摘要
为实现电压暂降信号的准确检测,提出了一种基于Kaiser窗函数改进S变换的电压暂降检测方法。首先,介绍基于Kaiser窗函数改进S变换的原理,分析其时频特性并给出电压暂降特征量的计算方法,而后给出基于Kaiser窗函数改进S变换的电压暂降检测流程。该方法对电压暂降信号进行基于Kaiser窗函数改进S变换,可分别得到信号的基波幅值特性曲线和基波相位特性曲线,然后由基波幅值差分曲线获得电压暂降起止时刻及持续时间;由基波幅值曲线的傅里叶变换得到电压暂降幅值;由基波相位特性曲线得到电压暂降相位跳变值。仿真试验表明,与现有检测方法相比,该方法具有准确度高、计算量少,不易受谐波与噪声影响等优点。构建的基于高性能数字信号处理器(DSP)的硬件测试平台验证了该方法的正确性和有效性。
In order to realize the accurate detection of voltage sag signal,a detection method for voltage sag based on modified S transformation with Kaiser window is proposed.The principle of improved S transform with Kaiser window is introduced,the time-frequency characteristic is analyzed and the calculation method of voltage sag characteristics is given.And then,the detection process of voltage sag based on modified S transformation with Kaiser window is presented.The fundamental amplitude characteristic curve and the fundamental phase characteristic curve of voltage sag can be obtained by the modified S transform with Kaiser window.Then the starting and ending time and duration of the voltage sag can be obtained from the fundamental amplitude difference curve.The fundamental wave magnitude curve combined with Fourier transform is used to calculate the magnitude of the voltage sag.The voltage sag phase jump is obtained from the fundamental wave phase characteristic curve.The simulations show that compared with the existing detection methods,the method has the advantages of high accuracy,low computation,and not easy to be affected by harmonics and noise.The hardware test platform based on high-performance digital signal processor(DSP)is constructed to verify the correctness and effectiveness of the method.
作者
徐勇
向运琨
曾麟
何哲
李建闽
XU Yong;XIANG Yunkun;ZENG Lin;HE Zhe;LI Jianmin(Hunan integrated Energy Service Co.,Ltd.,State Grid,Changsha 410007,China;College of Engineering and Design,Hunan Normal University,Changsha 410081,China)
出处
《自动化仪表》
CAS
2020年第10期61-66,73,共7页
Process Automation Instrumentation
关键词
电压暂降
改进S变换
Kaiser窗函数
特征检测
幅值矩阵
相位矩阵
幅值特性曲线
相位特性曲线
Voltage sag
Modified S transform
Kaiser window functrow
Characteristic detection
Magnitude matrix
Phase matrix
Magnitude characteristic curve
Phase characteristic curve
