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
Z=z;% 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..
Vm=z(ei);
Thm=z(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..
nm=nei+nfi+npi+nqi+npf+nqf; % total number of measurements
% robust parameters
tol=1;
maxiter=30;% maximal iteration for iteratively reweighted least squares (IRLS) algorithm
c=1.5; % for Huber-estimator
bm=mad_factor(nm); % correction factor to achieve unbiasness under Gaussian measurement noise
%%%%%%% GM-estimator%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%% flat initialization
iter=1;
s=1;
%% 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.
## [🌈](https://mp.weixin.qq.com/mp/appmsgalbum?__biz=Mzk0MDMzNzYwOA==&action=getalbum&album_id=2591810113208958977#wechat_redirect "🌈")****4 Matlab代码实现****
