Failure diagnosis and trend‐based performance losses routines for the detection and classification of incidents in large‐scale photovoltaic systems

Abstract

Fault detection and classification in photovoltaic (PV) systems through real-time monitoring is a fundamental task that ensures quality of operation and significantly improves the performance and reliability of operating systems. Different statistical and comparative approaches have already been proposed in the literature for fault detection; however, accurate classification of fault and loss incidents based on PV performance time series remains a key challenge. Failure diagnosis and trend-based performance loss routines were developed in this work for detecting PV underperformance and accurately identifying the different fault types and loss mechanisms. The proposed routines focus mainly on the differentiation of failures (e.g., inverter faults) from irreversible (e.g., degradation) and reversible (e.g., snow and soiling) performance loss factors based on statistical analysis. The proposed routines were benchmarked using historical inverter data obtained from a 1.8 MWp PV power plant. The results demonstrated the effectiveness of the routines for detecting failures and loss mechanisms and the capability of the pipeline for distinguishing underperformance issues using anomaly detection and change-point (CP) models.Finally, a CP model was used to extract significant changes in time series data, to detect soiling and cleaning events and to estimate both the performance loss and degradation rates of fielded PV systems.

Operation and Maintenance Decision Support System for Photovoltaic Systems

“…The RdTools incorporates the Year-on-Year (YOY) [38] method for estimating the PLR (in %/year) and the stochastic rate and recovery (SRR) [39] method for estimating the soiling losses and detecting cleaning events. Snow losses were detected by post-processing PV performance parameters (i.e., performance ratio values) along with the site's weather conditions (i.e., snowfall and ambient temperature measurements ) [40,41].…”

“…In parallel, the Facebook prophet (FBP) algorithm was used to identify the number and location(s) of change-point(s) in time series data by capturing linear and complex trends as well as abrupt profile changes [44,45]. The FBP has the capability of differentiating reversible from irreversible mechanisms, extracting the soiling losses and estimating both the performance loss rate (PLR) and degradation rate (RD) of PV systems [41]. This can be achieved by rating the detected changes and adjusting the flexibility of the algorithm (changepoint_prior_scale hyperparameter) to capture either changes due to performance loss factors, soiling loss or only degradation rate changes (by avoiding the influence of outliers/faults and temporary effects) [41].…”

“…The FBP has the capability of differentiating reversible from irreversible mechanisms, extracting the soiling losses and estimating both the performance loss rate (PLR) and degradation rate (RD) of PV systems [41]. This can be achieved by rating the detected changes and adjusting the flexibility of the algorithm (changepoint_prior_scale hyperparameter) to capture either changes due to performance loss factors, soiling loss or only degradation rate changes (by avoiding the influence of outliers/faults and temporary effects) [41]. More details about the FBP model calibration procedure and its usage for categorization of incidents are available in Livera et al [41].…”

“…This can be achieved by rating the detected changes and adjusting the flexibility of the algorithm (changepoint_prior_scale hyperparameter) to capture either changes due to performance loss factors, soiling loss or only degradation rate changes (by avoiding the influence of outliers/faults and temporary effects) [41]. More details about the FBP model calibration procedure and its usage for categorization of incidents are available in Livera et al [41]. Breaking down of system energy losses into 6 main categories (near zero power production incidents, reduced power production incidents due to faults, faulty/defective equipment, soiling, snow, degradation) was then performed to provide insights on the fault root causes.…”

“…The FBP algorithm was then used assess the overall plant health-state and to estimate both the PLR and the RD, by adjusting its flexibility hyperparameter. For PLR estimation, the FBP flexibility was set to 2.5 to capture a signal with all performance losses and then re-adjusted to 0.04 to capture only degradation changes [41]. By applying the ordinary least squares (OLS) method on the FBP extracted linear trend (see Fig.…”