基于云模型及DS融合技术的风电机组运行状态评价python代码实现

# 这是一个示例 Python 脚本。 # 按 Shift+F10 执行或将其替换为您的代码。 # 按 双击 Shift 在所有地方搜索类、文件、工具窗口、操作和设置。 import pandas as pd import csv import numpy as np import math def fusion(a):# 对矩阵进行DS融合求解函数，输入多维矩阵返回一维 result = a[0] deletetmp = [] for jishu in range(len(a)):# 对a的行数进行循环 m1 = np.array(result) deletetmp.append(jishu) if jishu >= len(a)-1: return result mtmp = a mtmp = np.delete(mtmp, deletetmp, axis=0)# 删除mtmp矩阵中的第deletetmp行 m2 = mtmp[0] k = 0 for i in range(a.shape[1]): for j in range(a.shape[1]): k = k + m1[i] * m2[j] # 计算冲突因子k res = 0 for q in range(a.shape[1]): res = res + m1[q] * m2[q] k = k - res # 计算冲突因子k list1 = [] for s in range(a.shape[1]): A = m1[s] * m2[s] / (1 - k) # 利用DS规则进行证据合成 list1.append(A) list2 = [] for t in range(a.shape[1]): # 归一化处理 P = list1[t] / np.sum(list1) list2.append(P) result = np.array(list2) return result ###############下一时刻指标值预测 def zhibiaoyuce(a, b): from sklearn.linear_model import LinearRegression model = LinearRegression() #导入linear_model回归模型 a = np.array(a, dtype=float) b = np.array(b, dtype=float) model.fit(a, b) #模型训练 y_pred = model.predict(a) return y_pred ###############动态劣化度求取 def dongtailiehuadu(xmin, xmax, xp): if xp< float(xmin): liehuadu = 0 if xp > float(xmax): liehuadu = 1 if xp >= float(xmin) and xp <= float(xmax): liehuadu = (xp-float(xmin))/(float(xmax)-float(xmin)) return liehuadu ###############云模型隶属度求取 def lishudu(gdx, k): exk = [0,0.35,0.65,1] enk = [0.117,0.117,0.1,0.117] u = math.exp(-(gdx-exk[k-1])*(gdx-exk[k-1])/2/enk[k-1]/enk[k-1]) return u ###############将读取的矩阵拆分生成预测样本函数 def yangben(r): r = r.T a = [] b = [] for k in range(r.shape[0]): for s in range(20): a.append(r[k, 5 * s:(5 * s + 5)]) b.append(r[k, 5 * s + 6]) a = np.array(a) b = np.array(b) c = b.reshape(20*(k+1), 1) tmp = np.concatenate((a, c), axis=1) return tmp ###############求列最大值 def liemax(r): max_val = r[0] for val in r: if val > max_val: max_val = val return max_val ###############求列最小值 def liemin(r): min_val = r[0] for val in r: if val < min_val: min_val = val return min_val filename = './rawData954683(final)-0618.csv' with open(filename,"r") as csvfile:# 读取数据 csvreader = csv.reader(csvfile) data = [] for row in csvreader: data.append(row) data = np.array(data) R1 = data[1:200, 3:11] #发电机参数矩阵 R2 = data[1:200, 11:18] #齿轮箱参数矩阵 R3 = data[1:200, 18:22] #机舱参数矩阵 R4 = data[1:200, 22:24] #主轴承参数矩阵 R5 = data[1:200, 24:28] #变流部件参数矩阵 R6 = data[1:200, 28:31] #外界因素参数矩阵 ###################R1mass矩阵生成######################### R1xy = yangben(R1) R1x = R1xy[:, 0:4] R1y = R1xy[:, 5] y = zhibiaoyuce(R1x, R1y) y_pred = [y[19], y[39], y[59], y[79], y[99], y[119], y[139], y[159]] #得到了R1中下一时刻8各指标的预测值 gdxtmp = [] rows = 8 cols = 4 massr1 = [[0 for col in range(cols)] for row in range(rows)] massr1 = np.zeros((rows, cols)) for i in range(len(y_pred)): liemin1 = liemin(R1[:,i]) liemax1 = liemax(R1[:,i]) gdxtmp.append(dongtailiehuadu(liemin1, liemax1, y_pred[i])) #求得R1中8各指标的动态劣化度 for j in range(4): massr1[i,j] = lishudu(gdxtmp[i], j) ###################R2mass矩阵生成######################### R2xy = yangben(R2) R2x = R2xy[:, 0:4] R2y = R2xy[:, 5] y = zhibiaoyuce(R2x, R2y) y_pred2 = [y[19], y[39], y[59], y[79], y[99], y[119], y[139]] # 得到了R1中下一时刻8各指标的预测值 gdxtmp2 = [] rows = 7 cols = 4 massr2 = [[0 for col in range(cols)] for row in range(rows)] massr2 = np.zeros((rows, cols)) for i in range(len(y_pred2)): liemin1 = liemin(R2[:, i]) liemax1 = liemax(R2[:, i]) gdxtmp2.append(dongtailiehuadu(liemin1, liemax1, y_pred2[i])) # 求得R1中8各指标的动态劣化度 for j in range(4): massr2[i, j] = lishudu(gdxtmp2[i], j) ###################R3mass矩阵生成######################### R3xy = yangben(R3) R3x = R3xy[:, 0:4] R3y = R3xy[:, 5] y = zhibiaoyuce(R3x, R3y) y_pred3 = [y[19], y[39], y[59], y[79]] # 得到了R1中下一时刻8各指标的预测值 gdxtmp3 = [] rows = 4 cols = 4 massr3 = [[0 for col in range(cols)] for row in range(rows)] massr3 = np.zeros((rows, cols)) for i in range(len(y_pred3)): liemin1 = liemin(R3[:, i]) liemax1 = liemax(R3[:, i]) gdxtmp3.append(dongtailiehuadu(liemin1, liemax1, y_pred3[i])) # 求得R1中8各指标的动态劣化度 for j in range(4): massr3[i, j] = lishudu(gdxtmp3[i], j) ###################R4mass矩阵生成######################### R4xy = yangben(R4) R4x = R4xy[:, 0:4] R4y = R4xy[:, 5] y = zhibiaoyuce(R4x, R4y) y_pred4 = [y[19], y[39]] # 得到了R1中下一时刻8各指标的预测值 gdxtmp4 = [] rows = 2 cols = 4 massr4 = [[0 for col in range(cols)] for row in range(rows)] massr4 = np.zeros((rows, cols)) for i in range(len(y_pred4)): liemin1 = liemin(R4[:, i]) liemax1 = liemax(R4[:, i]) gdxtmp4.append(dongtailiehuadu(liemin1, liemax1, y_pred4[i])) # 求得R1中8各指标的动态劣化度 for j in range(4): massr4[i, j] = lishudu(gdxtmp4[i], j) ###################R5mass矩阵生成######################### R5xy = yangben(R5) R5x = R5xy[:, 0:4] R5y = R5xy[:, 5] y = zhibiaoyuce(R5x, R5y) y_pred5 = [y[19], y[39], y[59], y[79]] # 得到了R1中下一时刻8各指标的预测值 gdxtmp5 = [] rows = 4 cols = 4 massR5 = [[0 for col in range(cols)] for row in range(rows)] massR5 = np.zeros((rows, cols)) for i in range(len(y_pred5)): liemin1 = liemin(R5[:, i]) liemax1 = liemax(R5[:, i]) gdxtmp5.append(dongtailiehuadu(liemin1, liemax1, y_pred5[i])) # 求得R1中8各指标的动态劣化度 for j in range(4): massR5[i, j] = lishudu(gdxtmp5[i], j) ###################R6mass矩阵生成######################### R6xy = yangben(R6) R6x = R6xy[:, 0:4] R6y = R6xy[:, 5] y = zhibiaoyuce(R6x, R6y) y_pred6 = [y[19], y[39], y[59]] # 得到了R1中下一时刻8各指标的预测值 gdxtmp6 = [] rows = 3 cols = 4 massR6 = [[0 for col in range(cols)] for row in range(rows)] massR6 = np.zeros((rows, cols)) for i in range(len(y_pred6)): liemin1 = liemin(R6[:, i]) liemax1 = liemax(R6[:, i]) gdxtmp6.append(dongtailiehuadu(liemin1, liemax1, y_pred6[i])) # 求得R1中8各指标的动态劣化度 for j in range(4): massR6[i, j] = lishudu(gdxtmp6[i], j) ################################################### 对矩阵进行DS融合求解函数，输入多维矩阵返回一维 Rdsm = [] massr = np.vstack((massr1, massr2, massr3, massr4, massR5, massR6)) Rdsm = fusion(massr)