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Photometric stereo uses images of objects illuminated from various directions to calculate surface normals which can be used to generate 3D meshes of the object. Such meshes can be used by engineers to estimate damage of a concrete surface, or track damage progression over time to inform maintenance decisions. This dataset [1] was collected to quantify the uncertainty in a photometric stereo test rig through both the comparison with a well characterised method (coordinate measurement machine) and experiment virtualisation. Data was collected for 9 real objects using both the test rig and the coordinate measurement machine. These objects range from clay statues to damaged concrete slabs. Furthermore, synthetic data for 12 objects was created via virtual renders generated using Blender (3D software) [2]. The two methods of data generation allowed the decoupling of the physical rig (used to light and photograph objects) and the photometric stereo algorithm (used to convert images and lighting information into 3D meshes). This data can allow users to: test their own photometric stereo algorithms, with specialised data created for structural health monitoring applications; provide an industrially relevant case study to develop and test uncertainty quantification methods on test rigs for structural health monitoring of concrete; or develop data processing methodologies for the alignment of scaled, translated, and rotated data.

Abstract Spot pricing is often suggested as a method of increasing demand‐side flexibility in electrical power load. However, few works have considered the vulnerability of spot pricing to financial fraud via false data injection (FDI) style attacks. The authors consider attacks which aim to alter the consumer load profile to exploit intraday price dips. The authors examine an anomaly detection protocol for cyber‐attacks that seek to leverage spot prices for financial gain. In this way the authors outline a methodology for detecting attacks on industrial load smart meters. The authors first create a feature clustering model of the underlying business, segregated by business type. The authors then use these clusters to create an incentive‐weighted anomaly detection protocol for false data attacks against load profiles. This clustering‐based methodology incorporates both the load profile and spot pricing considerations for the detection of injected load profiles. To reduce false positives, the authors model incentive‐based detection, which includes knowledge of spot prices, into the anomaly tracking, enabling the methodology to account for changes in the load profile which are unlikely to be attacks.

Traditionally, electricity distribution networks were designed for unidirectional power flow without the need to accommodate generation installed at the point of use. However, with the increase in Distributed Energy Resources and other Low Carbon Technologies, the role of distribution networks is changing. This shift brings challenges, including the need for intensive metering and more frequent reconfiguration to identify threats from voltage and thermal violations. Mitigating action through reconfiguration is informed by State Estimation, which is especially challenging for low voltage distribution networks where the constraints of low observability, non-linear load relationships, and highly unbalanced systems all contribute to the difficulty of producing accurate state estimates. To counter low observability, this paper proposes the application of a novel transfer learning methodology, based upon the concept of conditional online Bayesian transfer, to make forward predictions of bus pseudo-measurements. Day ahead load forecasts at a fully observed point on the network are adjusted using the intraday residuals at other points in the network to provide them with load forecasts without the need for a complete set of forecast models at all substations. These form pseudo-measurements that then inform the state estimates at future time points. This methodology is demonstrated on both a representative IEEE Test network and on an actual GB 11 kV feeder network.

To avoid the operational consequence of thermal rating exceedance and the financial consequence of excessive reinforcement, the impact of domestic charging of electric vehicles (EVs) on power distribution networks must be accurately assessed prior to accepting vehicle electrification at the community-scale. Although driven by routine, charging behaviour patterns are also influenced by geography, meteorological conditions and season, hence will have a localised element to them that could reduce the diversity of charging load profiles. To model this uncertainty, this article develops a probabilistic methodology to quantify EV home charging demands based on vehicle mobility data and underlying trip characteristics. Models articulate the departure time distribution using a mixture of von Mises distributions, and incorporate non-negative conditional distributions of trip durations, distances and parking durations, which in turn generalise localised charging behaviours. The resulting load profiles are used to drive a community electric network model based on a distribution feeder in Qatar, a country with high per km energy consumption, to quantify impact scenarios of high temperature and driving habit in terms of voltage and thermal stability. Results indicate that overnight domestic charging is sufficient to support daily trips and local networks are capable of hosting high EV penetration despite peaks.

Cables are the largest assets by volume on power distribution networks and the assets with the least health information routinely gathered. Projections over the next 8 years suggest increased penetration of low-carbon technology (LCT) at the distribution level with higher loads resulting from electric vehicle (EV) and heat pump uptake. Over this period, increased cable loading will directly influence their lifetimes and may mean that existing asset management practices need to be revised to understand the specific impact on end-of-life assessment. Accordingly, this paper uses a physics-based thermal lifetime model based on projected uptake trends for LCTs to evaluate the impact on distribution cable lifetime. Two case studies are presented considering portions of network and the ultimate impact on asset life over the next decade. Two commonly used cables are considered to quantify the lifetime reduction caused by LCT for asset fleets. The paper shows that the projected uptake of EVs and heat pumps will have a detrimental effect on cable life with a 30% reduction in cable lifetime possible.

Current liquid chromatography-tandem mass spectrometry (LC-MS/MS) applications for circulating androgen measurements are technically diverse. Previously, variable results have been reported for testosterone. Data are scarce for androstenedione and absent for dehydroepiandrosterone sulfate (DHEAS). We assessed the agreement of androstenedione, DHEAS and testosterone LC-MS/MS measurements among nine European centers and explored benefits of calibration system unification. Androgens were measured twice by laboratory-specific procedures in 78 patient samples and in EQA materials. Results were obtained by in-house and external calibration. Intra- and inter-laboratory performances were valued. Intra-laboratory CVs ranged between 4.2–13.2 % for androstenedione, 1.6–10.8 % for DHEAS, and 4.3–8.7 % and 2.6–7.1 % for female and male testosterone, respectively. Bias and trueness in EQA materials were within ±20 %. Median inter-laboratory CV with in-house vs. external calibration were 12.0 vs. 9.6 % for androstenedione (p<0.001), 7.2 vs. 4.9 % for DHEAS (p<0.001), 6.4 vs. 7.6 % for female testosterone (p<0.001) and 6.8 and 7.4 % for male testosterone (p=0.111). Median bias vs. all laboratory median with in-house and external calibration were −13.3 to 20.5 % and −4.9 to 18.7 % for androstenedione, −10.9 to 4.8 % and −3.4 to 3.5 % for DHEAS, −2.7 to 6.5 % and −11.3 to 6.6 % for testosterone in females, and −7.0 to 8.5 % and −7.5 to 11.8 % for testosterone in males, respectively. Methods showed high intra-laboratory precision but variable bias and trueness. Inter-laboratory agreement was remarkably good. Calibration system unification improved agreement in androstenedione and DHEAS, but not in testosterone measurements. Multiple components, such as commutability of calibrators and EQA materials and internal standard choices, likely contribute to inter-laboratory variability. [ABSTRACT FROM AUTHOR]

Used in many industrial applications, centrifugal pumps have optimal operating criteria specified at design. These criteria may not be precisely adhered to during operation which will ultimately reduce the life of the asset. Operators would therefore benefit from anticipating how often the design point is deviated from and hence how much asset degradation results. For centrifugal pumps, a novel set of covariates were proposed in this paper which formally partition observed operating zones with an Empirical Bivariate Quantile Partitioned distribution. This captured the dependency relation between operating parameters across plant configurations to predict the component wear that results from particular settings. The effectiveness of this was demonstrated through an operational case study in civil nuclear generation feedwater pumps where corroboration with bearing movements provides an indicator of plant wear. Such a technique is envisaged to inform operators of optimal plant configuration from multiple possibilities in advance of undertaking them.

While machine learning has made inroads into many industries, power systems have some unique application constraints and barriers that have motivated the creation of this Special Issue on their applications in condition monitoring [...]

Power distribution networks are increasingly challenged by ageing plant, environmental extremes and previously unforeseen operational factors. The combination of high loading and weather conditions is responsible for large numbers of recurring faults in legacy plants which have an impact on service quality. Owing to their scale and dispersed nature, it is prohibitively expensive to intensively monitor distribution networks to capture the electrical context these disruptions occur in, making it difficult to forestall recurring faults. In this paper, localised weather data are shown to support fault prediction on distribution networks. Operational data are temporally aligned with meteorological observations to identify recurring fault causes with the potentially complex relation between them learned from historical fault records. Five years of data from a UK Distribution Network Operator is used to demonstrate the approach at both HV and LV distribution network levels with results showing the ability to predict the occurrence of a weather related fault at a given substation considering only meteorological observations. Unifying a diverse range of previously identified fault relations in a single ensemble model and accompanying the predicted network conditions with an uncertainty measure would allow a network operator to manage their network more effectively in the long term and take evasive action for imminent events over shorter timescales.

Liquid chromatography-tandem mass spectrometry (LC-MS/MS) panels that include glucocorticoid-related steroids are increasingly used to characterize and diagnose adrenal cortical diseases. Limited information is currently available about reproducibility of these measurements among laboratories. The aim of the study was to compare LC-MS/MS measurements of corticosterone, 11-deoxycortisol and cortisone at eight European centers and assess the performance after unification of calibration. Seventy-eight patient samples and commercial calibrators were measured twice by laboratory-specific procedures. Results were obtained according to in-house and external calibration. We evaluated intra-laboratory and inter-laboratory imprecision, regression and agreement against performance specifications derived from 11-deoxycortisol biological variation. Intra-laboratory CVs ranged between 3.3 and 7.7%, 3.3 and 11.8% and 2.7 and 12.8% for corticosterone, 11-deoxycortisol and cortisone, with 1, 4 and 3 laboratories often exceeding the maximum allowable imprecision (MAI), respectively. Median inter-laboratory CVs were 10.0, 10.7 and 6.2%, with 38.5, 50.7 and 2.6% cases exceeding the MAI for corticosterone, 11-deoxycortisol and cortisone, respectively. Median laboratory bias vs. all laboratory-medians ranged from −5.6 to 12.3% for corticosterone, −14.6 to 12.4% for 11-deoxycortisol and −4.0 to 6.5% for cortisone, with few cases exceeding the total allowable error. Modest deviations were found in regression equations among most laboratories. External calibration did not improve 11-deoxycortisol and worsened corticosterone and cortisone inter-laboratory comparability. Method imprecision was variable. Inter-laboratory performance was reasonably good. However, cases with imprecision and total error above the acceptable limits were apparent for corticosterone and 11-deoxycortisol. Variability did not depend on calibration but apparently on imprecision, accuracy and specificity of individual methods. Tools for improving selectivity and accuracy are required to improve harmonization. [ABSTRACT FROM AUTHOR]