| 引用本文: | 秦心筱,张昌华,徐子豪,等.基于卷积神经网络的电力系统低频振荡主导模态
特征定性辨识[J].电力系统保护与控制,2021,49(10):51-58.[点击复制] |
| QIN Xinxiao,ZHANG Changhua,XU Zihao,et al.Research on qualitative identification of a low frequency oscillations dominant mode in power system based on a convolutional neural network[J].Power System Protection and Control,2021,49(10):51-58[点击复制] |
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| 摘要: |
| 低频振荡严重威胁电网的安全稳定运行。传统低频振荡辨识方法大都将被测信号视作平稳信号或进行平稳化处理，忽略了信号的非平稳特性。但在高比例新能源和电力电子设备的电力系统中，低频振荡参数具有大范围时变的特性，传统辨识方法难以准确识别。提出了一种基于卷积神经网络(CNN)的电力系统低频振荡辨识方法。首先对原始信号进行时域特征提取作为预处理操作，然后将16个时域特征的信号分别送入训练好的CNN网络，最后由全连接层综合各个网络的输出得到最终辨识结果。仿真实验表明，该方法可以快速准确地辨识出低频振荡信号的频率和衰减因子，具有很好的抗噪性。且与Prony方法相比，它能够辨识振荡过程中是否引入了新的振荡模态。 |
| 关键词: 电力系统 低频振荡 卷积神经网络 Prony 非平稳性 新能源发电 |
| DOI：DOI: 10.19783/j.cnki.pspc.200897 |
| 投稿时间：2020-07-28修订日期：2020-09-18 |
| 基金项目:四川省科技厅重点研发计划资助(2019YFG0142)；国网四川省电力公司经济技术研究院科技项目资助(SGSCJY00GHJS2000015) |
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| Research on qualitative identification of a low frequency oscillations dominant mode in power system based on a convolutional neural network |
| QIN Xinxiao1,ZHANG Changhua1,XU Zihao1,LI Qianyu1,WEI Jun 2,YE Shengyong2 |
| (1. School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China,
Chengdu 611731, China; 2. State Grid Sichuan Economic Research Institute, Chengdu 610072, China) |
| Abstract: |
| Low Frequency Oscillation (LFO) seriously threatens the stability and security of a power grid. Most traditional LFO identification methods regard the measured signals as stationary signals or process them as stationary signals. This ignores their non-stationary characteristic. However, in a power system with high proportion of renewables and power electronics, LFO parameters have a wide range of time-varying characteristics. Traditional methods find it difficult to identify them accurately. This paper proposes a low frequency oscillation identification method based on a Convolutional Neural Network (CNN). First, the time domain features of the original signals are extracted as a preprocessing operation. Then, 16 signals of different time-domain characteristics are sent to the trained CNN networks. Finally, the full connection layer synthesizes the outputs of each network to obtain the final identification results. Simulation results show that this method can identify the frequency and damping factor of LFO signals quickly and accurately, and has good anti-noise performance. In contrast to the Prony method, it can identify whether a new oscillation mode is introduced in the process of oscillation.
This work is supported by the Key Research and Development Program of Sichuan Science and Technology Department (No. 2019YFG0142) and Science and Technology Project of State Grid Sichuan Electric Power Economic and Technological Research Institute (No. SGSCJY00GHJS2000015). |
| Key words: power system low frequency oscillation convolutional neural network (CNN) Prony non-stationary renewable energy |
