Reactive Power Output Modeling of Synchronous Condenser in UHVDC Converter Station Based on Interlaced Superposition CNN-BiLSTM

Abstract

AbstractTo guarantee stable power system operation, a synchronous condenser (SC) is configured in an ultra-high voltage direct current (UHVDC) converter station to provide dynamic reactive power support to the power system. The research on the reactive power output modelling of a SC in an UHVDC converter station has important theoretical significance and practical value for the reactive power control of a SC in an UHVDC converter station. Focusing on the reactive power regulation system of the SC with strong coupling, multivariable, and nonlinear features, it is difficult for the universal analytic method to build the SC reactive power output model. A novel reactive power output model of the SC in the UHVDC converter station based on interlaced superposition convolutional neural network-bidirectional long short-term memory (CNN-BiLSTM) is proposed, in which a novel interlaced superposition CNN composed of convolution units with two different structures is built to increase the depth of the network and avoid over-fitting. Particularly the branch channels of convolution units with two different structures are connected by a convolution layer and a skip connection respectively. The interlaced superposition CNN-BiLSTM model is that the combination of the interlaced superposition CNN model and BiLSTM model for improving the model accuracy and generic capability. The Bayesian optimization method is used to optimize its hyperparameters. The application of interlaced Superposition CNN-BiLSTM in SC reactive power output modelling is a new technology. The excitation voltage and excitation current of the SC are used as inputs for the training and testing sampled data, and the reactive power of the SC is used as outputs for the training and testing sampled data. Therefore, the reactive power output model of a SC in an UHVDC converter station based on interlaced superposition CNN-BiLSTM is obtained, and it attains a low root mean square error (RMSE = 0.126750) and a high determination coefficient (R2 = 0.999999).