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Current Signature Analysis of Open Circuit Fault Diagnosis in 3Φ-Voltage Source Inverter under Variable Load Conditions

International Journal of Electronics and Communication Engineering
© 2023 by SSRG - IJECE Journal
Volume 10 Issue 6
Year of Publication : 2023
Authors : Vaishali Sonawane, S. B. Patil
10.14445/23488549/IJECE-V10I6P102
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How to Cite?

Vaishali Sonawane, S. B. Patil, "Current Signature Analysis of Open Circuit Fault Diagnosis in 3Φ-Voltage Source Inverter under Variable Load Conditions," SSRG International Journal of Electronics and Communication Engineering, vol. 10,  no. 6, pp. 7-16, 2023. Crossref, https://doi.org/10.14445/23488549/IJECE-V10I6P102

Abstract:

Accurately identifying Insulated Gate Bipolar Transistor switch failures is crucial for ensuring the dependability and durability of the 3Φ-Voltage Source Inverters. For the purpose of monitoring and diagnosing faults in three-phase inverters, signal processing is frequently utilized. The current study then uses an ANN approach to focus on the issue in variable load settings. The suitable Mother Wavelet is a Mother Wavelet with the greatest Energy to Shannon Entropy (ESE) ratio. An innovative technique with normalized characteristics is used to reduce the algorithm's complexity. The ReliefF algorithm is used to choose the best features. The most useful traits are used to instruct an ANN to detect errors. The suitable ANN structure is selected from a pool of training structures depending on accuracy. The recommended technique is novel in that it achieves OCFs in 3-VS Conditions of varying load by using the fewest features and the least amount of training data. To do this, extracted features are normalized before training an ANN. The data gathered shows that rank-based feature selection had improved the ANN classifier's accuracy.

Keywords:

3Φ-Voltage source inverters, Artificial neural networks, Current signature analysis, Energy to shannon entropy, Open circuit faults, Relief.

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