基于离散时间频率增益传感器的P级至M级PMU模型的实现（Matlab代码实现）

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📋📋📋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

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💥1 概述

低复杂度高精度P级到M级渐进式PMU模型，由Krzysztof Duda和Tomasz P. Zieliński设计。

基于离散时间频率增益传感器（DTFGT）和正弦斜率滤波器的P级至M级渐进式PMU模型的实现，以及在IEC/IEEE 60255-118-1标准动态调制测试中的应用。

📚2 运行结果

部分代码：

figure, 
subplot(1,2,1), hold on
    plot(fm, err_TVE_dF)    
    legend(text_legend_const,'Location','southeast')                
    xlabel('F_i_n (Hz)'), ylabel('TVE (%) for  straightforward A and \phi estimation')          
    title(text_title)
    set(gca,'YScale','log'),         
    axis tight, box on, grid on        
subplot(1,2,2), hold on
    plot(fm, err_TVE_dF_LS, '-')    
    legend(text_legend_const,'Location','southeast')                
    xlabel('F_i_n (Hz) '), ylabel('TVE (%) for LS based A and \phi estimation')          
    title(text_title)
    set(gca,'YScale','log'),         
    axis tight, box on, grid on        
figure, 
subplot(1,2,1), hold on
    plot(fm, err_Om_dF, '-')    
    legend(text_legend_const,'Location','southeast')           
    xlabel('F_i_n (Hz)'), ylabel('FE (Hz)'), title(text_title)
    set(gca,'YScale','log'),         
    axis tight, box on, grid on            
subplot(1,2,2), hold on
    plot(fm, err_Rocof_dF_LS, '-')    
    legend(text_legend_const,'Location','southeast')        
    xlabel('F_i_n (Hz)'), ylabel('RFE (Hz/s)'), title(text_title)
    set(gca,'YScale','log'),         
    axis tight, box on, grid on    
end

%###########################################################
function [Phasor, PhasorLS, Omr, ROCOFr] = PMU(x, P, N0, F0)
% PMU implementation with a cascade of rectangular filters
% and the Discrete-Time Frequency-Gain Transducer (DTFGT) 
% with the sine-shape slope filter 
% x  - sinusoidal signal x=A*cos(Om*n+p)
% P  - number of rectangular filters in the prefilter cascade
% Phasor   - estimated complex phasor reported at nominal frequency with straightforward amplitude and phase estimation
% PhasorLS - estimated complex phasor reported at nominal frequency with LS based amplitude and phase estimation 
% Omr - estimated frequency in radians reported at nominal frequency
% ROCOFr - estimated ROCOF in radians per second reported at nominal frequency
Nx = length(x);
Fs = N0*F0; %Hz
t  = (0:Nx-1)/Fs;
y  = x.*exp(-1i*2*pi*F0*t); % down-shifted sinusoidal signal x=A*cos(Om*n+p), Om=2*pi*f0/fs
%% for LS solution
ND2 =  N0/2-1;              % only one nominal period, although it could be more for longer cascade 
ND1 = -N0/2;   
%% sin frequency slope h = [1/2 0 -1/2];
w0= pi/(N0/2);
L = N0/4;
h = [1/2 zeros(1,L-1)  0  zeros(1,L-1) -1/2];  
%%
tr  = 1:N0:Nx;    % reporting times
Phasor   = zeros(length(tr), P);
PhasorLS = zeros(length(tr), P);
Omr    = zeros(length(tr), P);
ROCOFr = zeros(length(tr), P);

r0 = ones(1,N0)/N0; rp = 1;
for ind=1:P 

🎉3 参考文献

部分理论来源于网络，如有侵权请联系删除。

[1]Krzysztof Duda (2023). P2M_PMU 

🌈4 Matlab代码实现