基于小波多尺度分析和Kalman滤波的微机保护算法  

Microprocessor-based protection algorithm based on wavelet multi-scale analysis and Kalman filter

作　　者：吕思颖[1] 裴旵 秦昕[1] 要航[1] 

机构地区：[1]广西大学电气工程学院,广西南宁530004

出　　处：《电力系统保护与控制》2015年第21期54-59,共6页Power System Protection and Control

基　　金：广西研究生教育创新计划项目(YCSZ2014041)

摘　　要：提出了一种新的微机保护算法。采用小波多尺度变换对采集信号进行分解得到平滑信号和细节信号,在细节信号上利用模极大值法确定异常发生与否及其产生时刻。异常产生后进入故障处理程序,启动Kalman滤波器。利用平滑信号更新滤波器的观测值,减少故障信号暂态噪声的干扰,提高了滤波算法的收敛速度;利用细节信号实时在线计算测量噪声的方差,提高了滤波算法的收敛精度;小波分析对故障进行初次检测和判断,而滤波器估计出故障信号基波分量结合继电保护原理对故障进行再次判断,提高了保护算法的可靠性。在Matlab/Simulink环境下搭建仿真模型对算法进行验证与测试,仿真结果证实了算法的可行性和有效性。A new microprocessor-based protection algorithm is presented. The sampling signals are decomposed to smooth signals and detail signals by multi-scale wavelet transform. Utilizing modulus maximum method for detail signals to determine whether or not an exception occurs and when it occurs. Entering the fault handler once the exception generates and activating the Kalman filter. The smooth signals are adopted to update the observations of the filter to reduce the interference of fault signals＇ transient noise, which can improve the convergence speed of the filter algorithm. Detail signals are adopted to calculate the measurement noise variance in real-time to improve the convergence precision of the filter algorithm. Wavelet analysis method can detect and judge the fault for the first time, while the estimated fundamental frequency component of fault signals combined with relay protection principle are adopted to judge the fault again, which can improve the reliability of the protection algorithm. The Matlab/Simulink simulation system is built and the results show the feasibility and effectiveness of the algorithm.

关 键 词：小波变换 多尺度分析 模极大值 KALMAN滤波 微机保护算法 

分 类 号：TM774[电气工程—电力系统及自动化]