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

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

Estimating the Performance Loss Rate of Photovoltaic Systems Using Time Series Change Point Analysis

The accurate quantification of the performance loss rate of photovoltaic systems is critical for project economics. Following the current research activities in the photovoltaic performance and reliability field, this work presents a comparative assessment between common change point methods for performance loss rate estimation of fielded photovoltaic installations. An extensive testing campaign was thus performed to evaluate time series analysis approaches for performance loss rate evaluation of photovoltaic systems. Historical electrical data from eleven photovoltaic systems installed in Nicosia, Cyprus, and the locations’ meteorological measurements over a period of 8 years were used for this investigation. The application of change point detection algorithms on the constructed monthly photovoltaic performance ratio series revealed that the obtained trend might not always be linear. Specifically, thin film photovoltaic systems showed nonlinear behavior, while nonlinearities were also detected for some crystalline silicon photovoltaic systems. When applying several change point techniques, different numbers and locations of changes were detected, resulting in different performance loss rate values (varying by up to 0.85%/year even for the same number of change points). The results highlighted the importance of the application of nonlinear techniques and the need to extract a robust nonlinear model for detecting significant changes in time series data and estimating accurately the performance loss rate of photovoltaic installations

Cloud-Based Platform for Photovoltaic Assets Diagnosis and Maintenance

A cloud-based platform for reducing photovoltaic (PV) operation and maintenance (O&M) costs and improving lifetime performance is proposed in this paper. The platform incorporates a decision support system (DSS) engine and data-driven functionalities for data cleansing, PV system modeling, early fault diagnosis and provision of O&M recommendations. It can ensure optimum performance by monitoring in real time the operating state of PV assets, detecting faults at early stages and suggesting field mitigation actions based on energy loss analysis and incidents criticality evaluation. The developed platform was benchmarked using historical data from a test PV power plant installed in the Mediterranean region. The obtained results showed the effectiveness of the incorporated functionalities for data cleansing and system modeling as well as the platform’s capability for automated PV asset diagnosis and maintenance by providing recommendations for resolving the detected underperformance issues. Based on the DSS recommendations, approximately 7% of lost energy production could be recovered by performing field mitigation activities (e.g., corrective actions)

Cloud-Based Platform for Photovoltaic Assets Diagnosis and Maintenance

A cloud-based platform for reducing photovoltaic (PV) operation and maintenance (O&M) costs and improving lifetime performance is proposed in this paper. The platform incorporates a decision support system (DSS) engine and data-driven functionalities for data cleansing, PV system modeling, early fault diagnosis and provision of O&M recommendations. It can ensure optimum performance by monitoring in real time the operating state of PV assets, detecting faults at early stages and suggesting field mitigation actions based on energy loss analysis and incidents criticality evaluation. The developed platform was benchmarked using historical data from a test PV power plant installed in the Mediterranean region. The obtained results showed the effectiveness of the incorporated functionalities for data cleansing and system modeling as well as the platform’s capability for automated PV asset diagnosis and maintenance by providing recommendations for resolving the detected underperformance issues. Based on the DSS recommendations, approximately 7% of lost energy production could be recovered by performing field mitigation activities (e.g., corrective actions)

Direct Short-Term Net Load Forecasting Based on Machine Learning Principles for Solar-Integrated Microgrids

Accurate net load forecasting is a cost-effective technique, crucial for the planning, stability, reliability, and integration of variable solar photovoltaic (PV) systems in modern power systems. This work presents a direct short-term net load forecasting (STNLF) methodology for solar-integrated microgrids by leveraging machine learning (ML) principles. The proposed data-driven method comprises of an initial input feature engineering and filtering step, construction of forecasting model using Bayesian neural networks, and an optimization stage. The performance of the proposed model was validated on historical net load data obtained from a university campus solar-powered microgrid. The results demonstrated the effectiveness of the model for providing accurate and robust STNLF. Specifically, the optimally constructed model yielded a normalized root mean square error of 3.98% when benchmarked using a 1-year historical microgrid data. The $k$ -fold cross-validation method was then used and proved the stability of the forecasting model. Finally, the obtained ML-based forecasts demonstrated improvements of 17.77% when compared against forecasts of a baseline naïve persistence model. To this end, this work provides insights on how to construct high-performance STNLF models for solar-integrated microgrids. Such insights on the development of accurate STNLF architectures can have positive implications in actual microgrid decision-making by utilities/operators

Operation and maintenance decision support system for photovoltaic systems

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 large-scale 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 e/kW/year when performing O&M actions. For an electricity price of 59.98 e/MWh, a minimum of 8.4% energy loss per year is required for offsetting the annualized O&M cost value of 7.45 e/kW/year calculated by the SunSpec/National Renewable Energy Laboratory (NREL) PV O&M Cost Model

A comprehensive review of unmanned aerial vehicle-based approaches to support photovoltaic plant diagnosis

Accurate photovoltaic (PV) diagnosis is of paramount importance for reducing investment risk and increasing the bankability of the PV technology. The application of fault diagnostic solutions and troubleshooting on operating PV power plants is vital for ensuring optimal energy harvesting, increased power generation production and optimised field operation and maintenance (O&M) activities. This study aims to give an overview of the existing approaches for PV plant diagnosis, focusing on unmanned aerial vehicle (UAV)-based approaches, that can support PV plant diagnostics using imaging techniques and data-driven analytics. This review paper initially outlines the different degradation mechanisms, failure modes and patterns that PV systems are subjected and then reports the main diagnostic techniques. Furthermore, the essential equipment and sensor's requirements for diagnosing failures in monitored PV systems using UAV-based approaches are provided. Moreover, the study summarizes the operating conditions and the various failure types that can be detected by such diagnostic approaches. Finally, it provides recommendations and insights on how to develop a fully functional UAV-based diagnostic tool, capable of detecting and classifying accurately failure modes in PV systems, while also locating the exact position of faulty modules

International collaboration framework for the calculation of performance loss rates: Data quality, benchmarks, and trends (towards a uniform methodology)

The IEA PVPS Task 13 group, experts who focus on photovoltaic performance, operation, and reliability from several leading R&D centers, universities, and industrial companies, is developing a framework for the calculation of performance loss rates of a large number of commercial and research photovoltaic (PV) power plants and their related weather data coming across various climatic zones. The general steps to calculate the performance loss rate are (i) input data cleaning and grading; (ii) data filtering; (iii) performance metric selection, corrections, and aggregation; and finally, (iv) application of a statistical modeling method to determine the performance loss rate value. In this study, several high-quality power and irradiance datasets have been shared, and the participants of the study were asked to calculate the performance loss rate of each individual system using their preferred methodologies. The data are used for benchmarking activities and to define capabilities and uncertainties of all the various methods. The combination of data filtering, metrics (performance ratio or power based), and statistical modeling methods are benchmarked in terms of (i) their deviation from the average value and (ii) their uncertainty, standard error, and confidence intervals. It was observed that careful data filtering is an essential foundation for reliable performance loss rate calculations. Furthermore, the selection of the calculation steps filter/metric/statistical method is highly dependent on one another, and the steps should not be assessed individually

International collaboration framework for the calculation of performance loss rates: Data quality, benchmarks, and trends (towards a uniform methodology)

The IEA PVPS Task 13 group, experts who focus on photovoltaic performance, operation, and reliability from several leading R&D centers, universities, and industrial companies, is developing a framework for the calculation of performance loss rates of a large number of commercial and research photovoltaic (PV) power plants and their related weather data coming across various climatic zones. The general steps to calculate the performance loss rate are (i) input data cleaning and grading; (ii) data filtering; (iii) performance metric selection, corrections, and aggregation; and finally, (iv) application of a statistical modeling method to determine the performance loss rate value. In this study, several high-quality power and irradiance datasets have been shared, and the participants of the study were asked to calculate the performance loss rate of each individual system using their preferred methodologies. The data are used for benchmarking activities and to define capabilities and uncertainties of all the various methods. The combination of data filtering, metrics (performance ratio or power based), and statistical modeling methods are benchmarked in terms of (i) their deviation from the average value and (ii) their uncertainty, standard error, and confidence intervals. It was observed that careful data filtering is an essential foundation for reliable performance loss rate calculations. Furthermore, the selection of the calculation steps filter/metric/statistical method is highly dependent on one another, and the steps should not be assessed individually