Evaluation of operational amplifier immunity by means of Weibull distribution

The immunity of operational amplifiers is a trend topic for electromagnetic compatibility EMC community. Radiofrequency interference is usually applied to the operation amplifier and the voltage offset is monitored as a parameter to evaluate the EMC degradation. However, this method does not provide enough information to know the probability of failure to electromagnetic interference of the devices. In this paper, an alternative statistical analysis based on the Weibull distribution is used to analyze the electromagnetic immunity performance of operational amplifiers under different frequency interferences and modulation index. The results confirm the feasibility of the Weibull distribution to evaluate the radiofrequency interference RFI behavior.Peer ReviewedPostprint (author’s final draft

A Free Space Optic/Optical Wireless Communication: A Survey

The exponential demand for the next generation of services over free space optic and wireless optic communication is a necessity to approve new guidelines in this range. In this review article, we bring together an earlier study associated with these schemes to help us implement a multiple input/multiple output flexible platform for the next generation in an efficient manner. OWC/FSO is a complement clarification to radiofrequency technologies. Notably, they are providing various gains such as unrestricted authorizing, varied volume, essential safekeeping, and immunity to interference.

Characterization of the Spatial Distribution of the Electric Field Strength in Indoor Propagation at 2.45 GHz

Small-scale spatial variations of the electric field strength or “fast fading” are encountered in indoor environments, and are of particular concern for indoor wireless communication applications as well as for electromagnetic compatibility assessment. This thesis is motivated by the problem of electromagnetic interference with a critical-care medical equipment caused by fields radiated by portable electronic devices such as cell phones and tablet computers. Measurement and computer simulation of the electric field strength, in both controlled and real-world scenarios, are explored to estimate parameter values of statistical models for the fast fading in a region of interest inside a building. First, a method for measuring the dielectric constant of wall construction materials is developed for two reasons: little information available on electrical properties of such materials in the frequency range of interest, 2.4 GHz ISM band, and variations in material properties caused by different manufacturing processes employed by different manufacturers. The proposed technique, referred to as the parallel-path method, falls into the category of free-space methods and is shown to be more sensitive to the dielectric constant than free-space methods based on normal incidence only. Having determined the dielectric constant of gyproc slabs and of a wooden door, a controlled multipath environment is built inside an anechoic chamber. Two line-of-sight and a non-line-of-sight scenarios, each with about 4000 measurement points, are studied. We apply the Friedman’s goodness-of-fit test at 5% significance level to show that a ray-tracing technique based only on 3D geometrical optics is suitable for estimating the fast fading of the electromagnetic field at 2.45 GHz in a very controlled situation. Then the Anderson-Darling goodness-of-fit test, also at 5% significance level, is applied to show that in the vicinity of a transmitter the Ricean, Normal, Nakagami, and Weibull distributions can be equivalently used to represent the spatial fast fading for both line and non-line-of-sight scenarios. Furthermore, the effects of metal studs are shown to worsen not only point-by-point agreement between measurement and GO simulation, but also the agreement on the statistics of the fast fading in a 65 by 65 cm region. Another aspect of this thesis is the development of a new method for estimating the parameters of the Ricean probability density function. This new method is compared to the maximum-likelihood method, and is shown to provide accurate estimates with samples containing as few as 36 data points for regions within 2 m from a transmitter, and as few as 9 data points for regions farther away. This is a considerable improvement in term of computation time when compared to estimates based on approximately 4000 points, or even 200 data points. Together with GO simulations, this method reduces the initial and elaborated measurement approach to only a few simulated points and a statistical model. Finally, this methodology is extended and applied to real-world scenarios such as a long hallway and a conventional laboratory room. The agreement between measurement and GO simulation is not as good as that of the experiment conducted in a shielded anechoic chamber, but it is still reasonable, especially because the interior structures of walls such as metal studs are not modeled by the GO code. As for the statistical models used to describe the electric field strength variation in a region, it is shown that the Ricean, Normal, Nakagami, and the Weibull distributions can be employed. However, for the data collected in this work, the Normal distribution is the one that results in the worst fit to measured data for most of the cases. It is demonstrated that, even though diffracted rays are not taken into account, GO simulation allows for an accurate estimation of the parameters of a statistical model for the fast fading, for both controlled and most real-world scenarios, provided that the site geometry and electrical properties of walls, floor, and ceiling are known

Development of a quantitative health index and diagnostic method for efficient asset management of power transformers

Power transformers play a very important role in electrical power networks and are frequently operated longer than their expected design life. Therefore, to ensure their best operating performance in a transmission network, the fault condition of each transformer must be assessed regularly. For an accurate fault diagnosis, it is important to have maximum information about an individual transformer based on unbiased measurements. This can best be achieved using artificial intelligence (AI) that can systematically analyse the complex features of diagnostic measurements. Clustering techniques are a form of AI that is particularly well suited to fault diagnosis. To provide an assessment of transformers, a hybrid k-means algorithm, and probabilistic Parzen window estimation are used in this research. The clusters they form are representative of a single or multiple fault categories. The proposed technique computes the maximum probability of transformers in each cluster to determine their fault categories. The main focus of this research is to determine a quantitative health index (HI) to characterize the operating condition of transformers. Condition assessment tries to detect incipient faults before they become too serious, which requires a sensitive and quantified approach. Therefore, the HI needs to come from a proportionate system that can estimate health condition of transformers over time. To quantify this condition, the General Regression Neural Network (GRNN), a type of AI, has been chosen in this research. The GRNN works well with small sets of training data and avoids the needs to estimate large sets of model parameters, following a largely non-parametric approach. The methodology used here regards transformers as a collection of subsystems and summarizes their individual condition into a quantified HI based on the existing agreed benchmarks drawn from IEEE and CIGRE standards. To better calibrate the HI, it may be mapped to a failure probability estimate for each transformer over the coming year. Experimental results of the research show that the proposed methods are more effective than previously published approaches when diagnosing critical faults. Moreover, this novel HI approach can provide a comprehensive assessment of transformers based on the actual condition of their individual subsystems

Development of a quantitative health index and diagnostic method for efficient asset management of power transformers

Power transformers play a very important role in electrical power networks and are frequently operated longer than their expected design life. Therefore, to ensure their best operating performance in a transmission network, the fault condition of each transformer must be assessed regularly. For an accurate fault diagnosis, it is important to have maximum information about an individual transformer based on unbiased measurements. This can best be achieved using artificial intelligence (AI) that can systematically analyse the complex features of diagnostic measurements. Clustering techniques are a form of AI that is particularly well suited to fault diagnosis. To provide an assessment of transformers, a hybrid k-means algorithm, and probabilistic Parzen window estimation are used in this research. The clusters they form are representative of a single or multiple fault categories. The proposed technique computes the maximum probability of transformers in each cluster to determine their fault categories. The main focus of this research is to determine a quantitative health index (HI) to characterize the operating condition of transformers. Condition assessment tries to detect incipient faults before they become too serious, which requires a sensitive and quantified approach. Therefore, the HI needs to come from a proportionate system that can estimate health condition of transformers over time. To quantify this condition, the General Regression Neural Network (GRNN), a type of AI, has been chosen in this research. The GRNN works well with small sets of training data and avoids the needs to estimate large sets of model parameters, following a largely non-parametric approach. The methodology used here regards transformers as a collection of subsystems and summarizes their individual condition into a quantified HI based on the existing agreed benchmarks drawn from IEEE and CIGRE standards. To better calibrate the HI, it may be mapped to a failure probability estimate for each transformer over the coming year. Experimental results of the research show that the proposed methods are more effective than previously published approaches when diagnosing critical faults. Moreover, this novel HI approach can provide a comprehensive assessment of transformers based on the actual condition of their individual subsystems

Improved micro-contact resistance model that considers material deformation, electron transport and thin film characteristics

This paper reports on an improved analytic model forpredicting micro-contact resistance needed for designing microelectro-mechanical systems (MEMS) switches. The originalmodel had two primary considerations: 1) contact materialdeformation (i.e. elastic, plastic, or elastic-plastic) and 2) effectivecontact area radius. The model also assumed that individual aspotswere close together and that their interactions weredependent on each other which led to using the single effective aspotcontact area model. This single effective area model wasused to determine specific electron transport regions (i.e. ballistic,quasi-ballistic, or diffusive) by comparing the effective radius andthe mean free path of an electron. Using this model required thatmicro-switch contact materials be deposited, during devicefabrication, with processes ensuring low surface roughness values(i.e. sputtered films). Sputtered thin film electric contacts,however, do not behave like bulk materials and the effects of thinfilm contacts and spreading resistance must be considered. Theimproved micro-contact resistance model accounts for the twoprimary considerations above, as well as, using thin film,sputtered, electric contact