calcul flux linéaires

opti_continu.py

@@ -0,0 +1,53 @@
+import numpy as np
+import matplotlib.pyplot as plt
+import PyQt5 as qt
+
+# Vn = 4e5 # En V
+
+def make_Y(n):
+    Y = np.zeros((n, n))
+    return Y
+
+def connect_Y(x, y, Ys, Yp, Y):
+    Y[x, y] = -Ys
+    Y[y, x] = -Ys
+    Y[x, x] += Ys + Yp
+    Y[y, y] += Ys + Yp
+
+def spec(n, Y, Vn):
+    S = np.zeros((n, n))
+    for i in range(n):
+        for k in range(n):
+            if i == k:
+                S[i, k] = n
+            else:
+                S[i, k] = -1
+            S[i, k] *= Vn**2 * Y[i, k]
+    return S
+
+def delta_select(i, S):
+    for k in range(len(S)):
+        S[i, k] = 0
+    
+
+Y = make_Y(4)
+connect_Y(2, 3, 0.1, 0, Y)
+connect_Y(1, 3, 0.15, 0, Y)
+connect_Y(2, 1, 0.05, 0, Y)
+connect_Y(2, 0, 0.05, 0, Y)
+connect_Y(3, 0, 0.05, 0, Y)
+print("Admittance matrix :")
+print(Y)
+S = spec(4, Y, 2e5)
+print("System matrix :")
+print(S)
+invS = np.linalg.inv(S)
+print(invS)
+P = np.array([1000, -500, -250, -250])
+P = P * 1e6
+print("Power input :")
+print(P)
+delta = np.dot(invS, P)
+print("Delta (rad) :")
+print(delta)
+print(delta * 180 / 3.1415)

opti_prod.py

@@ -79,6 +79,7 @@ def minimize(f, x0, h=1e-4, step=1e-1, tol=1e-8, N=1e4, echo=False):
     return x
 
 def custom_minimize(f, x0, echo=False):
+    # Fonction avec plusieurs étapes successives de résolution, pour gagner en précision et éviter de diverger
     res_app = minimize(f, x0, step=5e-1)
     if echo:
         print(res_app)