Location of faults based on deep learning with feature selection for meter placement in distribution power grids
Author:
Affiliation:
1. CEMIM , Universidad Nacional de Mar del Plata , Mar del Plata , Argentina
2. Pladema , Universidad Nacional del Centro de la Provincia de Bueos Aires , Tandil , Argentina
3. CONICET , Tandil , Argentina
Abstract
Funder
Consejo Nacional de Investigaciones Científicas y Técnicas
Agencia Nacional de Promoción Científica y Tecnológica
Publisher
Walter de Gruyter GmbH
Subject
Energy Engineering and Power Technology
Link
https://www.degruyter.com/document/doi/10.1515/ijeeps-2023-0073/pdf
Reference18 articles.
1. Falaghi, H, Haghifam, M-R, Tabrizi, MRO. Fault indicators effects on distribution reliability indices. In: CIRED 2005 – 18th international conference and exhibition on electricity distribution; 2005:1–4 pp.
2. Mohammadi, P, Mehraeen, S, Nazaripouya, H. Sensitivity analysis-based optimal pmu placement for fault observability. IET Gener Transm Distrib 2021;15:737–50. https://doi.org/10.1049/gtd2.12055.
3. Li, W, Deka, D, Chertkov, M, Wang, M. Real-time faulted line localization and pmu placement in power systems through convolutional neural networks. IEEE Trans Power Syst 2019;34:4640–51. https://doi.org/10.1109/tpwrs.2019.2917794.
4. Naidu, O, Pradhan, AK. A traveling wave-based fault location method using unsynchronized current measurements. IEEE Trans Power Deliv 2018;34:505–13. https://doi.org/10.1109/tpwrd.2018.2875598.
5. Dekhandji, FZ, Azzougui, Y, Recioui, A. Pmu optimal placement for fault detection in wide area monitoring systems based on the drone squadron optimization. Algerian J Signal Syst 2019;4:25–31. https://doi.org/10.51485/ajss.v4i1.79.
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