Machine Learning Applications in Estimating Transformer Loss of Life

Transformer life assessment and failure diagnostics have always been important problems for electric utility companies. Ambient temperature and load profile are the main factors which affect aging of the transformer insulation, and consequently, the transformer lifetime. The IEEE Std. C57.911995 provides a model for calculating the transformer loss of life based on ambient temperature and transformer's loading. In this paper, this standard is used to develop a data-driven static model for hourly estimation of the transformer loss of life. Among various machine learning methods for developing this static model, the Adaptive Network-Based Fuzzy Inference System (ANFIS) is selected. Numerical simulations demonstrate the effectiveness and the accuracy of the proposed ANFIS method compared with other relevant machine learning based methods to solve this problem.Comment: IEEE Power and Energy Society General Meeting, 201

Structural Vulnerability Analysis of Electric Power Distribution Grids

Power grid outages cause huge economical and societal costs. Disruptions in the power distribution grid are responsible for a significant fraction of electric power unavailability to customers. The impact of extreme weather conditions, continuously increasing demand, and the over-ageing of assets in the grid, deteriorates the safety of electric power delivery in the near future. It is this dependence on electric power that necessitates further research in the power distribution grid security assessment. Thus measures to analyze the robustness characteristics and to identify vulnerabilities as they exist in the grid are of utmost importance. This research investigates exactly those concepts- the vulnerability and robustness of power distribution grids from a topological point of view, and proposes a metric to quantify them with respect to assets in a distribution grid. Real-world data is used to demonstrate the applicability of the proposed metric as a tool to assess the criticality of assets in a distribution grid

Optimal funding and investment strategies in defined contribution pension plans under Epstein-Zin utility

A defined contribution pension plan allows consumption to be redistributed from the plan member’s working life to retirement in a manner that is consistent with the member’s personal preferences. The plan’s optimal funding and investment strategies therefore depend on the desired pattern of consumption over the lifetime of the member. We investigate these strategies under the assumption that the member has an Epstein-Zin utility function, which allows a separation between risk aversion and the elasticity of intertemporal substitution, and we also take into account the member’s human capital. We show that a stochastic lifestyling approach, with an initial high weight in equity-type investments and a gradual switch into bond-type investments as the retirement date approaches is an optimal investment strategy. In addition, the optimal contribution rate each year is not constant over the life of the plan but reflects trade-offs between the desire for current consumption, bequest and retirement savings motives at different stages in the life cycle, changes in human capital over the life cycle, and attitude to risk

Forecasting Recharging Demand to Integrate Electric Vehicle Fleets in Smart Grids

Electric vehicle fleets and smart grids are two growing technologies. These technologies provided new possibilities to reduce pollution and increase energy efficiency. In this sense, electric vehicles are used as mobile loads in the power grid. A distributed charging prioritization methodology is proposed in this paper. The solution is based on the concept of virtual power plants and the usage of evolutionary computation algorithms. Additionally, the comparison of several evolutionary algorithms, genetic algorithm, genetic algorithm with evolution control, particle swarm optimization, and hybrid solution are shown in order to evaluate the proposed architecture. The proposed solution is presented to prevent the overload of the power grid

Review of recent research towards power cable life cycle management

Power cables are integral to modern urban power transmission and distribution systems. For power cable asset managers worldwide, a major challenge is how to manage effectively the expensive and vast network of cables, many of which are approaching, or have past, their design life. This study provides an in-depth review of recent research and development in cable failure analysis, condition monitoring and diagnosis, life assessment methods, fault location, and optimisation of maintenance and replacement strategies. These topics are essential to cable life cycle management (LCM), which aims to maximise the operational value of cable assets and is now being implemented in many power utility companies. The review expands on material presented at the 2015 JiCable conference and incorporates other recent publications. The review concludes that the full potential of cable condition monitoring, condition and life assessment has not fully realised. It is proposed that a combination of physics-based life modelling and statistical approaches, giving consideration to practical condition monitoring results and insulation response to in-service stress factors and short term stresses, such as water ingress, mechanical damage and imperfections left from manufacturing and installation processes, will be key to success in improved LCM of the vast amount of cable assets around the world

A proposed case for the cloud software engineering in security

This paper presents Cloud Software Engineering in Security (CSES) proposal that combines the benefits from each of good software engineering process and security. While other literature does not provide a proposal for Cloud security as yet, we use Business Process Modeling Notation (BPMN) to illustrate the concept of CSES from its design, implementation and test phases. BPMN can be used to raise alarm for protecting Cloud security in a real case scenario in real-time. Results from BPMN simulations show that a long execution time of 60 hours is required to protect real-time security of 2 petabytes (PB). When data is not in use, BPMN simulations show that the execution time for all data security rapidly falls off. We demonstrate a proposal to deal with Cloud security and aim to improve its current performance for Big Data

A sparse grid approach to balance sheet risk measurement

In this work, we present a numerical method based on a sparse grid approximation to compute the loss distribution of the balance sheet of a financial or an insurance company. We first describe, in a stylised way, the assets and liabilities dynamics that are used for the numerical estimation of the balance sheet distribution. For the pricing and hedging model, we chose a classical Black & Scholes model with a stochastic interest rate following a Hull & White model. The risk management model describing the evolution of the parameters of the pricing and hedging model is a Gaussian model. The new numerical method is compared with the traditional nested simulation approach. We review the convergence of both methods to estimate the risk indicators under consideration. Finally, we provide numerical results showing that the sparse grid approach is extremely competitive for models with moderate dimension.Comment: 27 pages, 7 figures. CEMRACS 201

Assessing the value dimensions of outsourced maintenance services

Purpose - The purpose of this paper is to investigate the diverse nature of tangible and intangible value dimensions that contribute to customers' perception of value from outsourced maintenance services. Design/methodology/approach - A multiple case study approach has been adopted. Repertory grid, an in-depth structured interviewing technique, has been used in order to draw out the respondents' hidden constructs in evaluating outsourced maintenance services. Data have been collected from four customer organizations of outsourced maintenance services, and a total of 33 interviews have been undertaken. Findings - The paper has identified a range of tangible and intangible value dimensions that are of importance in maintenance outsourcing decision making. The most important value dimensions for maintenance outsourcing were found to be specialist knowledge, accessibility (of the service provider), relational dynamic, range of products and services, delivery, pricing and locality. Although the paper has identified the most important value dimensions the paper also emphasizes the need to take into account the full range of value dimensions in order to understand the whole value pattern in an organization. Practical implications - The results will be of use for maintenance service providers to help them to improve value-adding capacity of maintenance services. The results can also be applied by customers to help them assess the value they receive from outsourced maintenance services. Originality/value - A different perspective on maintenance outsourcing value is provided. The value patterns in different organizations and the viewpoints of respondents in different organizational roles are described. The dynamic nature of these tangible or intangible values over time and their interrelationships has also been explored