Online model-based fault detection for grid connected PV systems monitoring

Harrou, Fouzi; Sun, Ying; Saidi, Ahmed

doi:10.1109/icee-b.2017.8192117

Cited by 6 publications

(3 citation statements)

References 20 publications

(23 reference statements)

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“…In [22]- [27], Harrou et al and Garoudja et al parametrize the PV system through fitting the single diode model to a set of outdoor I-V measurements, and simulate the dc operating current, voltage, and power of the system. They then apply different control chart variants to monitor the difference between measured and modeled values.…”

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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show abstract

“…Then, residuals of MPP current, voltage, and power are verified by the univariate and multivariate exponentially weighted moving average (EWMA) schemes for uncovering faults and partial shading [20]. In [21], the single one diode model and the cumulative sum (CUSUM) scheme are amalgamated to monitor the DC side of the PV system. However, detection thresholds in the previously discussed methods are computed based on the Gaussian assumption of residuals' distribution.…”

Section: Introductionmentioning

confidence: 99%

Monitoring of Photovoltaic Systems Using Improved Kernel-Based Learning Schemes

Harrou

Saidi

Sun

et al. 2021

IEEE J. Photovoltaics

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Data-based procedures for monitoring the operating performance of a PV system are proposed in this paper. The only information required to apply the procedures is the availability of system measurements, which are routinely on-line collected via sensors. Here, kernel-based machine learning methods, including support vector regression (SVR) and Gaussian process regression (GPR), are used to model multivariate data from the PV system for fault detection due to their flexibility and capability to nonlinear approximation. Essentially, the SVR and GPR models are adopted to obtain residuals to detect and identify occurred faults. Then, residuals are passed through an exponential smoothing filter to reduce noise and improve data quality. In this work, a monitoring scheme based on kernel density estimation is used to sense faults by examining the generated residuals. Several different scenarios of faults were considered in this study, including PV string fault, partial shading, PV modules shortcircuited, module degradation, and line-line faults on the PV array. Using data from a 20 MWp grid-connected PV system, the considered faults were successfully traced using the developed procedures. Also, it has been demonstrated that GPR-based monitoring procedures achieve better detection performance over SVRs to monitor PV systems.

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