Fallback Solution for a Low-Voltage Regulator Control using Artificial Neural Networks

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CIRED 2018 Ljubljana WS - 0237 - 20727.pdf (911.56 KB)

Paper number

0237

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Conference name

CIRED 2018 Ljubljana Workshop

Conference date

7 - 8 June 2018

Conference location

Ljubljana, Slovenia

Peer-reviewed

Yes

Short title

Convener

Authors

ma, haiyan, TU Kaiserslautern, Germany
Lang, Stefan, Pfalzwerke AG, Germany
Wellßow, Wolfram H., TU Kaiserslautern, Germany

Abstract

With the increase of renewable generation, violations of voltages or thermal limits are more likely to happen in particular in rural distribution networks. Instead of cost intensive grid expansions, other measures can be applied, such as transformers with on-load tap changers, voltage regulators or a novel regulator for power flow control in meshed grids. Depending on their control targets, these devices use remote measurement data, which are transferred by a communication network. However, in case of communication faults, the control processes are affected. A fallback solution using artificial neural networks is presented in this paper for estimating regulator tap positions and missing measurements. Simulation results show that the proposed solution is reliable and accurate.

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Publisher

AIM

Date

2018-06-07

Published in

CIRED 2018 Ljubljana Workshop on Microgrids and Local Energy Communities

Permanent link to this record

https://www.cired-repository.org/handle/20.500.12455/1250
http://dx.doi.org/10.34890/413

ISSN

2032-9628

ISBN

978-2-9602415-1-8