Condition Monitoring in Photovoltaic Systems by Semi-Supervised Machine Learning

Lars Maaløe, Ole Winther, Sergiu Spataru and Dezso Sera
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Lars Maaløe: Corti, Copenhagen, 1255 København, Denmark
Ole Winther: Applied Mathematics and Computer Science, Technical University of Denmark, 2800 Lyngby, Denmark
Sergiu Spataru: Department of Energy Technology, Aalborg University, 9100 Aalborg, Denmark
Dezso Sera: School of Electrical Engineering and Robotics, Queensland University of Technology, Brisbane City, QLD 4000, Australia

Energies, 2020, vol. 13, issue 3, 1-14

Abstract: With the rapid increase in photovoltaic energy production, there is a need for smart condition monitoring systems ensuring maximum throughput. Complex methods such as drone inspections are costly and labor intensive; hence, condition monitoring by utilizing sensor data is attractive. In order to recognize meaningful patterns from the sensor data, there is a need for expressive machine learning models. However, supervised machine learning, e.g., regression models, suffer from the cumbersome process of annotating data. By utilizing a recent state-of-the-art semi-supervised machine learning based on probabilistic modeling, we were able to perform condition monitoring in a photovoltaic system with high accuracy and only a small fraction of annotated data. The modeling approach utilizes all the unsupervised data by jointly learning a low-dimensional feature representation and a classification model in an end-to-end fashion. By analysis of the feature representation, new internal condition monitoring states can be detected, proving a practical way of updating the model for better monitoring. We present (i) an analysis that compares the proposed model to corresponding purely supervised approaches, (ii) a study on the semi-supervised capabilities of the model, and (iii) an experiment in which we simulated a real-life condition monitoring system.

Keywords: photovoltaic systems; condition monitoring; fault detection; machine learning; semi-supervised learning (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: 2020
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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