Mode identification of low‐frequency oscillations in power systems based on fourth‐order mixed mean cumulant and improved TLS‐ESPRIT algorithm
Wide area monitoring systems (WAMS) provide effective support for online identification of low‐frequency oscillations in power systems. The WAMS signal is sensitive to the surrounding environment, and it contains Gaussian white noise. The Gaussian white noise will produce Gaussian coloured noise thr...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2017-10-01
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| Series: | IET Generation, Transmission & Distribution |
| Subjects: | |
| Online Access: | https://doi.org/10.1049/iet-gtd.2016.2131 |
| Summary: | Wide area monitoring systems (WAMS) provide effective support for online identification of low‐frequency oscillations in power systems. The WAMS signal is sensitive to the surrounding environment, and it contains Gaussian white noise. The Gaussian white noise will produce Gaussian coloured noise through the filter, which will bring some errors to the mode identification. In order to solve the above problem, this study proposes a method to identify the low‐frequency oscillation modes of a single‐channel measurement of the power system based on the combination of the fourth‐order mixed mean cumulant and the improved TLS‐ESPRIT (total least square‐estimation parameter space rotation invariant technique). The actual signal is replaced by the processed signal using the fourth‐order mixed mean cumulant mathematical statistical method, thereby the Gaussian coloured noise is suppressed effectively; then, TLS‐ESPRIT algorithm is used to identify the signal, which is used to identify the low‐frequency oscillation of each mode. A novel relative change rate of singular value was also introduced in order to avoid the artificially threshold‐setting error. The simulation results show that the proposed method has better anti‐noise performance and higher accuracy of fitting than other methods reported in literature, besides being easy to be implemented and suitable for performing on‐line identification. |
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| ISSN: | 1751-8687 1751-8695 |