In modern power systems or new energy power stations, the medium voltage circuit breakers (MVCBs) are becoming more crucial and the operation reliability of the MVCBs could be greatly improved by online monitoring technology. The purpose of this research is to put forward a fault diagnosis approach based on vibration signal envelope analysis, including offline fault feature training and online fault diagnosis. During offline fault feature training, the envelope of the vibration signal is extracted from the historic operation data of the MVCB, and then the typical fault feature vector is built by using the wavelet packet-energy spectrum. In the online fault diagnosis process, the fault feature vector is built based on the extracted envelope of the real-time vibration signal, and the MVCB states are assessed by using the distance between the feature vectors and . The proposed method only needs to handle the envelope of the vibration signal, which dramatically reduces the signal bandwidth, and then the cost of the processing hardware and software could be cut down.
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http://dx.doi.org/10.3390/s23198331 | DOI Listing |
In this Letter, we propose and experimentally validate a high-fidelity and adaptive forward-phase-based vibration sensing using a Wiener filter (WF). In commercial coherent digital subcarrier multiplexing (DSCM) systems under external cavity lasers (ECLs), frequency-domain pilot tones (FPTs) in subcarrier intervals are employed for dynamic frequency offset estimation (FOE), carrier phase estimation (CPE), and polarization demultiplexing. The phase estimated by the CPE module is processed with the WF to achieve high-fidelity extraction of the vibration-induced phase.
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View Article and Find Full Text PDFNano Lett
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