The work presented examines the feasibility of using artificial neural networks (ANNs) and evolutionary algorithms (EAs) to model fault free and faulted switched reluctance motor (SRM) drive systems. SRMs are capable of functioning despite the presence of faults. Faults impart transient changes to machine inductances in a manner that is difficult to model analytically. After this transient period, SRMs are capable of functioning at a reduced level of performance. ANNs are applied for their well known interpolation capabilities for highly nonlinear systems. EAs are employed for their ability to search a complex structural and parametric space as necessary to find good ANN solutions. In this paper, the ANN structure and training regimen are described for application to an example SRM drive system under normal and abnormal operating conditions. >
Control techniques for electrical machines have been applied for many years to what are called electrical drives (EDs) or adjustable speed drives (ASDs), which are part of almost 25% of applications of electrical machines in the industry. Therefore, this technology has followed the evolution of performances of both power converters and electrical actuators or generators for more than 50 years and the invention of silicon-controlled rectifiers. The aim of this article is not to cover a survey of EDs or ASDs since it has already been covered recently [1] but rather to present trends of the control part related to this technology. Of course, the control part is fully dependent of the power part and cannot be treated as a stand-alone technique.
Closed-form expressions for the statistics of the harmonic mean of two independent and identically distributed gamma variates are presented. The probability density function of the harmonic mean of two F variates is also derived. These statistical results are then applied to study the performance of wireless communication systems with nonregenerative relays over flat Nakagami fading channels. More specifically, outage probability formulas for noise-limited systems as well as systems affected by interference are obtained. Furthermore, general expressions for average bit-error rates are also derived. Finally, comparisons between regenerative and nonregenerative systems are presented. Numerical results show that the former systems clearly outperform the latter ones for low average signal-to-noise ratios (SNRs). They also show that the two systems have similar performance at high average SNRs.