在“双碳”目标的推动下，大规模的分布式电源投入配电网，导致故障电流方向由单向变为双向，使得传统配电网故障定位方法不再适用。提出一种基于改进麻雀搜索算法的主动配电网故障区段定位方法。利用统计参数映射（statistical parametric mapping, SPM）混沌映射生成高质量初始麻雀种群，在发现者路径中融合正余弦算法，引入非线性惯性权重，平衡局部与全局搜索能力，加速算法收敛。同时在追随者路径引入螺旋搜索和Levy飞行，增强对未知区域探索，利于跳出局部最优。建立适用于主动配电网的开关状态函数，对传统目标函数加入防误判因子，以防信息失真。算例结果表明：该方法能够适应多重故障区段和不同位置分布式电源投切，定位准确率和收敛速度均有明显优势，具有良好容错性。

Under the impetus of the "dual carbon" goals, a large number of distributed generation have been integrated into the distribution network, transforming the fault current direction from unidirectional flow to bidirectional flow, which makes the traditional distribution network fault location method no longer applicable. An active distribution network fault section location method based on an improved sparrow search algorithm was proposed. High-quality initial sparrow populations were generated using statistical parametric mapping (SPM) chaotic mapping. The discoverer's path was enhanced by integrating the sine cosine algorithm and introducing a nonlinear inertia weight to balance local and global search capabilities, thus accelerating the convergence of the algorithm. Additionally, the follower's path incorporated spiral search and Levy flight to enhance exploration of unknown areas and facilitate escape from local optima. A switch status function suitable for active distribution networks was established, and an anti-misjudgment factor was added to the traditional objective function to prevent information distortion. Case study results indicate that the proposed method can adapt to multiple fault sections and the switching of distributed generation at different locations, significantly advantages both localization accuracy and convergence speed, and demonstrating good fault tolerance.