针对有源配电网故障定位中传统集中式方法所需通信量大，以及现有人工智能方法在分布式部署时面临模型复杂、资源受限等挑战，提出了一种分布式部署-集中式诊断的两阶段协同智能故障区段定位方法。该方法首先利用变分模态分解对各分布测量单元采集的电流信号提取高维局部故障特征，并通过轻量化多层感知机模型在终端完成初步诊断，仅将低维度的故障区段概率向量上传。随后，在集中式融合端以这些概率向量为元特征，借助K-Fold交叉验证生成元学习数据，构建基于Stacking的协同神经网络对多源信息进行端到端优化融合，输出最终故障区段判别结果。仿真实验表明，所提方法能够有效整合分布式观测信息，在多种故障工况下均能实现高精度的故障区段定位，其性能显著优于单个本地模型及简单融合策略，为配电网故障的快速精准定位提供了新的有效途径。

In view of the large communication volume required by traditional centralized methods in active distribution network fault section location, as well as the challenges faced by existing artificial intelligence methods such as complex models and resource constraints when deployed in a distributed manner, a two-stage collaborative intelligent fault section location method of distributed deployment and centralized diagnosis is proposed. This method first uses variational mode decomposition to extract high-dimensional local fault features from the current signals collected by each distributed measurement unit, and then completes the preliminary diagnosis at the terminal through a lightweight multi-layer perceptron model, only uploading the low-dimensional fault section probability vector. Subsequently, at the centralized fusion end, taking these probability vectors as meta-features, meta-learning data is generated with the aid of K-Fold cross-validation, and a collaborative neural network based on Stacking is constructed to optimize and fuse multi-source information end-to-end, in order to output the final fault section discrimination result. The simulation experiments show that the proposed method can effectively integrate distributed observation information and achieve high-precision fault section location under various fault conditions. Its performance is significantly better than that of a single local model and a simple fusion strategy, providing a new and effective way for the rapid and accurate location of distribution network faults.