A statistical process control approach for automatic anti-islanding detection using synchrophasors

Anti-islanding protection is becoming increasingly important due to the rapid installation of distributed generation from renewable resources like wind, tidal and wave, solar PV, bio-fuels, as well as from other resources like diesel. Unintentional islanding presents a potential risk for damaging utility plants and equipment connected from the demand side, as well as to public and personnel in utility plants. This paper investigates automatic islanding detection. This is achieved by deploying a statistical process control approach for fault detection with the real-time data acquired through a wide area measurement system, which is based on Phasor Measurement Unit (PMU) technology. In particular, the principal component analysis (PCA) is used to project the data into principal component subspace and residual space, and two statistics are used to detect the occurrence of fault. Then a fault reconstruction method is used to identify the fault and its development over time. The proposed scheme has been used in a real system and the results have confirmed that the proposed method can correctly identify the fault and islanding site.

Using ancestry-informative markers to identify fine structure across 15 populations of European origin

The Wellcome Trust Case Control Consortium 3 anorexia nervosa genome-wide association scan includes 2907 cases from 15 different populations of European origin genotyped on the Illumina 670K chip. We compared methods for identifying population stratification, and suggest list of markers that may help to counter this problem. It is usual to identify population structure in such studies using only common variants with minor allele frequency (MAF) >5%; we find that this may result in highly informative SNPs being discarded, and suggest that instead all SNPs with MAF >1% may be used. We established informative axes of variation identified via principal component analysis and highlight important features of the genetic structure of diverse European-descent populations, some studied for the first time at this scale. Finally, we investigated the substructure within each of these 15 populations and identified SNPs that help capture hidden stratification. This work can provide information regarding the designing and interpretation of association results in the International Consortia. 

Soil-Geochemical Factors controlling the Distribution and Oral Bioaccessibility of Nickel, Vanadium and Chromium in Northern Ireland Soils

Geogenic nickel (Ni), vanadium (V) and chromium (Cr) are present at elevated levels in soils in Northern Ireland. Whilst Ni, V and Cr total soil concentrations share common geological origins, their respective levels of oral bioaccessibility are influenced by different soil-geochemical factors. Oral bioaccessibility extractions were carried out on 145 soil samples overlying 9 different bedrock types to measure the bioaccessible portions of Ni, V and Cr. Principal component analysis identified two components (PC1 and PC2) accounting for 69% of variance across 13 variables from the Northern Ireland Tellus Survey geochemical data. PC1 was associated with underlying basalt bedrock, higher bioaccessible Cr concentrations and lower Ni bioaccessibility. PC2 was associated with regional variance in soil chemistry and hosted factors accounting for higher Ni and V bioaccessibility. Eight per cent of total V was solubilised by gastric extraction on average across the study area. High median proportions of bioaccessible Ni were observed in soils overlying sedimentary rock types. Whilst Cr bioaccessible fractions were low (max = 5.4%), the highest measured bioaccessible Cr concentration reached 10.0 mg kg-1, explained by factors linked to PC1 including high total Cr concentrations in soils overlying basalt bedrock.

Dietary patterns and cardiovascular risk factors in adolescents and young adults: the Northern Ireland Young Hearts Project

Dietary pattern (DP) analysis allows examination of the combined effects of nutrients and foods on the markers of CVD. Very few studies have examined these relationships during adolescence or young adulthood. Traditional CVD risk biomarkers were analysed in 12-15-year-olds (n 487; Young Hearts (YH)1) and again in the same individuals at 20-25 years of age (n 487; YH3). Based on 7 d diet histories, in the present study, DP analysis was performed using a posteriori principal component analysis for the YH3 cohort and the a priori Mediterranean Diet Score (MDS) was calculated for both YH1 and YH3 cohorts. In the a posteriori DP analysis, YH3 participants adhering most closely to the 'healthy' DP were found to have lower pulse wave velocity (PWV) and homocysteine concentrations, the 'sweet tooth' DP were found to have increased LDL concentrations, systolic blood pressure, and diastolic blood pressure and decreased HDL concentrations, the 'drinker/social' DP were found to have lower LDL and homocysteine concentrations, but exhibited a trend towards a higher TAG concentration, and finally the 'Western' DP were found to have elevated homocysteine and HDL concentrations. In the a priori dietary score analysis, YH3 participants adhering most closely to the Mediterranean diet were found to exhibit a trend towards a lower PWV. MDS did not track between YH1 and YH3, and nor was there a longitudinal relationship between the change in the MDS and the change in CVD risk biomarkers. In conclusion, cross-sectional analysis revealed that some associations between DP and CVD risk biomarkers were already evident in the young adult population, namely the association between the healthy DP (and the MDS) and PWV; however, no longitudinal associations were observed between these relatively short time periods.

When are total concentrations not total? Factors affecting geochemical analytical techniques for measuring element concentrations in soil

Inductively coupled plasma (ICP) following aqua regia digestion and X-ray fluorescence (XRF) are both geochemical techniques used to determine ‘total’ concentrations of elements in soil. The aim of this study is to compare these techniques, identify elements for which inconsistencies occur and investigate why they arise. A study area (∼14,000 km2) with a variety of total concentration controls and a large geochemical dataset (n = 7950) was selected. Principal component analysis determined underlying variance in a dataset composed of both geogenic and anthropogenic elements. Where inconsistencies between the techniques were identified, further numerical and spatial analysis was completed. The techniques are more consistent for elements of geogenic sources and lead, whereas other elements of anthropogenic sources show less consistency within rural samples. XRF is affected by sample matrix, while the form of element affects ICP concentrations. Depending on their use in environmental studies, different outcomes would be expected from the techniques employed, suggesting the choice of analytical technique for geochemical analyses may be more critical than realised.

The use of handheld near-infrared reflectance spectroscopy (NIRS) for the proximate analysis of poultry feed and to detect melamine adulteration of soya bean meal

The use of handheld near infrared (NIR) instrumentation, as a tool for rapid analysis, has the potential to be used widely in the animal feed sector. A comparison was made between handheld NIR and benchtop instruments in terms of proximate analysis of poultry feed using off-the-shelf calibration models and including statistical analysis. Additionally, melamine adulterated soya bean products were used to develop qualitative and quantitative calibration models from the NIRS spectral data with excellent calibration models and prediction statistics obtained. With regards to the quantitative approach, the coefficients of determination (R2) were found to be 0.94-0.99 with the corresponding values for the root mean square error of calibration and prediction were found to be 0.081-0.215 % and 0.095-0.288 % respectively. In addition, cross validation was used to further validate the models with the root mean square error of cross validation found to be 0.101-0.212 %. Furthermore, by adopting a qualitative approach with the spectral data and applying Principal Component Analysis, it was possible to discriminate between adulterated and pure samples.

S21 Culture Independent Identification Of Bacterial Communities In The Respiratory Tract Of Patients With Copd, Healthy Non-smokers And Healthy Smokers

Introduction and aims: The role bacteria play in the development and progression of Chronic Obstructive Pulmonary Disease (COPD) is unclear. We used culture-independent methods to describe differences and/or similarities in microbial communities in the lower airways of patients with COPD, healthy non-smokers and smokers.

Methods: Bronchial wash samples were collected from patients with COPD (GOLD 1–3; n = 18), healthy non-smokers (HV; n = 11) and healthy smokers (HS; n = 8). Samples were processed using the Illumina MiSeq platform. The Shannon-Wiener Index (SW) of diversity, lung obstruction (FEV1/FVC ratio) and ordination by Non-Metric Multidimensional Scaling (NMDS) on Bray-Curtis dissimilarity indices were analysed to evaluate how samples were related. Principal component analysis (PCA) was performed to assess the effect specific taxa had within each cohort. Characteristics of each cohort are shown in Table 1.

Results: There was no difference in taxa richness between cohorts (range: 69–71; p = 0.954). Diversity (SW Index) was significantly lower in COPD samples compared to samples from HV and HS (p = 0.009 and p = 0.033, respectively). There was no significant difference between HV and HS (p = 0.186). The FEV1/FVC ratio was significantly lower for COPD compared to HV (p = 9*10–8) and HS (p = 2*10–6), respectively. NMDS analysis showed that communities belonging to either of the healthy groups were more similar to each other than they were to samples belonging to the COPD group. PCA analysis showed that members of Streptococcus sp. and Haemophilus sp. had the largest effect on the variance explained in COPD. In HS, Haemophilus sp., Fusobaterium sp., Actinomyces sp., Prevotella sp. and Veillonella sp. had the largest effect on the variance explained, while in HV Neisseria sp., Porphyromonas sp., Actinomyces sp., Atopobium sp., Prevotella and Veillonella sp. had the largest effect on the variance explained.

Conclusions: The study demonstrates that microbial communities in the lower airways of patients with COPD are significantly different from that seen in healthy comparison groups. Patients with COPD had lower microbial diversity than either of the healthy comparison groups, higher relative abundance of members of Streptococcus sp. and lower relative abundance of a number of key anaerobes.Characteristics

Hydrophilic Interaction Ultra-High Performance Liquid Chromatography Coupled with Triple-Quadrupole Mass Spectrometry for Determination of Nucleosides and Nucleobases in Animal Horns

Traditional Chinese Medicines (TCMs) derived from animal horns are one of the most important types of Chinese medicine. In the present study, a fast and sensitive analytical method was established for qualitative and quantitative determination of 14 nucleosides and nucleobases in animal horns using hydrophilic interaction ultra-high performance liquid chromatography coupled with triple-quadruple tandem mass spectrometry (HILIC-UPLC-QQQ-MS/MS) in selective reaction monitoring (SRM) mode. The method was optimized and validated, and showed good linearity, precision, repeatability, and accuracy. The method was successfully used to determine contents of the 14 nucleosides and nucleobases in 25 animal horn samples. Hierarchical clustering analysis (HCA) and principal component analysis (PCA) were performed and the 25 samples were thereby divided into two groups, which agreed with taxonomy. The method may enable quick and effective search of substitutes for precious horns.

Material Identification of Loose Particles in Sealed Electronic Devices Using PCA and SVM

The existence of loose particles left inside the sealed electronic devices is one of the main factors affecting the reliability of the whole system. It is important to identify the particle material for analyzing their source. The conventional material identification algorithms mainly rely on time, frequency and wavelet domain features. However, these features are usually overlapped and redundant, resulting in unsatisfactory material identification accuracy. The main objective of this paper is to improve the accuracy of material identification. First, the principal component analysis (PCA) is employed to reselect the nine features extracted from time and frequency domains, leading to six less correlated principal components. And then the reselected principal components are used for material identification using a support vector machine (SVM). Finally, the experimental results show that this new method can effectively distinguish the type of materials including wire, aluminum and tin particles.

Major and trace elements in milk and Halloumi cheese as markers for authentication of goat feeding regimes and geographical origin

Sixty samples of milk, Halloumi cheese and local grazing plants (i.e. shrubs) were collected over a year from dairy farms located on three different locations of Cyprus. Major and trace elements were quantified using inductively coupled plasma-atomic emission spectroscopy (ICP-AES). Milk and Halloumi cheese produced in different geographical locations presented significant differences in the concentration of some of the elements analysed. Principal component analysis showed grouping of samples according to the region of production for both milk and cheese samples. These findings show that the assay of elements can provide useful fingerprints for the characterisation of dairy products.

Neural Network Based Attack on a Masked Implementation of AES

Masked implementations of cryptographic algorithms are often used in commercial embedded cryptographic devices to increase their resistance to side channel attacks. In this work we show how neural networks can be used to both identify the mask value, and to subsequently identify the secret key value with a single attack trace with high probability. We propose the use of a pre-processing step using principal component analysis (PCA) to significantly increase the success of the attack. We have developed a classifier that can correctly identify the mask for each trace, hence removing the security provided by that mask and reducing the attack to being equivalent to an attack against an unprotected implementation. The attack is performed on the freely available differential power analysis (DPA) contest data set to allow our work to be easily reproducible. We show that neural networks allow for a robust and efficient classification in the context of side-channel attacks.

Establishing the validity of English GP Patient Survey items evaluating out-of-hours care

Background A 2014 national audit used the English General Practice Patient Survey (GPPS) to compare service users’ experience of out-of-hours general practitioner (GP) services, yet there is no published evidence on the validity of these GPPS items. Objectives Establish the construct and concurrent validity of GPPS items evaluating service users’ experience of GP out-of-hours care. Methods Cross-sectional postal survey of service users (n=1396) of six English out-of-hours providers. Participants reported on four GPPS items evaluating out-of-hours care (three items modified following cognitive interviews with service users), and 14 evaluative items from the Out-of-hours Patient Questionnaire (OPQ). Construct validity was assessed through correlations between any reliable (Cochran's α>0.7) scales, as suggested by a principal component analysis of the modified GPPS items, with the ‘entry access’ (four items) and ‘consultation satisfaction’ (10 items) OPQ subscales. Concurrent validity was determined by investigating whether each modified GPPS item was associated with thematically related items from the OPQ using linear regressions. Results The modified GPPS item-set formed a single scale (α=0.77), which summarised the two-component structure of the OPQ moderately well; explaining 39.7% of variation in the ‘entry access’ scores (r=0.63) and 44.0% of variation in the ‘consultation satisfaction’ scores (r=0.66), demonstrating acceptable construct validity. Concurrent validity was verified as each modified GPPS item was highly associated with a distinct set of related items from the OPQ. Conclusions Minor modifications are required for the English GPPS items evaluating out-of-hours care to improve comprehension by service users. A modified question set was demonstrated to comprise a valid measure of service users’ overall satisfaction with out-of-hours care received. This demonstrates the potential for the use of as few as four items in benchmarking providers and assisting services in identifying, implementing and assessing quality improvement initiatives.

Using Graph Components Derived from an Associative Concept Dictionary to Predict fMRI Neural Activation Patterns that Represent the Meaning of Nouns

In this study, we introduce an original distance definition for graphs, called the Markov-inverse-F measure (MiF). This measure enables the integration of classical graph theory indices with new knowledge pertaining to structural feature extraction from semantic networks. MiF improves the conventional Jaccard and/or Simpson indices, and reconciles both the geodesic information (random walk) and co-occurrence adjustment (degree balance and distribution). We measure the effectiveness of graph-based coefficients through the application of linguistic graph information for a neural activity recorded during conceptual processing in the human brain. Specifically, the MiF distance is computed between each of the nouns used in a previous neural experiment and each of the in-between words in a subgraph derived from the Edinburgh Word Association Thesaurus of English. From the MiF-based information matrix, a machine learning model can accurately obtain a scalar parameter that specifies the degree to which each voxel in (the MRI image of) the brain is activated by each word or each principal component of the intermediate semantic features. Furthermore, correlating the voxel information with the MiF-based principal components, a new computational neurolinguistics model with a network connectivity paradigm is created. This allows two dimensions of context space to be incorporated with both semantic and neural distributional representations.

Wide-Area Phase-Angle Measurements for Islanding Detection—An Adaptive Nonlinear Approach

The integration of an ever growing proportion of large scale distributed renewable generation has increased the probability of maloperation of the traditional RoCoF and vector shift relays. With reduced inertia due to non-synchronous penetration in a power grid, system wide disturbances have forced the utility industry to design advanced protection schemes to prevent system degradation and avoid cascading outages leading to widespread blackouts. This paper explores a novel adaptive nonlinear approach applied to islanding detection, based on wide area phase angle measurements. This is challenging, since the voltage phase angles from different locations exhibit not only strong nonlinear but also time-varying characteristics. The adaptive nonlinear technique, called moving window kernel principal component analysis is proposed to model the time-varying and nonlinear trends in the voltage phase angle data. The effectiveness of the technique is exemplified using both DigSilent simulated cases and real test cases recorded from the Great Britain and Ireland power systems by the OpenPMU project.

Islanding Detection Based on Probabilistic PCA with Missing Values in PMU Data

This paper proposes a probabilistic principal component analysis (PCA) approach applied to islanding detection study based on wide area PMU data. The increasing probability of uncontrolled islanding operation, according to many power system operators, is one of the biggest concerns with a large penetration of distributed renewable generation. The traditional islanding detection methods, such as RoCoF and vector shift, are however extremely sensitive and may result in many unwanted trips. The proposed probabilistic PCA aims to improve islanding detection accuracy and reduce the risk of unwanted tripping based on PMU measurements, while addressing a practical issue on missing data. The reliability and accuracy of the proposed probabilistic PCA approach are demonstrated using real data recorded in the UK power system by the OpenPMU project. The results show that the proposed methods can detect islanding accurately, without being falsely triggered by generation trips, even in the presence of missing values.