Comparative Analysis of Machine Learning Algorithms for Eccentricity Fault Classification in Salient Pole Synchronous Machine

被引：1
|
作者：
Shejwalkar, Ashwin [1 ]
Yusuf, Latifa [2 ]
Ilamparithi, Thirumarai Chelvan [2 ]
机构：
[1] BITS Pilani, Elect & Elect Engn, KK Birla Goa Campus, Sancoale, Goa, India
[2] Univ Victoria, Elect & Comp Engn, Victoria, BC, Canada
来源：
2024 IEEE TEXAS POWER AND ENERGY CONFERENCE, TPEC | 2024年
基金：
加拿大自然科学与工程研究理事会;
关键词：
Artificial Neural Network (ANN); Convolutional Neural Network (CNN); classification; Dynamic Eccentricity (DE); features; Static Eccentricity (SE); Salient Pole Synchronous; Machine (SPSM);
D O I：
10.1109/TPEC60005.2024.10472206
中图分类号：
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号：
0807 ; 0820 ;
摘要：
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.
引用
收藏
页码：560 / 565
页数：6
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