Anomaly detection and predictive maintenance for photovoltaic systems

De Benedetti, M; Leonardi, F; Messina, F; Santoro, C; Vasilakos, A

Anomaly detection and predictive maintenance for photovoltaic systems

De Benedetti, M Leonardi, F Messina, F Santoro, C Vasilakos, A

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Publisher:: ELSEVIER
Publication Type:: Journal Article
Citation:: Neurocomputing, 2018, 310, pp. 59-68
Issue Date:: 2018-10-08

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Field	Value	Language
dc.contributor.author	De Benedetti, M
dc.contributor.author	Leonardi, F
dc.contributor.author	Messina, F
dc.contributor.author	Santoro, C
dc.contributor.author	Vasilakos, A https://orcid.org/0000-0003-1902-9877
dc.date.accessioned	2022-08-29T19:47:45Z
dc.date.available	2022-08-29T19:47:45Z
dc.date.issued	2018-10-08
dc.identifier.citation	Neurocomputing, 2018, 310, pp. 59-68
dc.identifier.issn	0925-2312
dc.identifier.issn	1872-8286
dc.identifier.uri	http://hdl.handle.net/10453/161043
dc.description.abstract	We present a learning approach designed to detect possible anomalies in photovoltaic (PV) systems in order to let an operator to plan predictive maintenance interventions. The anomaly detection algorithm presented is based on the comparison between the measured and the predicted values of the AC power production. The model designed to predict the AC power production is based on an Artificial Neural Network (ANN), that is capable of estimating the AC power production using solar irradiance and PV panel temperature measurements, and that is trained using a dataset previously gathered from the plant to be monitored. Live trend data coming from the PV system are then compared with the output of the model and the vector of residuals is analyzed to detect anomalies and generate daily predictive maintenance alerts; there residuals are aggregated over 1-day and processed to detect out-of-threshold samples and system degradation trends; these trends are extracted by computing the Triangular Moving Average (TMA) where the window size is automatically determined. The paper also reports experimental data results revealing that the model leads to a good anomaly detection rate, which is measured as a positive predictive detection rate greater than 90%. Moreover, the algorithm is able to recognize trends of system's deviations from normal operation behavior and generate predictive maintenance alerts as a decision support system for operatives, with the aim of avoiding possible incoming failures.
dc.language	English
dc.publisher	ELSEVIER
dc.relation.ispartof	Neurocomputing
dc.relation.isbasedon	10.1016/j.neucom.2018.05.017
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	08 Information and Computing Sciences, 09 Engineering, 17 Psychology and Cognitive Sciences
dc.subject.classification	Artificial Intelligence & Image Processing
dc.title	Anomaly detection and predictive maintenance for photovoltaic systems
dc.type	Journal Article
utslib.citation.volume	310
utslib.for	08 Information and Computing Sciences
utslib.for	09 Engineering
utslib.for	17 Psychology and Cognitive Sciences
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
utslib.copyright.status	closed_access	*
dc.date.updated	2022-08-29T19:47:44Z
pubs.publication-status	Published
pubs.volume	310

Abstract:

We present a learning approach designed to detect possible anomalies in photovoltaic (PV) systems in order to let an operator to plan predictive maintenance interventions. The anomaly detection algorithm presented is based on the comparison between the measured and the predicted values of the AC power production. The model designed to predict the AC power production is based on an Artificial Neural Network (ANN), that is capable of estimating the AC power production using solar irradiance and PV panel temperature measurements, and that is trained using a dataset previously gathered from the plant to be monitored. Live trend data coming from the PV system are then compared with the output of the model and the vector of residuals is analyzed to detect anomalies and generate daily predictive maintenance alerts; there residuals are aggregated over 1-day and processed to detect out-of-threshold samples and system degradation trends; these trends are extracted by computing the Triangular Moving Average (TMA) where the window size is automatically determined. The paper also reports experimental data results revealing that the model leads to a good anomaly detection rate, which is measured as a positive predictive detection rate greater than 90%. Moreover, the algorithm is able to recognize trends of system's deviations from normal operation behavior and generate predictive maintenance alerts as a decision support system for operatives, with the aim of avoiding possible incoming failures.

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http://hdl.handle.net/10453/161043