基于量子粒子群优化和扩展卡尔曼滤波的相量测量算法  

Phasor Measurement Algorithm Based on Quantum Particle Swarm Optimization and Extended Kalman Filter

作　　者：谢运华[1] 赵庆生[1] 郭贺宏 张学军[3] 

机构地区：[1]太原理工大学电力系统运行与控制山西省重点实验室,山西太原030024 [2]国网临汾供电公司,山西临汾041000 [3]山西大学电力工程系,山西太原030006

出　　处：《水电能源科学》2016年第5期194-198,共5页Water Resources and Power

基　　金：国家自然科学基金青年基金项目(51505317);山西省自然科学基金项目(2015011057)

摘　　要：电力系统中的信号往往含有各种噪声、谐波、间谐波的干扰,严重影响了相量测量的精度。对此,在复数扩展卡尔曼滤波(ECKF)的基础上,运用改进的量子粒子群优化(IQPSO)算法改进滤波迭代过程中的量测噪声协方差矩阵和模型噪声协方差矩阵,并测量电力信号的幅值、频率和相角。对各种非平稳正弦信号的仿真结果表明,相较修正的复数扩展卡尔曼滤波(RECKF)算法,IQPSO-ECKF算法提高了复数扩展卡尔曼滤波的相量测量精度和收敛速度。研究成果丰富了电力系统信号测量的内容。Signals in power systems often contain a variety of noise,harmonic and inter-harmonic interference,which seriously affected the accuracy of phasor measurement.Based on the extended complex Kalman filter（ECKF）,an improved quantum particle swarm optimization（IQPSO）is used to optimize measurement noise covariance matrix and model noise covariance matrix in the filtering iterative process and measure the amplitude,frequency and phase angle of electric signal.The simulation results of various nonstationary sinusoidal signals show that compared with the modified robust complex extended Kalman filtering（RECKF）algorithm,the IQPSO-ECKF can improve the accuracy and convergence rate of the phase measurement of complex extended Kalman filter.The research findings enrich the content of power system signal measurement.

关 键 词：量子粒子群优化 复数扩展卡尔曼滤波 相量测量 量测噪声协方差矩阵 模型噪声协方差矩阵 

分 类 号：TM71[电气工程—电力系统及自动化]