Replacing the ASBO with the injunction to prevent nuisance and annoyance: a plea for legislative scrutiny and amendment

In May 2013, the Coalition Government introduced a Bill which if passed will streamline the tools available to tackle anti-social behaviour. One of their proposals is to replace the controversial anti-social behaviour order (ASBO) with what is termed an Injunction to Prevent Nuisance and Annoyance (IPNA). Although designed to tackle criminal and sub-criminal behaviour, this new intervention will be a purely civil order replacing the civil-criminal hybrid ASBO. This article explores some of the more troubling aspects of this part of the Bill including its expansive definition of anti-social behaviour, the avoidance of due process protections, the extensive restrictions that respondents may face and the likely impact of its use on young people. With legislation presently under Parliamentary scrutiny, this article calls for amendments to avoid the most problematic aspects of the ASBO being not just replicated but amplified.

Reducing SCADA System Nuisance Alarms in the Water Industry in Northern Ireland

The advancement of telemetry control for the water industry has increased the difficulty of 14 managing large volumes of nuisance alarms (i.e. alarms that do not require a response). The aim 15 of this study was to identify and reduce the number of nuisance alarms that occur for Northern 16 Ireland (NI) Water by carrying-out alarm duration analysis to determine the appropriate length of 17 persistence (an advanced alarm management tool) that could be applied. All data was extracted 18 from TelemWeb (NI Water’s telemetry monitoring system) and analysed in Excel. Over a 6 19 week period, an average of 40,000 alarms occurred per week. The alarm duration analysis, which 20 has never been implemented before by NI Water, found that an average of 57% of NI Water 21 alarms had a duration of <5 minutes. Applying 5 minute persistence; therefore, could prevent an 22 average 26,816 nuisance alarms per week. Most of these alarms were from wastewater assets.

Identification of genetically and oceanographically distinct blooms of jellyfish

Reports of nuisance jellyfish blooms have increased worldwide during the last half-century, but the possible causes remain unclear.Apersistent difficulty lies in identifying whether blooms occur owing to local or regional processes. This issue can be resolved, in part, by establishing the geographical scales of connectivity among locations, which may be addressed using genetic analyses and oceanographic modelling. We used landscape genetics and Lagrangian modelling of oceanographic dispersal to explore patterns of connectivity in the scyphozoan jellyfish Rhizostoma octopus, which occurs en masse at locations in the Irish Sea and northeastern Atlantic. We found significant genetic structure distinguishing three populations, with both consistencies and inconsistencies with prevailing physical oceanographic patterns. Our analyses identify locations where blooms occur in apparently geographically isolated populations, locations where blooms may be the source or result of migrants, and a location where blooms do not occur consistently and jellyfish are mostly immigrant. Our interdisciplinary approach thus provides a means to ascertain the geographical origins of jellyfish in outbreaks, which may have wide utility as increased international efforts investigate jellyfish blooms. © 2013 The Authors.

Loss-of-Mains Detection by Internet Based ROCOF

This paper considers the enhancement of loss-of-mains detection by use of a differential rate-of-change-of-frequency relay to reduce nuisance tripping and improve sensitivity to small excursions in frequency. The telecommunications media which might carry the differential ROCOF signal are reviewed with a focus on channel latency, bandwidth and security.

Loss-of-Mains Protection System by Application of Phasor Measurement Unit Technology with Experimentally Assessed Threshold Settings

Loss-of-mains protection is an important component of the protection systems of embedded generation. The role of loss-of-mains is to disconnect the embedded generator from the utility grid in the event that connection to utility dispatched generation is lost. This is necessary for a number of reasons, including the safety of personnel during fault restoration and the protection of plant against out-of-synchronism reclosure to the mains supply. The incumbent methods of loss-of-mains protection were designed when the installed capacity of embedded generation was low, and known problems with nuisance tripping of the devices were considered acceptable because of the insignificant consequence to system operation. With the dramatic increase in the installed capacity of embedded generation over the last decade, the limitations of current islanding detection methods are no longer acceptable. This study describes a new method of loss-of-mains protection based on phasor measurement unit (PMU) technology, specifically using a low cost PMU device of the authors' design which has been developed for distribution network applications. The proposed method addresses the limitations of the incumbent methods, providing a solution that is free of nuisance tripping and has a zero non-detection zone. This system has been tested experimentally and is shown to be practical, feasible and effective. Threshold settings for the new method are recommended based on data acquired from both the Great Britain and Ireland power systems.

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.