为解决配电网在噪声环境与复杂扰动下故障特征提取不足、辨识精度低的难题，提出一种面向噪声环境下的多维熵特征与协同优化模型的鲁棒性故障辨识方法。首先，通过完全自适应噪声集合经验模态分解（Complete Ensemble Empirical Mode Decomposition with Adaptive Noise, CEEMDAN）对零序电流信号进行降噪，并创新性地融合八种异构熵特征，构建一个能深度刻画故障非线性动态特性的高维复杂度空间，以增强特征的内在抗扰动能力。其次，设计了一个协同诊断模型，利用核主成分分析（Kernel Principal Component Analysis, KPCA）精炼出对故障最敏感的低维特征，并采用改进麻雀搜索算法（Improved Sparrow Search Algorithm， ISSA）为支持向量机（Support Vector Machine, SVM）的惩罚因子和核函数参数进行协同优化。最后，建立KPCA-ISSA-SVM多维熵特征融合的配电网故障分类模型。在IEEE-33节点系统上的仿真验证，结果表明，所提方法在不同信噪比条件下均表现出优越的故障辨识精度，验证了该方法的有效性与先进性。

A robust fault identification method is proposed to address the challenges of insufficient fault feature extraction and low identification accuracy in distribution networks with noisy environments and complex disturbances, which integrates multidimensional entropy features with a cooperative optimization model. Firstly, the zero-sequence current signal is denoised by using the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN). It innovatively integrates eight heterogeneous entropy features to construct a high-dimensional complexity space that can deeply describe the nonlinear dynamic characteristics of faults, thereby enhancing the intrinsic anti-disturbance ability of the features. Secondly, a collaborative diagnostic model is developed, in which Kernel Principal Component Analysis (KPCA) is employed to extract the most fault-sensitive low-dimensional features, while an Improved Sparrow Search Algorithm (ISSA) is utilized to jointly optimize the penalty factor and kernel function parameters of the Support Vector Machine (SVM). Finally, a fault classification model for distribution networks is established based on multidimensional entropy feature fusion using KPCA-ISSA-SVM. Simulation studies on the IEEE 33-bus system demonstrate that the proposed method achieves superior fault identification accuracy under various signal-to-noise ratio conditions, thereby validating its effectiveness and advancement.