A Novel Approach to Fault Recognition in Multi-level Inverters through Artificial Neural Networks

International Journal of Electrical and Electronics Engineering

© 2024 by SSRG - IJEEE Journal

Volume 11 Issue 5

Year of Publication : 2024

Authors : E. Parimalasundar, S. Jayakumar, Sudha Dukkipati, S. Sudha, S. Sivarajan, B. Hemanth Kumar

10.14445/23488379/IJEEE-V11I5P115

How to Cite?

E. Parimalasundar, S. Jayakumar, Sudha Dukkipati, S. Sudha, S. Sivarajan, B. Hemanth Kumar, "A Novel Approach to Fault Recognition in Multi-level Inverters through Artificial Neural Networks," SSRG International Journal of Electrical and Electronics Engineering, vol. 11,  no. 5, pp. 161-174, 2024. Crossref, https://doi.org/10.14445/23488379/IJEEE-V11I5P115

Abstract:

This paper presents a novel methodology for detecting open and short circuit faults in multi-level inverters using a combination of time-frequency analysis, simulation, and experimental investigations. The proposed approach integrates the Discrete Wavelet Transform (DWT), Artificial Neural Networks (ANNs), and the back-propagation training technique to achieve accurate fault recognition. The utilization of DWT enables the extraction of fault-related features from the time-frequency domain, enhancing fault detection capabilities. These features are then utilized as inputs to an ANN, trained using the Back Propagation Training technique, to classify different fault conditions. Moreover, a LabVIEW real-time fault diagnosis model is developed to validate the effectiveness of the proposed approach through experimental implementation. This model provides a practical framework for real-time fault detection and diagnosis in multi-level inverters, contributing to improved reliability and operational efficiency of power electronics systems. By combining advanced signal processing techniques with artificial intelligence, the proposed methodology offers a comprehensive solution for fault recognition in multi-level inverters, addressing the challenges of modern power systems. The experimental validation underscores the effectiveness and feasibility of the proposed approach in real-world applications, highlighting its potential for enhancing the reliability and performance of multilevel inverter-based power systems.

Keywords:

Artificial Neural Networks, Back propagation training, Discrete Wavelet Transform, Fault detection, Multi-level inverters.

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