Browsing by Author "Shejwalkar, Ashwin"

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Comparative Analysis of Machine Learning Algorithms for Eccentricity Fault Classification in Salient Pole Synchronous Machine
(IEEE, 2024-03-22) Shejwalkar, Ashwin; Yusuf, Latifa; Ilamparithi, Thirumarai Chelvan
The paper performs a comparative study of Artificial Neural Networks (ANNs) and Convolutional Neural Networks (CNNs) for the classification of Static Eccentricity (SE) and Dynamic Eccentricity (DE) faults in a Salient Pole Synchronous Machine (SPSM). The SPSM was subjected to varying SE and DE severities, unbalanced source voltages, and load conditions. Stator and field current data were measured, and various time-domain and frequency-domain features were extracted from the above-mentioned data. Both networks were fed these features and compared based on classification accuracy, robustness, and computational complexity.
Effect of Power Factor of a Synchronous Machine on Eccentricity Faults Classification Accuracies
(IEEE, 2024-09-12) Yusuf, Latifa; Shejwalkar, Ashwin; Ilamparithi, Thirumarai Chelvan
The research work studies the effect of changing power factor of a Salient Pole Synchronous Machine (SPSM) on eccentricity fault classification accuracies of machine learning and deep learning models. The SPSM was subjected to static eccentricity (SE) and dynamic eccentricity (DE) with a severity of forty percent. Data was collected at different operating conditions, such as lagging, leading, and unity power factor. The data was used to train an Artificial Neural Network (ANN) and a one-dimensional Convolutional Neural Network (1D CNN) for eccentricity fault classification. Results show that the SPSM’s changing power factor significantly affected the classification accuracy of both neural networks.