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The traditional fault diagnosis methods for power transformers cannot detect the power faults accurately or ensure their normal operation. Therefore, a fault diagnosis method for power transformers based on wavelet packet transform and support vector machine (SVM) is proposed. For the power signal collected from a power transformer, the improved minimum noise fraction (MNF) transform denoising is used to denoise, and the noise matrix is estimated by the weighted neighborhood mean method. After the estimation, the improved MNF transform is used to effectively realize image dimensionality reduction and denoising, extract the signal characteristics, and divide the signal into low- and high-frequency part by means of wavelet packet transform to obtain the wavelet packet energy feature vector. The obtained wavelet packet energy feature vector is input into an SVM classifier, and the output results from the SVM classifier are used to realize the state recognition and fault diagnosis of power transformer. Experimental results show that the proposed method can effectively diagnose the faults in the power transformer, such as iron core short-circuit, coil interlayer short-circuit, bushing-to-ground breakdown, coil insulation resistance drop and bushing-to-bushing discharge, and the fault diagnosis accuracy was higher than 98.5%.
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| 科 Family | 属数 Number of genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) | 属 Genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) |
|---|---|---|---|---|---|---|
| 鹅膏菌科Amanitaceae | 2 | 11 | 5.26 | 鹅膏菌属 Amanita | 10 | 4.78 |
| 小菇科 Mycenaceae | 2 | 12 | 5.74 | 丝盖伞属 Inocybe | 5 | 2.39 |
| 多孔菌科 Polyporaceae | 8 | 14 | 6.70 | 蜡蘑属 Laccaria | 5 | 2.39 |
| 红菇科 Russulaceae | 3 | 23 | 11.00 | 小皮伞属 Marasmius | 6 | 2.87 |
| 小菇属 Mycena | 11 | 5.26 | ||||
| 光柄菇属 Pluteus | 5 | 2.39 | ||||
| 红菇属 Russula | 17 | 8.13 | ||||
| 栓菌属 Trametes | 5 | 2.39 |
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