Monitoring of Synchronization Failure for Power Electronics Converters

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F22%3APU146342" target="_blank" >RIV/00216305:26220/22:PU146342 - isvavai.cz</a>

Result on the web

<a href="https://ieeexplore.ieee.org/document/10067639" target="_blank" >https://ieeexplore.ieee.org/document/10067639</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/ICSRS56243.2022.10067639" target="_blank" >10.1109/ICSRS56243.2022.10067639</a>

Result language

angličtina

Original language name

Monitoring of Synchronization Failure for Power Electronics Converters

Original language description

The recent developments in the grid integration of power electronics converters have raised some serious concerns related to their synchronization. These concerns when unattended resulted in unintentional islanding of the distributed generation (DG) units and triggered the cyclic behavior of system disconnection. Hence, to overcome these drawbacks, timely detection of these synchronization failure with high accuracy is necessary. In this paper, the applicability of an advanced recurrent neural network that allows persistent information exchange is analyzed for distinguishing between the normal operation and synchronization failure in a converter network. To achieve this a long-short term memory (LSTM) autoencoder is designed as a classification approach and employed with the power converters operating in a network. The LSTM offers advantages with automated feature extraction and ranking which are the major aspects for improving the time detection of disturbances in a system with high accuracy. To develop this approach, a three-phase grid feeder integrating a three-phase rectifier with a three-phase inverter and a single-phase inverter is designed. Measurements and normal operation and synchronization failure are analyzed to train the algorithm. The trained algorithm is identified to achieve 99.49% training accuracy and 100% testing accuracy with a detection time of 0.2 milliseconds.

Czech name

—

Czech description

—

Type

D - Article in proceedings

CEP classification

—

OECD FORD branch

20201 - Electrical and electronic engineering

Project

<a href="/en/project/CK02000099" target="_blank" >CK02000099: Pilot project for power supply of traction line with AC/AC converter</a><br>

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2022

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection

2022 6th International Conference on System Reliability and Safety (ICSRS)

ISBN

978-1-6654-7092-6

ISSN

—

e-ISSN

—

Number of pages

6

Pages from-to

26-31

Publisher name

IEEE

Place of publication

NEW YORK

Event location

Venice

Event date

Nov 23, 2022

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

000981836500004