Fold back current control and admittance protection scheme for a distribution network containing distributed generators

Islanded operation, protection, reclosing and arc extinguishing are some of the challenging issues related to the connection of converter interfaced distributed generators (DGs) into a distribution network. The isolation of upstream faults in grid connected mode and fault detection in islanded mode using overcurrent devices are difficult. In the event of an arc fault, all DGs must be disconnected in order to extinguish the arc. Otherwise, they will continue to feed the fault, thus sustaining the arc. However, the system reliability can be increased by maximising the DG connectivity to the system: therefore, the system protection scheme must ensure that only the faulted segment is removed from the feeder. This is true even in the case of a radial feeder as the DG can be connected at various points along the feeder. In this paper, a new relay scheme is proposed which, along with a novel current control strategy for converter interfaced DGs, can isolate permanent and temporary arc faults. The proposed protection and control scheme can even coordinate with reclosers. The results are validated through PSCAD/EMTDC simulation and MATLAB calculations.

Development of a novel rolling-circle amplification technique to detect Banana streak virus which also discriminates between integrated and episomal virus sequences

Bananas are hosts to a large number of banana streak virus (BSV) species. However, diagnostic methods for BSV are inadequate because of the considerable genetic and serological diversity amongst BSV isolates and the presence of integrated BSV sequences in some banana cultivars which leads to false positives. In this study, a sequence non-specific, rolling-circle amplification (RCA) technique was developed and shown to overcome these limitations for the detection and subsequent characterisation of BSV isolates infecting banana. This technique was shown to discriminate between integrated and episomal BSV DNA, specifically detecting the latter in several banana cultivars known to contain episomal and/or integrated sequences of Banana streak Mysore virus (BSMyV), Banana streak OL virus (BSOLV) and Banana streak GF virus (BSGFV). Using RCA, the presence of BSMyV and BSOLV was confirmed in Australia, while BSOLV, BSGFV, Banana streak Uganda I virus (BSUgIV), Banana streak Uganda L virus (BSUgLV) and Banana streak Uganda M virus (BSUgMV) were detected in Uganda. This is the first confirmed report of episomally-derived BSUglV, BSUgLV and BSUgMV in Uganda. As well as its ability to detect BSV, RCA was shown to detect two other pararetroviruses, Sugarcane bacilliform virus in sugarcane and Cauliflower mosaic virus in turnip.

Spatially offset Raman spectroscopy (SORS) for the analysis and detection of packaged pharmaceuticals and concealed drugs

Spatially offset Raman spectroscopy (SORS) is a powerful new technique for the non-invasive detection and identification of concealed substances and drugs. Here, we demonstrate the SORS technique in several scenarios that are relevant to customs screening, postal screening, drug detection and forensics applications. The examples include analysis of a multi-layered postal package to identify a concealed substance; identification of an antibiotic capsule inside its plastic blister pack; analysis of an envelope containing a powder; and identification of a drug dissolved in a clear solvent, contained in a non-transparent plastic bottle. As well as providing practical examples of SORS, the results highlight several considerations regarding the use of SORS in the field, including the advantages of different analysis geometries and the ability to tailor instrument parameters and optics to suit different types of packages and samples. We also discuss the features and benefits of SORS in relation to existing Raman techniques, including confocal microscopy, wide area illumination and the conventional backscattered Raman spectroscopy. The results will contribute to the recognition of SORS as a promising method for the rapid, chemically-specific analysis and detection of drugs and pharmaceuticals.

Fat and thin adaptive HMM filters for vision based detection of moving targets

Computer vision is an attractive solution for uninhabited aerial vehicle (UAV) collision avoidance, due to the low weight, size and power requirements of hardware. A two-stage paradigm has emerged in the literature for detection and tracking of dim targets in images, comprising of spatial preprocessing, followed by temporal filtering. In this paper, we investigate a hidden Markov model (HMM) based temporal filtering approach. Specifically, we propose an adaptive HMM filter, in which the variance of model parameters is refined as the quality of the target estimate improves. Filters with high variance (fat filters) are used for target acquisition, and filters with low variance (thin filters) are used for target tracking. The adaptive filter is tested in simulation and with real data (video of a collision-course aircraft). Our test results demonstrate that our adaptive filtering approach has improved tracking performance, and provides an estimate of target heading not present in previous HMM filtering approaches.

Fault isolation in distributed generation connected distribution networks

Protection of a distribution network in the presence of distributed generators (DGs) using overcurrent relays is a challenging task due to the changes in fault current levels and reverse power flow. Specifically, in the presence of current limited converter interfaced DGs, overcurrent relays may fail to isolate the faulted section either in grid connected or islanded mode of operation. In this paper, a new inverse type relay is presented to protect a distribution network, which may have several DG connections. The new relay characteristic is designed based on the measured admittance of the protected line. The relay is capable of detecting faults under changing fault current levels. The relay performance is evaluated using PSCAD simulation and laboratory experiments.

Acoustic emission for diesel engine monitoring: A review and preliminary

Vibration analysis has been a prime tool in condition monitoring of rotating machines, however, its application to internal combustion engines remains a challenge because engine vibration signatures are highly non-stationary that are not suitable for popular spectrum-based analysis. Signal-to-noise ratio is a main concern in engine signature analysis due to severe background noise being generated by consecutive mechanical events, such as combustion, valve opening and closing, especially in multi-cylinder engines. Acoustic Emission (AE) has been found to give excellent signal-to-noise ratio allowing discrimination of fine detail of normal or abnormal events during a given cycle. AE has been used to detect faults, such as exhaust valve leakage, fuel injection behaviour, and aspects of the combustion process. This paper presents a review of AE application to diesel engine monitoring and preliminary investigation of AE signature measured on an 18-cylinder diesel engine. AE is compared with vibration acceleration for varying operating conditions: load and speed. Frequency characteristics of AE from those events are analysed in time-frequency domain via short time Fourier trasform. The result shows a great potential of AE analysis for detection of various defects in diesel engines.

A review of current statistical methodologies for in storage sampling and surveillance in the grains industry

Effective, statistically robust sampling and surveillance strategies form an integral component of large agricultural industries such as the grains industry. Intensive in-storage sampling is essential for pest detection, Integrated Pest Management (IPM), to determine grain quality and to satisfy importing nation’s biosecurity concerns, while surveillance over broad geographic regions ensures that biosecurity risks can be excluded, monitored, eradicated or contained within an area. In the grains industry, a number of qualitative and quantitative methodologies for surveillance and in-storage sampling have been considered. Primarily, research has focussed on developing statistical methodologies for in storage sampling strategies concentrating on detection of pest insects within a grain bulk, however, the need for effective and statistically defensible surveillance strategies has also been recognised. Interestingly, although surveillance and in storage sampling have typically been considered independently, many techniques and concepts are common between the two fields of research. This review aims to consider the development of statistically based in storage sampling and surveillance strategies and to identify methods that may be useful for both surveillance and in storage sampling. We discuss the utility of new quantitative and qualitative approaches, such as Bayesian statistics, fault trees and more traditional probabilistic methods and show how these methods may be used in both surveillance and in storage sampling systems.

The influence of temperature and introduction point on the detection of Rhyzopertha dominica in stored grain

The presence of insect pests in grain storages throughout the supply chain is a significant problem for farmers, grain handlers, and distributors world-wide. Insect monitoring and sampling programmes are used in the stored grains industry for the detection and estimation of pest populations. At the low pest densities dictated by economic and commercial requirements, the accuracy of both detection and abundance estimates can be influenced by variations in the spatial structure of pest populations over short distances. Geostatistical analysis of Rhyzopertha dominica populations in 2 and 3 dimensions showed that insect numbers were positively correlated over short (0.5 cm) distances, and negatively correlated over longer (.10 cm) distances. At 35 C, insects were located significantly further from the grain surface than at 25 and 30 C. Dispersion metrics showed statistically significant aggregation in all cases. The observed heterogeneous spatial distribution of R. dominica may also be influenced by factors such as the site of initial infestation and disturbance during handling. To account for these additional factors, I significantly extended a simulation model that incorporates both pest growth and movement through a typical stored-grain supply chain. By incorporating the effects of abundance, initial infestation site, grain handling, and treatment on pest spatial distribution, I developed a supply chain model incorporating estimates of pest spatial distribution. This was used to examine several scenarios representative of grain movement through a supply chain, and determine the influence of infestation location and grain disturbance on the sampling intensity required to detect pest infestations at various infestation rates. This study has investigated the effects of temperature, infestation point, and grain handling on the spatial distribution and detection of R. dominica. The proportion of grain infested was found to be dependent upon abundance, initial pest location, and grain handling. Simulation modelling indicated that accounting for these factors when developing sampling strategies for stored grain has the potential to significantly reduce sampling costs while simultaneously improving detection rate, resulting in reduced storage and pest management cost while improving grain quality.

Predicting fault-prone software modules with rank sum classification

The detection and correction of defects remains among the most time consuming and expensive aspects of software development. Extensive automated testing and code inspections may mitigate their effect, but some code fragments are necessarily more likely to be faulty than others, and automated identification of fault prone modules helps to focus testing and inspections, thus limiting wasted effort and potentially improving detection rates. However, software metrics data is often extremely noisy, with enormous imbalances in the size of the positive and negative classes. In this work, we present a new approach to predictive modelling of fault proneness in software modules, introducing a new feature representation to overcome some of these issues. This rank sum representation offers improved or at worst comparable performance to earlier approaches for standard data sets, and readily allows the user to choose an appropriate trade-off between precision and recall to optimise inspection effort to suit different testing environments. The method is evaluated using the NASA Metrics Data Program (MDP) data sets, and performance is compared with existing studies based on the Support Vector Machine (SVM) and Naïve Bayes (NB) Classifiers, and with our own comprehensive evaluation of these methods.

Selection and monitoring of navigation modes for an autonomous rover

Considering the wide spectrum of situations that it may encounter, a robot navigating autonomously in outdoor environments needs to be endowed with several operating modes, for robustness and efficiency reasons. Indeed, the terrain it has to traverse may be composed of flat or rough areas, low cohesive soils such as sand dunes, concrete road etc. . .Traversing these various kinds of environment calls for different navigation and/or locomotion functionalities, especially if the robot is endowed with different locomotion abilities, such as the robots WorkPartner, Hylos [4], Nomad or the Marsokhod rovers. Numerous rover navigation techniques have been proposed, each of them being suited to a particular environment context (e.g. path following, obstacle avoidance in more or less cluttered environments, rough terrain traverses...). However, seldom contributions in the literature tackle the problem of selecting autonomously the most suited mode [3]. Most of the existing work is indeed devoted to the passive analysis of a single navigation mode, as in [2]. Fault detection is of course essential: one can imagine that a proper monitoring of the Mars Exploration Rover Opportunity could have avoided the rover to be stuck during several weeks in a dune, by detecting non-nominal behavior of some parameters. But the ability to recover the anticipated problem by switching to a better suited navigation mode would bring higher autonomy abilities, and therefore a better overall efficiency. We propose here a probabilistic framework to achieve this, which fuses environment related and robot related information in order to actively control the rover operations.

Application of machine learning technique in wind turbine fault diagnosis

In this study, a machine learning technique called anomaly detection is employed for wind turbine bearing fault detection. Basically, the anomaly detection algorithm is used to recognize the presence of unusual and potentially faulty data in a dataset, which contains two phases: a training phase and a testing phase. Two bearing datasets were used to validate the proposed technique, fault-seeded bearing from a test rig located at Case Western Reserve University to validate the accuracy of the anomaly detection method, and a test to failure data of bearings from the NSF I/UCR Center for Intelligent Maintenance Systems (IMS). The latter data set was used to compare anomaly detection with SVM, a previously well-known applied method, in rapidly finding the incipient faults.

A gray-box neural network-based model identification and fault estimation scheme for nonlinear dynamic systems

A novel gray-box neural network model (GBNNM), including multi-layer perception (MLP) neural network (NN) and integrators, is proposed for a model identification and fault estimation (MIFE) scheme. With the GBNNM, both the nonlinearity and dynamics of a class of nonlinear dynamic systems can be approximated. Unlike previous NN-based model identification methods, the GBNNM directly inherits system dynamics and separately models system nonlinearities. This model corresponds well with the object system and is easy to build. The GBNNM is embedded online as a normal model reference to obtain the quantitative residual between the object system output and the GBNNM output. This residual can accurately indicate the fault offset value, so it is suitable for differing fault severities. To further estimate the fault parameters (FPs), an improved extended state observer (ESO) using the same NNs (IESONN) from the GBNNM is proposed to avoid requiring the knowledge of ESO nonlinearity. Then, the proposed MIFE scheme is applied for reaction wheels (RW) in a satellite attitude control system (SACS). The scheme using the GBNNM is compared with other NNs in the same fault scenario, and several partial loss of effect (LOE) faults with different severities are considered to validate the effectiveness of the FP estimation and its superiority.

Chlamydial infections of fish: Diverse pathogens and emerging causes of disease in aquaculture species

Chlamydial infections of fish are emerging as an important cause of disease in new and established aquaculture industries. To date, epitheliocystis, a skin and gill disease associated with infection by these obligate intracellular pathogens, has been described in over 90 fish species, including hosts from marine and fresh water environments. Aided by advances in molecular detection and typing, recent years have seen an explosion in the description of these epitheliocystis-related chlamydial pathogens of fish, significantly broadening our knowledge of the genetic diversity of the order Chlamydiales. Remarkably, in most cases, it seems that each new piscine host studied has revealed the presence of a phylogenetically unique and novel chlamydial pathogen, providing researchers with a fascinating opportunity to understand the origin, evolution and adaptation of their traditional terrestrial chlamydial relatives. Despite the advances in this area, much still needs to be learnt about the epidemiology of chlamydial infections in fish if these pathogens are to be controlled in farmed environments. The lack of in vitro methods for culturing of chlamydial pathogens of fish is a major hindrance to this field. This review provides an update on our current knowledge of the taxonomy and diversity of chlamydial pathogens of fish, discusses the impact of these infections on the health, and highlights further areas of research required to understand the biology and epidemiology of this important emerging group of fish pathogens of aquaculture species.