Failure diagnosis of short- and open-circuit fault conditions in PV systems

Livera, Andreas; Florides, Michalis; Theristis, Marios; Makrides, George; Georghiou, George E.

doi:10.1109/pvsc.2018.8548161

Cited by 12 publications

(7 citation statements)

References 7 publications

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“…Livera et al [20], [21] apply Schewart charts to the difference between the measured and modeled DC power, using the same parametric model as [19]. While Livera et al in [20] use a 3σ detection rule, Platon et al [21] use a 1σ rule, without justifying this choice.…”

Section: A Control Charts In the Pv System Literaturementioning

confidence: 99%

“…They then apply different control chart variants to monitor the difference between measured and modeled values. Platon et al [19] and Livera et al [20] apply CUSUM and multivariate CUSUM charts, respectively, Ramirez and Ramiŕez [18] and Livera et al [21] apply EWMA and multivariate EWMA charts, respectively, and Harrou et al in [22] and [23] apply a combination of wavelet multiscale representation of the data and EWMA charts (dubbed MW-EWMA). The multivariate variants simultaneously monitor the current, voltage, and power residuals, thus accounting for correlations in these values, reportedly giving better detection sensitivity.…”

Section: A Control Charts In the Pv System Literaturementioning

confidence: 99%

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Robust and Fast Detection of Small Power Losses in Large-Scale PV Systems

Skomedal

Øgaard

Haug

et al. 2021

IEEE J. Photovoltaics

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Due to the fast growth in global installed photovoltaic (PV) capacity, performance monitoring for large-scale PV systems is an increasingly relevant and important topic. A large volume of research exists in this field, but there is a need for comparison of different methods and their performance toward relevant metrics, a broad discussion of the different choices involved, and subsequent consolidation. In this article, we focus on the detection of small power losses on string-level. We discuss the different choices involved in building a robust string performance monitoring scheme. We suggest the following approach: 1) identify bad data quality and do data-filtering; 2) calculate the daily specific yield on string level; 3) calculate the relative difference in specific yield between the strings (relative yield); 4) identify historical faults; 5) correct for seasonal variations; and 6) apply control charts to detect performance losses in new data and issue alarms/report to the system operators. Based on data from a utility scale PV power plant we compare different control charts in terms of detection time and sensitivity. We show that the cumulative sum (CUSUM) median and the Tukey-CUSUM charts are the most promising fault detection methods of the ones we have tested. We can robustly detect faults causing a performance loss of about 1% within 35 days of the drop in performance.

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Section: A Control Charts In the Pv System Literaturementioning

confidence: 99%

Section: A Control Charts In the Pv System Literaturementioning

confidence: 99%

Robust and Fast Detection of Small Power Losses in Large-Scale PV Systems

Skomedal

Øgaard

Haug

et al. 2021

IEEE J. Photovoltaics

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“…When the PV plant (or part of it) needs to be taken offline for the execution of corrective actions, night time or low irradiation hours are considered to be the best practice for minimizing the energy loss [2]. For detecting faults in PV systems, several techniques have been proposed in the literature [18][19][20][21][22]. In general, fault diagnostic methods for PV systems are based on visual inspections, image processing and data analytic (including signal processing) techniques [18].…”

Section: Introductionmentioning

confidence: 99%

“…In principle, comparative, statistical and/or data-driven (e.g., artificial intelligence) analyses are performed, yielding useful information on the health-state and operation of the system. A number of failures including inverter shutdown, mismatch faults (partial shading), open-and short-circuit faults, line-to-line faults, string disconnections and bypass diode faults were reliably identified by such data analytic methods [20][21][22]. It is worth noting here that there is no consensus with respect to the most common technical issues that affect the PV plant power production.…”

Section: Introductionmentioning

confidence: 99%

Operation and Maintenance Decision Support System for Photovoltaic Systems

et al. 2022

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Operation and maintenance (O&M) and monitoring strategies are important for safeguarding optimum photovoltaic (PV) performance while also minimizing downtimes due to faults. An O&M decision support system (DSS) was developed in this work for providing recommendations of actionable decisions to resolve fault and performance loss events. The proposed DSS operates entirely on raw field measurements and incorporates technical asset and financial management features. Historical measurements from a largescale PV system installed in Greece were used for the benchmarking procedure. The results demonstrated the financial benefits of performing mitigation actions in case of near zero power production incidents. Stochastic simulations that consider component malfunctions and failures exhibited a net economic gain of approximately 4.17 €/kW/year when performing O&M actions. For an electricity price of 59.98 €/MWh, a minimum of 8.4% energy loss per year is required for offsetting the annualized O&M cost value of 7.45 €/kW/year calculated by the SunSpec/National Renewable Energy Laboratory (NREL) PV O&M Cost Model.

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“…This study presents an extensive review of AI-based methods and techniques of fault detection and diagnosis reported in various literatures. The contribution of the study is in outlining the characteristics of the reviewed AI-based methods and their effectiveness in rapidly and efficiently detecting faults with minimal error, since the effectiveness of a fault detection and diagnosis method depends on the following factors: Its ability to detect a fault and pinpoint its location in the shortest possible time; its relative affordability; and ease of use [38]. The structure of the remaining part of the paper is as follows.…”

Section: Introductionmentioning

confidence: 99%

Review of Artificial Intelligence-Based Failure Detection and Diagnosis Methods for Solar Photovoltaic Systems

2021

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In recent years, the overwhelming growth of solar photovoltaics (PV) energy generation as an alternative to conventional fossil fuel generation has encouraged the search for efficient and more reliable operation and maintenance practices, since PV systems require constant maintenance for consistent generation efficiency. One option, explored recently, is artificial intelligence (AI) to replace conventional maintenance strategies. The growing importance of AI in various real-life applications, especially in solar PV applications, cannot be over-emphasized. This study presents an extensive review of AI-based methods for fault detection and diagnosis in PV systems. It explores various fault types that are common in PV systems and various AI-based fault detection and diagnosis techniques proposed in the literature. Of note, there are currently fewer literatures in this area of PV application as compared to the other areas. This is due to the fact that the topic has just recently been explored, as evident in the oldest paper we could obtain, which dates back to only about 15 years. Furthermore, the study outlines the role of AI in PV operation and maintenance, and the main contributions of the reviewed literatures.

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Failure diagnosis of short- and open-circuit fault conditions in PV systems

Cited by 12 publications

References 7 publications

Robust and Fast Detection of Small Power Losses in Large-Scale PV Systems

Robust and Fast Detection of Small Power Losses in Large-Scale PV Systems

Operation and Maintenance Decision Support System for Photovoltaic Systems

Review of Artificial Intelligence-Based Failure Detection and Diagnosis Methods for Solar Photovoltaic Systems

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