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- 01HBWWKD9CTTGD2STHPS8SBEQG classification C1.
- 01HBWWKD9CTTGD2STHPS8SBEQG date "2023".
- 01HBWWKD9CTTGD2STHPS8SBEQG language "eng".
- 01HBWWKD9CTTGD2STHPS8SBEQG type conference.
- 01HBWWKD9CTTGD2STHPS8SBEQG hasPart 01HBWX0GQW5A3CVAGDCPH3NZTE.pdf.
- 01HBWWKD9CTTGD2STHPS8SBEQG subject "Technology and Engineering".
- 01HBWWKD9CTTGD2STHPS8SBEQG doi "10.1049/icp.2023.0576".
- 01HBWWKD9CTTGD2STHPS8SBEQG isbn "9781839538551".
- 01HBWWKD9CTTGD2STHPS8SBEQG presentedAt urn:uuid:70877f9d-7ae5-4d52-802b-49319cf4790a.
- 01HBWWKD9CTTGD2STHPS8SBEQG abstract "This paper discusses the employment of machine learning techniques to evaluate the voltage congestion on low-voltage distribution systems. Nowadays distribution network operators utilise computationally intensive digital twin models to evaluate the hosting capacity limitations. In fact, not all networks are incorporated into such digital environments. However, with the deployment of smart meters, new possibilities emerge that do not necessitate the modelling of these digital twins. In this work, end users' consumption data is utilised to predict emerging voltage congestion due to the rapid increase of electric vehicles and renewable energy sources. Additionally, not only does this allow to monitor the voltages, but it is also able to identify the grid standard violations. To this end, historical voltage profiles, power consumption and injection data derived from smart meters are exploited. All results are being validated through a power flow study applied on a low-voltage distribution network.".
- 01HBWWKD9CTTGD2STHPS8SBEQG author 3404C30C-A6F4-11E7-8878-09B7AD28A064.
- 01HBWWKD9CTTGD2STHPS8SBEQG author FA8A5676-F0ED-11E1-A9DE-61C894A0A6B4.
- 01HBWWKD9CTTGD2STHPS8SBEQG author urn:uuid:a4f7a4fc-dac8-47ef-a297-1f0a32f924a4.
- 01HBWWKD9CTTGD2STHPS8SBEQG author urn:uuid:ee12858d-c96b-459a-9a07-5630881e4a6c.
- 01HBWWKD9CTTGD2STHPS8SBEQG dateCreated "2023-10-04T08:24:20Z".
- 01HBWWKD9CTTGD2STHPS8SBEQG dateModified "2024-10-29T18:33:22Z".
- 01HBWWKD9CTTGD2STHPS8SBEQG name "Voltage congestion monitoring through machine learning".
- 01HBWWKD9CTTGD2STHPS8SBEQG pagination urn:uuid:134cedc0-f368-4d81-8772-71d0107b7af3.
- 01HBWWKD9CTTGD2STHPS8SBEQG publisher urn:uuid:eb1db28a-0cf5-4a5f-befb-afed5436f42e.
- 01HBWWKD9CTTGD2STHPS8SBEQG sameAs LU-01HBWWKD9CTTGD2STHPS8SBEQG.
- 01HBWWKD9CTTGD2STHPS8SBEQG sourceOrganization urn:uuid:c1d92224-5c3b-4469-bb9e-594bb8b6873c.
- 01HBWWKD9CTTGD2STHPS8SBEQG type C1.