Fault detection and monitoring systems for photovoltaic installations: A review

Triki-Lahiani, Asma; Abdelghani, Afef Bennani-Ben; Slama‐Belkhodja, Ilhem

doi:10.1016/j.rser.2017.09.101

Cited by 233 publications

(81 citation statements)

References 57 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The review provides an up‐to‐date knowledge with major issues to develop and/or choose an effective failure detection technique for small‐ and medium‐scale PV systems. The readers may refer also to additional recent review on failure detection and monitoring of PV systems …”

Section: Related Work On Failure Detection In Pv Systemsmentioning

confidence: 99%

Enhanced generalized likelihood ratio test for failure detection in photovoltaic systems

Mansouri

Hajji

Trabelsi

et al. 2018

Int Trans Electr Energ Syst

View full text Add to dashboard Cite

Summary In this paper, a new multiscale weighted generalized likelihood ratio test (MS‐WGLRT) chart is proposed for enhanced failure detection in photovoltaic systems. The main weakness of the classical generalized likelihood ratio test chart is in dealing with residual samples while ignoring their natural variances. By taking into consideration the nature variance of the detection residual and applying a multiscale representation, the proposed technique allows the reduction in false alarm and missed detection rates compared with the classical generalized likelihood ratio test chart. The multiscale representation of data is an efficient data analysis and feature extraction tool that has a great impact on the effectiveness of failure detection. The effectiveness of the proposed method is evaluated on a simulated photovoltaic data where the developed chart is used for detecting single and multiple failures (eg, bypass, mix, and shading failures). The simulation results show that the multiscale weighted generalized likelihood ratio test method offers better performance compared with the classical generalized likelihood ratio chart.

show abstract

Section: Related Work On Failure Detection In Pv Systemsmentioning

confidence: 99%

Enhanced generalized likelihood ratio test for failure detection in photovoltaic systems

Mansouri

Hajji

Trabelsi

et al. 2018

Int Trans Electr Energ Syst

View full text Add to dashboard Cite

show abstract

“…Many papers in the literature have been devoted to such an analysis (that is carried out by analysing the shape of the I-V and/or P-V characteristics for positive voltages). The analysis is nearly always coupled with the adoption of irradiance and/or temperature sensors [46][47][48][49][50]. The accuracy of the mismatching detection is strongly related to the capillarity of the distribution of the adopted sensors.…”

Section: Detection Of Mismatching Operating Conditionsmentioning

confidence: 99%

On the Link between the Operating Point and the Temperature Distribution in PV Arrays Working under Mismatching Conditions

Costanzo

Vitelli

2018

International Journal of Photoenergy

View full text Add to dashboard Cite

Mismatching operating conditions negatively affect the extracted energy in photovoltaic (PV) systems. They may also lead to dangerous localized heating phenomena (hot spots) that can cause, in turn, accelerated ageing and reduced reliability. Since the adoption of bypass diodes or smart active switches does not prevent the occurrence of hotspots, it is necessary to investigate alternative strategies. A promising solution is represented by the proper regulation of the operating point of the PV cells in the current vs. voltage (I-V) or power vs. voltage (P-V) planes when mismatching conditions occur. In particular, in this paper, the existence of operating points allowing a suitable compromise between maximization of the extracted power and minimization of thermal stresses, due to hot spots, is experimentally evidenced. Experimental results highlighting the link existing between the operating point in the I-V plane and the PV cell temperature distribution under uniform and mismatching operating conditions are presented and discussed. On the basis of the obtained experimental results, it is possible to state that, when mismatching conditions occur, it is mandatory to properly choose the operating point: the global maximum power point may not be the best operating point. Hence, it is crucial to gain information about the eventual occurrence of mismatching conditions in order to be able to properly choose the best operating point. Therefore, another crucial aspect that is evidenced in this paper is represented by the fact that the detection of the occurrence of mismatching conditions, based on the analysis of the shape of the I-V and/or P-V characteristics, is effective only if the analysis is carried out for both positive and negative voltages.

show abstract

“…In recent years, the PV industry has developed rapidly as the cost of PV modules has been greatly reduced. The installed capacity of PV power plants is increasing rapidly [1]; by the end of 2018, the cumulative PV installed capacity of China reached 174.63 GW, with an additional installed capacity of 44.1 GW. The PV power plant has a large number of modules, which works under the natural environment, so the module or array often failures in PV power plants [2].…”

Section: Introductionmentioning

confidence: 99%

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

Zhao¹,

Sun²,

Zhou³

et al. 2020

International Journal of Photoenergy

View full text Add to dashboard Cite

There are a large number of photovoltaic (PV) arrays in large-scale PV power plants or regional distributed PV power plants, and the output of different arrays fluctuates with the external conditions. The deviation and evolution information of the array output are easily covered by the random fluctuations of the PV output, which makes the fault diagnosis of PV arrays difficult. In this paper, a fault diagnosis method based on the deviation characteristics of the PV array output is proposed. Based on the current of the PV array on the DC (direct current) side, the deviation characteristics of the PV array output under different arrays and time series are analyzed. Then, the deviation function is constructed to evaluate the output deviation of the PV array. Finally, the fault diagnosis of a PV array is realized by using the probabilistic neural network (PNN), and the effectiveness of the proposed method is verified. The main contributions of this paper are to propose the deviation function that can extract the fault characteristics of PV array and the fault diagnosis method just using the array current which can be easily applied in the PV plant.

show abstract

Fault detection and monitoring systems for photovoltaic installations: A review

Cited by 233 publications

References 57 publications

Enhanced generalized likelihood ratio test for failure detection in photovoltaic systems

Enhanced generalized likelihood ratio test for failure detection in photovoltaic systems

On the Link between the Operating Point and the Temperature Distribution in PV Arrays Working under Mismatching Conditions

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

Contact Info

Product

Resources

About