| 引用本文: | 金 涛,李鸿南,刘 对.基于BPSOGA的含风电机组的配电线路故障区段定位[J].电力自动化设备,2016,36(6): |
| JIN Tao,LI Hongnan,LIU Dui.Faulty section location based on BPSOGA for distribution line with wind turbine generator[J].Electric Power Automation Equipment,2016,36(6): |
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| 摘要: |
| 风电机组等分布式电源并入配电线路中，将导致传统的故障区段定位方法不再适用。对传统的开关函数和适应度函数进行改进，统一假定开关的正方向，提出基于粒子群优化算法和遗传算法的二进制混合算法。该算法采用双种群进化和信息交换的策略，在寻优搜索开始时产生2个子种群，双种群在进化过程中互不干扰，在每一代进化完成后相互共享信息，选择最优信息进行2个种群下一代的进化，直至得出最优解。仿真结果表明：所提方法对风电机组的并网数量和位置不作限制，适用于单一故障和多重故障的定位，并且具有一定的容错性。与单独的二进制粒子群优化算法和遗传算法对比，所提混合算法性能较高、收敛速度较快，能明显降低出现“未成熟收敛”的概率。 |
| 关键词: 粒子群优化算法 遗传算法 二进制混合算法 风电机组 配电线路 故障区段定位 |
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| 基金项目:欧盟FP7国际科技合作基金资助项目（909880）；国家自然科学基金资助项目（61304260）；福建省杰出青年科学基金资助项目（2012J06012） |
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| Faulty section location based on BPSOGA for distribution line with wind turbine generator |
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JIN Tao, LI Hongnan, LIU Dui
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College of Electrical Engineering and Automation，Fuzhou University，Fuzhou 350116，China
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| Abstract: |
| Since the traditional methods of faulty section location could not be applied to the distribution line with DG（Distributed Generation），such as wind turbine generator，the traditional switching function and fitness function are modified，a unified positive direction of switches is assumed，and a binary system mixed algorithm based on PSO（Particle Swarm Optimization） algorithm and GA（Genetic Algorithm） is proposed，which adopts the strategies of double-population evolution and information exchange. Two sub-populations are generated in the beginning of optimization. They do not interfere with each other during the evolution of each generation，but share the information of this generation after the evolution and select the optimal information for the next generation until the final optimal solution is obtained. Simulative results show that，the proposed error-tolerant method is immune to the quantity and location of grid-connected wind turbine generators and suitable for both single and multiple faults. Compared to sole binary PSO algorithm or GA，the proposed method has better performance，faster convergence speed and much lower premature convergence probability. |
| Key words: particle swarm optimization algorithm genetic algorithms binary system mixed algorithm wind turbines distribution line faulty section location |
