State Space Model of the LLC, Intuitive Approach using Time Domain Analysis скачать в хорошем качестве

State Space Model of the LLC, Intuitive Approach using Time Domain Analysis

Script: import numpy as np import matplotlib.pyplot as plt from scipy.signal import StateSpace, step Define LLC parameters f_r = 100e3 V_dc = 400 V_o = 50 P_o = 2000 R_L = V_o**2/P_o L_r = 5.16e-6 L_M = 25.8e-6 C_r = 490e-9 turn_ratio = 4 C_out = 2000e-6 Define system x_label = ["i_r", "v_cr", "i_M", "v_o"] A = np.array([ [0, -1 / L_r, 0, -turn_ratio / L_r], [1 / C_r, 0, 0, 0], [0, 0, 0, turn_ratio / L_M], [turn_ratio / C_out, 0, -turn_ratio / C_out, -1 / (C_out * R_L)], ]) B = np.array([ [1 / L_r], [0], [0], [0], ]) Initial conditions and time vector x0 = np.array([0,100,0, V_o]) # Initial state u = V_dc T = np.linspace(0, 1/f_r/2, 1000) # Time vector Create state-space system system = StateSpace(A, B * u, np.eye(A.shape[0]), np.zeros((A.shape[0], B.shape[1]))) Compute step response T, y = step(system, T=T, X0=x0) Plot step response plt.figure(figsize=(10, 6)) for i in range(0, y.shape[1], 2): plt.plot(T, y[:, i], label=x_label[i]) plt.xlabel("Time (s)") plt.ylabel("Current (A)") plt.legend() plt.grid() plt.show() plt.figure(figsize=(10, 6)) for i in range(1, y.shape[1], 2): plt.plot(T, y[:, i], label=x_label[i]) plt.xlabel("Time (s)") plt.ylabel("Voltage (V)") plt.legend() plt.grid() plt.show()