Anomaly detection by comparing photovoltaic systems with machine learning methods
Conference: NEIS 2020 - Conference on Sustainable Energy Supply and Energy Storage Systems
09/14/2020 - 09/15/2020 at Hamburg, Deutschland
Proceedings: NEIS 2020
Pages: 6Language: englishTyp: PDF
Benninger, Moritz; Hofmann, Martina; Liebschner, Marcus (University of Applied Sciences Aalen, Aalen, Germany)
The digital interconnection of several photovoltaic systems enables a comprehensive monitoring and detection of failures. The concept of this work is based on the similar behaviour of individual photovoltaic systems under same conditions. By recording electrical data, individual strings can be compared with each other by machine learning methods. For this purpose, a k-Nearest-Neighbours algorithmn and a Multi-layer Perceptron are used for processing the data and detecting divergent characteristics of the electrical current. With this approach, a reliable and reproducible fault monitoring can be guaranteed without large measuring effort.