Identification of Power Disturbances Using Generalized Morphological Open-closing and Close-opening Undecimated Wavelet
| Autor: | Mengshi Li, Qiqiang Wu, T.Y. Ji, Yong Zhang |
|---|---|
| Rok vydání: | 2015 |
| Předmět: |
Discrete wavelet transform
Engineering Morphological gradient Lifting scheme business.industry Stationary wavelet transform Second-generation wavelet transform 020208 electrical & electronic engineering ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Wavelet transform Pattern recognition 02 engineering and technology Wavelet packet decomposition Wavelet Control and Systems Engineering 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Computer vision Artificial intelligence Electrical and Electronic Engineering business |
| Zdroj: | IEEE Transactions on Industrial Electronics. :1-1 |
| ISSN: | 1557-9948 0278-0046 |
| DOI: | 10.1109/tie.2015.2499728 |
| Popis: | This paper proposes a new technique, generalized morphological open-closing and close-opening undecimated wavelet (GMOCUW), based on which a power disturbance identification scheme is developed. In order to extract features of power disturbances, the proposed scheme employs undecimated wavelet transform for its advantage in retaining information and reducing waveform distortion, multiscale morphological analysis for its ability in frequency analysis, and generalized morphological open-closing (GMOC) and generalized morphological close-opening (GMCO) operations for their advantages in information preserving. Power system computer aided design/electro-magnetic transient in dc system (PSCAD/EMTDC) was employed to construct a test power system to simulate eight types of power disturbances. Additionally, a laboratory platform was established to generate power quality (PQ) signals under real operating conditions. The performance of GMOCUW has been compared with that of morphological gradient wavelet (MGW), new dual neural-network-based methodology (NDNM), S-transform (ST), and Daubechies 4 wavelet (DB4W). Comparison results have proved that, in power disturbance detection, GMOCUW is more accurate and faster than these methods. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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