A unified model of the electrical power network

Traditionally, the different infrastructure layers, technologies and management activities associated with the design, control and protection operation of the Electrical Power Systems have been supported by numerous independent models of the real world network. As a result of increasing competition in this sector, however, the integration of technologies in the network and the coordination of complex management processes have become of vital importance for all electrical power companies. The aim of the research outlined in this paper is to develop a single network model which will unify the generation, transmission and distribution infrastructure layers and the various alternative implementation technologies. This 'unified model' approach can support ,for example, network fault, reliability and performance analysis. This paper introduces the basic network structures, describes an object-oriented modelling approach and outlines possible applications of the unified model

Power system fault prediction using artificial neural networks

The medium term goal of the research reported in this paper was the development of a major in-house suite of strategic computer aided network simulation and decision support tools to improve the management of power systems. This paper describes a preliminary research investigation to access the feasibility of using an Artificial Intelligence (AI) method to predict and detect faults at an early stage in power systems. To achieve this goal, an AI based detector has been developed to monitor and predict faults at an early stage on particular sections of power systems. The detector only requires external measurements taken from the input and output nodes of the power system. The AI detection system is capable of rapidly predicting a malfunction within the system . Simulation will normally take place using equivalent circuit representation. Artificial Neural Networks (ANNs) are used to construct a hierarchical feed-forward structure which is the most important component in the fault detector. Simulation of a transmission line (2-port circuit ) has already been carried out and preliminary results using this system are promising. This approach provided satisfactory results with accuracy of 95% or higher

Update of an early warning fault detection method using artificial intelligence techniques

This presentation describes a research investigation to access the feasibility of using an Artificial Intelligence (AI) method to predict and detect faults at an early stage in power systems. An AI based detector has been developed to monitor and predict faults at an early stage on particular sections of power systems. The detector for this early warning fault detection device only requires external measurements taken from the input and output nodes of the power system. The AI detection system is capable of rapidly predicting a malfunction within the system. Artificial Neural Networks (ANNs) are being used as the core of the fault detector. In an earlier paper [11], a computer simulated medium length transmission line has been tested by the detector and the results clearly demonstrate the capability of the detector. Today’s presentation considers a case study illustrating the suitability of this AI Technique when applied to a distribution transformer. Furthermore, an evolutionary optimisation strategy to train ANNs is also briefly discussed in this presentation, together with a ‘crystal ball’ view of future developments in the operation and monitoring of transmission systems in the next millennium

Early warning fault detection using artificial intelligent methods

This paper describes a research investigation to access the feasibility of using an Artificial Intelligence (AI) method to predict and detect faults at an early stage in power systems. An AI based detector has been developed to monitor and predict faults at an early stage on particular sections of power systems. The detector for this early warning fault detection device only requires external measurements taken from the input and output nodes of the power system. The AI detection system is capable of rapidly predicting a malfunction within the system. Artificial Neural Networks (ANNs) are being used as the core of the fault detector. A simulated medium length transmission line has been tested by the detector and the results demonstrate the capability of the detector. Furthermore, comments on an evolutionary technique as the optimisation strategy for ANNs are included in this paper

Digital measurement of lightning impulse parameters using curving fitting algorithms

This paper describes the application of curve fitting algorithms to aid the evaluation of lightning impulse parameters. A number of popular curve fitting algorithms have been evaluated and compared. Investigations using the genetic algorithm and other optimisation methods for the purpose of curve fitting have also been carried out and will be described

Reflection of underwater sound from surface waves

Author Posting. © Acoustical Society of America, 2009. This article is posted here by permission of Acoustical Society of America for personal use, not for redistribution. The definitive version was published in Journal of the Acoustical Society of America 125 (2009): 66-72, doi:10.1121/1.3035828.A tank experiment has been conducted to measure reflection of underwater sound from surface waves. Reflection from a wave crest leads to focusing and caustics and results in rapid variation in the received waveform as the surface wave moves. Theoretical results from wavefront modeling show that interference of three surface reflected eigenrays for each wave crest produces complicated interference waveforms. There is good agreement between theory and experiment even on the shadow side of caustics where there are two surface reflected arrivals but only one eigenray.The support of the Office of Naval Research, Grant No. N00014-04-1-0728, is gratefully acknowledge

Immunological responses in human papillomavirus 16 E6/E7-transgenic mice to E7 protein correlate with the presence of skin disease

The human papillomavirus (HPV) oncogenes, E6 and E7, are believed to contribute to the development of cervical cancers in women infected with certain HPV genotypes, most notably HPV-16 and HPV-18. Given their expression in tumor tissue, E6 and E7 have been implicated as potential tumor-specific antigens. We have examined an HPV-16 E6- and E7-transgenic mouse lineage for immune responses to these viral oncoproteins. Mice in this lineage express the HPV-16 E6 and E7 genes in their skin and eyes, and on aging, these mice frequently develop squamous cell carcinomas and lenticular tumors. Young transgenic mice, which had measurable E7 protein in the eye but not in the skin, were immunologically naive to E7 protein. They mounted an immune response to E7 on immunization comparable to that of nontransgenic controls, suggesting a lack of immune tolerance to this protein. Older line 19 mice, which are susceptible to skin disease associated with transcription of the E6 and E7 open reading frames, had measurable E7 protein in their skin. These older transgenic mice spontaneously developed antibody responses to endogenous E7 protein, particularly in association with skin disease. Also detected in older mice were delayed-type hypersensitivity responses to E7. These finding parallel the humoral immune response to E7 protein in patients with HPV-associated cervical cancer and suggest that line 19 mice may provide a model for studying the immunobiology of HPV-associated cancers

Breast cancer instructs dendritic cells to prime interleukin 13–secreting CD4+ T cells that facilitate tumor development

We previously reported (Bell, D., P. Chomarat, D. Broyles, G. Netto, G.M. Harb, S. Lebecque, J. Valladeau, J. Davoust, K.A. Palucka, and J. Banchereau. 1999. J. Exp. Med. 190: 1417–1426) that breast cancer tumors are infiltrated with mature dendritic cells (DCs), which cluster with CD4+ T cells. We now show that CD4+ T cells infiltrating breast cancer tumors secrete type 1 (interferon γ) as well as high levels of type 2 (interleukin [IL] 4 and IL-13) cytokines. Immunofluorescence staining of tissue sections revealed intense IL-13 staining on breast cancer cells. The expression of phosphorylated signal transducer and activator of transcription 6 in breast cancer cells suggests that IL-13 actually delivers signals to cancer cells. To determine the link between breast cancer, DCs, and CD4+ T cells, we implanted human breast cancer cell lines in nonobese diabetic/LtSz-scid/scid β2 microglobulin–deficient mice engrafted with human CD34+ hematopoietic progenitor cells and autologous T cells. There, CD4+ T cells promote early tumor development. This is dependent on DCs and can be partially prevented by administration of IL-13 antagonists. Thus, breast cancer targets DCs to facilitate its development

Optimism may moderate screening mammogram frequency in Medicare: A longitudinal study

Higher trait optimism and/or lower cynical hostility are associated with healthier behaviors and lower risk of morbidity and mortality, yet their association with health care utilization has been understudied. Whether these psychological attitudes are associated with breast cancer screening behavior is unknown. To assess the association of optimism and cynical hostility with screening mammography in older women and whether sociodemographic factors acted as mediators of these relationships, we used Women\u27s Health Initiative (WHI) observational cohort survey data linked to Medicare claims. The sample includes WHI participants without history of breast cancer who were enrolled in Medicare Parts A and B for \u3e /=2 years from 2005-2010, and who completed WHI baseline attitudinal questionnaires (n = 48,291). We used survival modeling to examine whether screening frequency varied by psychological attitudes (measured at study baseline) after adjusting for sociodemographic characteristics, health conditions, and healthcare-related variables. Psychological attitudes included trait optimism (Life Orientation Test-Revised) and cynical hostility (Cook Medley subscale), which were self-reported at study baseline. Sociodemographic, health conditions, and healthcare variables were self-reported at baseline and updated through 2005 as available. Contrary to our hypotheses, repeated events survival models showed that women with the lowest optimism scores (i.e., more pessimistic tendencies) received 5% more frequent screenings after complete covariate adjustment (p \u3c .01) compared to the most optimistic group, and showed no association between cynical hostility and frequency of screening mammograms. Sociodemographic factors did not appear to mediate the relationship between optimism and screenings. However, higher levels of education and higher levels of income were associated with more frequent screenings (both p \u3c .01). We also found that results for optimism were primarily driven by women who were aged 75 or older after January 2009, when changes to clinical guidelines lead to uncertainty about risks and benefits of screening in this age group. The study demonstrated that lower optimism, higher education, and higher income were all associated with more frequent screening mammograms in this sample after repeated events survival modeling and covariate adjustment