Hierarchical Fault Location Method for Distribution Network with Special Load
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摘要: 特殊负荷接入配电网，增强了电源、负荷与电网之间的互动性，对故障定位提出了更高的要求，为此，提出了一种快速定位故障点的分层定位方法. 首先分析基于遗传算法的单层含特殊负荷配电网故障定位的不完备性；其次在单层模型的理论基础上，提出改进量子免疫算法的区域定位方法和隐枚举法的区段定位方法；最后对所提模型和基于单一智能算法的单层模型进行了对比仿真实验. 结果表明：与遗传算法、免疫算法和改进量子免疫算法构建的单层定位模型相比，所提分层定位方法大大简化了故障辨识模型的复杂度，在提高定位效率的同时保证了定位的容错性和稳定性，可将故障搜索维度降低69%，故障定位耗时在1 s以内，故障辨识率达到100%.Abstract: Special load is connected to the distribution network, which enhances the interaction between the power supply, load and power grid, and imposes higher requirements for fault location. To deal with this, a hierarchical location method for fast locating fault points is proposed. Firstly, the incompleteness of the genetic algorithm based fault location model is analyzed for single-layer distribution network with special load. Secondly, based on the theory of the single-layer model, a regional positioning method and a segment method are proposed, which respectively improves quantum immune algorithm and implicit enumeration method. Finally, a comparative simulation is carried out between the proposed model and the single-layer model that is based on a single intelligent algorithm. Compared with the genetic algorithm, immune algorithm and improved single-layer positioning model constructed by quantum immune algorithm, the hierarchical positioning method can greatly simplify the complexity of the fault identification model, ensure the fault tolerance and stability of the positioning while improving the positioning efficiency. In addition, this method reduces the fault search dimension by 69%, takes less than 1s to locate the fault, and the fault identification rate is 100%.
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图 4 单层配电网故障定位仿真
Figure 4. Fault location simulation of single-layer distribution network
图 5 分层配电网故障定位仿真
Figure 5. Simulation of fault location in layered distribution network
表 1 故障区域、节点和电流方向
Table 1. Fault areas，nodes，and current directions
区域 区域端节点 故障电流方向 一 1 1 二 3 1 三 9 −1 四 12 0 五 15 −1 六 17 −1 七 22 0 八 24 −1 
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表 2 单一故障仿真结果
Table 2. Single failure simulation results
假定
故障区段{K1,K2,K3} 畸变
位置区域
定位结果区段
定位结果（5） {0,0,1} 无 二 （5） （9） {0,1,1} 无 三 （9） （18） {0,1,1} 无 六 （18） （7） {1,1,1} 无 二 （7） （5） {1,1,1} 10 二 （5） （7） {1,1,1} 24 二 （7） （24） {1,1,1} 11 八 （24）
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表 3 双重故障仿真结果
Table 3. Double-fault simulation results
假定
故障区段{K1,K2,K3} 畸变
位置区域
定位结果区段
定位结果（5）、（7） {0,0,1} 无 二 （5）、（7） （7）、（9） {0,1,1} 无 二、三 （7）、（9） （15）、（18） {0,1,1} 无 五、六 （15）、（18） （21）、（24） {1,1,1} 10 六、八 （21）、（24） （19）、（25） {1,1,1} 18 六、八 （19）、（25）
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表 4 区段定位结果
Table 4. Segment positioning results
判断故障区域 适应度 区段状态编码 区域状态信息 二 1.000 [0 0 0 0 0 0] 0 三 0.667 [0 1 0] 1 六 0.667 [0 1 0 0 0] 1 八 1.000 [0 0 0] 0
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表 5 4种定位模型性能对比
Table 5. Performance comparison of four positioning models
模型 准确次数/次 平均迭代次数/次 GA 24 36 IA 27 21 IQIA1 30 18 QIA+IEM 30 10
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