Identification of Partial Discharge Through Cable-Specific Adaption and Neural Network Ensemble
[EN] This paper proposes to administer a multi-step artificial intelligence approach with an ensemble of adaptive neural networks (NNs) trained on 50000 samples to identify partial discharge (PD) diagnostic measurements for in-service medium voltage (MV) power cables. To evaluate the performance of...
| Autores: | , , , , , , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2021 |
| País: | España |
| Institución: | Universitat Politècnica de València (UPV) |
| Repositorio: | RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia |
| Idioma: | inglés |
| OAI Identifier: | oai:riunet.upv.es:10251/181822 |
| Acceso en línea: | https://riunet.upv.es/handle/10251/181822 |
| Access Level: | acceso abierto |
| Palabra clave: | Partial discharge Neural networks Medium voltage cables INGENIERIA ELECTRICA |
| Sumario: | [EN] This paper proposes to administer a multi-step artificial intelligence approach with an ensemble of adaptive neural networks (NNs) trained on 50000 samples to identify partial discharge (PD) diagnostic measurements for in-service medium voltage (MV) power cables. To evaluate the performance of the algorithm, a case study was performed on cables deliberately selected to contain both uncomplicated measurements and disruptive irregularities representative of conditions during field testing. The experimental test results prove that the proposed cable-specific adaptation improves PD identification accuracy, with further increment through the NN ensembles. The main contribution of the approach is in both the cable-specific adaption and the NN ensemble being applied to MV cable field measurements. |
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