Inter-turn fault detection in PM synchronous motor by neuro-fuzzy technique
- PDF / 2,907,877 Bytes
- 12 Pages / 595.276 x 790.866 pts Page_size
- 59 Downloads / 163 Views
ORIGINAL ARTICLE
Inter-turn fault detection in PM synchronous motor by neurofuzzy technique Reihaneh Amiri Ahouee1
•
Mahmood Mola2
Received: 2 February 2019 / Revised: 15 June 2020 Ó The Society for Reliability Engineering, Quality and Operations Management (SREQOM), India and The Division of Operation and Maintenance, Lulea University of Technology, Sweden 2020
Abstract In this paper, a method for detecting the stator internal coil fault detection for a permanent magnet synchronous motor (PMSM) using the ANFIS algorithm is proposed and described. At first, the dynamic model of the synchronous motor along with its certain fault will be introduced. Since fault detection in these engines is very important and has a high value, different methods have been proposed for detecting stator deflection in electric machines. To determine the fault percentage in the permanent magnet synchronous motor, a neuro-fuzzy adaptive inference system is used to identify the fault. The advantages of the proposed algorithm are the ability to detect faults with different domains. It is flexible enough to be used for offline and online identification. For this reason, we have used neuro-comparative learning techniques in fuzzy logic in this paper. The inputs of the proposed algorithm are two PMSM current and torque signals in normal and faulty conditions. In the proposed algorithm, the membership function structure was created with the fuzzy C-means clustering method. The simulation results show that the proposed algorithm can accurately determine where and with what speed the fault occurs.
& Reihaneh Amiri Ahouee [email protected] Mahmood Mola [email protected] 1
Department of Electrical Engineering, Faculty of Engineering, Islamic Azad University, Central Tehran Branch, Tehran, Iran
2
Industrial Control Systems Laboratory, Department of Electrical Engineering, Faculty of Engineering, Ayatollah Boroujerdi University, Borujerd, Iran
Keywords Permanent magnet synchronous motor Stator internal coil fault Fault detection Fuzzy C-mean clustering ANFIS
1 Introduction Permanent magnet synchronous motors are considered as among the important electric machines, with advantages such as high efficiency, reliable performance, and high power density compared to all types of electric machines (Chen et al. 2014; Hang et al. 2015). Due to the sensitive application of these types of engines, maintenance of them is essential. But avoiding faults in engineering systems is not an easy task. Over the past decades, attempts have been made to detect faults in electrical devices and to respond best to them. Therefore, it is very important to identify and diagnose a fault very quickly (Malloy et al. 2015). The fault diagnosis in electrical machines has three major parts. In part one, the machine must be modeled. In general, there are three general methods for modeling PM machines: mathematical modeling (Hang et al. 2016), finite element modeling (FE) (Arumugam et al. 2015), and modeling based on mathematical composition and FE (Khoobroo and
Data Loading...
