企业网站配色,网站的信任度,seo文章,网站系统维护中#x1f4a5;#x1f4a5;#x1f49e;#x1f49e;欢迎来到本博客❤️❤️#x1f4a5;#x1f4a5; #x1f3c6;博主优势#xff1a;#x1f31e;#x1f31e;#x1f31e;博客内容尽量做到思维缜密#xff0c;逻辑清晰#xff0c;为了方便读者。 ⛳️座右铭欢迎来到本博客❤️❤️ 博主优势博客内容尽量做到思维缜密逻辑清晰为了方便读者。 ⛳️座右铭行百里者半于九十。 本文目录如下 目录 1 概述 2 运行结果 3 参考文献 4 Matlab代码实现 1 概述
稳健的电力系统状态估计器对于监测和控制应用至关重要。根据我们的经验我们发现使用投影统计的鲁棒广义最大似然GM估计器是文献中最好的方法之一。它对多个交互和符合不良数据、不良杠杆点、不良零注入以及某些类型的网络攻击具有鲁棒性。此外它的计算效率很高使其适用于在线应用程序。除了GM估计器良好的击穿点外它在高斯或其他厚尾非高斯测量噪声下具有很高的统计效率。使用SCADA测量的GM估计器的原始版本是由Mili和他的同事在1996年提出的[1]。通过在 [R2] 中使用吉文斯旋转其数值稳定性得到了增强。在[R3]中GM估计器被扩展为同时估计变压器抽头位置和系统状态。不良的零点注入也得到了解决。在[R4]中提出了GM估计器来处理创新和观测异常值以及动态状态估计中的测量损失。测试系统包括 IEEE 14 总线、30 总线和 118 总线系统。仅包括 SCADA 测量值。
由于结果图比较多本文仅展现IEEE118节点运行结果图。
2 运行结果 部分代码
zdata zconv(nbus); % Get Conventional Measurement data.. [bsh g b] line_mat_func(nbus); % Get conductance and susceptance matrix type zdata(:,2); % Type of measurement, % type 1 voltage magnitude p.u % type 2 Voltage phase angle in degree % type 3 Real power injections % type 4 Reactive power injection % type 5 Real power flow % type 6 Reactive power flow z zdata(:,3); % Measurement values Zz;% for ploting figures fbus zdata(:,4); % From bus tbus zdata(:,5); % To bus Ri diag(zdata(:,6)); % Measurement Error Covariance matrix e ones(nbus,1); % Initialize the real part of bus voltages f zeros(nbus,1);% Initialize the imaginary part of bus voltages E [f;e]; % State Vector comprising of imaginary and real part of voltage G real(ybus); B imag(ybus); ei find(type 1); % Index of voltage magnitude measurements.. fi find(type 2); % Index of voltage angle measurements.. ppi find(type 3); % Index of real power injection measurements.. qi find(type 4); % Index of reactive power injection measurements.. pf find(type 5); % Index of real power flow measurements.. qf find(type 6); % Index of reactive power flow measurements.. Vmz(ei); Thmz(fi); z(ei)Vm.*cosd(Thm); % converting voltage from polar to Cartesian z(fi)Vm.*sind(Thm); nei length(ei); % Number of Voltage measurements(real) nfi length(fi); % Number of Voltage measurements(imaginary) npi length(ppi); % Number of Real Power Injection measurements.. nqi length(qi); % Number of Reactive Power Injection measurements.. npf length(pf); % Number of Real Power Flow measurements.. nqf length(qf); % Number of Reactive Power Flow measurements.. nmneinfinpinqinpfnqf; % total number of measurements % robust parameters tol1; maxiter30;% maximal iteration for iteratively reweighted least squares (IRLS) algorithm c1.5; % for Huber-estimator bmmad_factor(nm); % correction factor to achieve unbiasness under Gaussian measurement noise %%%%%%% GM-estimator%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %% flat initialization iter1; s1; %% For the GM-estimator to be able to handle two conforming outliers located on the same bus %% the local redundancy must be large enough %% add outliers %%
3 参考文献 文章中一些内容引自网络会注明出处或引用为参考文献难免有未尽之处如有不妥请随时联系删除。 [R1] L. Mili, M. Cheniae, N. Vichare, and P. Rousseeuw, Robust state estimation based on projection statistics, IEEE Trans. Power Syst, vol. 11, no. 2, pp. 1118--1127, 1996. [R2] R. C. Pires, A. S. Costa, L. Mili, Iteratively reweighted least-squares state estimation through givens rotation, IEEE Trans. Power Syst., Vol. 14, no. 4, pp. 1499--1507, 1999. [R3] R. C. Pires, L. Mili, F. A. Becon Lemos, Constrained robust estimation of power system state variables and transformer tap positions under erroneous zero-injections, IEEE Trans. Power Syst., vol. 29, no. 3, pp. 1144--1152, May 2014. [R4] J. B. Zhao, M. Netto, L. Mili, A robust iterated extended Kalman filter for power system dynamic state estimation, IEEE Trans. Power Syst., DOI:10.1109/TPWRS.2016.2628344, in press.
4 Matlab代码实现