Analysis and Classification of Faults in Switched Reluctance Motors Using Deep Learning Neural Networks
- PDF / 5,747,690 Bytes
- 20 Pages / 595.276 x 790.866 pts Page_size
- 108 Downloads / 230 Views
RESEARCH ARTICLE–ELECTRICAL ENGINEERING
Analysis and Classification of Faults in Switched Reluctance Motors Using Deep Learning Neural Networks E. Fantin Irudaya Raj1
· M. Balaji2
Received: 4 June 2020 / Accepted: 20 October 2020 © King Fahd University of Petroleum & Minerals 2020
Abstract The simple and robust construction, less weight, wide operating speed range, and higher fault tolerance capability of switched reluctance (SR) motor make it a viable contender for the conventional dc and ac machines. The faults at the rotor, winding, stator converters, and sensors lead to overcurrent, increase torque ripples, and sudden breakdown of the system. Thus, it is an urgent requirement to recognize and classify the faults that exist in switched reluctance motors, thereby the reliability, robustness, and widespread utilization of SR motors can be increased. The phases of the SR Motor are excited by using an asymmetric bridgeless resonance converter. This paper proposes an automatic diagnosis and classification of faults using radial basis function neural network (RBFNN). A mathematical model of the SR motor is established to determine the state of the art of fault condition of SR motors. The speed data of SR motor are utilized by RBFNN to generate fault information. Gabor filter is used for preprocessing the input data, and segmentation is achieved using high accurate DCT-DOST transformation. A gray-level co-occurrence matrix optimized with a genetic algorithm is used to extract the features in the speed signal of the motor. A test setup was developed in MATLAB to measure the performances of the RBFNN classifier in real-time. The effectiveness of the simulated fault classification model is verified by comparing the results of the conventional PI controller with several optimized algorithm-based tuned PI controllers. Keywords SR motor · Fault classification · Speed control · RBFNN · BR converter · Genetic algorithm
1 Introduction Switched reluctance motor (SRM) with a large number of promising features like simple construction, less weight, robust, higher torque to inertia ratio, less maintenance requirement, low cost, and wide range of operating speed is a potential candidate for variable speed applications. The salient poles of the stator and rotor of the SR motor are made of steel lamination. The windings are connected in each phase (opposite poles) of the stator with no windings on the rotor. The proper excitation of the current with respect to the position of the rotor decides the performance of SRM. When a current flows in any one of the phases, the torque gets generated due to the rotor movement so as to line up
B
E. Fantin Irudaya Raj [email protected]
1
Department of Electrical and Electronics Engineering, Dr. Sivanthi Aditanar College of Engineering, Tiruchendur, India
2
Department of Electrical and Electronics Engineering, SSN College of Engineering, Kalavakkam, India
with the energized stator pole. The direction of the generated torque changes due to the rotor motion in accordance with t
Data Loading...
