Detection of short circuit faults in two-level voltage source inverter using convolution neural network

Abstract

Voltage source inverters (VSIs) play a critical role in modern industrial systems, particularly in controlling the operation of equipment such as induction motors. Ensuring their reliable performance is crucial, as faults like short circuits can severely disrupt industrial processes. This paper introduces a new diagnostic approach for detecting and localizing short circuit faults in VSIs. The method uses Lissajous curves derived from the Clark transformation of the VSI’s 3-phase voltage components (V_α, V_β). These curves serve as input data for a convolutional neural networks (CNNs) model, enabling the accurate classification of single and double short circuit faults. Simulation results using MATLAB/Simulink demonstrate that the proposed method achieves 100% classification accuracy within 100 ms, highlighting its suitability for real-time applications. The approach offers significant advantages in speed and accuracy over traditional techniques, with potential implications for enhancing the reliability and safety of inverter-driven systems in industrial environments.