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

Thumbnail Image

Files

CIRED 2019 - 958.pdf (538.56 KB)

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

Published in

CIRED 2019 Conference

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