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The identification of microbiological infection is usually a diagnostic investigation, a complex process that is firstly initiated by clinical suspicion. With the emergence of high-throughput sequencing (HTS) technologies, metagenomic analysis has unveiled the power to identify microbial DNA/RNA from a diverse range of clinical samples (1). Metagenomic analysis of whole human genomes at the clinical/research interface bypasses the steps of clinical scrutiny and targeted testing and has the potential to generate unexpected findings relating to infectious and sometimes transmissible disease. There is no doubt that microbial findings that may have a significant impact on a patient's treatment and their close contacts should be reported to those with clinical responsibility for the sample-donating patient. There are no clear recommendations on how such findings that are incidental, or outside the original investigation, should be handled. Here we aim to provide an informed protocol for the management of incidental microbial findings as part of the 100,000 Genomes Project which may have broader application in this emerging field. As with any other clinical information, we aim to prioritise the reporting of data that are most likely to be of benefit to the patient and their close contacts. We also set out to minimize risks, costs and potential anxiety associated with the reporting of results that are unlikely to be of clinical significance. Our recommendations aim to support the practice of microbial metagenomics by providing a simplified pathway that can be applied to reporting the identification of potential pathogens from metagenomic datasets. Given that the ambition for UK sequenced human genomes over the next 5 years has been set to reach 5 million and the field of metagenomics is rapidly evolving, the guidance will be regularly reviewed and will likely adapt over time as experience develops. © 2019 Magiorkinis G et al.

A method for synthesising wind speed time series (WSTS) from limited data is required that can be used for reliability examination of wind farms and maintenance strategies for a range of wind speed scenarios. Key characteristics of the wind resource need to be captured, including energy availability and maintenance weather windows. 4 WSTS simulators were used to produce synthetic WSTS based on benchmark data from a meteorological mast data at the offshore Egmond aan Zee wind farm in the Netherlands. These synthetic WSTS were compared with test criteria to determine their suitability for reliability analysis. This included comparing the synthetic WSTS to the benchmark data in terms of the energy availability in the wind and from a typical turbine, residence time at wind speeds, number of transitions between 1m/s wind speed bins, replication of seasonal characteristics including weather windows, and underlying statistical properties. Based on the chosen criteria, the most appropriate WSTS simulator was the modified Markov process. However, no modelling technique performed best against all criteria and none capture the autocorrelation function (ACF) as closely as desired. Therefore, there is scope for a more advanced technique for wind speed modelling for reliability analysis which combines the best aspects of the models used in this work.

Wind energy is the most rapidly expanding source of renewable energy in the UK and has supplied 5% of the country’s electricity requirements since 2010. With finite space available for future development, investors and manufacturers are constantly looking for new methods to increase the efficiency of both active and planned wind farms. Wind turbine wake effects are a common cause of energy loss within a wind farm; capable of reducing a trailing turbine’s output by up to 30%. Through the use of wake estimation models, wind farms can be designed and operated to reduce the wake effects experienced by turbines. The Jensen model is a fast wake estimation model, derived from the momentum equation, capable of providing wind speed and wake radius values at distances downstream of a turbine. The simplicity and low computation time of the model have made it highly popular in the commercial market. This paper critically reviews the Jensen model for on-shore wind farms. This critical review demonstrates that the Jensen model is appropriate as a rapid wake estimator that can be used as part of an on-line wind farm control system for wake optimisation.

This work presents a dynamic wind farm controller for maximising power output of a wind farm. The controller uses a coordinated control approach where the output of the upstream turbines is varied to minimise wake effects on the downstream turbines. The speed deficit due to wakes is calculated using a modified version of the Jensen wake flow model. This model gives the wind speed at different locations in the wind farm. Particle Swarm Optimisation (PSO) is used to generate different sets of coefficients of power (CP) for all the turbines and select the one which results in maximum farm output. The Brazos wind farm is used as a case study. The controller optimises a row of seven wind turbines in less than 5 seconds and increases the farm output by 9%. High computational efficiency and accuracy make the proposed controller very suitable for practical implementation in industry.

Studies have shown that the reliability of fully rated converters (FRC) in permanent magnet synchronous generator (PMSG) wind turbines is critical. This paper outlines an experimental rig that applies PMSG wind turbine specific operating profiles to a machine side converter (MSC) power module. A number of test regimes have been designed to verify thermal models of the power module, validate the use of cycle life vs. Tj profile manufacturing data, and determine the wind turbine operational profiles that cause the most damage to the MSC.

This summary presents the key messages from the final analysis of the baseline domestic profile control group in the Customer-Led Network Revolution (CLNR) monitoring trials. It presents outputs from the largest study of household electricity use in the UK and provides integrated socio-technical analysis of domestic customer loads and electrical consumption on the basis of interdisciplinary multi-method research. We present analysis of the baseline control group test cell 1a (TC1a) that collected smart meter data for ca. 9000 customers across a two year period May 2011 to April 2013. This dataset has been used as a comparison against the impacts of technology and load-shifting interventions trialled in the CLNR project. These interventions are designed to modify how customers place demand on the electrical distribution network, e.g. by shifting demand out of peak periods, automatically pausing certain electrical goods, etc. Using a socio-technical approach we take account of the ways in which electricity use and its flexibility are shaped by social and material factors.

This summary presents the key messages from the final analysis of the domestic heat pump group (test cell 3) in the Customer-Led Network Revolution monitoring trials. It presents outputs from the largest study of household electricity use in the UK and provides integrated socio-technical analysis of domestic customer loads and electrical consumption on the basis of interdisciplinary multi-method research. This report draws on qualitative interviews and home energy tours conducted with 18 households recruited from the 331 domestic customers involved in the CLNR air-source-to-water heat pump (ASWHP) trial (Test Cell 3). Participants with ASWHP were contacted directly by one of the research team, using information provided by the energy retailer, which had previously identified households that were willing to participate. The semi-structured interviews focused on building rapport with the participant while discussing their energy use in general terms. These conversations included information about occupancy, major electrical loads, heating regimes, washing and cooking practices, thoughts and feelings about electricity use, seasonality and other temporal factors as well as experiences of and responses to new technologies. Interviews were focused on two clusters within the regional network: social housing tenants in South Tyneside and County Durham. Social housing landlords had installed loft and wall insulation, where feasible, and retrofitted an ASWHP at no cost to the tenants. Interview participants had lived with the ASWHP for between 6-12 months, including the winter months. Interviews were conducted between January and March 2013.

This report examines the nature and practice of energy use by domestic customers in the CLNR project who generated their own solar photovoltaic (PV) electricity. It brings together power monitoring data capable of providing insight into how electricity is generated and used at the household level with data concerning how households respond to these new forms of energy generation. The report draws on the data for test cell 5 (solar PV) and test cell 20 IHD (solar PV with an in-home display showing consumption, export and import readings). Over the period 1st January 2013 – 31st December 2013, there were 143 customers in TC5 and 149 customers in TC20 IHD. The TC5 and TC20 data is compared to the analysis of TC1a (a control set of customers with no interventions except smart meters and in-home displays of consumption) which contained a total of 8415 customers with data monitored over the period 1st October 2012 – 30th September 2013. Social science research was conducted with households to investigate their current patterns and practices of electricity use and how this had changed along with the use of low carbon technologies and participation in the interventions developed under the CLNR project). In total, the analysis draws on 46 separate interviews with house tours.

This report describes the CLNR trial which examined electric vehicles usage patterns and expected network loading in the event of large-scale take-up of electric vehicles. The trial involved domestic customers who owned an electric vehicle and had access to a home charger, analysis being carried out by Durham University’s CLNR project engineering and social science teams. Observations are based on a semi-qualitative analysis of EV dataset collated from online survey, face to face interviews with householders enrolled in the CLNR project, and power monitoring data collected from households and electric vehicle (EV) chargers. The CLNR project output is the largest socio-technical study of domestic EV charging in the UK and brings together monitoring data to examine electric vehicle usage patterns and expected network loading in the event of large-scale take-up of electric vehicles. Customers on the trial exhibit “working household” house demand profiles with EV demand profiles that track these. EV charging strongly follows domestic occupancy, especially as it relates to working patterns; the standard working day rhythms define and constrain EV charging patterns with weekend charging load different from weekdays. The EV charging practices show diurnal as well as seasonal patterns of activities. The EV load increases in winter months, likely due to battery charging demand (increased vehicle heating) and decreases in summer months (possibly) due to other factors such as summer holidays. This supports the predominant use the EV as a week-day car used to commute to work.

This report presents the key messages from the final analysis of the time of use (ToU) tariff trial group in the Customer-Led Network Revolution monitoring trials. It presents outputs from a significant study of household electricity use and ToU tariff behaviour in the UK and provides integrated socio-technical analysis on the basis of interdisciplinary multi-method research. The aim of this research has been to study how smart grid interventions might be designed and implemented and to understand social responses to such interventions. A total of 628 participants in ToU trial (Test Cell 9a) volunteered to undertake a trial of a three band static time of use tariff and were equipped with an in-home display unit which provided a near real time signal of their current electricity load through a traffic light system and retrospective visualisations of gas and electricity consumption. Participants in the control trial were also equipped with smart meters and the same in-home display units as the participants in Test Cell 1a (the baseline domestic profile control group,). The rates and time bands of the tariff are shown in Table 1. While the trial of the tariff ran for almost two years, this report analyses the data gathered for the 12 months of the trial (October 2012 – September 2013) when the largest number of participants’ energy consumption records could be analysed.

This report presents an analysis of the performance of domestic Micro-CHP devices, installed at 11 separate domestic locations for the CLNR project. The report addresses Learning Objective 1 of the project, which is to enhance understanding of current, emerging and possible future customer load and generation characteristics. The technology installed is a Baxi Ecogen Stirling engine, with a maximum heat output of 6kW, a maximum electrical output of 1kW, and an overall efficiency of 90% [1], producing a heat to power ratio of 6:1, an electric efficiency of 13%, and a heat efficiency of 77%. In each location, two parameters were monitored: the electrical demand from and generation produced by the micro-CHP engine; and the amount of electricity imported (or exported) from the grid by the house as a whole. Both were measured in average watts per measured interval, with measurements taken every minute. Trial monitoring began in December 2012 and ended in March 2014. Trial monitoring data was provided by British Gas. The analysis in this report focuses on two areas; the economic and carbon savings of the Micro-CHP and the operating profile of the Micro-CHP and thus the potential impact on distribution networks.

This paper outlines proposed testing criteria for wind speed time series (WSTS) models. The objective is to assess their suitability for reliability analysis that is dependent on an accurate representation of weather patterns. Two WSTS models were analysed for their suitability against these criteria. The Markov model was found to be suitable for resource assessment, but would require modification before it could represent weather patterns, whilst the random sampling model could represent weather patterns more accurately, but could not be used for resource assessment.

This paper introduces a wind speed model for simulating the distribution of wind speeds within a wind farm. The model combines a macro scale wind speed time series (WSTS) model based on a continuous Markov process with a wake flow model, based on the Jensen model, to produce wind speeds upwind of every wind turbine. This model has been designed for use in the testing of turbine coordinated control algorithms and for use in detailed reliability analysis. An example analysis was carried out to investigate the Annual Energy Not Produced (AENP) due to wake effects on a single string wind farm. It was found that the wakes accounted for a 20.2% reduction in energy production compared to the wakeless scenario, highlighting the need to model these wake effects.

The development of electrical control system faults leads to increased mechanical component degradation, severe reduction of asset performance, and a direct increase in annual maintenance costs. This paper presents a highly accurate data driven classification system for the diagnosis of electrical control system faults, in particular, wind turbine pitch faults. Early diagnosis of these faults can enable operators to move from traditional corrective or time based maintenance towards a predictive maintenance strategy, whilst simultaneously mitigating risks and requiring no further capital expenditure. Our approach provides transparent, human-readable rules for maintenance operators which have been validated by an independent domain expert. Data from 8 wind turbines was collected every 10 min over a period of 28 months with 10 attributes utilised to diagnose pitch faults. Three fault classes are identified, each represented by 6,000 instances in each of the testing and training sets. Of the turbines, 4 are used to train the system with a further 4 for validation. Repeated random sampling of the majority fault class was used to reduce computational overheads whilst retaining information content and balancing the training and validation sets. A classification accuracy of 85.50 % was achieved with 14 human readable rules generated via the RIPPER inductive rule learner. Of these, 11 were described as “useful and intuitive” by an independent domain-expert. An expert system was developed utilising the model along with domain knowledge, resulting in a pitch fault diagnostic accuracy of 87.05 % along with a 42.12 % reduction in pitch fault alarms.

Many current wind turbine (WT) studies focus on improving their reliability and reducing the cost of energy, particularly when WTs are operated offshore. WT Supervisory Control and Data Acquisition (SCADA) systems contain alarms and signals that provide significant important information. A possible WT fault can be detected through a rigorous analysis of the SCADA data. This paper proposes a new method for analysing WT SCADA data by using Adaptive Neuro-Fuzzy Inference System (ANFIS) with the aim to achieve automated detection of significant pitch faults. Two existing statistical analysis approaches were applied to detect common pitch fault symptoms. Based on the findings, an ANFIS Diagnosis Procedure was proposed and trained. The trained system was then applied in a wind farm containing 26 WTs to show its prognosis ability for pitch faults. The result was compared to a SCADA Alarms approach and the comparison has demonstrated that the ANFIS approach gives prognostic warning of pitch faults ahead of pitch alarms. Finally, a Confusion Matrix analysis was made to show the accuracy of the proposed approach.

Peak electricity demand poses a particular challenge both to network operators and to energy suppliers. A reduction in peak demand would allow existing networks to accommodate load growth with lower investment, and also reduce the cost of electricity generation during peak periods. To explore the potential for peak reduction, the Customer-Led Network Revolution project has trialled a Time of Use (ToU) tariff scheme. By increasing electricity prices during the weekday peak period (4pm-8pm) for throughout the year and reducing prices in off-peak periods, the tariff incentivises a shift in consumption out of the peak period. A static ToU tariff was used – that is, the tariff remained constant and did not change dynamically depending on expected network loading. Based on smart meter data and survey responses, test cell (TC) 9a investigated the electricity use patterns of 574 domestic users on a ToU tariff between October 2012 and September 2013 and compared them to those of the control group (test cell 1a). The impact of demographic profile on customers’ responses to the ToU tariff was also considered. On average, when compared to consumers in the control cell TC1a, customers on the ToU tariff had lower consumption during the peak period on weekdays, and higher consumption at other times – indicating that the tariff achieved the intended behaviour change. There was a small net reduction (0.8%) in annual consumption, although this was not enough to be statistically significant. In particular, the trial showed; Lower electricity consumption during the peak periods (between 1.5% and 11.3% less than TC1a). This is in line with our qualitative research where customers claim changing time of use of certain appliances. Lower average peak power demands1 during the peak period (between 3.2% and 12.5% lower than TC1a when averaged throughout the year and across all customers). On average, customers showed a lower maximum half-hourly peak demand (between 2.1% – 10.3% lower than TC1a) during the peak period. However at the time of greatest system peak demand – specifically a single half-hour in the year there was no (statistically significant) difference in the mean peak demand observed between TC9a and TC1a

Due to the fluid nature of the early stages of the design process, it is difficult to obtain deterministic product design evaluations. This is primarily due to the flexibility of the design at this stage, namely that there can be multiple interpretations of a single design concept. However, it is important for designers to understand how these design concepts are likely to fulfil the original specification, thus enabling the designer to select or bias towards solutions with favourable outcomes. One approach is to create a stochastic model of the design domain. This paper tackles the issues of using a product database to induce a Bayesian model that represents the relationships between the design parameters and characteristics. A greedy learning algorithm is presented and illustrated using a simple case study.

The Information Pump (IP) is a methodology that aims to counter the problems arising from traditional subjective product data collection. The IP is a game theory based process that aims to maximise information extracted from a panel of subjects, while maintaining their interest in the process through a continuous panelist scoring method. The challenge with implementing this arises from the difficulty in executing the 'game'. In its original format, there is an assumption that the game is played with each player using their own PC to interact with the process. While this in theory allows information and scores to flow in a controlled manner between the players, it actually provides a major barrier to the wider adoption of the IP method. This barrier is two-fold: it is costly and complex, and it is not a natural manner for exchanging information. The core objective is to develop a low cost version of the IP method. This will use the game theory approach to maintain interest among participants and maximise information extraction, but remove the need for each participant to have their own PC interface to the game. This will require replacing both the inter-player communication method and the score keeping/reporting.

We study steady-state pattern-forming instabilities on R2. A uniform initial state that is invariant under the Euclidean group E(2) of translations, rotations and reflections of the plane loses linear stability to perturbations with a non-zero wavenumber kc. We identify branches of solutions that are periodic on a square lattice that inherits a reducible action of the symmetry group D4 x T2. Reducible group actions occur naturally when we consider solutions that are periodic on real-space lattices that are much more widely spaced than the wavelength of the pattern-forming instability. They thus apply directly to computations in large domains where periodic boundary conditions are applied.\ud The normal form for the bifurcation is calculated, taking the presence of various hidden symmetries into account and making use of previous work by Crawford [8]. We compute the stability (relative to other branches of solutions that exist on this lattice) of the solution branches that we can guarantee by applying the equivariant branching lemma. These computations involve terms higher than third order in the normal form, and are affected by the hidden symmetries. The effects of hidden symmetries that we elucidate are relevant also to bifurcations from fully nonlinear patterns.\ud \ud In addition, other primary branches of solutions with submaximal symmetry are found always to exist; their existence cannot be deduced by applying the equivariant branching lemma. These branches are stable in open regions of the space of normal form coefficients.\ud \ud The relevance of these results is illustrated by numerical simulations of a simple pattern-forming PDE.\ud

Two-dimensional convection can become unstable to a mean shear flow. In three dimensions, with periodic boundary conditions in the two horizontal directions, this instability can cause the alignment of convection rolls to alternate between the x and y axes. Rolls with their axes in the y-direction become unstable to a shear flow in the x-direction that tilts and suppresses the rolls, but this flow does not affect rolls whose axes are aligned with it. New rolls, orthogonal to the original rolls, can grow, until they in turn become unstable to the shear flow instability. This behaviour is illustrated both through numerical simulations and through low-order models, and the sequence of local and global bifurcations is determined.

Numerical experiments on two-dimensional convection with or without a vertical magnetic field reveal a bewildering variety of periodic and aperiodic oscillations. Steady rolls can develop a shearing instability, in which rolls turning over in one direction grow at the expense of rolls turning over in the other, resulting in a net shear across the layer. As the temperature difference across the fluid is increased, two-dimensional pulsating waves occur, in which the direction of shear alternates. We analyse the nonlinear dynamics of this behaviour by first constructing appropriate low-order sets of ordinary differential equations, which show the same behaviour, and then analysing the global bifurcations that lead to these oscillations by constructing one-dimensional return maps. We compare the behaviour of the partial differential equations, the models and the maps in systematic two-parameter studies of both the magnetic and the non-magnetic cases, emphasising how the symmetries of periodic solutions change as a result of global bifurcations. Much of the interesting behaviour is associated with a discontinuous change in the leading direction of a fixed point at a global bifurcation; this change occurs when the magnetic field is introduced.

Numerical experiments on compressible magnetoconvection reveal a new type of periodic oscillation, associated with alternating streaming motion. Analogous behaviour in a Boussinesq fluid is constrained by extra symmetry. A low-order model confirms that these pulsating waves appear via a pitchfork-Hopf-gluing bifurcation sequence from the steady state.

The problem of Boussinesq magnetoconvection with periodic boundary conditions is\ud studied using standard perturbation techniques. It is fbund that either travelling\ud waves or standing waves can be stable at the onset of oscillatory convection,\ud depending on the parameters of the problem. When travelling waves occur, a steady\ud shearing flow is present that is quadratic in the amplitude of the convective flow. The\ud weakly nonlinear predictions are confirmed by comparison with numerical solutions\ud of the full partial differential equations at Rayleigh numbers 10% above critical.\ud Modulated waves (through which stability is transferred between travelling and\ud standing waves) are found near the boundary between the regions in parameter space\ud where travelling waves and standing waves are preferred.