Fault Location and Restoration of Microgrids via Particle Swarm Optimization
Fault Location and Restoration of Microgrids via Particle Swarm Optimization
复制标题
基于粒子群优化的微电网故障定位与恢复
作者：
W. Lin;Wei;Kai;Hong;Chun
这项工作的目的是开发一种集成的微电网(MG)故障定位和恢复方法。这部作品包括两个部分。第一部分介绍了故障定位算法，第二部分介绍了故障恢复算法。提出的算法用粒子群优化算法(PSO)实现。故障测距算法基于节点注入对支路电流和支路电流对母线电压(BCBV)矩阵的修正，形成新的系统拓扑。故障前潮流分析采用向前/后掠法。故障发生后，利用Zbus修正算法对Zbus进行修正，计算出各母线的电压变化。然后，利用粒子群算法计算电压误差矩阵，搜索故障区段。在故障区段配置完成后，利用粒子群算法实现多目标函数，并对其约束条件进行优化恢复。最后，以IEEE 37节点分布式电源测试系统为样本系统进行了一系列的仿真分析。仿真结果表明，该优化算法能有效地解决发电机故障定位与恢复问题。
This aim of this work was to develop an integrated fault location and restoration approach for microgrids (MGs). The work contains two parts. Part I presents the fault location algorithm, and Part II shows the restoration algorithm. The proposed algorithms are implemented by particle swarm optimization (PSO). The fault location algorithm is based on network connection matrices, which are the modifications of bus-injection to branch-current and branch-current to bus-voltage (BCBV) matrices, to form the new system topology. The backward/forward sweep approach is used for the prefault power flow analysis. After the occurrence of a fault, the voltage variation at each bus is calculated by using the Zbus modification algorithm to modify Zbus. Subsequently, the voltage error matrix is computed to search for the fault section by using PSO. After the allocation of the fault section, the multi-objective function is implemented by PSO for optimal restoration with its constraints. Finally, the IEEE 37-bus test system connected to distributed generations was utilized as the sample system for a series simulation and analysis. The outcomes demonstrated that the proposed optimal algorithm can effectively solve fault location and restoration problems in MGs.