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To tackle the issues of low execution efficiency and poor fault tolerance in traditional fault localization methods for active distribution networks using swarm intelligence optimization algorithms，a two-stage fault location method was introduced based on the SSA-RF algorithm and cosine similarity. Firstly，the fault current state equation was used to create a fault feature database of the target distribution network by stochastically simulating single-point and multi-point faults. Next，an enhanced random forest（RF）classification model that integrates the sparrow search algorithm（SSA）was introduced. Through model training，a high-dimensional mapping correlation between the fault current direction matrix and the line segment containing the fault point was established.This trained SSA-RF classification model was utilized for the initial localization of the faulted line segment.Subsequently，cosine similarity of fault current direction information of neighboring segmented lines within the identified segment was computed for precise fault location. Experimental results on the modified IEEE 33-node test distribution network demonstrate that the proposed two-stage fault locatlizaion method achieves superior accuracy and anti-interference capabilities compared to fault location methods based on swarm intelligent optimization algorithms.
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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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