Received signal strength in large-scale wireless relay sensor network: a stochastic ray approach

The authors consider a point percolation lattice representation of a large-scale wireless relay sensor network (WRSN) deployed in a cluttered environment. Each relay sensor corresponds to a grid point in the random lattice and the signal sent by the source is modelled as an ensemble of photons that spread in the space, which may 'hit' other sensors and are 'scattered' around. At each hit, the relay node forwards the received signal to its nearest neighbour through direction-selective relaying. The authors first derive the distribution that a relay path reaches a prescribed location after undergoing certain number of hops. Subsequently, a closed-form expression of the average received signal strength (RSS) at the destination can be computed as the summation of all signal echoes' energy. Finally, the effect of the anomalous diffusion exponent ß on the mean RSS in a WRSN is studied, for which it is found that the RSS scaling exponent e is given by (3ß-1)/ß. The results would provide useful insight into the design and deployment of large-scale WRSNs in future. © 2011 The Institution of Engineering and Technology.

Computational learning of the conditional phase-type (C-Ph) distribution: Learning C-Ph distributions

This paper presents a new algorithm for learning the structure of a special type of Bayesian network. The conditional phase-type (C-Ph) distribution is a Bayesian network that models the probabilistic causal relationships between a skewed continuous variable, modelled by the Coxian phase-type distribution, a special type of Markov model, and a set of interacting discrete variables. The algorithm takes a dataset as input and produces the structure, parameters and graphical representations of the fit of the C-Ph distribution as output.The algorithm, which uses a greedy-search technique and has been implemented in MATLAB, is evaluated using a simulated data set consisting of 20,000 cases. The results show that the original C-Ph distribution is recaptured and the fit of the network to the data is discussed.

The Conditional Phase-type distribution: Graphical Models for Continuous Non-Normal Data:The Conditional Phase-type (C-Ph) distribution:

Conditional Gaussian (CG) distributions allow the inclusion of both discrete and continuous variables in a model assuming that the continuous variable is normally distributed. However, the CG distributions have proved to be unsuitable for survival data which tends to be highly skewed. A new method of analysis is required to take into account continuous variables which are not normally distributed. The aim of this paper is to introduce the more appropriate conditional phase-type (C-Ph) distribution for representing a continuous non-normal variable while also incorporating the causal information in the form of a Bayesian network.

Accumulation, Subcellular Distribution and Toxicity of Copper in Earthworm (Eisenia fetida) in the Presence of Ciprofloxacin

Land application of wastes from concentrated animal feeding operations results in accumulation of copper (Cu) and antimicrobials in terrestrial systems. Interaction between Cu and antimicrobials may change Cu speciation in soil solution, and affect Cu bioavailability and toxicity. In this study, earthworms were exposed to quartz sand percolated with different concentrations of Cu and ciprofloxacin (CIP). Copper uptake by earthworms, its subcellular partition, and toxicity were studied. An increase in the applied CIP decreased the free Cu ion concentration in external solution and mortalities of earthworm, while Cu contents in earthworms increased. Copper and CIP in earthworms were fractionated into five fractions: a granular fraction (D), a fraction consisting of tissue fragments, cell membranes, and intact cells (E), a microsomal fraction (F), a denatured proteins fraction (G), and a heat-stable proteins fraction (H). Most of the CIP in earthworms was in fraction H. Copper was redistributed from the metal-sensitive fraction E to fractions D, F, G, and H with increasing CIP concentration. These results challenge the free ion activity model and suggested that Cu may be partly taken up as Cu-CIP complexes in earthworms, changing the bioavailability, subcellular distribution, and toxicity of Cu to earthworms.

Capability Curve Based Enhanced Reactive Power Control Strategy for Stability Enhancement and Network Voltage Management

Reactive power has become a vital resource in modern electricity networks due to increased penetration of distributed generation. This paper examines the extended reactive power capability of DFIGs to improve network stability and capability to manage network voltage profile during transient faults and dynamic operating conditions. A coordinated reactive power controller is designed by considering the reactive power capabilities of the rotor-side converter (RSC) and the grid-side converter (GSC) of the DFIG in order to maximise the reactive power support from DFIGs. The study has illustrated that, a significant reactive power contribution can be obtained from partially loaded DFIG wind farms for stability enhancement by using the proposed capability curve based reactive power controller; hence DFIG wind farms can function as vital dynamic reactive power resources for power utilities without commissioning additional dynamic reactive power devices. Several network adaptive droop control schemes are also proposed for network voltage management and their performance has been investigated during variable wind conditions. Furthermore, the influence of reactive power capability on network adaptive droop control strategies has been investigated and it has also been shown that enhanced reactive power capability of DFIGs can substantially improve the voltage control performance.

Driven optomechanical systems for mechanical entanglement distribution

We consider the distribution of entanglement from a multimode optical driving source to a network of remote and independent optomechanical systems. By focusing on the tripartite case, we analyse the effects that the features of the optical input states have on the degree and sharing structure of the distributed, fully mechanical, entanglement. This study, which is conducted looking at the mechanical steady state, highlights the structure of the entanglement distributed among the nodes and determines the relative efficiency between bipartite and tripartite entanglement transfer. We discuss a few open points, some of which are directed towards the bypassing of such limitations.

Experimental Distribution of Entanglement with Separable Carriers

The key requirement for quantum networking is the distribution of entanglement between nodes. Surprisingly, entanglement can be generated across a network without direct transfer - or communication - of entanglement. In contrast to information gain, which cannot exceed the communicated information, the entanglement gain is bounded by the communicated quantum discord, a more general measure of quantum correlation that includes but is not limited to entanglement. Here, we experimentally entangle two communicating parties sharing three initially separable photonic qubits by exchange of a carrier photon that is unentangled with either party at all times. We show that distributing entanglement with separable carriers is resilient to noise and in some cases becomes the only way of distributing entanglement through noisy environments.

Network Usage Determination for Transmission Cost Allocation using a Transformer Analogy

This study presents a new method for determining the transmission network usage by loads and generators, which can then be used for transmission cost/loss allocation in an explainable and justifiable manner. The proposed method is based on solid physical grounds and circuit theory. It relies on dividing the currents through the network into two components; the first one is attributed to power flows from generators to loads, whereas the second one is because of the generators only. Unlike almost all the available methods, the proposed method is assumption free and hence it is more accurate than similar methods even those having some physical basis. The proposed method is validated through a transformer analogy, and theoretical derivations. The method is verified through application to the IEEE 30 bus system and the IEEE 118 test system. The results obtained verified many desirable features of the proposed method. Being more accurate in determining the network usage, in an explainable transparent manner, and in giving accurate cost signals, indicating the best locations to add loads and generation, are among the many desirable features.

On-off based charging strategies for EVs connected to a Low Voltage distributon network

The development of appropriate Electric Vehicle (EV) charging strategies has been identified as an effective way to accommodate an increasing number of EVs on Low Voltage (LV) distribution networks. Most research studies to date assume that future charging facilities will be capable of regulating charge rates continuously, while very few papers consider the more realistic situation of EV chargers that support only on-off charging functionality. In this work, a distributed charging algorithm applicable to on-off based charging systems is presented. Then, a modified version of the algorithm is proposed to incorporate real power system constraints. Both algorithms are compared with uncontrolled and centralized charging strategies from the perspective of both utilities and customers. © 2013 IEEE.

Integrating petrography, mineralogy and hydrochemistry to constrain the influence and distribution of groundwater contributions to baseflow in poorly productive aquifers: Insights from Gortinlieve catchment, Co. Donegal, NW Ireland

Identifying groundwater contributions to baseflowforms an essential part of surfacewater body characterisation. The Gortinlieve catchment (5 km2) comprises a headwater stream network of the Carrigans River, itself a tributary of the River Foyle, NW Ireland. The bedrock comprises poorly productive metasediments that are characterised by fracture porosity. We present the findings of a multi-disciplinary study that integrates new hydrochemical and mineralogical investigations with existing hydraulic, geophysical and structural data to identify the scales of groundwater flow and the nature of groundwater/bedrock interaction (chemical denudation). At the catchment scale, the development of deep weathering profiles is controlled by NE-SW regional scale fracture zones associated with mountain building during the Grampian orogeny. In-situ chemical denudation of mineral phases is controlled by micro- to meso-scale fractures related to Alpine compression during Palaeocene to Oligocene times. The alteration of primary muscovite, chlorite (clinochlore) and albite along the surfaces of these small-scale fractures has resulted in the precipitation of illite, montmorillonite and illite/montmorillonite clay admixtures. The interconnected but discontinuous nature of these small-scale structures highlights the role of larger scale faults and fissures in the supply and transportation of weathering solutions to/from the sites of mineral weathering. The dissolution of primarily mineral phases releases the major ions Mg, Ca and HCO3 that are shown to subsequently formthe chemical makeup of groundwaters. Borehole groundwater and stream baseflow hydrochemical data are used to constrain the depths of groundwater flow pathways influencing the chemistry of surface waters throughout the stream profile. The results show that it is predominantly the lower part of the catchment, which receives inputs from catchment/regional scale groundwater flow, that is found to contribute to the maintenance of annual baseflow levels. This study identifies the importance
of deep groundwater in maintaining annual baseflow levels in poorly productive bedrock systems.

The Vertical Distribution and Evolution of Slam Pressure on an Oscillating Wave Surge Converter

The accurate definition of the extreme wave loads which act on offshore structures represents a significant challenge for design engineers and even with decades of empirical data to base designs upon there are still failures attributed to wave loading. The environmental conditions which cause these loads are infrequent and highly non-linear which means that they are not well understood or simple to describe. If the structure is large enough to affect the incident wave significantly further non-linear effects can influence the loading. Moreover if the structure is floating and excited by the wave field then its responses, which are also likely to be highly non-linear, must be included in the analysis. This makes the description of the loading on such a structure difficult to determine and the design codes will often suggest employing various tools including small scale experiments, numerical and analytical methods, as well as empirical data if available.
Wave Energy Converters (WECs) are a new class of offshore structure which pose new design challenges, lacking the design codes and empirical data found in other industries. These machines are located in highly exposed and energetic sites, designed to be excited by the waves and will be expected to withstand extreme conditions over their 25 year design life. One such WEC is being developed by Aquamarine Power Ltd and is called Oyster. Oyster is a buoyant flap which is hinged close to the seabed, in water depths of 10 to 15m, piercing the water surface. The flap is driven back and forth by the action of the waves and this mechanical energy is then converted to electricity.
It has been identified in previous experiments that Oyster is not only subject to wave impacts but it occasionally slams into the water surface with high angular velocity. This slamming effect has been identified as an extreme load case and work is ongoing to describe it in terms of the pressure exerted on the outer skin and the transfer of this short duration impulsive load through various parts of the structure.
This paper describes a series of 40th scale experiments undertaken to investigate the pressure on the face of the flap during the slamming event. A vertical array of pressure sensors are used to measure the pressure exerted on the flap. Characteristics of the slam pressure such as the rise time, magnitude, spatial distribution and temporal evolution are revealed. Similarities are drawn between this slamming phenomenon and the classical water entry problems, such as ship hull slamming. With this similitude identified, common analytical tools are used to predict the slam pressure which is compared to that measured in the experiment.

New multi-stage DC-DC converters for grid-connected photovoltaic systems

Renewable energy is high on international and national agendas. Currently, grid-connected photovoltaic (PV) systems are a popular technology to convert solar energy into electricity. Existing PV panels have a relatively low and varying output voltage so that the converter installed between the PVs and the grid should be equipped with high step-up and versatile control capabilities. In addition, the output current of PV systems is rich in harmonics which affect the power quality of the grid. In this paper, a new multi-stage hysteresis control of a step-up DC-DC converter is proposed for integrating PVs into a single-phase power grid. The proposed circuitry and control method is experimentally validated by testing on a 600W prototype converter. The developed technology has significant economic implications and could be applied to many distributed generation (DG) systems, especially for the developing countries which have a large number of small PVs connected to their single-phase distribution network. 

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.

Secure data networks for electrical distribution applications

Smart Grids are characterized by the application of information communication technology (ICT) to solve electrical energy challenges. Electric power networks span large geographical areas, thus a necessary component of many Smart Grid applications is a wide area network (WAN). For the Smart Grid to be successful, utilities must be confident that the communications infrastructure is secure. This paper describes how a WAN can be deployed using WiMAX radio technology to provide high bandwidth communications to areas not commonly served by utility communications, such as generators embedded in the distribution network. A planning exercise is described, using Northern Ireland as a case study. The suitability of the technology for real-time applications is assessed using experimentally obtained latency data.