基于时频域融合与置信度增强模型的复杂电能质量扰动分类方法

许慧燕; 余子文; 洪典; 李建闽

许慧燕,余子文,洪典,李建闽.基于时频域融合与置信度增强模型的复杂电能质量扰动分类方法[J].电测与仪表,2026,63(1):72-82.
XU Huiyan,YU Ziwen,HONG Dian,LI Jianmin.A complex power quality disturbance classification method based on time-frequency domain fusion and confidence enhancement model[J].Electrical Measurement & Instrumentation,2026,63(1):72-82.

基于时频域融合与置信度增强模型的复杂电能质量扰动分类方法

A complex power quality disturbance classification method based on time-frequency domain fusion and confidence enhancement model

DOI：10.19753/j.issn1001-1390.2026.01.008

中文关键词: 电能质量扰动时频域融合标签增强因子多标签学习

英文关键词:power quality disturbance, time-frequency domain fusion, label enhancement factor, multi-label learning

基金项目:国家自然科学基金（51907062）；湖南省自然科学基金（2021JJ40354）

作者中文名	作者英文名	单位
许慧燕	XU Huiyan	1. 湖南涉外经济学院,信息科学与工程学院,长沙 410205; 3. 湖南师范大学信息科学与工程学院,长沙 410081
余子文	YU Ziwen	布里斯托大学工程数学与技术学院, 英国布里斯托尔 BS8 1TH
洪典	HONG Dian	湖南师范大学信息科学与工程学院, 长沙 410081
李建闽	LI Jianmin	湖南师范大学工程与设计学院, 长沙 410081

摘要点击次数: 49

中文摘要:

传统电能质量扰动(power quality disturbance, PQD)分类方法通常依赖有限类型的样本训练，难以有效识别未见过的复杂多重扰动类型。为此，提出了一种基于时频域融合与置信度增强模型的复杂电能质量扰动分类方法。该方法先对PQD信号进行快速傅里叶变换，获取其频谱信息。接着，利用时序卷积网络和卷积神经网络分别提取时域与频域特征，并融合所得的时频特征以增强特征表达。然后，在多标签学习框架下，引入类别标签以区分单一扰动与多重扰动类型，并通过置信度得分预测各扰动标签的存在性。最后，为提升模型对未训练多重扰动类型的识别能力，进一步设计标签增强因子，在不影响已训练PQD类型识别的前提下，优化多重扰动的置信度分布。仿真结果表明，该方法仅使用单一与双重扰动样本训练的情况下，在未包含于训练集的多重扰动类型上识别准确率能达到96.75%以上。在实际测试中，对未知扰动类型的识别率保持在91.67%以上，展现出良好的泛化能力。该方法在电网运行状态多变，扰动叠加复杂的实际场景具有较高的应用价值。

英文摘要:

Traditional power quality disturbance (PQD) classification methods often rely on a limited set of disturbance types for training, making it challenging to accurately identify previously unseen complex and multiple disturbance types. To address this issue, this paper proposes a novel PQD classification method based on time-frequency domain fusion and confidence enhancement model. Firstly, the PQD signal is transformed using the fast Fourier transform to obtain its spectral information. Then, a temporal convolutional network and a convolutional neural network are employed to extract features from the time and frequency domains, respectively. The extracted features are fused to enhance the overall feature representation. Within a multi-label learning framework, class labels are introduced to differentiate between single and multiple disturbance types, and confidence scores are predicted to determine the presence of each disturbance label. Finally, to further improve the ability of model to identify unseen multiple disturbance types, a label enhancement factor is designed to optimize the confidence is tribution for multiple disturbances without affecting the recognition performance of known PQD types. Simulation results show that the proposed method achieves an dentification accuracy of over 96. 75% for multiple disturbance types not included in the training set, even when trained only on single and dual disturbance samples. In real-world tests, the method maintains a recognition rate above 91.67% for unknown disturbance types, demonstrating strong generalization capabilities. The proposed method offers high application value in real-world scenarios where power grid operating conditions are variable and disturbance patterns are complex and superimposed.

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