Recognition of Composite Power Quality Disturbances Based on Time-Frequency Fusion and Confidence Optimization

MENG Xiangliang; WANG limin; XIAO xinghua; SUN zhensheng

doi:10.11959/j.issn.1000-0801.DXKX260186

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Recognition of Composite Power Quality Disturbances Based on Time-Frequency Fusion and Confidence Optimization

更新时间：2026-06-23

- Recognition of Composite Power Quality Disturbances Based on Time-Frequency Fusion and Confidence Optimization
- Telecommunications Science (2026)
- 作者机构：
  
  1.国网信息通信产业集团有限公司，北京 100053
  2.国网信通亿力科技有限责任公司，福建福州 350003
- 作者简介：
- 基金信息：
- DOI：10.11959/j.issn.1000-0801.DXKX260186
  CLC： TN929.5;TP399
- Received：20 March 2026，
  
  Revised：2026-05-11，
  
  Accepted：23 June 2026，
- 稿件说明：
移动端阅览
MENG Xiangliang, WANG limin, XIAO xinghua, et al. Recognition of Composite Power Quality Disturbances Based on Time-Frequency Fusion and Confidence Optimization[J/OL]. Telecommunications Science, 2026.
DOI：

MENG Xiangliang, WANG limin, XIAO xinghua, et al. Recognition of Composite Power Quality Disturbances Based on Time-Frequency Fusion and Confidence Optimization[J/OL]. Telecommunications Science, 2026. DOI： 10.11959/j.issn.1000-0801.DXKX260186.

摘要

随着新能源发电技术的广泛应用，电网中电能质量扰动（PQD）的类型日益复杂，多重扰动叠加现象频发，传统分类方法因依赖完备训练样本，难以有效识别未见过的复合扰动类型。为此，提出一种基于时频特征融合与置信度优化（time-frequency feature fusion and confidence optimization

TFFF-CO）的电能质量扰动分类方法。首先，对PQD信号进行快速傅里叶变换（FFT）获取频谱信息，利用注意力增强型门控循环单元（AGRU-ATT）提取时域动态特征，采用轻量型多尺度卷积网络（MS-CNN-Lite）捕捉频域局部特征；其次，通过自适应权重分配策略融合时频特征，增强特征表征的完整性与针对性；然后，在多标签学习框架下引入类别区分标签，实现单一与多重扰动的初步划分，并预测各扰动标签的置信度；最后，设计置信度补偿因子，在不影响已知扰动识别精度的前提下，提升未知多重扰动的置信度区分能力。仿真实验表明，该方法仅采用单一和双重扰动样本训练时，对三重、四重扰动的识别准确率分别达到99.42%和96.38%。

Abstract

With the wide application of new energy power generation technologies

the types of power quality disturbances (PQD) in power grids are becoming increasingly complex

and the phenomenon of multiple disturbances superimposing frequently occurs. Traditional classification methods

which rely on complete training samples

are difficult to effectively identify unseen compound disturbance types. Therefore

a power quality disturbance classification method based on time-frequency feature fusion and confidence optimization (TFFF-CO) is proposed. Firstly

the PQD signal is subjected to fast Fourier transform (FFT) to obtain spectral information

and the time-domain dynamic features are extracted using the attention-enhanced gated recurrent unit (AGRU-ATT)

while the frequency-domain local features are captured by the lightweight multi-scale convolutional neural network (MS-CNN-Lite). Secondly

the time-frequency features are fused through an adaptive weight allocation strategy to enhance the integrity and specificity of feature representation. Then

in the multi-label learning framework

category discrimination labels are introduced to achieve the preliminary division of single and multiple disturbances and predict the confidence of each disturbance label. Finally

a dynamic confidence optimization factor is designed to improve the confidence discrimination ability of unknown multiple disturbances without affecting the recognition accuracy of known disturbances. Simulation experiments show that when this method is trained only with single and double disturbance samples

the recognition accuracy for triple and quadruple disturbances reaches 99.42% and 96.38%

respectively.

关键词

Keywords

references

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