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.

An Autopilot Based on a Local Control Network Design for an Unmanned Surface Vehicle

Over recent years, a number of marine autopilots designed using linear techniques have underperformed owing to their inability to cope with nonlinear vessel dynamics. To this end, a new design framework for the development of nonlinear autopilots is proposed herein. Local control networks (LCNs) can be used in the design of nonlinear control systems. In this paper, a LCN approach is taken in the design of a nonlinear autopilot for controlling the nonlinear yaw dynamics of an unmanned surface vehicle known as Springer. It is considered the approach is the first of its kind to be used in marine control systems design. Simulation results are presented and the performance of the nonlinear autopilot is compared with that of an existing Springer linear quadratic Gaussian (LQG) autopilot using standard system performance criteria. From the results it can be concluded the LCN autopilot out performed that based on LQG techniques in terms of the selected criteria. Also it provided more energy saving control strategies and would thereby increase operational duration times for the vehicle during real-time missions.

CoWiSMoN: A Framework for Cognitive Wireless Sensor Mobile Network System for Emergency Rescue Management

One of the crucial aspects of disaster management of emergency situations is the early assessment of needs and damages. In most disaster situations, higher fatality and increased casualty results from lack of access to timely available emergency services rather than the initial disaster itself. This is usually caused by lack of access to the affected area in order to properly assess the situation for relevant and urgent measures. Cognitive wireless sensor networks provide an opportunity to overcome this situation especially through interconnection via mobile systems. This paper presents a cognitive wireless sensor mobile networks-based framework (CoWiSMoN), designed to offer real-time emergency services to victims and rescue personnel in event of disasters. Critical issues underlying the implementation of such a system are discussed and analyzed.

Characterization and Modeling of the Indoor Radio Channel at 868 MHz for a Mobile Bodyworn Wireless Personal Area Network

This letter reports the statistical characterization and modeling of the indoor radio channel for a mobile wireless personal area network operating at 868 MHz. Line of sight (LOS) and non-LOS conditions were considered for three environments: anechoic chamber, open office area and hallway. Overall, the Nakagami-m cdf best described fading for bodyworn operation in 60% of all measured channels in anechoic chamber and open office area environments. The Nakagami distribution was also found to provide a good description of Rician distributed channels which predominated in the hallway. Multipath played an important role in channel statistics with the mean recorded m value being reduced from 7.8 in the anechoic chamber to 1.3 in both the open office area and hallway.

Genetic algorithms for local controller network construction

Local Controller Networks (LCNs) provide nonlinear control by interpolating between a set of locally valid, subcontrollers covering the operating range of the plant. Constructing such networks typically requires knowledge of valid local models. This paper describes a new genetic learning approach to the construction of LCNs directly from the dynamic equations of the plant, or from modelling data. The advantage is that a priori knowledge about valid local models is not needed. In addition to allowing simultaneous optimisation of both the controller and validation function parameters, the approach aids transparency by ensuring that each local controller acts independently of the rest at its operating point. It thus is valuable for simultaneous design of the LCNs and identification of the operating regimes of an unknown plant. Application results from a highly nonlinear pH neutralisation process and its associated neural network representation are utilised to illustrate these issues.

Low Insertion Loss Substrate Integrated Waveguide Quasi–Elliptic Filters for V- Band Wireless Personal Area Network Applications

Novel V-band substrate integrated waveguide (SIW) filters have been presented. Design procedures for the filters synthesis and mechanisms providing quasi-elliptic response have been explained. The insertion loss of the filters has been measured below 2 dB with microstrip-to-SIW transitions being included.

Local Area Plans: Best Practice Manual

This Manual has been prepared as a companion document for the Planning Guidelines, Local Area Plans: Creating Sustainable Communities - Guidelines for Planning Authorities (Hereafter referred to as ‘The Planning Guidelines’).

The aim of the Manual is to reinforce the key aspects of the Planning Guidelines by emphasising better and more consistent processes and highlighting examples of good practice.

Average household income, crime, and smoking behaviour in a local area: The Finnish 10-Town study

Social environments, like neighbourhoods, are increasingly recognised as determinants of health. While several studies have reported an association of low neighbourhood socio-economic status with morbidity, mortality and health risk behaviour, little is known of the health effects of neighbourhood crime rates. Using the ongoing 10-Town study in Finland, we examined the relations of average household income and crime rate measured at the local area level, with smoking status and intensity by linking census data of local area characteristics from 181 postal zip codes to survey responses to smoking behaviour in a cohort of 23,008 municipal employees. Gender-stratified multilevel analyses adjusted for age and individual occupational status revealed an association between low local area income rate and current smoking. High local area crime rate was also associated with current smoking. Both local area characteristics were strongly associated with smoking intensity. Among ever-smokers, being an ex-smoker was less likely among residents in areas with low average household income and a high crime rate. In the fully adjusted model, the association between local area income and smoking behaviour among women was substantially explained by the area-level crime rate. This study extends our knowledge of potential pathways through which social environmental factors may affect health. (c) 2007 Elsevier Ltd. All rights reserved.