Monitoring and Fault Location Sensor Network for Underground Distribution Lines

One of the fundamental tasks of electric distribution utilities is guaranteeing a continuous supply of electricity to their customers. The primary distribution network is a critical part of these facilities because a fault in it could affect thousands of customers. However, the complexity of this network has been increased with the irruption of distributed generation, typical in a Smart Grid and which has significantly complicated some of the analyses, making it impossible to apply traditional techniques. This problem is intensified in underground lines where access is limited. As a possible solution, this paper proposes to make a deployment of a distributed sensor network along the power lines. This network proposes taking advantage of its distributed character to support new approaches of these analyses. In this sense, this paper describes the aquiculture of the proposed network (adapted to the power grid) based on nodes that use power line communication and energy harvesting techniques. In this sense, it also describes the implementation of a real prototype that has been used in some experiments to validate this technological adaptation. Additionally, beyond a simple use for monitoring, this paper also proposes the use of this approach to solve two typical distribution system operator problems, such as: fault location and failure forecasting in power cables.Ministerio de Economía y Competitividad, Government of Spain project Sistema Inteligente Inalámbrico para Análisis y Monitorización de Líneas de Tensión Subterráneas en Smart Grids (SIIAM) TEC2013-40767-RMinisterio de Educación, Cultura y Deporte, Government of Spain, for the funding of the scholarship Formación de Profesorado Universitario 2016 (FPU 2016

Advanced flight control system study

A fly by wire flight control system architecture designed for high reliability includes spare sensor and computer elements to permit safe dispatch with failed elements, thereby reducing unscheduled maintenance. A methodology capable of demonstrating that the architecture does achieve the predicted performance characteristics consists of a hierarchy of activities ranging from analytical calculations of system reliability and formal methods of software verification to iron bird testing followed by flight evaluation. Interfacing this architecture to the Lockheed S-3A aircraft for flight test is discussed. This testbed vehicle can be expanded to support flight experiments in advanced aerodynamics, electromechanical actuators, secondary power systems, flight management, new displays, and air traffic control concepts

Investigation of Motor Supply Signature Analysis to Detect Motor Resistance Imbalances

The trend to use inverter drives in industry is well established. It is desirable to monitor the condition of the motor/drive combination with the minimum of system intervention and at the same time retaining compatibility with the latest generation of AC PWM vector drives. This paper studies the effect of stator resistance asymmetry on the performance of the motor driven by a latest-generation unmodified AC PWM drive under varying speed conditions. The asymmetry of increased resistance in one phase is intended to simulate the onset of a failing connection between drive and motor but one that is non-critical and will remain undetected in use because the resistance increase is small and does not appear to affect the motor operation significantly. The performance is compared against baseline motor data for the resistance increase. Moreover, it is also examined following an auto-tune on the drive with the asymmetric motor in order to observe if any effects of resistance imbalance can be shown on the sensorless vector control algorithms. Initial results from the motor tests clearly show a difference in values measured from the motor current and voltage signals, which can be a useful indication of the asymmetry of the drive system

A brief network analysis of Artificial Intelligence publication

In this paper, we present an illustration to the history of Artificial Intelligence(AI) with a statistical analysis of publish since 1940. We collected and mined through the IEEE publish data base to analysis the geological and chronological variance of the activeness of research in AI. The connections between different institutes are showed. The result shows that the leading community of AI research are mainly in the USA, China, the Europe and Japan. The key institutes, authors and the research hotspots are revealed. It is found that the research institutes in the fields like Data Mining, Computer Vision, Pattern Recognition and some other fields of Machine Learning are quite consistent, implying a strong interaction between the community of each field. It is also showed that the research of Electronic Engineering and Industrial or Commercial applications are very active in California. Japan is also publishing a lot of papers in robotics. Due to the limitation of data source, the result might be overly influenced by the number of published articles, which is to our best improved by applying network keynode analysis on the research community instead of merely count the number of publish.Comment: 18 pages, 7 figure

Technical and vocational skills (TVS): a means of preventing violence among youth in Nigeria

Technical and vocational skills are an important tool for reducing violence among youth, especially in Nigeria, who face security challenges due to different kinds of violence. This paper focusses on the policies and programmes intended to provide youth with skills that can help them improve their life instead of engaging in violence. The paper also studies youth participation in violence. The study shows that youth in Nigeria participate in violence because of unemployment and economic pressure. These youth are mostly from poor families and are mostly used by others to achieve their own unlawful ambition. The data were collected from various secondary sources such as textbooks, journals and conference papers that were carefully reviewed. The results obtained from the literature revealed that youth are not committed, sensitised and mobilised to taking advantage of the opportunities available to them. The results also revealed that almost all the programmes meant to provide youths with skills have failed. Poverty alleviation programmes established to create jobs, self-employment and self-reliance have been unsuccessful. Therefore, alternatives must be provided to help the younger generations. Based on the literature reviewed, the paper discusses related issues and outcomes and ends with recommendations to improve the situation

Radiation Induced Fault Detection, Diagnosis, and Characterization of Field Programmable Gate Arrays

The development of Field Programmable Gate Arrays (FPGAs) has been a great achievement in the world of micro-electronics. One of these devices can be programmed to do just about anything, and replace the need for thousands of individual specialized devices. Despite their great versatility, FPGAs are still extremely vulnerable to radiation from cosmic waves in space and from adversaries on the ground. Extensive research has been conducted to examine how radiation disrupts different types of FPGAs. The results show, unfortunately, that the newer FPGAs with smaller technology are even more susceptible to radiation damage than the older ones. This research incorporates and enhances current methods of radiation detection. The design consists of 15 sensor networks that each have 29 sensors. The sensors are simple inverters, but they have the ability to detect flipped bits and delay errors caused by radiation. Analyzers process the outputs of each sensor to determine if the value agrees with what is expected. This information is fed to a reporter that creates an easy-to-read output that describes which network the fault is in, what type of fault is present, how many are in the network, how long they have been there, and the percent slowdown if it is a delay issue. Each network reports any fault data, to the computer screen in real time. This design does need some improvement, but once those improvements are made and tested, this system can be incorporated with FPGA reconfiguration methods that automatically place application logic away from failing errors of the FPGA. This system has great potential to become a great too in fault mitigation

Diagnostic and prognostic analysis tools for monitoring degradation in aged structures

This research addresses the problem of prolonging the life of aged structures of historical value that have already outlived their original designed lives many times. While a lot of research has been carried out in the field of structural monitoring, diagnostics and prognostics for high tech industries, this is not the case for historical aged structures. Currently most maintenance projects for aged structures have focused on the instrumentation and diagnostic techniques required to detect any damage with a certain degree of success. This research project involved the development of diagnostic and prognostic tools to be used for monitoring and predicting the ‘health’ of aged structures. The diagnostic and prognostic tools have been developed for the monitoring of Cutty Sark iron structures as a first application. The concept of canary and parrot sensor devices are developed where canary devices are small, accelerated devices, which will fail according to similar failure mechanisms occurring in an aged structures and parrot devices are designed to fail at the same rate as the structure, thus mimicking the structure. The model-driven prognostic tool uses a Physics-of-Failure (PoF) model to predict remaining life of a structure. It uses a corrosion model based on the decrease in corrosion rate over time to predict remaining life of an aged iron structures. The data-driven diagnostic tool developed uses Mahalanobis Distance analysis to detect anomalies in the behaviour of a structure. Bayesian Network models are then used as a fusion method, integrating remaining life predictions from the model-driven prognostic tool with information of possible anomalies from data-driven diagnostic tool to provide a probability distribution of predicted remaining life. The diagnostics and prognostic tools are validated and tested through demonstration example and experimental tests. This research primarily looks at applying diagnostic and prognostic technologies used in high-tech industries to aged iron structures. In order to achieve this, the model-driven and data-driven techniques commonly used had to be adapted taking into consideration the particular constraints of monitoring and maintaining aged structures. The fusion technique developed is a novel approach for prognostics for aged structures and provides the flexibility often needed for diagnostic and prognostic tools

Fault diagnosis and comparing risk for the steel coil manufacturing process using statistical models for binary data

[EN] Advanced statistical models can help industry to design more economical and rational investment plans. Fault detection and diagnosis is an important problem in continuous hot dip galvanizing. Increasingly stringent quality requirements in the automotive industry also require ongoing efforts in process control to make processes more robust. Robust methods for estimating the quality of galvanized steel coils are an important tool for the comprehensive monitoring of the performance of the manufacturing process. This study applies different statistical regression models: generalized linear models, generalized additive models and classification trees to estimate the quality of galvanized steel coils on the basis of short time histories. The data, consisting of 48 galvanized steel coils, was divided into sets of conforming and nonconforming coils. Five variables were selected for monitoring the process: steel strip velocity and four bath temperatures. The present paper reports a comparative evaluation of statistical models for binary data using Receiver Operating Characteristic (ROC) curves. A ROC curve is a graph or a technique for visualizing, organizing and selecting classifiers based on their performance. The purpose of this paper is to examine their use in research to obtain the best model to predict defective steel coil probability. In relation to the work of other authors who only propose goodness of fit statistics, we should highlight one distinctive feature of the methodology presented here, which is the possibility of comparing the different models with ROC graphs which are based on model classification performance. Finally, the results are validated by bootstrap procedures.The authors are indebted to the anonymous referees whose suggestions improved the original manuscript. This work was supported by a grant from PAID-06-08 (Programa de Apoyo a la Investigacion y Desarrollo) of the Universitat Politecnica de Valencia.Debón Aucejo, AM.; García-Díaz, JC. (2012). Fault diagnosis and comparing risk for the steel coil manufacturing process using statistical models for binary data. Reliability Engineering and System Safety. 100:102-114. https://doi.org/10.1016/j.ress.2011.12.022S10211410