| Autor: |
CHEN Gang, HU Kaikai, CHEN Yanan, ZHANG Jiayou, XIONG Wenhao |
| Jazyk: |
čínština |
| Rok vydání: |
2024 |
| Předmět: |
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| Zdroj: |
Kongzhi Yu Xinxi Jishu, Iss 2, Pp 32-39 (2024) |
| Druh dokumentu: |
article |
| ISSN: |
2096-5427 |
| DOI: |
10.13889/j.issn.2096-5427.2024.02.005 |
| Popis: |
Periodic abnormal power fluctuations of wind turbines can easily cause low-frequency oscillations of the power grid, threatening the safety of the power grid. Wind turbine power signals are nonlinear and non-stationary signals, and their abnormal fluctuation Frequencies are uncertain, which makes it difficult to extract and detect abnormal power fluctuation features. This paper proposes a method for detecting abnormal power fluctuations of wind turbine based on subband processing and correlation coefficient. For the application of this method, the author first removed trend items of the original power signal by high-pass filter, and decomposed the signal by the subband processing filter bank to obtain a series of subband signals; calculated the cross-correlation coefficient between the power signal with trend items removed and each subband signal respectively, located the subband where periodic abnormal fluctuation occurred and filtered useful subband according to cross-correlation coefficients; obtained high signal-to-noise ratio signal reconstructed based on the filtered subband signal; and by combining the peak detection method and the autocorrelation analysis method, identified the time period where periodic abnormal fluctuations of specific amplitude of the reconstructed signal, thus realizing the detection of periodic abnormal power fluctuations of wind turbine. Field data tests show that for periodic abnormal power fluctuations of different frequencies such as 0.06Hz and 0.32Hz, the proposed method can effectively identify the subband where periodic abnormal fluctuations occur and the periodic abnormal fluctuation characteristics. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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