基于时频图和时序特征组合的电能质量复合扰动识别

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摘要针对电能质量扰动（PQDs）识别难题,本文提出一种基于LIRC-BiLSTM的双分支多模态融合轻量化识别模型。该模型首先对原始PQDs信号进行S变换,生成时频图像并作为卷积注意力模块（CBAM）支路输入;同时,将原始PQDs一维时序信号向量输入双向长短期记忆网络（BiLSTM）支路。在CBAM支路中,采用多尺度特征提取模块提取不同分辨率的图像特征,再引入CBAM自适应增强通道与空间关注信息,以聚焦时频图像的关键模式与整体趋势;在BiLSTM支路中,先对时序矩阵进行轻量卷积预处理,再送入BiLSTM,并通过自注意力机制对时序特征进行强化。最后,将两条支路的输出进行时频特征和时序特征融合,完成PQDs类型判别。仿真实验表明,所提LIRC-BiLSTM模型能够有效融合时频图像与时序细节信息,显著提升了对多类电能质量扰动的识别准确率与抗噪性能。

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	毕贵红
	刘大卫
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	陈世轲

关键词 ：电能质量扰动, S变换, 多模态特征融合, 深度学习

Abstract：To address the challenge of identifying power quality disturbances (PQDs), this paper proposes a lightweight two-branch multimodal fusion recognition model, LIRC-BiLSTM. The model first applies an S transform to the raw PQD signals to produce time-frequency images that are fed to a convolutional block attention module (CBAM) branch, while the raw one-dimensional PQD time series vectors are sent to a bidirectional long short-term memory network (BiLSTM) branch. In the CBAM branch, a multi-scale feature-extraction module captures image features at different resolutions, and a CBAM is introduced to adaptively enhance channel and spatial attention, focusing on key patterns and overall trends in the time-frequency images. In the BiLSTM branch, the time-series matrix undergoes lightweight convolutional preprocessing before being input to a BiLSTM, and a self-attention mechanism is applied to strengthen the temporal features. Finally, the outputs of both branches are fused to combine time-frequency and temporal features for PQDs type classification. Simulation results show that the proposed LIRC-BiLSTM model effectively integrates time-frequency images with temporal detail, significantly improving classification accuracy and noise robustness for multiple classes of power quality disturbances.

Key words： power quality disturbances S transform multimodal feature fusion deep learning

收稿日期: 2025-08-11

基金资助:国家自然科学基金（51767012）

作者简介: 毕贵红（1968—）,男,云南省昆明市人,博士,教授,研究方向为能源系统建模、优化与智能数据分析。

引用本文:

毕贵红, 刘大卫, 陈仕龙, 张维, 陈世轲. 基于时频图和时序特征组合的电能质量复合扰动识别[J]. 电气技术, 2026, 27(1): 9-19. BI Guihong, SINN SIN, CHEN Shilong, ZHANG Wei, CHEN Shike. Power quality composite disturbance identification based on combination of time-frequency diagram and timing features. Electrical Engineering, 2026, 27(1): 9-19.

链接本文:

https://dqjs.cesmedia.cn/CN/Y2026/V27/I1/9

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