation model is built to verify the validity of the LSSVM algorithm in the Bayesian framework by comparing it with the model of LSSVM and the Support Vector Machine (SVM). Moreover, a 5 × 3 photovoltaic array and a reference photovoltaic string are established and experimentally tested to validate the performance of the proposed method.

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“…The source for these accidents was a fault on the DC side that was not identified early [2,3]. Therefore, an efficient real time monitoring and fault detection strategy is very important, not only to reduce time and cost of maintenance, but also to avoid any kind of energy loss, equipment damages and disastrous accidents [4]. Several monitoring strategies have been proposed in the literature.…”

Section: Rightsmentioning

confidence: 99%