Browsing by Author "Ilamparithi, Thirumarai Chelvan"

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Analysis of Reluctance Synchronous Motor Under Hybrid Fault Condition
(IEEE, 2023-09) Ghalavand, Fatemeh; Yusuf, Latifa; Ilamparithi, Thirumarai Chelvan
A small degree of static eccentricity is inevitable due to manufacturing tolerances and assembly imperfections. Therefore, when stator inter-turn fault happens, it is important to analyze it along with static eccentric condition. Unfortunately, there is not much literature that analyzes such a condition. This paper focuses on the analysis of a Reluctance Synchronous Machine (RSM) when subjected to stator inter-turn and static eccentricity faults simultaneously. In particular, the work focuses on determining the impact of relative position between the minimum airgap point and the stator inter-turn fault. The goal of the paper is achieved by simulating a 1.5 hp, 4-pole, RSM using Finite Element (FE) software. Line current data is captured under different fault conditions and motor current signature analysis is carried out. Furthermore, the lower sideband harmonic frequency is reconstructed in time domain using Inverse Fourier Fast Transform. Clarke’s transformation is applied on the reconstructed harmonic frequency currents to estimate the alpha, beta components. Afterwards, Principal Component Analysis (PCA) is implemented on the alpha, beta currents. The major benefits of the work include establishing the impact of hybrid faults on motor current signatures, developing a new measure to predict the relative position of the point of minimum airgap.
Classification and Severity Estimation of Eccentricity Faults in Salient Pole Synchronous Machine using Deep Learning
(IEEE, 2025) Yusuf, Latifa; Moa, Belaid; Ilamparithi, Thirumarai Chelvan
The presented research work is focused on the classification and severity estimation of eccentricity faults in Salient Pole Synchronous Machines. Building on our comparative study of Artificial Neural Network and Convolutional Neural Network for eccentricity fault classification, we propose an end-to-end deep learning model, namely Hierarchical Convolutional Neural Network, for eccentricity classification and severity estimation. The deep learning model inherently consists of an eccentricity detection component for fault classification and a severity estimation component for fault quantification. The deep learning model is built using the experimental data of a 3-phase, 2-kW, salient pole synchronous machine. The machine is subjected to 20%, 40%, and 60% severities of static and dynamic eccentricity faults under different loading conditions. Stator line currents and line-to-line voltages obtained from different operating conditions are used to train, validate and test the proposed model. To enhance the model's performance, time delay construction was incorporated to augment the datasets and carefully evaluate the impact of selected raw input features, specifically stator currents and voltages, as well as the load. Among the evaluated scenarios, the use of voltage with time delay (V, TD) as input features produced the best results, achieving 100% classification accuracy and a root mean square error of 0.0046 for static eccentricity and 0.0188 for dynamic eccentricity estimation. Results indicate that the model performs excellently in fault classification and severity estimation. Compared to traditional machine learning models, the presented model is an end-to-end deep learning architecture devoid of manual feature extraction and is robust to load variations.
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.