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To solve the current problem that the monitoring methods of power cable are relatively simple and lack of multi-parameter comprehensive monitoring and judgment, this paper integrated various partial discharge signals of cable joints, and proposed a cable joint fault monitoring method based on deep learning fusion evidence theory. Then fault identification research was conducted for three situations, including no defects, internal insulation defects, and joint moisture in cable joints. According to convolutional neural network algorithm model, the partial discharge signal graphs were trained and tested separately, and data fusion was carried out by D-S evidence theory to realize fault type identification. The results show that for the situation of internal insulation defects and joint moisture, the best recognition effect of single information source is high frequency partial discharge, and its average recognition rate can reach 85.6%, and the average recognition rate of ultrasonic method is the lowest, which is 78.7%, while the average recognition rate of the identify method proposed by this paper can reach 95.7%. When there is a misjudgment in the recognition result of one of the multi-dimensional information sources, D-S evidence theory fusion can eliminate the interference of the wrong information source, accurately identify the discharge type.
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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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