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

Contextualising water use in residential settings: a survey of non-intrusive techniques and approaches

Water monitoring in households is important to ensure the sustainability of fresh water reserves on our planet. It provides stakeholders with the statistics required to formulate optimal strategies in residential water management. However, this should not be prohibitive and appliance-level water monitoring cannot practically be achieved by deploying sensors on every faucet or water-consuming device of interest due to the higher hardware costs and complexity, not to mention the risk of accidental leakages that can derive from the extra plumbing needed. Machine learning and data mining techniques are promising techniques to analyse monitored data to obtain non-intrusive water usage disaggregation. This is because they can discern water usage from the aggregated data acquired from a single point of observation. This paper provides an overview of water usage disaggregation systems and related techniques adopted for water event classification. The state-of-the art of algorithms and testbeds used for fixture recognition are reviewed and a discussion on the prominent challenges and future research are also included

ANOMALY INFERENCE BASED ON HETEROGENEOUS DATA SOURCES IN AN ELECTRICAL DISTRIBUTION SYSTEM

Harnessing the heterogeneous data sets would improve system observability. While the current metering infrastructure in distribution network has been utilized for the operational purpose to tackle abnormal events, such as weather-related disturbance, the new normal we face today can be at a greater magnitude. Strengthening the inter-dependencies as well as incorporating new crowd-sourced information can enhance operational aspects such as system reconfigurability under extreme conditions. Such resilience is crucial to the recovery of any catastrophic events. In this dissertation, it is focused on the anomaly of potential foul play within an electrical distribution system, both primary and secondary networks as well as its potential to relate to other feeders from other utilities. The distributed generation has been part of the smart grid mission, the addition can be prone to electronic manipulation. This dissertation provides a comprehensive establishment in the emerging platform where the computing resources have been ubiquitous in the electrical distribution network. The topics covered in this thesis is wide-ranging where the anomaly inference includes load modeling and profile enhancement from other sources to infer of topological changes in the primary distribution network. While metering infrastructure has been the technological deployment to enable remote-controlled capability on the dis-connectors, this scholarly contribution represents the critical knowledge of new paradigm to address security-related issues, such as, irregularity (tampering by individuals) as well as potential malware (a large-scale form) that can massively manipulate the existing network control variables, resulting into large impact to the power grid

Disaggregating high-resolution gas metering data using pattern recognition

© 2018 Elsevier B.V. Growing concern about the scale and extent of the gap between predicted and actual energy performance of new and retrofitted UK homes has led to a surge in the development of new tools and technologies trying to address the problem. A vital aspect of this work is to improve ease and accuracy of measuring in-use performance to better understand the extent of the gap and diagnose its causes. Existing approaches range from low cost but basic assessments allowing very limited diagnosis, to intensively instrumented experiments that provide detail but are expensive and highly disruptive, typically requiring the installation of specialist monitoring equipment and often vacating the house for several days. A key challenge in reducing the cost and difficulty of complex methods in occupied houses is to disaggregate space heating energy from that used for other uses without installing specialist monitoring equipment. This paper presents a low cost, non-invasive approach for doing so for a typical occupied UK home where space heating, hot water and cooking are provided by gas. The method, using dynamic pattern matching of total gas consumption measurements, typical of those provided by a smart meter, was tested by applying it to two occupied houses in the UK. The findings revealed that this method was successful in detecting heating patterns in the data and filtering out coinciding use

Web interactive non intrusive load disaggregation system for active demand in smart grids

A Smart Grid combines the use of traditional technology with innovative digital solutions, making the management of the electricity grid more flexible. It allows for monitoring, analysis, control and communication within the supply chain to improve efficiency, reduce the energy consumption and cost, and maximize the transparency and reliability of the energy supply chain. The optimization of energy consumption in Smart Grids is possible by using an innovative system based on Non Intrusive Appliance Load Monitoring (NIALM) algorithms, in which individual appliance power consumption information is disaggregated from single-point measurements, that provide a feedback in such a way to make energy more visible and more amenable to understanding and control. We contribute with an approach for monitoring consumption of electric power in households based on both a NILM algorithm, that uses a simple load signatures, and a web interactive systems that allows an active role played by users

Energy storage systems for smart meter privacy: a study of public perceptions

Smart meters are a vital step for transitioning to a smart grid architecture. Studies have shown that it is possible to extract appliance usage information through non-intrusive load monitoring methods. This data can be used by third-parties for unwanted activities like targeted marketing, home invasion, etc. It is postulated that the data leakage will be minimum when the power flow from/to the grid is piecewise linear. To achieve linearity, the use of energy storage systems is investigated. Energy storage systems (ESS) are being increasingly used by customers having solar energy production. In this project, an algorithm for the energy management unit (EMU) to control the ESS is proposed which maintains piecewise linearity. Two types of users are considered for the study: 1. user who injects excess energy to the grid 2. user who does not (or is not allowed by law) to inject power to the grid. The effect of the algorithm on both users is studied. The minimum capacity of ESS for data leakage prevention is analysed for both cases. Data from four different households is used in different combinations to obtain the mean capacity required. Using this data, an equation is formulated for the minimum capacity of ESS required to maintain linearity in power flows. The second part of the study is to understand how people perceive smart meter privacy issues and how much they are willing to spend for mitigating privacy breaches. The survey is done in Sweden. Sweden was the first European country to have 100% smart meter roll-out. In 2020, the smart meters installed during the first roll-out will reach their economic lifespan. Hence, the country is preparing for a second-generation mass roll-out of smart meters. The perception of people regarding smart meters is identified from two perspectives. First, the consumers are directly surveyed for estimating their awareness of smart meter privacy problems and their willingness to invest in technologies that prevent such issues. Second, different stakeholders in smart metering are surveyed regarding their experience during first and second roll-out. The methods currently employed to safeguard consumer data is also explored during the second survey

EU Privacy seals project: Challenges and Possible Scope of an EU Privacy Seal Scheme. Final Report Study Deliverable 3.4

The objective of this report is focus on the challenges of implementing an effective EU privacy seal and its possible scope. It returns the focus to privacy and data protection, and presents further groundwork to feed into Task 4 of the Study (Proposals and evaluation of options for an EU-wide privacy seals scheme). Where relevant, research results and analyses of Tasks 1 and 2 are used. First, the report assesses the gaps in current privacy seal sector. Next, it highlights the advantages of, priorities for and possible scope of an EU privacy seal scheme. Eventually, four case studies (CCTV systems, cloud services, smart metering systems and biometric systems) illustrate the possible scope of an EU privacy seal scheme and demonstrate whether an EU privacy seals scheme would bring any added value to privacy and data protection.JRC.G.6-Digital Citizen Securit

Intelligent Remote Monitoring and Management system for Type1 Diabetes

The work presented in this thesis focuses on developing a telemedicine system for better management of type1 diabetes in children and teenagers. The research and development of the system is motivated by the inadequate communication in the current system of management of the disease, which results in non-compliance of patients following the regimen. This non-compliance generally results in uncontrolled blood glucose levels, which can result in hypoglycaemia, hyperglycaemia and later life health complications. This further results in an increase in health care costs. In this context, the thesis presents a novel end-to-end, low cost telemedicine system, WithCare+, developed in close collaboration between the University of Sheffield (Electronics & Electrical Engineering) and Sheffield Children’s Hospital. The system was developed to address the challenges of implementing modern telemedicine in type 1 diabetic care with particular relevance to National Health Service children’s clinics in the United Kingdom, by adopting a holistic care driven approach (involving all stakeholders) based on specific key enabler technologies such as low cost and reconfigurable design. However, one of the major issues with current telemedicine system is non-compliance of the patients due to invasive procedure of the glucose measurement which could be clearly addressed by non-invasive method of glucose measurement. Hence, the thesis also makes a contribution towards non-invasive glucose measurement using Near Infrared spectroscopy in terms of addressing the calibration challenge; two methods are proposed to improve the calibration of the Near Infrared instrument. The first method combines locally weighted regression and partial least square regression and the second method combines digital band pass filtering with support vector regression. The efficacy of the proposed methods is validated in experiments carried out in a non-controlled environment and the results obtained demonstrate that the proposed methods improved the performance of the calibration model in comparison to traditional calibration techniques such as Principal Component Regression and Partial Least Squares regression