A Model-Data Hybrid Driven Diagnosis Method for Open-Switch Faults in Three-Phase T-Type Grid-Connected Converters
Bo Long, ZhongLin He, Cristian García, José Rodríguez, Kil To Chong
Abstract
With the widespread application of 3LT <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> Cs, fault diagnosis has become increasingly important. Existing diagnostic methods can be divided into two types: model-driven and data-driven. Model-driven diagnosis is fast and accurate, but defining diagnosis rules can be complicated and difficult, making it less feasible. On the other hand, using artificial neural networks (ANN) for fault diagnosis is relatively easier, but it requires heavy calculations and takes a long diagnose time. To combine the advantages of both methods, this paper proposes a model-data hybrid driven diagnosis method for open-circuit faults in 3LT <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> C. First, a model is constructed based on the circuit topology. Secondly, the input and output parameters of the neural network are determined. Finally, the constructed back-propagation neural network (BPNN) is trained using experimental data, based on this, a three-layer BP neural network with three inputs and thirteen outputs is constructed to achieve open-circuit fault diagnosis. The effectiveness of the proposed fault diagnostic algorithms is verified through experimental results.