Statistical Methods for Degradation Estimation and Anomaly Detection in Photovoltaic Plants

Photovoltaic (PV) plants typically suffer from a significant degradation in performance over time due to multiple factors. Operation and maintenance systems aim at increasing the efficiency and profitability of PV plants by analyzing the monitoring data and by applying data-driven methods for assessing the causes of such performance degradation. Two main classes of degradation exist, being it either gradual or a sudden anomaly in the PV system. This has motivated our work to develop and implement statistical methods that can reliably and accurately detect the performance issues in a cost-effective manner. In this paper, we introduce different approaches for both gradual degradation assessment and anomaly detection. Depending on the data available in the PV plant monitoring system, the appropriate method for each degradation class can be selected. The performance of the introduced methods is demonstrated on data from three different PV plants located in Slovenia and Italy monitored for several years. Our work has led us to conclude that the introduced approaches can contribute to the prompt and accurate identification of both gradual degradation and sudden anomalies in PV plants.

[1]  Ying Sun,et al.  Reliable fault detection and diagnosis of photovoltaic systems based on statistical monitoring approaches , 2018 .

[2]  Emad Maher Natsheh,et al.  Intelligent PV Panels Fault Diagnosis Method Based on NARX Network and Linguistic Fuzzy Rule-Based Systems , 2020, Sustainability.

[3]  Gaël Varoquaux,et al.  Scikit-learn: Machine Learning in Python , 2011, J. Mach. Learn. Res..

[4]  Gabi Friesen,et al.  Review of Failures of Photovoltaic Modules , 2014 .

[5]  Donald J. Berndt,et al.  Using Dynamic Time Warping to Find Patterns in Time Series , 1994, KDD Workshop.

[6]  S. W. Roberts,et al.  Control Chart Tests Based on Geometric Moving Averages , 2000, Technometrics.

[7]  Aminmohammad Saberian,et al.  Modelling and Prediction of Photovoltaic Power Output Using Artificial Neural Networks , 2014 .

[8]  D. Ruppert.  The Elements of Statistical Learning: Data Mining, Inference, and Prediction , 2004 .

[9]  Aissa Chouder,et al.  Automatic supervision and fault detection of PV systems based on power losses analysis , 2010 .

[10]  Martin Halwachs,et al.  Statistical evaluation of PV system performance and failure data among different climate zones , 2019, Renewable Energy.

[11]  Marc Rußwurm,et al.  Tslearn, A Machine Learning Toolkit for Time Series Data , 2020, J. Mach. Learn. Res..

[12]  Junho Bang,et al.  An Intelligent Fault Detection Model for Fault Detection in Photovoltaic Systems , 2020, J. Sensors.

[13]  George Makrides,et al.  Nonlinear Photovoltaic Degradation Rates: Modeling and Comparison Against Conventional Methods , 2020, IEEE Journal of Photovoltaics.

[14]  Donald S. Zinger,et al.  Review on Methods of Fault Diagnosis in Photovoltaic System Applications , 2019, Journal of Engineering Science and Technology Review.

[15]  L. Neumaier,et al.  Scientific and economic comparison of outdoor characterisation methods for photovoltaic power plants , 2019 .

[16]  Robson Ribeiro Linhares,et al.  A Monitoring System for Online Fault Detection and Classification in Photovoltaic Plants , 2020, Sensors.

[17]  Chotirat Ratanamahatana,et al.  An Enhanced Parameter-Free Subsequence Time Series Clustering for High-Variability-Width Data , 2014, SCDM.

[18]  Dirk Jordan,et al.  Compendium of photovoltaic degradation rates , 2016 .

[19]  Diane J. Cook,et al.  A survey of methods for time series change point detection , 2017, Knowledge and Information Systems.

[20]  M. Dhimish,et al.  Photovoltaic Degradation Rate Affected by Different Weather Conditions: A Case Study Based on PV Systems in the UK and Australia , 2020, Electronics.

[21]  B. Marion.  Comparison of predictive models for photovoltaic module performance , 2008, 2008 33rd IEEE Photovoltaic Specialists Conference.

[22]  M. Topič,et al.  Performance of Different Types of ETSC Solar Irradiance Sensors , 2013 .

[23]  Dirk C. Jordan,et al.  The Dark Horse of Evaluating Long-Term Field Performance—Data Filtering , 2014, IEEE Journal of Photovoltaics.

[24]  Fernando Morgado-Dias,et al.  Machine Learning in PV Fault Detection, Diagnostics and Prognostics: A Review , 2017 .

[25]  Tingting Pei,et al.  A Fault Detection Method for Photovoltaic Systems Based on Voltage and Current Observation and Evaluation , 2019, Energies.

[26]  Marcelo Gradella Villalva,et al.  Comprehensive Approach to Modeling and Simulation of Photovoltaic Arrays , 2009, IEEE Transactions on Power Electronics.

[27]  David Moser,et al.  Review of Statistical and Analytical Degradation Models for Photovoltaic Modules and Systems as Well as Related Improvements , 2018, IEEE Journal of Photovoltaics.

[28]  Michael G. Deceglie,et al.  PV degradation methodology comparison — A basis for a standard , 2016, 2016 IEEE 43rd Photovoltaic Specialists Conference (PVSC).

[29]  P. Ingenhoven,et al.  Comparison of Statistical and Deterministic Smoothing Methods to Reduce the Uncertainty of Performance Loss Rate Estimates , 2018, IEEE Journal of Photovoltaics.

[30]  R. G. Ross,et al.  Flat-plate photovoltaic array design optimization , 1980 .

[31]  Spyros Theocharides,et al.  Machine learning algorithms for photovoltaic system power output prediction , 2018, 2018 IEEE International Energy Conference (ENERGYCON).