Distributed Adaptive Learning Framework for Wide Area Monitoring of Power Systems Integrated with Distributed Generations

This paper presents a preliminary study of developing a novel distributed adaptive real-time learning framework for wide area monitoring of power systems integrated with distributed generations using synchrophasor technology. The framework comprises distributed agents (synchrophasors) for autonomous local condition monitoring and fault detection, and a central unit for generating global view for situation awareness and decision making. Key technologies that can be integrated into this hierarchical distributed learning scheme are discussed to enable real-time information extraction and knowledge discovery for decision making, without explicitly accumulating and storing all raw data by the central unit. Based on this, the configuration of a wide area monitoring system of power systems using synchrophasor technology, and the functionalities for locally installed open-phasor-measurement-units (OpenPMUs) and a central unit are presented. Initial results on anti-islanding protection using the proposed approach are given to illustrate the effectiveness.

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.

Using hypnosis to facilitate direct observation of multiple tics and self-monitoring in a typically developing teenager

A hypnotic induction technique was used to facilitate direct observation of multiple tics in a typically developing teenager in a home setting. A comprehensive habit reversal program then was implemented, including awareness training, competing response training, relaxation training, self-monitoring, social support and contingency management. Duration of relaxation was then self-monitored using a changing criterion design. The procedure eliminated multiple tics and achieved long-term maintenance of treatment gains. Implications for assessing and treating tics are discussed.

Plug Force Monitoring for the Control and Optimisation of the Thermoforming Process

Thermoforming processes generally employ sheet temperature monitoring as the primary means of process control. In this paper the development of an alternative system that monitors plug force is described. Tests using a prototype device have shown that the force record over a forming cycle creates a unique map of the process operation. Key process features such as the sheet modulus, sheet sag and the timing of the process stages may be readily observed, and the effects of changes in all of the major processing parameters are easily distinguished. Continuous, cycle-to-cycle tests show that the output is consistent and repeatable over a longer time frame, providing the opportunity for development of an on-line process control system. Further testing of the system is proposed.

Monitoring growth in asthmatic children treated with high dose inhaled glucocorticoids does not predict adrenal suppression

Aims: To determine whether routine outpatient monitoring of growth predicts adrenal suppression in prepubertal children treated with high dose inhaled glucocorticoid.

Methods: Observational study of 35 prepubertal children (aged 4–10 years) treated with at least 1000 µg/day of inhaled budesonide or equivalent potency glucocorticoid for at least six months. Main outcome measures were: changes in HtSDS over 6 and 12 month periods preceding adrenal function testing, and increment and peak cortisol after stimulation by low dose tetracosactrin test. Adrenal suppression was defined as a peak cortisol 500 nmol/l.

Results: The areas under the receiver operator characteristic curves for a decrease in HtSDS as a predictor of adrenal insufficiency 6 and 12 months prior to adrenal testing were 0.50 (SE 0.10) and 0.59 (SE 0.10). Prediction values of an HtSDS change of –0.5 for adrenal insufficiency at 12 months prior to testing were: sensitivity 13%, specificity 95%, and positive likelihood ratio of 2.4. Peak cortisol reached correlated poorly with change in HtSDS ( = 0.23, p = 0.19 at 6 months; = 0.33, p = 0.06 at 12 months).

Conclusions: Monitoring growth does not enable prediction of which children treated with high dose inhaled glucocorticoids are at risk of potentially serious adrenal suppression. Both growth and adrenal function should be monitored in patients on high dose inhaled glucocorticoids. Further research is required to determine the optimal frequency of monitoring adrenal function.

Selective migration from deprived areas in Northern Ireland and the spatial distribution of inequalities: implications for monitoring health and inequalities in health

Much of the evidence suggesting that inequalities in health have been increasing over the last two decades has come from studies that compared the changes in relative health status of areas over time. Such studies ignore the movement of people between areas. This paper examines the population movement between small areas in Northern Ireland in the year prior to the 1991 census as well as the geographical distribution of migrants to Northern Ireland over the same period. It shows that deprived areas tended to become depopulated and that those who left these areas were the more affluent residents. While immigrants differed a little from the indigenous population, the overall effect of their distribution would be to maintain the geographical socio-economic status quo. The selective movement of people between areas would result in the distribution of health and ill-health becoming more polarized, i.e. produce a picture of widening inequalities between areas even though the distribution between individuals is unchanged. These processes suggest potential significant problems with the area-based approaches to monitoring health and inequalities in health.