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Fault Location of Active Distribution Network Based on Improved Gray Wolf Algorithm

机译:基于改进灰狼算法的主动分配网络故障定位

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As the scale of the power grid continues to expand and a large number of distributed power sources are connected, the difficulty of locating faults is increasing. The gray wolf optimization algorithm is simple in principle, easy to implement and has good global performance, so it is widely used, but the algorithm also has the defects of low optimization accuracy and easy to fall into the local optimal solution. Based on the gray wolf algorithm based on dynamic update weights, this paper proposes an improved gray wolf optimization algorithm. The algorithm first improved the C strategy to balance the global exploration and local development capabilities of the algorithm; then the sine and cosine global optimization strategy was used to guide and update the position again to reduce the premature probability and improve the algorithm optimization accuracy; finally, the adaptive local optimization strategy was used to select the optimal solution through neighborhood comparison to avoid the algorithm falling into the local optimal solution. The gray wolf optimization algorithm before and after the improvement is used to simulate and analyze the fault section location, which further verifies the correctness and superiority of the algorithm.
机译:随着电网的规模继续扩展并且连接大量分布式电源,定位故障的难度正在增加。灰狼优化算法原则上简单,易于实施,具有良好的全球性能,因此它被广泛使用,但算法还具有低优化精度的缺陷,易于落入本地最佳解决方案。基于基于动态更新权重的灰狼算法,本文提出了一种改进的灰羽优化算法。该算法首先改进了C策略,以平衡算法的全球探索和局部开发能力;然后使用正弦和余弦全局优化策略来指导和更新该位置,再次降低过早概率并提高算法优化准确性;最后,使用自适应局部优化策略来通过邻域比较选择最佳解决方案,以避免落入本地最佳解决方案的算法。改进之前和之后的灰狼优化算法用于模拟和分析故障部分位置,这进一步验证了算法的正确性和优越性。

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