基于特征子空间分解的高精度实时电网频率测量方法研究

项目名称： 基于特征子空间分解的高精度实时电网频率测量方法研究

项目编号： No.51207160

项目类型： 青年科学基金项目

立项/批准年度： 2013

项目学科： 电气科学与工程学科

项目作者： 薛蕙

作者单位： 中国农业大学

项目金额： 25万元

中文摘要： 随着智能电网和微电网技术的快速发展，准确、实时的电网频率测量对电网安全、可靠和高效运行的意义越来越重要。本项目针对电气观测信号波形畸变及动态时变的特点，把特征子空间分解方法引入电网频率测量，利用特征子空间分解方法高分辨率特性消除观测信号中各种波形畸变和动态时变干扰对频率测量的影响，提高电网频率测量的精度；通过子空间迭代和更新算法减小特征子空间分解的计算复杂度，实现特征子空间的实时跟踪和估计，提高频率测量算法的响应速度；研究观测信号模型、采样数据窗长度及信号阶次估计方法对频率测量精度和算法响应速度的影响，通过时变观测模型的正确建立、数据窗长度的合理选取、信号阶次的准确估计等方法进一步提高电网频率测量精度和算法响应速度。在上述工作的基础上，建立基于特征子空间分解的高精度实时电网频率测量方法，并通过仿真实验和理论分析的手段评价和比较所建立方法的性能，以满足电网频率测量的准确性和实时性要求。

中文关键词： 电网；频率测量；信号子空间；噪声子空间；特征分解

英文摘要： With the rapid development of smart grid and microgrid, accurate and real time power grid frequency measurement is increasingly important to safe, reliable and efficient performance of power grid. Fully considering the waveform distortions and time-varying characters of power grid electrical signal, this project introduces eigen-subspace decomposition algorithm into power grid frequency measurement. The high frequency resolution character of eigen-subspace decompostion algorithm is used to eliminate frequency measurement errors caused by the interferences of waveform distortions and time-varying disturbances in the electrical measurement signal, thus improving power grid frequency measurement precision. Subspace iteration and update techniques are used to reduce the computation complexity and implement real time tracking and estimation of eigen-subspace, thus improving the response speed of power grid frequency measurement method. The factors, such as measurement signal model, sample data window length and estimation methods for measurement signal order, which affect the frequency measurement precision and response speed are studied, and frequency measurement precision and response speed are improved by the accurate construction of time-varying measurement model, approporiate setting of data window length and p

英文关键词： Power grid；frequency measurement；signal subspace；noise subspace；eigen decomposition