Update PhyRes-LSTM model file

Update PhyRes-LSTM model file

model/ResNetBlock.py

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+import torch
+import torch.nn as nn
+
+# 定义ResNet块
+class ResNetBlock(nn.Module):
+    def __init__(self, in_channels, out_channels):
+        super(ResNetBlock, self).__init__()
+        self.conv1 = nn.Conv1d(in_channels, out_channels, kernel_size=3, padding=1)
+        self.bn1 = nn.BatchNorm1d(out_channels)
+        self.relu = nn.ReLU()
+        self.conv2 = nn.Conv1d(out_channels, out_channels, kernel_size=3, padding=1)
+        self.bn2 = nn.BatchNorm1d(out_channels)
+        self.shortcut = nn.Conv1d(in_channels, out_channels, kernel_size=1)
+
+    def forward(self, x):
+        shortcut = self.shortcut(x)
+        x = self.conv1(x)
+        x = self.bn1(x)
+        x = self.relu(x)
+        x = self.conv2(x)
+        x = self.bn2(x)
+        x += shortcut
+        x = self.relu(x)
+        return x
+
+# 定义模型
+class ResNetLSTMModel(nn.Module):
+    def __init__(self, input_size, resnet_channels, lstm_hidden_size, output_size):
+        super(ResNetLSTMModel, self).__init__()
+        self.resnet_x1 = ResNetBlock(input_size, resnet_channels)
+        self.resnet_x2 = ResNetBlock(input_size, resnet_channels)
+        self.lstm_x1 = nn.LSTM(resnet_channels, lstm_hidden_size, batch_first=True)
+        self.lstm_x2 = nn.LSTM(resnet_channels, lstm_hidden_size, batch_first=True)
+        self.fc_lstm = nn.Linear(lstm_hidden_size, 2)  # LSTM的输出连接到全连接层，输出2个值
+        self.fc_x1_y1 = nn.Linear(3, 1)  # x1输出和y1通过全连接层
+        self.fc_x2_y2 = nn.Linear(3, 1)  # x2输出和y2通过全连接层
+
+    def forward(self, x1, x2, y1, y2):
+        x1 = self.resnet_x1(x1)
+        x2 = self.resnet_x2(x2)
+        x1 = x1.permute(0, 2, 1)  # 调整输入形状为 (batch_size, sequence_length, channels)
+        x2 = x2.permute(0, 2, 1)  # 调整输入形状为 (batch_size, sequence_length, channels)
+        x1, _ = self.lstm_x1(x1)
+        x2, _ = self.lstm_x2(x2)
+        x1 = x1[:, -1, :]  # 取最后一个时间步的输出
+        x2 = x2[:, -1, :]  # 取最后一个时间步的输出
+
+        lstm_output_x1 = self.fc_lstm(x1)
+        lstm_output_x2 = self.fc_lstm(x2)
+
+        x1_pred_input = torch.cat((lstm_output_x1, y1), dim=1)
+        x2_pred_input = torch.cat((lstm_output_x2, y2), dim=1)
+
+        x1_pred = self.fc_x1_y1(x1_pred_input)
+        x2_pred = self.fc_x2_y2(x2_pred_input)
+
+        return torch.cat((x1_pred, x2_pred), dim=1)

model/physics_driven_block.py

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+import numpy as np
+import matplotlib.pyplot as plt
+def Physics_driven_block(Vi, R_load, D1, D2, D_phi):
+    # Define parameters
+    Ls = 80e-6  # Transformer leakage inductor
+    C2 = 100e-6  # DC Output Link Capacitance
+    R_load = 15  # Load resistance
+    R_T = 0.085  # AC Inductor Resistance
+    Vi = 100  # DC Input Voltage
+    n = 1  # Transformer Turns Ratio
+    f = 20000  # Switching Frequency
+    T = 1 / f  # Period
+    T_hs = T / 2  # Half period
+
+    D1 = 0.2212  # Duty cycle for primary side
+    D2 = 0.2212  # Duty cycle for secondary side
+    D_phi = 0.3957  # Phase shift between primary and secondary
+
+    # Define time step and simulation time
+    dt = 1e-7
+    t_end = 0.5  # Simulation time
+    t = np.arange(0, t_end, dt)  # Time vector
+
+    # Initialize primary side bridge voltage Vab
+    Vab = np.zeros_like(t)
+    # Initialize secondary side bridge voltage Vcd
+    Vcd = np.zeros_like(t)
+
+    # Initialize v_C2 and i_L
+    v_C2 = np.zeros_like(t)
+    i_L = np.zeros_like(t)
+
+    # Record primary side bridge voltage Vab waveform
+    for i in range(len(t)):
+        t_mod = t[i] % T  # Get the current time point within the period
+        if D1 * T_hs <= t_mod < T_hs:
+            Vab[i] = 1
+        elif (1 + D1) * T_hs <= t_mod < T:
+            Vab[i] = -1
+
+        if (D1 + D_phi) * T_hs <= t_mod < (D1 + D_phi + 1 - D2) * T_hs:
+            Vcd[i] = 1
+        elif (t_mod < (D1 + D_phi - D2) * T_hs or ((1 + D1 + D_phi) * T_hs <= t_mod < T)):
+            Vcd[i] = -1
+
+        # 4th order Runge-Kutta method for v_C2 and i_L
+        if i > 1:
+            k1_vC2 = (n * Vcd[i - 1] * i_L[i - 1] - v_C2[i - 1] / R_load) / C2
+            k1_iL = (Vab[i - 1] * Vi - n * Vcd[i - 1] * v_C2[i - 1] - R_T * i_L[i - 1]) / Ls
+
+            k2_vC2 = (n * Vcd[i - 1] * (i_L[i - 1] + k1_iL * dt / 2) - (v_C2[i - 1] + k1_vC2 * dt / 2) / R_load) / C2
+            k2_iL = (Vab[i - 1] * Vi - n * Vcd[i - 1] * (v_C2[i - 1] + k1_vC2 * dt / 2) - R_T * (
+                        i_L[i - 1] + k1_iL * dt / 2)) / Ls
+
+            k3_vC2 = (n * Vcd[i - 1] * (i_L[i - 1] + k2_iL * dt / 2) - (v_C2[i - 1] + k2_vC2 * dt / 2) / R_load) / C2
+            k3_iL = (Vab[i - 1] * Vi - n * Vcd[i - 1] * (v_C2[i - 1] + k2_vC2 * dt / 2) - R_T * (
+                        i_L[i - 1] + k2_iL * dt / 2)) / Ls
+
+            k4_vC2 = (n * Vcd[i - 1] * (i_L[i - 1] + k3_iL * dt) - (v_C2[i - 1] + k3_vC2 * dt) / R_load) / C2
+            k4_iL = (Vab[i - 1] * Vi - n * Vcd[i - 1] * (v_C2[i - 1] + k3_vC2 * dt) - R_T * (
+                        i_L[i - 1] + k3_iL * dt)) / Ls
+
+            v_C2[i] = v_C2[i - 1] + (k1_vC2 + 2 * k2_vC2 + 2 * k3_vC2 + k4_vC2) * dt / 6
+            i_L[i] = i_L[i - 1] + (k1_iL + 2 * k2_iL + 2 * k3_iL + k4_iL) * dt / 6
+
+    tp = np.arange(1, len(t) // 2 + 1)
+
+    # Record only waveform change points
+    record_t = []
+    record_v_C2 = []
+    record_i_L = []
+
+    # Record initial values
+    record_t.append(t[0])
+    record_v_C2.append(v_C2[0])
+    record_i_L.append(i_L[0])
+
+    # Record the points where Vab and Vcd change
+    for i in tp[1:]:
+        if Vab[i] != Vab[i - 1] or Vcd[i] != Vcd[i - 1]:
+            record_t.append(t[i])
+            record_v_C2.append(v_C2[i])
+            record_i_L.append(i_L[i])
+
+    record_t = np.array(record_t)
+    record_v_C2 = np.array(record_v_C2)
+    record_i_L = np.array(record_i_L)
+
+    # 绘制record_t, record_v_C2, record_i_L
+    plt.figure()
+    plt.plot(record_t, record_v_C2, 'b', label='v_C2')
+    plt.plot(record_t, record_i_L, 'r', label='i_L')
+    plt.xlabel('time')
+    plt.ylabel('Amplitude')
+    plt.legend()
+    plt.title('Recorded Waveform Data')
+    plt.show()
+
+    return record_t, record_v_C2, record_i_L