上海交通大学学报

上海交通大学学报

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面向噪声环境的多维熵特征融合的配电网故障辨识方法

  

  1. 1. 昆明理工大学 电力工程学院，昆明 650500；2. 上海交通大学 电子信息与电气工程系，上海 200240；3. 云南电网有限责任公司电力科学研究院，昆明650217
  • 作者简介:沈赋（1988－），副教授，从事新型电力系统建模、电网运行与调度等研究
  • 基金资助:
    国家自然科学基金资助项目(52107097)；云南省兴滇英才支持计划项目（KKRD202204021）；云南省应用基础研究计划资助项目（202501AT070350）

Fault Identification Method for Distribution Networks Based on Multi-Dimensional Entropy Feature Fusion in Noisy Environments

  1. 1. Faculty of Electric Power Engineering, Kunming University of Science and Technology, Kunming 650500, China; 2. Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China; 3. Electric Power Research Institute of Yunnan Power Grid Co., Ltd., Kunming 650217, China

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摘要： 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。

关键词:

配电网, 故障辨识, 熵特征, 核主成分分析, 麻雀搜素算法, 支持向量机

Abstract:

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.

Key words: distribution network, fault identification, entropy characteristics, kernel principal component analysis(KPCA), improved sparrow search algorithm(ISSA), support vector machine(SVM)

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