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.

Effects of Undaria pinnatifida, Himanthalia elongata and Porphyra umbilicalis extracts on in vitro α-glucosidase activity and glucose diffusion

Background: Seaweeds are good sources of dietary fibre, which can influence glucose uptake and glycemic control.Objective: To investigate and compare the in vitro inhibitory activity of different extracts from Undaria pinnatifida (Wakame), Himanthalia elongata (Sea spaghetti) and Porphyra umbilicalis (Nori) on α-glucosidase activity and glucose diffusion.Methods: The in vitro effects chloroform-, ethanol- and water-soluble extracts of the three algae were assayed on α- glucosidase activity and glucose diffusion through membrane. Principal Components Analysis (PCA) was applied to identify patterns in the data and to discriminate which extract will show the most proper effect.Results: Only water extracts of Sea spaghetti possessed significant in vitro inhibitory effects on α-glucosidase activity (26.2% less mmol/L glucose production than control, p < 0.05) at 75 min. PCA distinguished Sea spaghetti effects, supporting that soluble fibre and polyphenols were involved. After 6 h, Ethanol-Sea spaghetti and water-Wakame extracts exerted the highest inhibitory effects on glucose diffusion (65.0% and 60.2% vs control, respectively). This extracts displayed the lowest slopes for glucose diffusion-time lineal adjustments (68.2% and 62.8% vs control, respectively).Conclusions: The seaweed hypoglycemic effects appear multi-faceted and not necessarily concatenated. According to present results, ethanol and water extracts of Sea spaghetti, and water extracts of Wakame could be useful for the development of functional foods with specific hypoglycemic properties.

A unified view on the effect of fibers and loading on sfrc creep through linear projection to latent structures

Creep of Steel Fiber Reinforced Concrete (SFRC) under flexural loads in the cracked state and to what extent different factors determine creep behaviour are quite understudied topics within the general field of SFRC mechanical properties. A series of prismatic specimens have been produced and subjected to sustained flexural loads. The effect of a number of variables (fiber length and slenderness, fiber content, and concrete compressive strength) has been studied in a comprehensive fashion. Twelve response variables (creep parameters measured at different times) have been retained as descriptive of flexural creep behaviour. Multivariate techniques have been used: the experimental results have been projected to their latent structure by means of Principal Components Analysis (PCA), so that all the information has been reduced to a set of three latent variables. They have been related to the variables considered and statistical significance of their effects on creep behaviour has been assessed. The result is a unified view on the effects of the different variables considered upon creep behaviour: fiber content and fiber slenderness have been detected to clearly modify the effect that load ratio has on flexural creep behaviour.

Experimentally validated partial least squares model for dynamic line rating

This study presents a model based on partial least squares (PLS) regression for dynamic line rating (DLR). The model has been verified using data from field measurements, lab tests and outdoor experiments. Outdoor experimentation has been conducted both to verify the model predicted DLR and also to provide training data not available from field measurements, mainly heavily loaded conditions. The proposed model, unlike the direct measurement based DLR techniques, enables prediction of line rating for periods ahead of time whenever a reliable weather forecast is available. The PLS approach yields a very simple statistical model that accurately captures the physical performance of the conductor within a given environment without requiring a predetermination of parameters as required by many physical modelling techniques. Accuracy of the PLS model has been tested by predicting the conductor temperature for measurement sets other than those used for training. Being a linear model, it is straightforward to estimate the conductor ampacity for a set of predicted weather parameters. The PLS estimated ampacity has proven its accuracy through an outdoor experiment on a piece of the line conductor in real weather conditions.

Partial least squares model for dynamic rating of overhead line in wind intensive areas based on field measurements

This paper presents a statistical model for the thermal behaviour of the line model based on lab tests and field measurements. This model is based on Partial Least Squares (PLS) multi regression and is used for the Dynamic Line Rating (DLR) in a wind intensive area. DLR provides extra capacity to the line, over the traditional seasonal static rating, which makes it possible to defer the need for reinforcement the existing network or building new lines. The proposed PLS model has a number of appealing features; the model is linear, so it is straightforward to use for predicting the line rating for future periods using the available weather forecast. Unlike the available physical models, the proposed model does not require any physical parameters of the line, which avoids the inaccuracies resulting from the errors and/or variations in these parameters. The developed model is compared with physical model, the Cigre model, and has shown very good accuracy in predicting the conductor temperature as well as in determining the line rating for future time periods. 

Principal Component Analysis of Wide Area Phasor Measurements for Islanding Detection – A Geometric View

This paper presents a new technique for the detectionof islanding conditions in electrical power systems. This problem isespecially prevalent in systems with significant penetrations of distributedrenewable generation. The proposed technique is based onthe application of principal component analysis (PCA) to data setsof wide-area frequency measurements, recorded by phasor measurementunits. The PCA approach was able to detect islandingaccurately and quickly when compared with conventional RoCoFtechniques, as well as with the frequency difference and change-ofangledifference methods recently proposed in the literature. Thereliability and accuracy of the proposed PCA approach is demonstratedby using a number of test cases, which consider islandingand nonislanding events. The test cases are based on real data,recorded from several phasor measurement units located in theU.K. power system.

Robust 2D Ear Registration and Recognition Based on SIFT Point Matching

Significant recent progress has shown ear recognition to be a viable biometric. Good recognition rates have been demonstrated under controlled conditions, using manual registration or with specialised equipment. This paper describes a new technique which improves the robustness of ear registration and recognition, addressing issues of pose variation, background clutter and occlusion. By treating the ear as a planar surface and creating a homography transform using SIFT feature matches, ears can be registered accurately. The feature matches reduce the gallery size and enable a precise ranking using a simple 2D distance algorithm. When applied to the XM2VTS database it gives results comparable to PCA with manual registration. Further analysis on more challenging datasets demonstrates the technique to be robust to background clutter, viewing angles up to +/- 13 degrees and with over 20% occlusion.

Patient-reported 'ever had' and 'current' long term physical symptoms following prostate cancer treatments

OBJECTIVE: To document prostate cancer patient reported 'ever experienced' and 'current' prevalence of disease specific physical symptoms stratified by primary treatment received.
PATIENTS: 3,348 prostate cancer survivors 2-15 years post diagnosis.
METHODS: Cross-sectional, postal survey of 6,559 survivors diagnosed 2-15 years ago with primary, invasive PCa (ICD10-C61) identified via national, population based cancer registries in Northern Ireland and Republic of Ireland. Questions included symptoms at diagnosis, primary treatments and physical symptoms (impotence/urinary incontinence/bowel problems/breast changes/loss of libido/hot flashes/fatigue) experienced 'ever' and at questionnaire completion ("current"). Symptom proportions were weighted by age, country and time since diagnosis. Bonferroni corrections were applied for multiple comparisons.
RESULTS: Adjusted response rate 54%; 75% reported at least one 'current' physical symptom ('ever':90%), with 29% reporting at least three. Prevalence varied by treatment; overall 57% reported current impotence; this was highest following radical prostatectomy (RP)76% followed by external beam radiotherapy with concurrent hormone therapy (HT); 64%. Urinary incontinence (overall 'current' 16%) was highest following RP ('current'28%, 'ever'70%). While 42% of brachytherapy patients reported no 'current' symptoms; 43% reported 'current' impotence and 8% 'current' incontinence. 'Current' hot flashes (41%), breast changes (18%) and fatigue (28%) were reported more often by patients on HT.
CONCLUSION: Symptoms following prostate cancer are common, often multiple, persist long-term and vary by treatment. They represent a significant health burden. An estimated 1.6% of men over 45 is a prostate cancer survivor currently experiencing an adverse physical symptom. Recognition and treatment of physical symptoms should be prioritised in patient follow-up. This information should facilitate men and clinicians when deciding about treatment as differences in survival between radical treatments is minimal.

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.

Synchrophasor-Based Islanding Detection for Distributed Generation Systems Using Systematic Principal Component Analysis Approaches

Systematic principal component analysis (PCA) methods are presented in this paper for reliable islanding detection for power systems with significant penetration of distributed generations (DGs), where synchrophasors recorded by Phasor Measurement Units (PMUs) are used for system monitoring. Existing islanding detection methods such as Rate-of-change-of frequency (ROCOF) and Vector Shift are fast for processing local information, however with the growth in installed capacity of DGs, they suffer from several drawbacks. Incumbent genset islanding detection cannot distinguish a system wide disturbance from an islanding event, leading to mal-operation. The problem is even more significant when the grid does not have sufficient inertia to limit frequency divergences in the system fault/stress due to the high penetration of DGs. To tackle such problems, this paper introduces PCA methods for islanding detection. Simple control chart is established for intuitive visualization of the transients. A Recursive PCA (RPCA) scheme is proposed as a reliable extension of the PCA method to reduce the false alarms for time-varying process. To further reduce the computational burden, the approximate linear dependence condition (ALDC) errors are calculated to update the associated PCA model. The proposed PCA and RPCA methods are verified by detecting abnormal transients occurring in the UK utility network.

A novel radial basis function neural network principal component analysis scheme for PMU-based wide-area power system monitoring

A novel model-based principal component analysis (PCA) method is proposed in this paper for wide-area power system monitoring, aiming to tackle one of the critical drawbacks of the conventional PCA, i.e. the incapability to handle non-Gaussian distributed variables. It is a significant extension of the original PCA method which has already shown to outperform traditional methods like rate-of-change-of-frequency (ROCOF). The ROCOF method is quick for processing local information, but its threshold is difficult to determine and nuisance tripping may easily occur. The proposed model-based PCA method uses a radial basis function neural network (RBFNN) model to handle the nonlinearity in the data set to solve the no-Gaussian issue, before the PCA method is used for islanding detection. To build an effective RBFNN model, this paper first uses a fast input selection method to remove insignificant neural inputs. Next, a heuristic optimization technique namely Teaching-Learning-Based-Optimization (TLBO) is adopted to tune the nonlinear parameters in the RBF neurons to build the optimized model. The novel RBFNN based PCA monitoring scheme is then employed for wide-area monitoring using the residuals between the model outputs and the real PMU measurements. Experimental results confirm the efficiency and effectiveness of the proposed method in monitoring a suite of process variables with different distribution characteristics, showing that the proposed RBFNN PCA method is a reliable scheme as an effective extension to the linear PCA method.

Loss-of-Main Monitoring and Detection for Distributed Generations Using Dynamic Principal Component Analysis

In this paper, our previous work on Principal Component Analysis (PCA) based fault detection method is extended to the dynamic monitoring and detection of loss-of-main in power systems using wide-area synchrophasor measurements. In the previous work, a static PCA model was built and verified to be capable of detecting and extracting system faulty events; however the false alarm rate is high. To address this problem, this paper uses a well-known ‘time lag shift’ method to include dynamic behavior of the PCA model based on the synchronized measurements from Phasor Measurement Units (PMU), which is named as the Dynamic Principal Component Analysis (DPCA). Compared with the static PCA approach as well as the traditional passive mechanisms of loss-of-main detection, the proposed DPCA procedure describes how the synchrophasors are linearly
auto- and cross-correlated, based on conducting the singular value decomposition on the augmented time lagged synchrophasor matrix. Similar to the static PCA method, two statistics, namely T2 and Q with confidence limits are calculated to form intuitive charts for engineers or operators to monitor the loss-of-main situation in real time. The effectiveness of the proposed methodology is evaluated on the loss-of-main monitoring of a real system, where the historic data are recorded from PMUs installed in several locations in the UK/Ireland power system.

Regulatory Change and Capital Adjustment of US Credit Unions

We investigate the determinants of US credit union capital-to-assets ratios, before and after the implementation of the current capital adequacy regulatory framework in 2000. Capitalization varies pro-cyclically, and until the financial crisis credit unions classified as adequately capitalized or below followed a faster adjustment path than well capitalized credit unions. This pattern was reversed, however, in the aftermath of the crisis. The introduction of the PCA regulatory regime achieved a reduction in the proportion of credit unions classified as adequately capitalized or below that continued until the onset of the crisis. Since the crisis, the speed of recovery of credit unions in this category following an adverse capitalization shock was sharply reduced.

The role of secreted frizzled-related protein 2 expression in prostate cancer

AIMS: Improved prostate cancer (PCa)-specific biomarkers are urgently required to distinguish between indolent and aggressive disease, in order to avoid overtreatment. In this study, we investigated the prostatic tissue expression of secreted frizzled-related protein (SFRP)-2.

METHODS AND RESULTS: Following immunohistochemical analysis on PCa tissue microarrays with samples from 216 patients, strong/moderate SFRP-2 expression was observed in epithelial cells of benign prostatic hyperplasia, and negative/weak SFRP-2 expression was observed in the majority of tumour epithelia. However, among Gleason grade 5 carcinomas, 40% showed strong/moderate SFRP-2 expression and 60% showed negative SFRP-2 expression in epithelial cells. Further microscopic evaluation of Gleason grade 5 tumours revealed different morphological patterns, corresponding with differential SFRP-2 expression. The first subgroup (referred to as Type A) appeared to have a morphologically solid growth pattern, whereas the second subgroup (referred to as Type B) appeared to have a more diffuse pattern. Furthermore, 100% (4/4) of Type A patients experienced biochemical recurrence, as compared with 0% (0/6) of Type B patients.

CONCLUSIONS: These results imply: (i) that there is a loss of SFRP-2 expression from benign to malignant prostate glands; and (ii) differential SFRP-2 expression among two possible subgroups of Gleason grade 5 tumours.