scholarly journals Analysis of the Fault Tolerance of a Switched Reluctance Machine with Distributed Inverter

2012 ◽  
Vol 5 (2) ◽  
pp. 482-493 ◽  
Author(s):  
Martin Hennen ◽  
Matthias Boesing ◽  
Rik De Doncker
Keyword(s):  
Fault Tolerance ◽  
Switched Reluctance Machine ◽  
Switched Reluctance ◽  
Reluctance Machine

scholarly journals An Overview of Fault-Diagnosis and Fault-Tolerance Techniques for Switched Reluctance Machine Systems

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 174822-174838 ◽  
Author(s):  
Chun Gan ◽  
Yu Chen ◽  
Ronghai Qu ◽  
Zhiyue Yu ◽  
Wubin Kong ◽  
...  
Keyword(s):  
Fault Tolerance ◽  
Fault Diagnosis ◽  
Switched Reluctance Machine ◽  
Switched Reluctance ◽  
Reluctance Machine ◽  
Machine Systems

Radial Force Characteristics of Switched Reluctance Machine

1999 ◽  
Author(s):  
Neil R. Garrigan ◽  
Albert Storace ◽  
Wen L. Soong ◽  
Thomas A. Lipo ◽  
Charles M. Stephens
Keyword(s):  
Radial Force ◽  
Switched Reluctance Machine ◽  
Switched Reluctance ◽  
Reluctance Machine ◽  
Force Characteristics

scholarly journals Multi-Objective Optimization of Switched Reluctance Machine Design Using Jaya Algorithm (MO-Jaya)

Mathematics ◽  
2021 ◽  
Vol 9 (10) ◽  
pp. 1107
Author(s):  
Mohamed Afifi ◽  
Hegazy Rezk ◽  
Mohamed Ibrahim ◽  
Mohamed El-Nemr
Keyword(s):  
Optimization Techniques ◽  
Design Parameters ◽  
Jaya Algorithm ◽  
Switched Reluctance Machine ◽  
Nsga Ii ◽  
Switched Reluctance ◽  
Multi Objective ◽  
Reluctance Machine ◽  
Linear Relationships ◽  
Forward Equations

The switched reluctance machine (SRM) design is different from the design of most of other machines. SRM has many design parameters that have non-linear relationships with the performance indices (i.e., average torque, efficiency, and so forth). Hence, it is difficult to design SRM using straight forward equations with iterative methods, which is common for other machines. Optimization techniques are used to overcome this challenge by searching for the best variables values within the search area. In this paper, the optimization of SRM design is achieved using multi-objective Jaya algorithm (MO-Jaya). In the Jaya algorithm, solutions are moved closer to the best solution and away from the worst solution. Hence, a good intensification of the search process is achieved. Moreover, the randomly changed parameters achieve good search diversity. In this paper, it is suggested to also randomly change best and worst solutions. Hence, better diversity is achieved, as indicated from results. The optimization with the MO-Jaya algorithm was made for 8/6 and 6/4 SRM. Objectives used are the average torque, efficiency, and iron weight. The results of MO-Jaya are compared with the results of the non-dominated sorting genetic algorithm (NSGA-II) for the same conditions and constraints. The optimization program is made in Lua programming language and executed by FEMM4.2 software. The results show the success of the approach to achieve better objective values, a broad search, and to introduce a variety of optimal solutions.

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