Adjustment of model parameters to estimate distribution transformers remaining lifespan

Currently, the electrical system in Argentina is working at its maximum capacity, decreasing the margin between the installed power and demanded consumption, and drastically reducing the service life of transformer substations due to overload (since the margin for summer peaks is small). The advent of the Smart Grids allows electricity distribution companies to apply data analysis techniques to manage resources more efficiently at different levels (avoiding damages, better contingency management, maintenance planning, etc.). The Smart Grids in Argentina progresses slowly due to the high costs involved. In this context, the estimation of the lifespan reduction of distribution transformers is a key tool to efficiently manage human and material resources, maximizing the lifetime of this equipment. Despite the current state of the smart grids, the electricity distribution companies can implement it using the available data. Thermal models provide guidelines for lifespan estimation, but the adjustment to particular conditions, brands, or material quality is done by adjusting parameters. In this work we propose a method to adjust the parameters of a thermal model using Genetic Algorithms, comparing the estimation values of top-oil temperature with measurements from 315 kVA distribution transformers, located in the province of Tucumán, Argentina. The results show that, despite limited data availability, the adjusted model is suitable to implement a transformer monitoring system.Fil: Jimenez, Victor Adrian. Universidad Tecnológica Nacional. Facultad Regional Tucumán. Centro de Investigación en Tecnologías Avanzadas de Tucumán; ArgentinaFil: Will, Adrian L. E.. Universidad Tecnológica Nacional. Facultad Regional Tucumán. Centro de Investigación en Tecnologías Avanzadas de Tucumán; ArgentinaFil: Gotay Sardiñas, Jorge. Universidad Tecnológica Nacional. Facultad Regional Tucumán. Centro de Investigación en Tecnologías Avanzadas de Tucumán; ArgentinaFil: Rodriguez, Sebastian Alberto. Universidad Tecnológica Nacional. Facultad Regional Tucumán. Centro de Investigación en Tecnologías Avanzadas de Tucumán; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tucumán; Argentin

Working Paper 03-07 - Capital services and total factor productivity measurements : impact of various methodologies for Belgium

This Working Paper presents the different methodologies currently used to construct a volume index of capital services and analyzes the effects of methodological changes on capital services and total factor productivity estimates for Belgium over the period 1970-2004. The measurement of capital services is realized in two steps. First, productive capital stocks have to be estimated for each type of asset. Two methodologies are generally used: the geometric and the hyperbolic profile. Secondly, these stocks are aggregated, using the user costs of capital (exante or ex-post approach) as weights to derive an overall index. For the economy as a whole and the entire period, under an ex-post approach, the volume indices of capital services estimated with a hyperbolic age-efficiency profile grow at a higher rate than the indices estimated with a geometric profile. This general conclusion is, however, not observed in every sector. Under an ex-ante approach, the different volume indices are quite similar for the whole economy, even if the indices grow generally at a slightly higher rate in the case of a geometric pattern. A higher growth rate of the volume indices generates a higher capital contribution and, consequently, a lower TFP contribution. Over long periods of time, the different TFP estimates are relatively similar. Over shorter periods, the different methodologies generate more significant variations in the TFP contribution.TFP

Development of a whole life cycle cost model for electrification options on the UK rail system

Projects to deliver Overhead Line Equipment (OLE) electrification on the UK rail infrastructure system presents technical challenges which the rail industry in Britain have not traditionally had to consider. Whole Life Cycle assessment provides decision makers with cost estimates for the installation phase and over the entire service life of the system, including disposal. The OLE projects face a particular problem when analysing the best option for overbridges. Much of the rail infrastructure has not traditionally had to consider overhead clearances and therefore many of the bridges are only a little taller than the rolling stock. In addition to the difficulties in assessing the Life-Cycle costs of assets that have historically been used in very limited scales, the Whole Life Cycle assessment must consider the various engineering options that are available for projects. The three competing options (bridge rebuild, track lowering, reduced clearance) are all going to have very different capital expenditure (CAPEX) and operating expenditure (OPEX) costs. This work presents a model created to predict these costs over the anticipated assessment period. The developed model predicts capital expenditures, maintenance and service disruption costs and links them to the three major assets options involved in OLE underbridges

Internet of Things-aided Smart Grid: Technologies, Architectures, Applications, Prototypes, and Future Research Directions

Traditional power grids are being transformed into Smart Grids (SGs) to address the issues in existing power system due to uni-directional information flow, energy wastage, growing energy demand, reliability and security. SGs offer bi-directional energy flow between service providers and consumers, involving power generation, transmission, distribution and utilization systems. SGs employ various devices for the monitoring, analysis and control of the grid, deployed at power plants, distribution centers and in consumers' premises in a very large number. Hence, an SG requires connectivity, automation and the tracking of such devices. This is achieved with the help of Internet of Things (IoT). IoT helps SG systems to support various network functions throughout the generation, transmission, distribution and consumption of energy by incorporating IoT devices (such as sensors, actuators and smart meters), as well as by providing the connectivity, automation and tracking for such devices. In this paper, we provide a comprehensive survey on IoT-aided SG systems, which includes the existing architectures, applications and prototypes of IoT-aided SG systems. This survey also highlights the open issues, challenges and future research directions for IoT-aided SG systems

Assessing the Real-Time Informational Content of Macroeconomic Data Releases for Now-/Forecasting GDP: Evidence for Switzerland

This study utilizes the dynamic factor model of Giannone et al. (2008) in order to make now-/forecasts of GDP quarter-on-quarter growth rates in Switzerland. It also assesses the informational content of macroeconomic data releases for forecasting of the Swiss GDP. We find that the factor model offers a substantial improvement in forecast accuracy of GDP growth rates compared to a benchmark naive constant-growth model at all forecast horizons and at all data vintages. The largest forecast accuracy is achieved when GDP nowcasts for an actual quarter are made about three months ahead of the official data release. We also document that both business tendency surveys as well as stock market indices possess the largest informational content for GDP forecasting although their ranking depends on the underlying transformation of monthly indicators from which the common factors are extracted.Business tendency surveys, Forecasting, Nowcasting, Real-time data, Dynamic factor model

Diagnostic Applications for Micro-Synchrophasor Measurements

This report articulates and justifies the preliminary selection of diagnostic applications for data from micro-synchrophasors (µPMUs) in electric power distribution systems that will be further studied and developed within the scope of the three-year ARPA-e award titled Micro-synchrophasors for Distribution Systems

Offshoring and Specialisation: Are Industries Moving Abroad?

This paper investigates the impact of off-shoring on specialisation via its effect on national endowments and productivity. We use different definition of off-shoring to properly capture international fragmentation of production, while controlling for countries? stocks of R&D and ICT capital. Using industry data for the US, Japan and Europe we show that while offshoring of materials can benefit a wide range of industries, service and intra-industry offshoring can decrease specialisation in high-tech industry, both within manufacturing and services. This effect can be compensated with increasing R&D investments.

RF Localization in Indoor Environment

In this paper indoor localization system based on the RF power measurements of the Received Signal Strength (RSS) in WLAN environment is presented. Today, the most viable solution for localization is the RSS fingerprinting based approach, where in order to establish a relationship between RSS values and location, different machine learning approaches are used. The advantage of this approach based on WLAN technology is that it does not need new infrastructure (it reuses already and widely deployed equipment), and the RSS measurement is part of the normal operating mode of wireless equipment. We derive the Cramer-Rao Lower Bound (CRLB) of localization accuracy for RSS measurements. In analysis of the bound we give insight in localization performance and deployment issues of a localization system, which could help designing an efficient localization system. To compare different machine learning approaches we developed a localization system based on an artificial neural network, k-nearest neighbors, probabilistic method based on the Gaussian kernel and the histogram method. We tested the developed system in real world WLAN indoor environment, where realistic RSS measurements were collected. Experimental comparison of the results has been investigated and average location estimation error of around 2 meters was obtained