| 基于Prony滑动平均窗算法的电力系统低频振荡特征分析 |
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| 引用本文： | 张俊峰,杨婷,陈珉,张甜甜,萧珺,毛承雄.基于Prony滑动平均窗算法的电力系统低频振荡特征分析[J].电力自动化设备,2018,38(10). |
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| 作者姓名： | 张俊峰 杨婷 陈珉 张甜甜 萧珺 毛承雄 |
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| 作者单位： | 广东电网公司电力科学研究院;华中科技大学电气与电子工程学院 |
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| 摘 要： | Prony算法能根据实测数据辨识系统的相关特性参数,有助于分析系统低频振荡。针对传统Prony算法只能分析部分数据且对噪声敏感的问题,提出一种Prony滑动平均窗算法,分窗口对数据进行分析,不仅能充分利用数据,而且采用求和取平均的方法在一定程度上能削弱噪声,即使在信噪比非常小的情况下仍能得到准确的辨识结果。基于PSASP软件的仿真分析验证了Prony滑动平均窗算法所得结果的准确性。
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| 关 键 词： | 电力系统 低频振荡 Prony算法 滑动平均窗 信噪比 |
Power system low-frequency oscillation characteristic analysisbased on Prony moving average window algorithm |
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| ZHANG Junfeng,YANG Ting,CHEN Min,ZHANG Tiantian,XIAO Jun and MAO Chengxiong.Power system low-frequency oscillation characteristic analysisbased on Prony moving average window algorithm[J].Electric Power Automation Equipment,2018,38(10). |
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| Authors: | ZHANG Junfeng YANG Ting CHEN Min ZHANG Tiantian XIAO Jun and MAO Chengxiong |
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| Affiliation: | Electric Power Research Institute of Guangdong Power Grid Corporation, Guangzhou 510080, China,College of Electrical and Electronic Engineering, Huazhong University of Science and Technology, Wuhan 430074, China,College of Electrical and Electronic Engineering, Huazhong University of Science and Technology, Wuhan 430074, China,College of Electrical and Electronic Engineering, Huazhong University of Science and Technology, Wuhan 430074, China,College of Electrical and Electronic Engineering, Huazhong University of Science and Technology, Wuhan 430074, China and College of Electrical and Electronic Engineering, Huazhong University of Science and Technology, Wuhan 430074, China |
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| Abstract: | Prony algorithm can identify related characteristic parameters of power system according to the measured data, which can help to analyze the low-frequency oscillations of the system. However, the traditional Prony algorithms are sensitive to noise and can only analyze partial of the data. A Prony moving average window algorithm is proposed to analyze the data in separate windows, which can not only make full use of the data, but also weaken the noise and obtain correct identification results even if the SNR(Signal-to-Noise Ratio) is very small. The simulative results based on PSASP software verify the accuracy of the Prony moving average window algorithm. |
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| Keywords: | electric power systems low-frequency oscillation Prony algorithm moving average window SNR |
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