基于IMTF-DCAMCNN的电能质量扰动识别
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华北电力大学 电气与电子工程学院

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TM71

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国家电网有限公司科技项目资助(5400-202319202A-1-1-ZN)


Power Quality Disturbance Identification Based on IMTF-DCAMCNN
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School of Electrical and Electronic Engineering,North China Electric Power University

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    摘要:

    分布式能源并网以及电力电子设备的应用推进了新型电力系统的发展,但也引起了复杂的电能质量扰动(power quality disturbance,PQDs)问题。为精确识别现代电力系统中复杂的电能质量扰动,提出一种基于改进的马尔可夫转移场(Improved Markov Transition Field,IMTF)与双通道注意力机制卷积神经网络(Dual-channel attention mechanism convolutional neural networks,DCAMCNN)的电能质量识别方法。首先,将电能质量时序信号经过MTF转化为二维图像,并利用HSV(Hue,Saturation,Value)颜色空间对MTF图像进行二次颜色编码;然后构建双通道注意力机制,将其与多尺度卷积神经网络融合,沿两通道关注PQDs重要信息并抑制轻量级特征;最后将改进后的MTF图像输入到所构建的模型中训练进行参数优化,利用最优模型输出扰动分类结果。实验结果表明,与传统的图像转换方法及其他网络算法相比,所提方法具有更高的识别准确率和抗噪能力。

    Abstract:

    The integration of distributed energy and the application of power electronic equipment have promoted the development of new power systems, but also caused complex power quality disturbances (PQDs). In order to accurately identify complex power quality disturbances in modern power systems, a power quality identification method based on Improved Markov Transition Field (IMTF) and Dual-channel attention mechanism convolutional neural networks (DCAMCNN) is proposed. Firstly, the power quality time series signal is transformed into a two-dimensional image through MTF, and the HSV (Hue,Saturation,Value) color space is used to perform secondary color coding on the MTF image. Then, a dual-channel attention mechanism is constructed and fused with a multi-scale convolutional neural network to focus on the important information of PQDs along two channels and suppress lightweight features. Finally, the improved MTF image is input into the constructed model to train and optimize the parameters, and the optimal model is used to output the disturbance classification results. Experimental results show that the proposed method has higher recognition accuracy and anti-noise ability than traditional image transformation methods and other network algorithms.

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谷伟康,尹忠东,HE Jing,等. 基于IMTF-DCAMCNN的电能质量扰动识别[J]. 科学技术与工程, , ():

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  • 收稿日期:2024-04-03
  • 最后修改日期:2024-04-24
  • 录用日期:2024-04-25
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