随着光伏接入的新型电力系统的不断发展,电能质量扰动（PQDs）的类型变得更加复杂,针对复合扰动占比的增加使得传统方法难以在人工经验指导下准确执行复杂的PQDs分类任务的问题,提出一种基于图像融合和多模态特征驱动的复杂PQDs分类方法。在数据处理方面,引入二维图像编码方法,即递归图（RP）与连续小波变换（CWT）将一维PQDs信号转换为二维图像,并通过水平拼接产生RP-CWT特征图像,实现特征增强。在模型层面,建立重复层卷积网络（RepLKNet）和双向门控单元（BiGRU）并行优化的多模态融合的集成模型,同时考虑时域、频域、空间维度上的高维特征,能快速准确地识别信号中的扰动特征。最后,基于仿真数据和PQDs实验平台进行测试,结果表明所提方法可对复杂PQDs进行准确分类。
Abstract
With the continuous development of new power systems integrated with photovoltaics, the types of power quality disturbances （PQDs） have become more complex. In response to the increasing proportion of composite disturbances, which makes it difficult for traditional methods to accurately classify complex PQDs under the guidance of human experience, a complex PQDs classification method based on image fusion and multimodal feature-driven approach is proposed. In terms of data processing, two-dimensional image encoding methods—recurrence plot （RP） and continuous wavelet transform （CWT）—are introduced to convert one-dimensional PQDs signals into two-dimensional images. RP-CWT feature images are generated through horizontal concatenation to achieve feature enhancement. At the model level, an ensemble model for multimodal fusion with parallel optimization of re-parameterized large kernel network （RepLKNet） and bidirectional gated recurrent unit （BiGRU） is established. This model captures high-dimensional features in the time, frequency, and spatial dimensions, enabling rapid and accurate identification of disturbance features in signals. Finally, tests based on simulation data and a PQDs experimental platform show that the proposed method can accurately classify complex PQDs.
关键词
电能质量扰动 /
多模态融合 /
连续小波变换 /
递归图 /
双向门控单元 /
重复层卷积网络
Key words
power quality disturbances /
multimodal fusion /
continuous wavelet transform /
recurrence plot /
bidirectional gated recurrent unit /
re-parameterized large kernel network
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基金
国家自然科学基金面上项目（12072203）; 中国铁路北京局集团有限公司科技研究开发计划（2025CGD02）
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