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⛄ 内容介绍
风电数据存在复杂的非线性特征,传统的预测模型精度不足以满足现行的预测要求.为此本文提出了基于变分模态分解的麻雀优化-核极限学习机的预测模型,并对风电数据进行预测.首先,采用VMD程序把边坡位移序列分解为一系列有限带宽的子序列;其次,对各子序列分别采用相空间重构,并利用核极限学习机进行预测,采用鸟群算法优化相空间重构的嵌入维度、KELM中惩罚系数和核参数三个数值,以取得最优预测模型;最后,将各个子序列预测值叠加,得到边坡位移的最终预测值.结果表明,与KELM模型相比,基于VMD模型的SSA-KELM模型预测精度更高,为风电数据的预测提供一种更有效的方法.
⛄ 部分代码
function [Convergence_curve,bestX]=SSA(N, dim, ub, lb,M,hiddennum_best, inputn, outputn, output_train, inputn_test ,outputps, output_test)
P_percent = 0.2; % 发现者的种群规模占总种群规模的百分比
pNum = round(N*P_percent); % 发现者数量20%
SD = pNum/2; % 警戒者数量10%
ST = 0.8; % 安全阈值
% 初始化
X = initialization(N, dim, ub, lb);
for i = 1:N
% X(i, :) = lb + (ub - lb) .* rand(1, dim);
fitness1(i) = fitness(X(i, :),hiddennum_best, inputn, outputn, output_train, inputn_test ,outputps, output_test);
end
pFit = fitness1;
pX = X; % 与pFit相对应的个体最佳位置
[fMin, bestI] = min(fitness1); % fMin表示全局最优解
bestX = X(bestI, :); % bestX表示全局最优位置
%% 开始进化
Convergence_curve = ones(M,1); % 初始化每次迭代得到的最佳的适应度
%% 迭代寻优
for t = 1 : M
[~, sortIndex] = sort(pFit); % 排序
[fmax, B] = max(pFit);
worst = X(B, :);
%% 发现者位置更新
r2 = rand(1);
if r2 < ST
for i = 1:pNum % Equation (3)
r1 = rand(1);
X(sortIndex(i), :) = pX(sortIndex(i), :)*exp(-(i)/(r1*M));
X(sortIndex(i), :) = Bounds(X(sortIndex(i), :), lb, ub);
fitness1(sortIndex(i)) = fitness(X(sortIndex(i), :),hiddennum_best, inputn, outputn, output_train, inputn_test ,outputps, output_test);
end
else
for i = 1:pNum
X(sortIndex(i), :) = pX(sortIndex(i), :)+randn(1)*ones(1, dim);
X(sortIndex(i), :) = Bounds(X(sortIndex(i), :), lb, ub);
fitness1(sortIndex(i)) = fitness(X(sortIndex(i), :),hiddennum_best, inputn, outputn, output_train, inputn_test ,outputps, output_test);
end
end
[~, bestII] = min(fitness1);
bestXX = X(bestII, :);
%% 跟随者位置更新
for i = (pNum+1):N % Equation (4)
A = floor(rand(1, dim)*2)*2-1;
if i > N/2
X(sortIndex(i), :) = randn(1)*exp((worst-pX(sortIndex(i), :))/(i)^2);
else
X(sortIndex(i), :) = bestXX+(abs((pX(sortIndex(i), :)-bestXX)))*(A'*(A*A')^(-1))*ones(1, dim);
end
X(sortIndex(i), :) = Bounds(X(sortIndex(i), :), lb, ub);
fitness1(sortIndex(i)) = fitness(X(sortIndex(i), :),hiddennum_best, inputn, outputn, output_train, inputn_test ,outputps, output_test);
end
%% 警戒者位置更新
c = randperm(numel(sortIndex));
b = sortIndex(c(1:SD));
for j = 1:length(b) % Equation (5)
if pFit(sortIndex(b(j))) > fMin
X(sortIndex(b(j)), :) = bestX+(randn(1, dim)).*(abs((pX(sortIndex(b(j)), :) -bestX)));
else
X(sortIndex(b(j)), :) = pX(sortIndex(b(j)), :)+(2*rand(1)-1)*(abs(pX(sortIndex(b(j)), :)-worst))/(pFit(sortIndex(b(j)))-fmax+1e-50);
end
X(sortIndex(b(j)), :) = Bounds(X(sortIndex(b(j)), :), lb, ub);
fitness1(sortIndex(b(j))) = fitness(X(sortIndex(b(j)), :),hiddennum_best, inputn, outputn, output_train, inputn_test ,outputps, output_test);
end
for i = 1:N
% 更新个体最优
if fitness1(i) < pFit(i)
pFit(i) = fitness1(i);
pX(i, :) = X(i, :);
end
% 更新全局最优
if pFit(i) < fMin
fMin = pFit(i);
bestX = pX(i, :);
end
end
Convergence_curve(t) = fMin;
disp(['SSA: At iteration ', num2str(t), ' ,the best fitness is ', num2str(fMin)]);
end
⛄ 运行结果
⛄ 参考文献
[1]郑小霞, 蒋海生, 刘静,等. 基于变分模态分解与灰狼算法优化极限学习机的滚动轴承故障诊断[J]. 轴承, 2021(9):6.
