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1 加了算例A的收敛曲线展示。

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2 算离散结果时，重新初始化松弛变量。
3 结果对松弛变量的初衷也比较敏感。

Signed-off-by: dugg@lab-desk <dugg@lab-desk>
This commit is contained in:
dugg@lab-desk
2014-09-09 16:19:29 +08:00
parent 382763f559
commit 466c90d8a7
28 changed files with 582 additions and 125 deletions
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14
@ForThesis1/AddPDQDPGQG.m Normal file
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function this=AddPDQDPGQG( this, PD,QD,PG,QG)
%ADDPD Summary of this function goes here
% Detailed explanation goes here
if this.currentPos>this.sampleNum
error('出入的数太多了');
return;
end
this.currentPos=this.currentPos+1;
this.PDArray(:,this.currentPos)=PD;
this.QDArray(:,this.currentPos)=QD;
this.PGArray(this.currentPos)=PG;
this.QGArray(this.currentPos)=QG;
end

30
@ForThesis1/ForThesis.m Normal file
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classdef ForThesis
%UNTITLED Summary of this class goes here
% Detailed explanation goes here
properties
currentPos=0;
sampleNum=0;
LoadNum=0;
PDArray=0;
QDArray=0;
PGArray=0;
QGArray=0;
end
methods
function this=ForThesis(sampleNum,LoadNum)
this.currentPos=0;%记录当前用到的列，不要超过100列
this.sampleNum=sampleNum;
this.LoadNum=LoadNum;
this.PDArray=zeros(LoadNum,sampleNum);
this.QDArray=zeros(LoadNum,sampleNum);
this.PGArray=zeros(sampleNum,1);
this.QGArray=zeros(sampleNum,1);
end
end
end

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@ForThesis1/MaxBranchDeviation.m Normal file
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function [ output_args ] = MaxBranchDeviation( ~, Linei,Linej,Liner,Linex,Lineb,Transfori,Transforj,Transforr,Transforx,Branchi,Branchg,Branchb,Transfork0,Volt0,UAngel0,Volt,UAngel,FileName,PD0,QD0)
%% 最大支路功率偏差
% 支路功率包括线路和变压器
%% 先用加了误差的负荷功率计算潮流值
[Busnum,Balance,PQstandard,Precision,~,~,~,~,~,kmax,~ ,...
~,~,~,~,~,~,~,Pointpoweri,PG,QG,PD,QD,PVi,PVu,~,~,~,~,~,~,~]= openfile2(FileName);
PD=PD0;
QD=QD0;
%% 形成节点导纳矩阵
[~,Y,r,c,Angle] = admmatrix(Busnum,Linei,Linej,Liner,Linex,Lineb,Transfori,Transforj,Transforr,...
Transforx,Transfork0,Branchi,Branchg,Branchb);
[P0,Q0,U,Uangle] = Initial(PG,PD,PQstandard,Pointpoweri,QG,QD,Busnum); %求功率不平衡量
%disp('迭代次数i 最大不平衡量');
%% 循环体计算
for i = 0:kmax
[Jacob,PQ,U,Uangle] = jacobian(Busnum,Balance,PVi,PVu,U,Uangle,Y,Angle,P0,Q0,r,c); %形成雅克比矩阵
% disp('第一次雅克比');
m = max(abs(PQ));
m=full(m);
%fprintf(' %u %.8f \n',i,m);
if m > Precision %判断不平衡量是否满足精度要求
[Uangle,U] = solvefun(Busnum,Jacob,PQ,Uangle,U); %求解修正方程，更新电压变量
else
disp(['收敛，迭代次数为',num2str(i),'次']);
break %若满足精度要求，则计算收敛
end
end
Volt0=U;
UAngel0=Uangle;
[dispLineloss0 dispTransloss0]=Lineloss(Linei,Linej,Liner,Linex,Lineb,Transfori,Transforj,Transforr,Transforx,Branchi,Branchg,Branchb,Transfork0,Volt0,UAngel0);%测量值
[dispLineloss dispTransloss]=Lineloss(Linei,Linej,Liner,Linex,Lineb,Transfori,Transforj,Transforr,Transforx,Branchi,Branchg,Branchb,Transfork0,Volt,UAngel);%估计值
t1=(dispLineloss0(:,3) - dispLineloss(:,3))./dispLineloss0(:,3);
t2=(dispTransloss0(:,3) - dispTransloss(:,3))./dispTransloss0(:,3);
t11=dispLineloss0(:,3)>1e-5;% 太小的值不计算
t22=dispTransloss0(:,3)>1e-5;% 太小的值不计算
t3=abs([t1(t11);t2(t22)]);
output_args=max(t3(t3~=Inf));
end

11
@ForThesis1/MaxDeviation.m Normal file
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function [output_arg]=MaxDeviation(this,PG0,QG0,PD0,QD0)
PD0Array=repmat(PD0,1,this.sampleNum);
QD0Array=repmat(QD0,1,this.sampleNum);
PDMaxDev=max(abs( (this.PDArray-PD0Array)./PD0Array ),[],2);
QDMaxDev=max(abs( (this.QDArray-QD0Array)./QD0Array ),[],2);
PG0Array=repmat(PG0,this.sampleNum,1);
QG0Array=repmat(QG0,this.sampleNum,1);
PGMaxDev=max(abs( (PG0Array-this.PGArray)./PG0Array ));
QGMaxDev=max( abs( (QG0Array-this.QGArray)./QG0Array ) );
output_arg=max([PDMaxDev;QDMaxDev;PGMaxDev;QGMaxDev;]);
end

7
@ForThesis1/MeanPD.m Normal file
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function [ output_args ] = MeanPD(this)
%MEANPD Summary of this function goes here
% Detailed explanation goes here
output_args=sum(this.PDArray,2)/this.sampleNum;
end

7
@ForThesis1/MeanPG.m Normal file
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function [ output_args ] = MeanPG( this )
%MEANPG Summary of this function goes here
% Detailed explanation goes here
output_args=sum(this.PGArray,1)/this.sampleNum;
end

7
@ForThesis1/MeanQD.m Normal file
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function [ output_args ] = MeanQD(this)
%MEANQD Summary of this function goes here
% Detailed explanation goes here
output_args=sum(this.QDArray,2)/this.sampleNum;
end

7
@ForThesis1/MeanQG.m Normal file
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function [ output_args ] = MeanQG( this )
%MEAQG Summary of this function goes here
% Detailed explanation goes here
output_args=sum(this.QGArray,1)/this.sampleNum;
end

12
@ForThesis1/PercentOfPass.m Normal file
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function [ output_args ] = PercentOfPass( this, PG0,QG0,PD0,QD0 )
%% 估计值合格率
PD=this.PDArray(:,end);%用最后一列
QD=this.QDArray(:,end);%用最后一列
t1=PD-PD0;
t2=QD-QD0;
t3=abs(t1)/3*0.05;
t4=abs(t2)/3*0.05;
output_args=sum(t3<=1)+sum(t4<=1);
end

13
@ForThesis1/SqareDeviation.m Normal file
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function [ output_args ] = SqareDeviation( this, PG0,QG0,PD0,QD0)
%% 其实就是负荷系数不为0的目标函数
PD=this.PDArray(:,end);%用最后一列
QD=this.QDArray(:,end);%用最后一列
t1=PD-PD0;
t2=t1./0.05;
t3=QD-QD0;
t4=t3./0.05;
output_args=sqrt(sum(t2.^2)+sum(t4.^2));
end

19
@ForThesis1/StatBranchDeviation.m Normal file
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function [ output_args ] = StatBranchDeviation( ~, Linei,Linej,Liner,Linex,Lineb,Transfori,Transforj,Transforr,Transforx,Branchi,Branchg,Branchb,Transfork0,Volt0,UAngel0,Volt,UAngel )
%STATBRANCHDEVIATION Summary of this function goes here
% Detailed explanation goes here
%% 支路功率统计偏差 L2 范数
% 支路功率包括线路和变压器
%%
[dispLineloss0 dispTransloss0]=Lineloss(Linei,Linej,Liner,Linex,Lineb,Transfori,Transforj,Transforr,Transforx,Branchi,Branchg,Branchb,Transfork0,Volt0,UAngel0);
[dispLineloss dispTransloss]=Lineloss(Linei,Linej,Liner,Linex,Lineb,Transfori,Transforj,Transforr,Transforx,Branchi,Branchg,Branchb,Transfork0,Volt,UAngel);
t1=(dispLineloss0(:,3) - dispLineloss(:,3))./dispLineloss0(:,3);
t2=(dispTransloss0(:,3) - dispTransloss(:,3))./dispTransloss0(:,3);
t11=dispLineloss0(:,3)>1e-5;% 太小的值不计算
t22=dispTransloss0(:,3)>1e-5;% 太小的值不计算
t3=[t1(t11);t2(t22)];
t4=t3.^2;
t5=sum(t4).^.5;
output_args=t5;
end

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@ForThesis1/StatDeviation.m Normal file
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function [ output_args ] = StatDeviation( this,PG0,QG0,PD0,QD0 )%ͳ¼ÆÎó²î
%STATDEVIATION Summary of this function goes here
% Detailed explanation goes here
PD0Array=repmat(PD0,1,this.sampleNum);
QD0Array=repmat(QD0,1,this.sampleNum);
PDDev=(this.PDArray-PD0Array)/0.05;
QDDev=(this.QDArray-QD0Array)/0.05;
% PG0Array=repmat(PG0,this.sampleNum,1);
% QG0Array=repmat(QG0,this.sampleNum,1);
% PGDev=(PG0Array-this.PGArray)/0.01;
% QGDev=(QG0Array-this.QGArray)/0.01;
wholeMat=[PDDev;QDDev;];
t1=wholeMat.^2;
t2=sum(t1,1);
t3=t2/size(t1,1);
t4=t3.^.5;
output_args=sum(t4)/length(t4);
end