Anomaly Detection in Photovoltaic Production Factories via Monte Carlo Pre-Processed Principal Component Analysis

Eleonora Arena, Alessandro Corsini, Roberto Ferulano, Dario Alfio Iuvara, Eric Stefan Miele, Lorenzo Ricciardi Celsi, Nour Alhuda Sulieman and Massimo Villari
Additional contact information
Eleonora Arena: Enel Green Power S.p.A., Contrada Blocco Torrazze sn, Zona Industriale, 95121 Catania, Italy
Alessandro Corsini: Dipartimento di Ingegneria Astronautica, Elettrica ed Energetica, Sapienza Università di Roma via Eudossiana 18, 00184 Roma, Italy
Roberto Ferulano: ELIS Innovation Hub, via Sandro Sandri 81, 00159 Roma, Italy
Dario Alfio Iuvara: Enel Green Power S.p.A., Contrada Blocco Torrazze sn, Zona Industriale, 95121 Catania, Italy
Eric Stefan Miele: Dipartimento di Ingegneria Astronautica, Elettrica ed Energetica, Sapienza Università di Roma via Eudossiana 18, 00184 Roma, Italy
Lorenzo Ricciardi Celsi: ELIS Innovation Hub, via Sandro Sandri 81, 00159 Roma, Italy
Nour Alhuda Sulieman: Dipartimento di Scienze Matematiche e Informatiche, Scienze Fisiche e Scienze Della Terra, Università di Messina, Piazza Pugliatti 1, 98122 Messina, Italy
Massimo Villari: Dipartimento di Scienze Matematiche e Informatiche, Scienze Fisiche e Scienze Della Terra, Università di Messina, Piazza Pugliatti 1, 98122 Messina, Italy

Energies, 2021, vol. 14, issue 13, 1-16

Abstract: This paper investigates a use case of robust anomaly detection applied to the scenario of a photovoltaic production factory—namely, Enel Green Power’s 3SUN solar cell production plant in Catania, Italy—by considering a Monte Carlo based pre-processing technique as a valid alternative to other typically used methods. In particular, the proposed method exhibits the following advantages: (i) Outlier replacement, by contrast with traditional methods which are limited to outlier detection only, and (ii) the preservation of temporal locality with respect to the training dataset. After pre-processing, the authors trained an anomaly detection model based on principal component analysis and defined a suitable key performance indicator for each sensor in the production line based on the model errors. In this way, by running the algorithm on unseen data streams, it is possible to isolate anomalous conditions by monitoring the above-mentioned indicators and virtually trigger an alarm when exceeding a reference threshold. The proposed approach was tested on both standard operating conditions and an anomalous scenario. With respect to the considered use case, it successfully anticipated a fault in the equipment with an advance of almost two weeks, but also demonstrated its robustness to false alarms during normal conditions.

Keywords: anomaly detection; principal component analysis; Monte Carlo simulation; PV cell production line; predictive maintenance (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6)

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