Contactor Modeling Technology Based on an Artificial Neural Network

Abstract

We propose a new contactor modeling method that incorporates the back propagation (BP) neural network to map the complex nonlinear electromechanical coupling relation of the contactor to build its model. First, the artificial neural network (ANN) model collects the actual operational data of the contactor, including the coil voltage, coil current and moving core displacement, and then uses the strong nonlinear fitting ability of the BP neural network to perform the model training. When the training is completed, the ANN model can output the precise displacement according to the input data of the coil voltage and the coil current. Through a simple training process, this method can complete the modeling of any electromagnetic contactor. This method avoids the need to solve the complex magnetic circuit equation of the contactor and thus provides a simple and universal method for calculating the displacement of the electromagnetic switch. The co-simulation system is used to model, train, and analyze the contactor ANN model. Finally, a relevant experiment is conducted to confirm the effectiveness of the ANN model.