| 引用本文: | 孙国强,卫志农,唐利锋,李育燕,缪立恒.多目标配电网故障定位的Pareto进化算法[J].电力自动化设备,2012,32(5): |
| SUN Guoqiang,WEI Zhinong,TANG Lifeng,LI Yuyan,MIAO Liheng.Pareto evolutionary algorithm for multi-objective fault location of distribution network[J].Electric Power Automation Equipment,2012,32(5): |
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| 摘要: |
| 提出一种用于配电网故障定位的多目标优化模型，采用带精英策略的快速非支配排序遗传算法（NSGA-II）进行求解。传统多目标优化问题通过加权方式转换为单目标问题，对权值比较敏感，且每次只能得到一种权值下的最优解。NSGA-II则避免了传统加权求解时权值的选择和解的偏好性。该算法采用快速非支配排序机制，计算复杂性低；同时考虑个体拥挤距离，从而保证种群的多样性；最后，提出适用于故障定位的最优解集处理方法，便于从多目标最优解集中筛选出唯一符合故障情况的解。算例测试分别模拟单点、多点故障，以及信息完备和部分信息畸变的情况，测试结果表明，所提方法均能准确地定位故障区段。 |
| 关键词: 配电网 故障定位 优化 模型 Pareto 非支配排序遗传算法 遗传算法 进化算法 |
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| 基金项目:国家自然科学基金资助项目（50877024，51107032， 61104045）；中央高校基本科研业务费资助项目（2010B05914） |
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| Pareto evolutionary algorithm for multi-objective fault location of distribution network |
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SUN Guoqiang1, WEI Zhinong1, TANG Lifeng1, LI Yuyan2, MIAO Liheng3
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1.College of Energy and Electrical Engineering，Hohai University，Nanjing 210098，China;2.Guodian Nanjing Automation Co.，Ltd.，Nanjing 210003，China;3.Wuxi Guang Ying Power Design Co.，Ltd.，Wuxi 214000，China
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| Abstract: |
| A multi-objective optimization model of fault location for distribution network is proposed and the non-dominated sorting genetic algorithm（NSGA-II） is adopted to get its solution. Traditional multi-objective optimization model is converted to mono-objective optimization model by weighting，which is sensitive to weight and has only one solution in one calculation. Without weight selection，NSGA-II has low computational complexity，and it considers the individual crowding distance to guarantee the population diversity. The optimal solution set approach for fault location is provided for detecting the only proper one from the multi-objective solution set. Single-point and multi-point faults are simulated in two conditions：with and without partial information distortion. Results show that the presented method locates the faulty section（s） accurately. |
| Key words: distribution network electric fault location optimization mathematical models Pareto principle NSGA-II genetic algorithms evolutionary algorithms |
