2025年7月20日 周日
首页 > 过刊浏览>2024年第39卷第4期 >102-111. DOI:10.19781/j.issn.1673-9140.2024.04.012
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基于MTF可视化和改进DenseNet神经网络的电能质量扰动识别算法
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[cstr]
作者:
作者单位:

(1.上海电力大学电气工程学院，上海 200090;2.上海电力大学计算机与技术学院，上海 200090;3.上海海洋大学信息学院，上海 201306;4.上海电力大学数理学院，上海 200090;5.上海电力大学经济与管理学院，上海 200090)

通讯作者:

时 帅（1987—），男，博士，讲师，主要从事电力系统运行等研究；E?mail：ssglasgow@163.com

中图分类号:

TM721

基金项目:

国家自然科学基金（61972240）


An identification method based on MTF visualization and improved DenseNet for power quality disturbances
Author:
Affiliation:

((1.College of Electrical Power Engineering,Shanghai University of Electric Power, Shanghai 200090, China;2.College of Computer Science and Technology, Shanghai University of Electric Power,Shanghai 200090, China;3.College of Information Technology,Shanghai Ocean University,Shanghai 201306, China;4.College of Mathematics and Physics,Shanghai University of Electric Power, Shanghai 200090,China;5.College of Economics and Management,Shanghai University of Electric Power, Shanghai 200090, China))

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    摘要:

    针对传统电能质量扰动(power quality disturbances, PQDs）分类器人工选取特征过程复杂、精细化程度不足的问题，提出一种基于马尔科夫迁移场（Markov translate filed，MTF）可视化和改进密集卷积网络（dense convolutional networks， DenseNet）的PQDs识别新方法。首先将一维PQD信号经MTF映射为二维图像，接着将图像输入到具有新型通道注意力机制的改进DenseNet中，最后训练网络自行从海量样本中提取特征，实现PQDs信号的正确识别。算例结果表明：在无噪声和信噪比为20、30 dB情况下，所提改进DenseNet能有效克服传统方法中主观性强、抗噪性能差等特征缺点，可以更好地提取复合PQD特征信息，对复合PQD识别率高。

    Abstract:

    Aiming at the problems of complex process and insufficient refinement of artificial feature selection in traditional power quality disturbances (PQDs) classifier, a new PQD recognition method based on Markov transition field visualization and improved DenseNet is proposed. Firstly, the one-dimensional PQD signal is mapped into a two-dimensional image by MTF. Then, the image is input into an improved DenseNet with a new channel attention mechanism. Finally, the network is trained to extract features from a large number of samples by itself, so as to realize the correct recognition of PQD signals. The example results show that: in the case of no noise and signal-to-noise ratio of 20dB and 30dB, the proposed improved DenseNet can effectively overcome the shortcomings of traditional methods, such as strong subjectivity of feature selection and poor anti-noise performance. It can better extract the feature information of complex PQD, and has a high recognition rate for complex PQD.

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时 帅,陈子文,黄冬梅,等.基于MTF可视化和改进DenseNet神经网络的电能质量扰动识别算法[J].电力科学与技术学报,2024,39(4):102-111.
SHI Shuai, CHEN Ziwen, HUANG Dongmei, et al. An identification method based on MTF visualization and improved DenseNet for power quality disturbances[J]. Journal of Electric Power Science and Technology,2024,39(4):102-111.

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