Object Detection Based Power Quality Expert System for an Electric Vehicle Infrastructure

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Paper number
958
Working Group Number
Conference name
CIRED 2019
Conference date
3-6 June 2019
Conference location
Madrid, Spain
Peer-reviewed
Yes
Short title
Convener
Authors
Streubel, Tim , University of Stuttgart- Institute of Power Transmission and High Voltage Technology, Germany
Eisenmann, Adrian , University of Stuttgart Institute of Power Transmission and High Voltage Technology, Germany
Abstract
The shift in power systems from large power stations towards smaller decentralized generation units imposes new challenges on grid operators regarding power quality. The steady increase of power electronics connected to the grid results in elevated harmonic distortion levels, inducing additional losses and causing malfunctioning of control devices. This development has led to a rising number of monitoring systems, in order to ensure adequate power quality within the mandatory limits and boundaries. Consequently, detection and classification of power quality disturbances is a vital component of the mentioned systems. This paper proposes a new method for detecting power quality issues and identifying their underlying causes, based on historical data. The implemented automated image classification algorithm continuously analyses the FFT spectrogram of disturbances and simultaneously determines the type of disturbance and its cause without the need of a segmentation and feature extraction process. The results are validated using measurement from an electric vehicle car charging station.
Table of content
Keywords
Publisher
AIM
Date
2019-06-03
Permanent link to this record
https://cired-repository.org/handle/20.500.12455/218
http://dx.doi.org/10.34890/427
ISSN
2032-9644
ISBN
978-2-9602415-0-1