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data accompany the paper: + S. Zhao, Y. Peng, Y. Zhang, and H. Wang, "Parameter Estimation of Power Electronic + Converters with Physics-informed Machine Learning", IEEE Trans. Power Electronics, 2022 + + * The Tensorflow verion is 1.15.0. The code is developed based on the github repository + https://github.com/maziarraissi/PINNs, where more useful information can be found therein. +================================================================================================== +""" + +import sys +import tensorflow as tf +import numpy as np +import time +import scipy.io + +np.random.seed(1234) +tf.set_random_seed(1234) + + +class PhysicsInformedNN: + # Initialize the class + def __init__(self, x0, u0, x1, u1, layers, dt, lb, ub, q, splitIdx1, splitIdx2, splitIdx3, otherParams): + + self.lb = lb + self.ub = ub + + self.x0 = x0 + self.x1 = x1 + + self.u0 = u0 + self.u1 = u1 + + self.layers = layers + self.dt = dt + self.q = max(q, 1) + + self.splitIdx1 = splitIdx1 + self.splitIdx2 = splitIdx2 + self.splitIdx3 = splitIdx3 + + # Initialize NN + self.weights, self.biases = self.initialize_NN(layers) + self.otherParams = otherParams + + self.L = tf.Variable([2], dtype=tf.float32) + self.RL = tf.Variable([0.039], dtype=tf.float32) + self.C = tf.Variable([0.412], dtype=tf.float32) + self.RC = tf.Variable([1.59], dtype=tf.float32) + self.Rdson = tf.Variable([1.22], dtype=tf.float32) + self.Rload1 = tf.Variable([1.22], dtype=tf.float32) + self.Rload2 = tf.Variable([1.22], dtype=tf.float32) + self.Rload3 = tf.Variable([1.22], dtype=tf.float32) + self.vIn = tf.Variable([0.87], dtype=tf.float32) + self.vF = tf.Variable([0.1], dtype=tf.float32) + + tmp = np.float32(np.loadtxt( + 'Butcher_tableau/Butcher_IRK%d.txt' % (q), + ndmin=2)) + weights = np.reshape(tmp[0:q ** 2 + q], (q + 1, q)) + self.IRK_alpha = weights[0:-1, :] + self.IRK_beta = weights[-1:, :] + self.IRK_times = tmp[q ** 2 + q:] + + # tf placeholders and graph + self.sess = tf.Session(config=tf.ConfigProto(allow_soft_placement=True, + log_device_placement=True)) + + self.x0_tf = tf.placeholder(tf.float32, shape=(None, self.x0.shape[1])) + self.x1_tf = tf.placeholder(tf.float32, shape=(None, self.x1.shape[1])) + self.u0_tf = tf.placeholder(tf.float32, shape=(None, self.u0.shape[1])) + self.u1_tf = tf.placeholder(tf.float32, shape=(None, self.u1.shape[1])) + + self.U0_pred, self.V0_pred = self.net_U0(self.x0_tf) # 2*N0 x q + self.U1_pred, self.V1_pred = self.net_U1(self.x1_tf) # 2*N1 x q + + self.loss = tf.reduce_sum(tf.square(self.u0_tf[:, 1:2] - self.V0_pred)) + \ + tf.reduce_sum(tf.square(self.u1_tf[:, 1:2] - self.V1_pred)) + \ + tf.reduce_sum(tf.square(self.u0_tf[:, 0:1] - self.U0_pred)) + \ + tf.reduce_sum(tf.square(self.u1_tf[:, 0:1] - self.U1_pred)) # + \ + + self.optimizer = tf.contrib.opt.ScipyOptimizerInterface(self.loss, + method='L-BFGS-B', + options={'iprint': 0, + 'maxiter': otherParams.LBFGSEpoch, + 'maxfun': otherParams.LBFGSEpoch, + 'maxcor': 50, + 'maxls': 100, + 'ftol': 1.0 * np.finfo(float).eps}) + + self.optimizer_Adam = tf.train.AdamOptimizer() + self.train_op_Adam = self.optimizer_Adam.minimize(self.loss) + + init = tf.global_variables_initializer() + self.sess.run(init) + + def initialize_NN(self, layers): # initialize a fully-connected neural network + weights = [] + biases = [] + num_layers = len(layers) + for l in range(0, num_layers - 1): + W = self.xavier_init(size=[layers[l], layers[l + 1]]) + b = tf.Variable(tf.zeros([1, layers[l + 1]], dtype=tf.float32), dtype=tf.float32) + weights.append(W) + biases.append(b) + return weights, biases + + def xavier_init(self, size): + in_dim = size[0] + out_dim = size[1] + xavier_stddev = np.sqrt(2 / (in_dim + out_dim)) + return tf.Variable(tf.truncated_normal([in_dim, out_dim], stddev=xavier_stddev), dtype=tf.float32) + + def neural_net(self, X, weights, biases): + num_layers = len(weights) + 1 + + H = 2.0 * (X - self.lb) / (self.ub - self.lb) - 1.0 + for l in range(0, num_layers - 2): + W = weights[l] + b = biases[l] + H = tf.tanh(tf.add(tf.matmul(H, W), b)) + W = weights[-1] + b = biases[-1] + Y = tf.add(tf.matmul(H, W), b) + return Y + + def net_U0(self, x): # establish the backward relationship eq. (4) + + L = tf.exp(self.L) * 1e-4 + RL = tf.exp(self.RL) * 1e-1 + C = tf.exp(self.C) * 1e-4 + RC = tf.exp(self.RC) * 1e-1 + Rdson = tf.exp(self.Rdson) * 1e-1 + Rload1 = tf.exp(self.Rload1) + Rload2 = tf.exp(self.Rload2) + Rload3 = tf.exp(self.Rload3) + vIn = tf.exp(self.vIn) * 1e1 + vF = tf.exp(self.vF) + + y = x[:, 2:3] + yOff = x[:, 3:4] + dt = x[:, 4:5] + + Rload = tf.concat((tf.ones((self.splitIdx1 * 2, 1)) * Rload1, tf.ones((self.splitIdx2 * 2, 1)) * Rload2, + tf.ones((self.splitIdx3 * 2, 1)) * Rload3), 0) + + current_and_voltage = self.neural_net(x, self.weights, self.biases) + u = current_and_voltage[:, 0:self.q] + v = current_and_voltage[:, self.q:self.q * 2] + + F_u = -((y * (RL + Rdson)) * u + (yOff * (RL)) * u + v - y * vIn + yOff * vF) / L # inductor current model + U0 = u - dt * tf.matmul(F_u, self.IRK_alpha.T) + F_v = (C * RC * Rload * (F_u) + Rload * u - v) / (C * (RC + Rload)) # output voltage model + V0 = v - dt * tf.matmul(F_v, self.IRK_alpha.T) + + return U0, V0 + + def net_U1(self, x): # establish the forward relationship eq. (7) + + L = tf.exp(self.L) * 1e-4 + RL = tf.exp(self.RL) * 1e-1 + C = tf.exp(self.C) * 1e-4 + RC = tf.exp(self.RC) * 1e-1 + Rdson = tf.exp(self.Rdson) * 1e-1 + Rload1 = tf.exp(self.Rload1) + Rload2 = tf.exp(self.Rload2) + Rload3 = tf.exp(self.Rload3) + vIn = tf.exp(self.vIn) * 1e1 + vF = tf.exp(self.vF) + + y = x[:, 2:3] + yOff = x[:, 3:4] + dt = x[:, 4:5] + + Rload = tf.concat((tf.ones((self.splitIdx1 * 2, 1)) * Rload1, tf.ones((self.splitIdx2 * 2, 1)) * Rload2, + tf.ones((self.splitIdx3 * 2, 1)) * Rload3), 0) + + current_and_voltage = self.neural_net(x, self.weights, self.biases) + u = current_and_voltage[:, 0:self.q] + v = current_and_voltage[:, self.q:self.q * 2] + + F_u = -((y * (RL + Rdson)) * u + (yOff * (RL)) * u + v - y * vIn + yOff * vF) / L # inductor current model + U1 = u + dt * tf.matmul(F_u, (self.IRK_beta - self.IRK_alpha).T) + F_v = (C * RC * Rload * (F_u) + Rload * u - v) / (C * (RC + Rload)) # output voltage model + V1 = v + dt * tf.matmul(F_v, (self.IRK_beta - self.IRK_alpha).T) + + return U1, V1 + + def callback(self, loss, L, RL, C, RC, Rdson, Rload1, Rload2, Rload3, vIn, vF): + L_value = np.abs(np.exp(L) / self.otherParams.nL * 100 - 100) + RL_value = np.abs(np.exp(RL) / self.otherParams.nRL * 100 - 100) + C_value = np.abs(np.exp(C) / self.otherParams.nC * 100 - 100) + RC_value = np.abs(np.exp(RC) / self.otherParams.nRC * 100 - 100) + Rdson_value = np.abs(np.exp(Rdson) / self.otherParams.nRdson * 100 - 100) + Rload1_value = np.abs(np.exp(Rload1) / self.otherParams.nRload1 * 100 - 100) + Rload2_value = np.abs(np.exp(Rload2) / self.otherParams.nRload2 * 100 - 100) + Rload3_value = np.abs(np.exp(Rload3) / self.otherParams.nRload3 * 100 - 100) + vIn_value = np.abs(np.exp(vIn) / self.otherParams.nVin * 100 - 100) + vF_value = np.abs(np.exp(vF) / self.otherParams.nVF * 100 - 100) + meanError = np.mean([L_value, RL_value, C_value, + RC_value, Rdson_value, Rload1_value, + Rload2_value, Rload3_value, + vIn_value, vF_value]) + + print( + 'Loss: %.3e, mean: %.1f, L: %.1f, RL: %.1f, C: %.1f, RC: %.1f, Rdson: %.1f, Rload1: %.1f, Rload2: %.1f, Rload3: %.1f, vIn: %.1f, vF: %.1f' % ( + loss, meanError, L_value, RL_value, + C_value, RC_value, + Rdson_value, + Rload1_value, Rload2_value, + Rload3_value, + vIn_value, vF_value)) + + def train(self, nIter): + tf_dict = {self.x0_tf: self.x0, self.u0_tf: self.u0, + self.x1_tf: self.x1, self.u1_tf: self.u1} + + start_time = time.time() + for it in range(nIter): + self.sess.run(self.train_op_Adam, tf_dict) + # Print training info + if it % 10000 == 0: + elapsed = time.time() - start_time + loss_value = self.sess.run(self.loss, tf_dict) + L = np.abs(np.exp(self.sess.run(self.L)) / self.otherParams.nL * 100 - 100) + RL = np.abs(np.exp(self.sess.run(self.RL)) / self.otherParams.nRL * 100 - 100) + C = np.abs(np.exp(self.sess.run(self.C)) / self.otherParams.nC * 100 - 100) + RC = np.abs(np.exp(self.sess.run(self.RC)) / self.otherParams.nRC * 100 - 100) + Rdson = np.abs(np.exp(self.sess.run(self.Rdson)) / self.otherParams.nRdson * 100 - 100) + Rload1 = np.abs(np.exp(self.sess.run(self.Rload1)) / self.otherParams.nRload1 * 100 - 100) + Rload2 = np.abs(np.exp(self.sess.run(self.Rload2)) / self.otherParams.nRload2 * 100 - 100) + Rload3 = np.abs(np.exp(self.sess.run(self.Rload3)) / self.otherParams.nRload3 * 100 - 100) + vIn = np.abs(np.exp(self.sess.run(self.vIn)) / self.otherParams.nVin * 100 - 100) + vF = np.abs(np.exp(self.sess.run(self.vF)) / self.otherParams.nVF * 100 - 100) + meanError = np.mean([L, RL, C, RC, Rdson, Rload1, Rload2, Rload3, vIn, vF]) + + print( + 'Iterations: %d, Loss: %.3e, mean: %.1e, L: %.1f, RL: %.1f, C: %.1f, RC: %.1f, Rdson: %.1f, Rload1: %.1f, Rload2: %.1f, Rload3: %.1f, vIn: %.1f, vF: %.1f, Time: %.2f' % + (it, loss_value, meanError, L, RL, C, RC, Rdson, Rload1, Rload2, Rload3, vIn, vF, elapsed)) + start_time = time.time() + + self.optimizer.minimize(self.sess, + feed_dict=tf_dict, + fetches=[self.loss, self.L, self.RL, self.C, self.RC, self.Rdson, self.Rload1, + self.Rload2, self.Rload3, self.vIn, self.vF], + loss_callback=self.callback) + + def predict(self, x_star_Backward, x_star_Forward, dt): + + U0_star = self.sess.run(self.U0_pred, + {self.x0_tf: x_star_Backward}) + V0_star = self.sess.run(self.V0_pred, + {self.x0_tf: x_star_Backward}) + U1_star = self.sess.run(self.U1_pred, + {self.x1_tf: x_star_Forward}) + V1_star = self.sess.run(self.V1_pred, + {self.x1_tf: x_star_Forward}) + + return U0_star, V0_star, U1_star, V1_star + + +class otherParams: # simulation settings + def __init__(self): + self.AdamEpoch = 200000 + self.AdamLearningRate = 1e-3 + self.LBFGSEpoch = 50000 + self.nL = 7.25 + self.nRL = 3.14 + self.nC = 1.645 + self.nRC = 2.01 + self.nRdson = 2.21 + self.nRload1 = 3.1 + self.nRload2 = 10.2 + self.nRload3 = 6.1 + self.nVin = 4.8 + self.nVF = 1 + + +if __name__ == "__main__": + + for testIdx in range(0, 7, 1): # loop to test all 7 cases automatically + fileNameAutoGen = 'Simulation_data/buckSimulation_%s.mat' % testIdx + data = scipy.io.loadmat(fileNameAutoGen) + xCurrent = data['CurrentInput'] + xVoltage = data['VoltageInput'] + xSwitch = data['Dswitch'] + yCurrent = data['Current'] + yVoltage = data['Voltage'] + Indicator = data['forwaredBackwaredIndicator'] + dt = data['dt'] + + idx_forward = np.transpose(np.nonzero(Indicator[:, 0:1] == -2)) + idx_forward = idx_forward[:, 0:1] + idx_backward = np.transpose(np.nonzero(Indicator[:, 0:1] == 2)) + idx_backward = idx_backward[:, 0:1] + + q = 20 + layers = [5, 50, 50, 50, 50, 50, q * 2] # architecture of the neural network + X = np.concatenate([xCurrent, xVoltage, xSwitch, 1 - xSwitch, dt], 1) + lb = X.min(0) + ub = X.max(0) + + x0 = np.concatenate( + [xCurrent[idx_forward], xVoltage[idx_forward], xSwitch[idx_forward], 1 - xSwitch[idx_forward], + dt[idx_forward]], 1) + x0 = np.reshape(x0, (-1, 5)) + u0 = np.concatenate([yCurrent[idx_forward], yVoltage[idx_forward]], 1) + u0 = np.reshape(u0, (-1, 2)) + + x1 = np.concatenate( + [xCurrent[idx_backward], xVoltage[idx_backward], xSwitch[idx_backward], 1 - xSwitch[idx_backward], + dt[idx_backward]], 1) + x1 = np.reshape(x1, (-1, 5)) + u1 = np.concatenate([yCurrent[idx_backward], yVoltage[idx_backward]], 1) + u1 = np.reshape(u1, (-1, 2)) + + # the information of number of data points in each trasient regimes. + splitIdx1 = 120 # otherwise it will be 1 for no data case of the signal transient + splitIdx2 = 120 + splitIdx3 = 120 + Params = otherParams() + + model = PhysicsInformedNN(x0, u0, x1, u1, layers, dt, lb, ub, q, splitIdx1, splitIdx2, splitIdx3, Params) + model.train(Params.AdamEpoch) + + L = np.exp(model.sess.run(model.L)) + RL = np.exp(model.sess.run(model.RL)) + C = np.exp(model.sess.run(model.C)) + RC = np.exp(model.sess.run(model.RC)) + Rdson = np.exp(model.sess.run(model.Rdson)) + Rload1 = np.exp(model.sess.run(model.Rload1)) + Rload2 = np.exp(model.sess.run(model.Rload2)) + Rload3 = np.exp(model.sess.run(model.Rload3)) + vIn = np.exp(model.sess.run(model.vIn)) + vF = np.exp(model.sess.run(model.vF)) + RLRdson = RL + Rdson + + # Norminal values of the simulation setting, please refer to Table I in the paper. + L_value = np.abs(np.mean(L) / Params.nL * 100 - 100) + RL_value = np.abs(np.mean(RL) / Params.nRL * 100 - 100) + C_value = np.abs(np.mean(C) / Params.nC * 100 - 100) + RC_value = np.abs(np.mean(RC) / Params.nRC * 100 - 100) + Rdson_value = np.abs(np.mean(Rdson) / Params.nRdson * 100 - 100) + Rload1_value = np.abs(np.mean(Rload1) / Params.nRload1 * 100 - 100) + Rload2_value = np.abs(np.mean(Rload2) / Params.nRload2 * 100 - 100) + Rload3_value = np.abs(np.mean(Rload3) / Params.nRload3 * 100 - 100) + vIn_value = np.abs(np.mean(vIn) / Params.nVin * 100 - 100) + vF_value = np.abs(np.mean(vF) / Params.nVF * 100 - 100) + + meanError = np.mean([L_value, RL_value, C_value, + RC_value, Rdson_value, Rload1_value, + Rload2_value, Rload3_value, vIn_value, vF_value]) + + text_file = open("Result.txt", "a+") + text_file.write( + "buckSimulation_%s: mean:%.2f, L: %.2f, RL: %.2f, C: %.2f, RC: %.2f, Rdson: %.2f, Rload1: %.2f, Rload2: %.2f, Rload3: %.2f, vIn: %.2f, vF: %.2f\n" % ( + testIdx, meanError, L_value, RL_value, C_value, RC_value, Rdson_value, Rload1_value, + Rload2_value, + Rload3_value, vIn_value, vF_value)) + text_file.close() diff --git a/Result.txt b/Result.txt new file mode 100644 index 0000000..2455ea3 --- /dev/null +++ b/Result.txt @@ -0,0 +1,7 @@ +buckSimulation_0: mean:0.03, L: 0.01, RL: 0.02, C: 0.03, RC: 0.03, Rdson: 0.09, Rload1: 0.00, Rload2: 0.00, Rload3: 0.00, vIn: 0.00, vF: 0.09 +buckSimulation_1: mean:0.13, L: 0.00, RL: 0.23, C: 0.07, RC: 0.12, Rdson: 0.52, Rload1: 0.00, Rload2: 0.04, Rload3: 0.02, vIn: 0.01, vF: 0.25 +buckSimulation_2: mean:1.53, L: 0.35, RL: 0.54, C: 0.03, RC: 5.70, Rdson: 0.13, Rload1: 0.01, Rload2: 0.02, Rload3: 0.01, vIn: 0.17, vF: 8.37 +buckSimulation_3: mean:0.49, L: 0.13, RL: 0.30, C: 0.05, RC: 2.76, Rdson: 0.66, Rload1: 0.02, Rload2: 0.15, Rload3: 0.07, vIn: 0.01, vF: 0.79 +buckSimulation_4: mean:1.93, L: 0.21, RL: 1.16, C: 0.65, RC: 5.57, Rdson: 1.05, Rload1: 0.00, Rload2: 0.27, Rload3: 0.19, vIn: 0.22, vF: 9.93 +buckSimulation_5: mean:3.71, L: 0.84, RL: 6.42, C: 0.95, RC: 5.22, Rdson: 12.22, Rload1: 0.04, Rload2: 0.04, Rload3: 0.06, vIn: 0.26, vF: 11.01 +buckSimulation_6: mean:5.11, L: 1.03, RL: 13.22, C: 1.04, RC: 4.38, Rdson: 27.59, Rload1: 0.10, Rload2: 0.13, Rload3: 0.12, vIn: 0.16, vF: 3.30 diff --git a/Simulation_data/Dataset details.md b/Simulation_data/Dataset details.md new file mode 100644 index 0000000..e790590 --- /dev/null +++ b/Simulation_data/Dataset details.md @@ -0,0 +1,5 @@ +# Dataset details + +The dataset includes six .mat files, from **buckSimulation_0** to **buckSimulation_6**. They correspond to the testing cases in the Table II in the paper, i.e., clean data, ADC error, Sync error, 5 noise, 10 noise, ADC-Sync-5noise, ADC-Sync-10noise. + +The datasets are generated by using the Buck converter model implemented in Matlab. The Matlab script is available upon request to szh@energy.aau.dk. \ No newline at end of file diff --git a/Simulation_data/buckSimulation_0.mat b/Simulation_data/buckSimulation_0.mat new file mode 100644 index 0000000..c1c7d4e Binary files /dev/null and b/Simulation_data/buckSimulation_0.mat differ diff --git a/Simulation_data/buckSimulation_1.mat b/Simulation_data/buckSimulation_1.mat new file mode 100644 index 0000000..7e8ea29 Binary files /dev/null and b/Simulation_data/buckSimulation_1.mat differ diff --git a/Simulation_data/buckSimulation_2.mat b/Simulation_data/buckSimulation_2.mat new file mode 100644 index 0000000..f4390cd Binary files /dev/null and b/Simulation_data/buckSimulation_2.mat differ diff --git a/Simulation_data/buckSimulation_3.mat b/Simulation_data/buckSimulation_3.mat new file mode 100644 index 0000000..0253e27 Binary files /dev/null and b/Simulation_data/buckSimulation_3.mat differ diff --git a/Simulation_data/buckSimulation_4.mat b/Simulation_data/buckSimulation_4.mat new file mode 100644 index 0000000..ad29820 Binary files /dev/null and b/Simulation_data/buckSimulation_4.mat differ diff --git a/Simulation_data/buckSimulation_5.mat b/Simulation_data/buckSimulation_5.mat new file mode 100644 index 0000000..f349661 Binary files /dev/null and b/Simulation_data/buckSimulation_5.mat differ diff --git a/Simulation_data/buckSimulation_6.mat b/Simulation_data/buckSimulation_6.mat new file mode 100644 index 0000000..284c494 Binary files /dev/null and b/Simulation_data/buckSimulation_6.mat differ