Photovoltaic Array Fault Detection by Automatic Reconfiguration

Dong, Ji; Cai, Zhang; Lv, Mingsong; Ma, Ye; Guan, Nan

doi:10.3390/en10050699

Cited by 31 publications

(21 citation statements)

References 22 publications

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“…An I-V curve tracer can be used to examine the behavior of the PV panels, taking into consideration solar irradiance and ambient temperature [36]. Using an I-V curve tracer, it is possible to readily identify anomalies caused by short circuit bypass diodes [29] and fault detection [37]. The time to assess PV panel health using an I-V curve tracer is minimal, estimated at less than one minute per panel [37].…”

Section: Current Approaches and Opportunities For Pv Predictive Maintmentioning

confidence: 99%

“…Using an I-V curve tracer, it is possible to readily identify anomalies caused by short circuit bypass diodes [29] and fault detection [37]. The time to assess PV panel health using an I-V curve tracer is minimal, estimated at less than one minute per panel [37]. Although there are many benefits to using handheld tools to manually assess the performance of the panels, challenges and opportunities remain primarily because of the length of time required to assess an entire array or plant, and the potential for human error during the manual data collection process.…”

Section: Current Approaches and Opportunities For Pv Predictive Maintmentioning

confidence: 99%

See 1 more Smart Citation

PV System Predictive Maintenance: Challenges, Current Approaches, and Opportunities

et al. 2020

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Within the United States solar energy industry, there is a general motto of “set it and forget it” with solar energy. This notion is derived from much of the research and reliability studies around the photovoltaic (PV) panels themselves, not necessarily the PV system as a whole (including the inverter and other components). This implies that maintenance and regular monitoring is not needed. Yet many things can go wrong to cause the actual performance to deviate from the expected performance. If failures and/or unanticipated degradation issues go undetected, they will lead to reduced energy generation (and associated electricity credits) and/or potential loss of component warranty because of manufacturer turnover. Given the size of the problem and gaps with current solutions, the authors propose that PV system owners need an unbiased third-party off-the-shelf system-level predictive maintenance tool to optimize return-on-investment and minimize time to warranty claim in PV installations. This paper reviews the literature highlighting challenges, current approaches, and opportunities for PV predictive maintenance. The paper concludes with a call to action for establishing a collaborative agenda toward prioritizing PV predictive maintenance.

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Section: Current Approaches and Opportunities For Pv Predictive Maintmentioning

confidence: 99%

Section: Current Approaches and Opportunities For Pv Predictive Maintmentioning

confidence: 99%

PV System Predictive Maintenance: Challenges, Current Approaches, and Opportunities

et al. 2020

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“…The applied method is based on the residuals obtained from a 1‐diode mode. Ji et al proposed an automatic technique for detecting PV failures. For instance, in Chine et al, artificial neural network is adopted in the literature where 2 various algorithms are then established to identify and isolate 8 different types of failures.…”

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

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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.

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“…In PV modules manufacturing technology, manufacturing tolerances of ±3% to ±5% in their maximum power rating are generally allowed [22]. MML is recognized and investigated by many researchers on different size of PV array configurations using different reconfiguration techniques of PV modules [23][24][25].…”

Section: Introductionmentioning

confidence: 99%

Performance Comparison of Mismatch Power Loss Minimization Techniques in Series-Parallel PV Array Configurations

2019

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The mismatch in current-voltage (I-V) characteristics of photovoltaic (PV) modules causes significant power loss in a large PV array, which is known as mismatch power loss (MML). The PV array output power generation can be improved by minimizing MML using different techniques. This paper investigates the performance of different module arrangement techniques to minimize MML both for long series string (LSS) and long parallel branch (LPB) in series-parallel (SP) array configurations at uniform irradiance condition. To investigate the significance of MML LSS-SP configuration with dimensions: 1 × 40, 2 × 20, 4 × 10, 5 × 8 and LPB-SP configuration with dimensions: 40 × 1, 20 × 2, 10 × 4, 8 × 5 were used. A comparative analysis is made to find the effectiveness of MML reduction techniques on PV arrays with three different power ratings. Simulation results show that the PV modules arrangement obtained by the genetic algorithm (GA) and current based arrangement (Im) performed better than the arrangements obtained by all other techniques in terms of PV array output power and MML minimization. The performance of the proposed technique was analyzed for both LSS-SP and LPB-SP array configurations in 400 W, 3400 W, and 9880 W arrays. To substantiate the simulation results experiment was performed using a 400 W PV array in outdoor weather condition and obtained similar results. It was also observed that the percentage of recoverable energy (%RE) obtained by arranging the modules using the GA method was higher than Im based method for both LSS-SP and LPB-SP array configurations. A maximum %RE of 4.159 % was recorded for a 5 × 8 LSS-SP array configuration by applying the GA based MML reduction method.

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Photovoltaic Array Fault Detection by Automatic Reconfiguration

Cited by 31 publications

References 22 publications

PV System Predictive Maintenance: Challenges, Current Approaches, and Opportunities

PV System Predictive Maintenance: Challenges, Current Approaches, and Opportunities

Enhanced generalized likelihood ratio test for failure detection in photovoltaic systems

Performance Comparison of Mismatch Power Loss Minimization Techniques in Series-Parallel PV Array Configurations

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