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
Working Group Number
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