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基于正弦波/指数函数自相关的运动状态估计模型,提出一种基于卡尔曼滤波(Kalman filter,KF)的电能质量扰动(power quality disturbances,PQD)检测方法(KFPQD检测方法).提出方法针对基波频率的纯正弦电压信号模型,以及基波频率纯正弦信号叠加指数衰减的电流信号模型,选取电压信号及其一阶导数形成二维电压状态向量,选取电流信号及其一阶导数、衰减直流分量形成三维电流状态向量,进行状态空间建模,离散化后采用KF算法对电压/电流及其变化率进行状态估计,跟踪检测基波电压/电流信号的跳变.仿真和微电网PQD检测实验结果表明:提出方法在跟踪精度、抗干扰性和敏感性方面均优于传统KFPQD方法,提高对电压与电流跳变的跟踪性能.“,”This paper was based on dynamic model of sinusoidal/exponential autocorrelation.A new power quality disturbances (PQD) detection method based on Kalman filter (KF) (KFPQD detection method) was proposed.The new proposed method was based on the pure sinusoidal voltage signal model for fundamental frequency and the current signal model for the pure sinusoidal signal adding exponential decay.The voltage signal and its first derivative were selected to form a two-dimensional voltage state vector.The current signal,its first derivative and decay the DC component were selected to form the three-dimensional current state vector.The space state models were established and discretized.Then,the KF method was adopted to estimate the voltage/current and their change rate,to detect the transitions of fundamental voltage/current.Simulations and experiments for micro-grid PQD detection show that:the new proposed KFPQD detection method is superior to the traditional KFPQD method in tracking accuracy,anti-interference and sensitivity.The tracking performance for voltage and current transitions was improved.