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The development of the flexible DC fault line selection technology plays an important role for DC distri-bution network. In this paper, a novel algorithm is proposed to solve the problem that there is less available fault infor-mation about the existing flexible DC fault, which makes full use of the advantages of ensemble empirical mode decom-position (EEMD), principal component analysis (PCA) and the correlation coefficient algorithm. First, the transient cur-rent sample signal is extracted, and the data matrix represented by the orthogonal basis function is obtained by EEMD. Then, the feature vector of the matrix element is transformed into the principal component based on PCA, and the sam-ple signal is projected into the principal component space to realize coordinate transformation, so as to obtain the clus-tering and identification results of the sample data. Finally, fault line identification is performed based on the correlation coefficient. The EEMD of the proposed algorithm reveals the internal variation law of the original historical data, while PCA can effectively select the effective fault features. A large num-ber of experiments show that the novel algorithm is accurate and effective. Compared with other existing methods, it has ad-vantages in the cases of unclear fault information and different transition resistances.
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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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