Intelligent Circuits and Systems

ICICS-2020 is the third conference initiated by the School of Electronics and Electrical Engineering at Lovely Professional University that explored recent innovations of researchers working for the development of smart and green technologies in the fields of Energy, Electronics, Communications, Computers, and Control. ICICS provides innovators to identify new opportunities for the social and economic benefits of society.　 This conference bridges the gap between academics and R&D institutions, social visionaries, and experts from all strata of society to present their ongoing research activities and foster research relations between them. It provides opportunities for the exchange of new ideas, applications, and experiences in the field of smart technologies and finding global partners for future collaboration. The ICICS-2020 was conducted in two broad categories, Intelligent Circuits & Intelligent Systems and Emerging Technologies in Electrical Engineering

NASA scientific and technical publications: A catalog of special publications, reference publications, conference publications, and technical papers, 1991-1992

This catalog lists 458 citations of all NASA Special Publications, NASA Reference Publications, NASA Conference Publications, and NASA Technical Papers that were entered into the NASA Scientific and Technical Information database during accession year 1991 through 1992. The entries are grouped by subject category. Indexes of subject terms, personal authors, and NASA report numbers are provided

NASA Tech Briefs, March 1995

This issue contains articles with a special focus on Computer-Aided design and engineering amd a research report on the Ames Research Center. Other subjects in this issue are: Electronic Components and Circuits, Electronic Systems, Physical Sciences, Materials, Computer Programs, Mechanics, Machinery, Manufacturing/Fabrication, Mathematics and Information Sciences and Life Science

Some Studies on Breakdown of Solid Insulations and it’s Modeling using Soft Computing Techniques

Electrical power systems are experiencing significant changes at the worldwide scale both in size and in complexities. The generating capacities of power plants and application of high voltage has intensively increased due to their inherent advantages, such as, greater efficiency and cost effectiveness. It is, thus essential to know the property of the insulating materials for optimum solution in terms of cost and insulating capability. Out of so many properties of insulation materials, determination of the breakdown voltage continues to evoke a lot of interest to the Electrical Engineers in general and High Voltage Engineers in particular. Hence, it is possible to develop solid insulating materials with excellent breakdown strength and any attempt at modeling the phenomenon with the presence of void would go a long way in assessing the insulation quality. Some of the few important topics reviewed at the beginning of the thesis are the factors affecting the breakdown voltage in general, breakdown voltage study of different composite insulating materials and the factors affecting the breakdown voltage due to Partial Discharges (PD) in voids

A framework for developing a prognostic model using partial discharge data from electrical trees

Insulation breakdown is a key failure mode of high voltage (HV) equipment, with progressive faults such as electrical treeing leading to potentially catastrophic failure. Electrical treeing proceeds from defects in solid insulation, and cables are particularly affected. Research has shown that diagnosis of the fault can be achieved based on partial discharge (PD) analysis. Nonetheless, after diagnosis of a defect, engineers need to know how long they have to take action. This requires prognosis of remaining insulation life. The progression of a defect is far less well understood than diagnosis, making prognosis a key challenge requiring new approaches to defect modelling. The practical deployment of prognostics for cable monitoring is not currently feasible, due to the lack of understanding of degradation mechanisms and limited data relating defect inception to plant failure. However, this thesis advances the academic state of the art, with an eye towards practical deployment in the future. The expected beneficiaries of this work are therefore researchers in the field of HV condition monitoring in general, and electrical treeing within cables in particular. This research work develops a prognostic model of insulation failure due to the electrical treeing phenomenon by utilising the associated PD data from previous experiment. Both phase-resolved and pulse sequence approaches were employed for PD features extraction. The performance of the PD features as prognostic parameters were evaluated using three metrics, monotonicity, prognosability and trendability. The analysis revealed that features from pulse sequence approach are better than phase-resolved approach in terms of monotonicity and prognosability. The key contributions to knowledge of this work are three-fold: the selection of the most appropriate prognostic parameter for PD in electrical trees, through thorough analysis of the behaviour of a number of candidate parameters; a prognostic modelling approach for this parameter based on curve-fitting; and a generalised framework for prognostic modelling using data-driven techniques.Insulation breakdown is a key failure mode of high voltage (HV) equipment, with progressive faults such as electrical treeing leading to potentially catastrophic failure. Electrical treeing proceeds from defects in solid insulation, and cables are particularly affected. Research has shown that diagnosis of the fault can be achieved based on partial discharge (PD) analysis. Nonetheless, after diagnosis of a defect, engineers need to know how long they have to take action. This requires prognosis of remaining insulation life. The progression of a defect is far less well understood than diagnosis, making prognosis a key challenge requiring new approaches to defect modelling. The practical deployment of prognostics for cable monitoring is not currently feasible, due to the lack of understanding of degradation mechanisms and limited data relating defect inception to plant failure. However, this thesis advances the academic state of the art, with an eye towards practical deployment in the future. The expected beneficiaries of this work are therefore researchers in the field of HV condition monitoring in general, and electrical treeing within cables in particular. This research work develops a prognostic model of insulation failure due to the electrical treeing phenomenon by utilising the associated PD data from previous experiment. Both phase-resolved and pulse sequence approaches were employed for PD features extraction. The performance of the PD features as prognostic parameters were evaluated using three metrics, monotonicity, prognosability and trendability. The analysis revealed that features from pulse sequence approach are better than phase-resolved approach in terms of monotonicity and prognosability. The key contributions to knowledge of this work are three-fold: the selection of the most appropriate prognostic parameter for PD in electrical trees, through thorough analysis of the behaviour of a number of candidate parameters; a prognostic modelling approach for this parameter based on curve-fitting; and a generalised framework for prognostic modelling using data-driven techniques

Light-weight materials selection for high-speed naval craft

Thesis (Nav. E.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering; and, (S.M.)--Massachusetts Institute of Technology, Dept. of Civil and Environmental Engineering, 2007.Includes bibliographical references (p. 109-111).A decision analysis study was conducted on the process of materials selection for high-speed naval craft using the Modified Digital Logic (MDL) method proposed by B. Dehgham-Manshadi et al in ref [17]. The purpose is to show how this method along with Ashby's material selection process can be integrated to provide a comprehensive tool designed specifically for light-weight material optimization. Using Ashby's Material Selection Charts and the MDL method, a step by step material selection process is outlined. Furthermore, a comparison of the materials based on equivalent plate uni-axial ideal elastic compressive stress was completed using the American Bureau of Shipbuilding (ABS) Guide for Building and Classing High-Speed Naval Craft and then an evaluation was done to optimize material selection depending on the designer's preference for weight and cost. The potential materials for evaluation were selected using Naval Sea Systems Command (NAVSEA) references for the most current materials in use, or being evaluated, for light weight naval construction. The results demonstrate the feasibility of using the MDL method to select one or more materials based on desired mechanical and structural characteristics. The study also introduces the potential use of non-traditional materials in Naval Architecture, such as Ultra High-Performance Concrete Composite (UHP2C) DUCTAL ©.by Joseph B. Torrez.S.M.Nav.E

Dielectric Properties, Partial Discharge Properties, and Dissolved Gas Analysis of Ricinnus Oils as Biodegradable Liquid Insulating Materials (Archive)

In this paper partial discharge (PD) characteristics of ricinnus oil and dissolved gas analysis (DGA) due to PD are investigated. The examination showed that the properties of ricinnus oil as a dielectric material such as breakdown voltage, dielectric constant, loss factor, neutralization number, and flash point complied with IEC standard. The I I-q-n pattern of PD and the change of the pattern with applied voltage were observed. Dissolved gas analyses due to PD in oil were also performed. The results were compared with ones in mineral oil which have been using as insulating materials in high voltage apparatus. The results showed that the PDIV of ricinnus oil is higher than one of mineral oil. Types and pattern of dissolved gas due to PD in ricinnus oil were similar with ones in mineral oil. The total combustible gas in ricinnus oil is a little bit higher than one in mineral oil. The measurement of the cumulative charge during 5 minutes showed that PD activity in ricinus oil is higher than one in mineral oil. The results showed that the gassing tendency due to PD of ricinus oil is good with small amount of combustible gase

Methods for modeling degradation of electrical engineering components for lifetime prognosis

Reliability of electrical components is an issue studied to improve the quality of products, and to plan maintenance in case of failure. Reliability is measured by studying the causes of failure and the mean time to failure. One of the methods applied in this field is the study of component aging, because failure often occurs after degradation. The objective of this thesis is to model the degradation of components in electrical engineering, in order to estimate their lifetime. More specifically, this thesis will study large area organic white light sources (OLEDs). These sources offer several advantages in the world of lighting thanks to their thinness, their low energy consumption and their ability to adapt to a wide range of applications. The second components studied are electrical insulators applied to pairs of twisted copper wires, which are commonly used in low voltage electrical machines. First, the degradation and failure mechanisms of the various electrical components, including OLEDs and insulators, are studied. This is done to identify the operational stresses for including them in the aging model. After identifying the main causes of aging, general physical models are studied to quantify the effects of operational stresses. Empirical models are also presented when the physics of degradation is unknown or difficult to model. Next, methods for estimating the parameters of these models are presented, such as multilinear and nonlinear regression, as well as stochastic methods. Other methods based on artificial intelli­gence and online diagnosis are also presented, but they will not be studied in this thesis. These methods are applied to degradation data of organic LEDs and twisted pair insulators. For this purpose, accelerated and multifactor aging test benches are designed based on factorial experimental designs and response surface methods, in order to optimize the cost of the experiments. Then, a measurement protocol is described, in order to optimize the inspection time and to collect periodic data. Finally, estimation methods tackle unconstrained deterministic degradation models based on the measured data. The best empirical model of the degradation trajectory is then chosen based on model selection criteria. In a second step, the parameters of the degradation trajectories are modeled based on operational constraints. The parameters of the aging factors and their interactions are estimated by multilinear regression and according to different learning sets. The significance of the parameters is evaluated by statistical methods if possible. Finally, the lifetime of the experiments in the validation sets is predicted based on the parameters estimated by the different learning sets. The training set with the best lifetime prediction rate is considered the best

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