System configuration, fault detection, location, isolation and restoration: a review on LVDC Microgrid protections

Low voltage direct current (LVDC) distribution has gained the significant interest of research due to the advancements in power conversion technologies. However, the use of converters has given rise to several technical issues regarding their protections and controls of such devices under faulty conditions. Post-fault behaviour of converter-fed LVDC system involves both active converter control and passive circuit transient of similar time scale, which makes the protection for LVDC distribution significantly different and more challenging than low voltage AC. These protection and operational issues have handicapped the practical applications of DC distribution. This paper presents state-of-the-art protection schemes developed for DC Microgrids. With a close look at practical limitations such as the dependency on modelling accuracy, requirement on communications and so forth, a comprehensive evaluation is carried out on those system approaches in terms of system configurations， fault detection, location, isolation and restoration

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

Association of the CCR5 gene with juvenile idiopathic arthritis

The CC chemokine receptor 5 (CCR5) has been shown to be important in the recruitment of T-helper cells to the synovium, where they accumulate, drive the inflammatory process and the consequent synovitis and joint destruction. A 32 base-pair insertion/deletion variant (CCR5Δ32) within the gene leads to a frame shift and a nonfunctional receptor. CCR5Δ32 has been investigated for its association with juvenile idiopathic arthritis (JIA), with conflicting results. The aim of this study was to investigate whether CCR5Δ32 is associated with JIA in an UK population. CCR5Δ32 was genotyped in JIA cases (n=1054) and healthy controls (n=3129) and genotype and allele frequencies were compared. A meta-analysis of our study combined with previously published studies was performed. CCR5Δ32 was significantly associated with protection from developing JIA, in this UK data set (P(trend)=0.006, odds ratio (OR) 0.79 95% confidence interval (95% CI): 0.66-0.94). The meta-analysis of all published case-control association studies confirmed the protective association with JIA (P=0.001 OR 0.82 95% CI: 0.73-0.93). CCR5Δ32 is a functional variant determining the number of receptors on the surface of T cells, and it is hypothesized that the level of CCR5 expression could influence the migration of proinflammatory T cells into the synovium and thus susceptibility to JIA

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

Child, Victim, or Prostitute? Justice Through Immunity for Prostituted Children

Tessa L. Dysart, Regent University School of La

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