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

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

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

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

LDEF Space Plasma-High Voltage Drainage Experiment post-flight results

The Space Plasma-High Voltage Drainage Experiment (SP-HVDE) was comprised of two identical experimental trays. With one tray located on the leading (ram facing, B10) edge and the other located on the trailing (wake facing, D4) edge of the Long Duration Exposure Facility (LDEF), it was possible to directly compare the effects of ram and wake spacecraft environments on charged dielectric materials. Six arrays of Kapton dielectric samples of 2 mil, 3 mil, and 5 mil thicknesses maintained at +/- 300, +/- 500, and +/- 1000 voltage bias formed the experimental matrix of each tray. In addition, each tray carried two solar cell strings, one biased at +300 volts and the other at -300 volts, to study current leakage from High Voltage Solar Arrays (HVSA). The SP-HVDE provides the first direct, long-term, in-flight measurements of average leakage current through dielectric materials under electric stress. The experiment also yields information on the long term stability of the bulk dielectric properties of such materials. Data and findings of the SP-HVDE are an extension of those from shorter term flight experiments such as the PIX-1 (Plasma Interaction Experiment) and PIX-2 and are therefore valuable in the design and evaluation of long-lived space systems with high voltage systems exposed to the low earth orbital environment. A summary of the SP-HVDE post flight analysis final report delivered to the LDEF Project Office under contract to the National Aeronautics and Space Administration is presented

Development of an animal-borne blood sample collection device and its deployment for the determination of cardiovascular and stress hormones in phocid seals

The research was supported by Bilateral Program between Japan and the United Kingdom and a Grant-in-Aid for Scientific Research (23247010) to Y. Takei and by Grant-in-Aid for challenging Exploratory Research (15K14567) to I. Suzuki from the Japan Society for the Promotion of Science. This work was also supported by funding from the U.K. Natural Environment Research Council (Grant SMRU1001).An animal-borne blood sampler with data-logging functions was developed for phocid seals, which collected two blood samples for the comparison of endocrino-logical/biochemical parameters under two different conditions. The sampler can be triggered by preset hydrostatic pressure, acceleration (descending or ascending), temperature, and time, and also man-ually by light. The sampling was reliable with 39/50 (78%) successful attempts to collect blood samples. Contamination of fluids in the tubing to the next blood sample was 1%, following the prior clearance of the tubing to a waste syringe. In captive harbor seals (Phoca vitulina), the automated blood-sampling method was less stressful than direct blood withdrawal, as evidenced by lower levels of stress hormones (P < 0.05 for ACTH and P = 0.078 for cortisol). HPLC analyses showed that both cortisol and cortisone were circu-lating in seal blood. Using the sampler, plasma levels of cardiovascular hormones, atrial natriuretic peptide (ANP), AVP, and ANG II were compared in grey seals (Halichoerus grypus), between samples collected when the animals were on land and in the water. HPLC analyses determined that [Met12] ANP (1-28) and various forms of angiotensins (ANG II, III, and IV) were circulating in seal blood. Although water immersion profoundly changes the plasma levels of cardiovascular hormones in terrestrial mammals, there were only tendencies toward an increase in ANP (P = 0.069) and a decrease in AVP (P = 0.074) in the seals. These results suggest that cardiovascular regulation in phocid seals may have undergone adaptation during evolution of the carnivore to a semiaquatic lifestyle.PostprintPeer reviewe

Influence of lifestyle choices on risks of CYP1B1 polymorphisms for prostate cancer.

Cytochrome P450 1B1 (CYP1B1) converts xenobiotics to carcinogens and how lifestyle choices may interact with CYP1B1 polymorphisms and affect prostate cancer risk was assessed. Blood genomic DNA from a Caucasian population was analysed at polymorphic sites of the 5' untranslated region of CYP1B1 using TaqMan genotyping assays. Overall, drinker status and minor alleles at rs2551188, rs2567206 and rs10175368 were associated with prostate cancer. Linkage was observed between rs2551188, rs2567206, rs2567207 and rs10175368, and the G-C-T-G haplotype (major allele at respective sites) was decreased in cancer. Interestingly when classified by lifestyle factors, no associations of genotypes were found for non-smokers and non-drinkers, whereas on the contrary, minor type at rs2567206 and rs10175368 increased and major G-C-T-G decreased risk for cancer among smokers and drinkers. Interestingly, rs2551188, rs2567206 and rs10175368 minor genotypes correlated with increased tissue CYP1B1 as determined by immunohistochemistry. Further, rs10175368 enhanced luciferase activity and mobility shift show stronger binding of nuclear factor for the minor allele. These results demonstrate smoking and alcohol consumption to modify the risks of CYP1B1 polymorphisms for prostate cancer which may be through rs10175368, and this is of importance in understanding their role in the pathogenesis and as a biomarker for this disease