Internal Overvoltage Identification of Distribution Network via Time-Frequency Atomic Decomposition
| Autor: | Mou-Fa Guo, Yan Yang, Wei Gao, Yu-Fei Liao, Rong-Jong Wai |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
General Computer Science
Computer science 020209 energy 020208 electrical & electronic engineering General Engineering 02 engineering and technology Topology Time–frequency analysis Distribution network hierarchical identification Overvoltage 0202 electrical engineering electronic engineering information engineering Waveform General Materials Science lcsh:Electrical engineering. Electronics. Nuclear engineering effective atoms lcsh:TK1-9971 internal overvoltage atomic decomposition Voltage |
| Zdroj: | IEEE Access, Vol 7, Pp 85110-85122 (2019) |
| ISSN: | 2169-3536 |
| DOI: | 10.1109/access.2019.2925108 |
| Popis: | Internal overvoltage accidents in the distribution network are likely to cause an equipment insulation breakdown and result in system power outages and economic losses. Therefore, an internal overvoltage identification method based on the time-frequency atomic decomposition is investigated in this study. Firstly, the overvoltage waveforms are divided into four time periods. Then, the waveforms during these four time periods are decomposed by the atomic decomposition algorithm to obtain the effective atoms from the waveforms. Moreover, the root-mean-square (RMS) value of the zero-sequence voltage, the dominant atom frequency, the total relative matching degree, and the effective atom frequency are extracted as major features. In addition, the layered identification of the overvoltage types can be realized by combining the corresponding identification criteria. The salient advantage is the features with low dimension and a high degree of discrimination. Various overvoltage types can be identified just by the corresponding thresholds, and it is easier to deploy in the field than conventional methods based on classifier training. The effectiveness of the proposed method is verified by experimental results, and it concludes that the proposed algorithm has high accuracy and strong adaptability. |
| Databáze: | OpenAIRE |
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