Anomaly detection in power quality measurements using proximity-based unsupervised machine learning techniques

Publikation: Beitrag in Buch/Konferenzbericht/Sammelband/GutachtenBeitrag in Buch/Sammelband/GutachtenBeigetragenBegutachtung



Power quality (PQ) data analysis has become an issue of great interest in past few years. Due to numerous impacts, PQ can exhibit rare and significant deviations of a time series from its typical characteristic. Such unusual deviations are known as anomalies and the process of identifying such changes is referred to anomaly detection. In this paper, four proximity-based machine learning (ML) techniques are applied to original (nontransformed) PQ data for automatic anomaly detection. In contrast to standard classification tasks, these techniques are applied on unlabeled data, taking only the characteristics of the PQ data into account. The process of parameters tuning and their influence on methods performance are described. The methods performance has been evaluated on different time series, which represent the typical variation in PQ data. Finally, the ability to detect different types of anomalies is analyzed.


Titel2019 Electric Power Quality and Supply Reliability Conference and 2019 Symposium on Electrical Engineering and Mechatronics, PQ and SEEM 2019
Herausgeber (Verlag)Institute of Electrical and Electronics Engineers Inc.
ISBN (Print)9781728126500
PublikationsstatusVeröffentlicht - 1 Juni 2019


Reihe2019 Electric Power Quality and Supply Reliability Conference (PQ) & 2019 Symposium on Electrical Engineering and Mechatronics (SEEM)

Externe IDs

Scopus 85072776054



  • Anomaly detection, Power quality, Time series analysis, Unsupervised machine learning