The European Large Area ISO Survey II: mid-infrared extragalactic source counts

We present preliminary source counts at 6.7um and 15um from the Preliminary Analysis of the European Large Area ISO survey, with limiting flux densities of \~2mJy at 15um & ~1mJy at 6.7um. We separate the stellar contribution from the extragalactic using identifications with APM sources made with the likelihood ratio technique. We quantify the completeness & reliability of our source extraction using (a) repeated observations over small areas, (b) cross-IDs with stars of known spectral type, (c) detections of the PSF wings around bright sources, (d) comparison with independent algorithms. Flux calibration at 15um was performed using stellar IDs; the calibration does not agree with the pre-flight estimates, probably due to effects of detector hysteresis and photometric aperture correction. The 6.7um extragalactic counts are broadly reproduced in the Pearson & Rowan-Robinson model, but the Franceschini et al. (1997) model underpredicts the observed source density by ~0.5-1 dex, though the photometry at 6.7um is still preliminary. At 15um the extragalactic counts are in excellent agreement with the predictions of the Pearson & Rowan-Robinson (1996), Franceschini et al. (1994), Guiderdoni et al. (1997) and the evolving models of Xu et al. (1998), over 7 orders of magnitude in 15um flux density. The counts agree with other estimates from the ISOCAM instrument at overlapping flux densities (Elbaz et al. 1999), provided a consistent flux calibration is used. Luminosity evolution at a rate of (1+z)^3, incorporating mid-IR spectral features, provides a better fit to the 15um differential counts than (1+z)^4 density evolution. No-evolution models are excluded, and implying that below around 10mJy at 15um the source counts become dominated by an evolving cosmological population of dust-shrouded starbursts and/or active galaxies.Comment: MNRAS in press. 14 pages, uses BoxedEPS (included). For more information on the ELAIS project see http://athena.ph.ic.ac.uk

Visual 3-D SLAM from UAVs

The aim of the paper is to present, test and discuss the implementation of Visual SLAM techniques to images taken from Unmanned Aerial Vehicles (UAVs) outdoors, in partially structured environments. Every issue of the whole process is discussed in order to obtain more accurate localization and mapping from UAVs flights. Firstly, the issues related to the visual features of objects in the scene, their distance to the UAV, and the related image acquisition system and their calibration are evaluated for improving the whole process. Other important, considered issues are related to the image processing techniques, such as interest point detection, the matching procedure and the scaling factor. The whole system has been tested using the COLIBRI mini UAV in partially structured environments. The results that have been obtained for localization, tested against the GPS information of the flights, show that Visual SLAM delivers reliable localization and mapping that makes it suitable for some outdoors applications when flying UAVs

An improved DC fault protection scheme independent of boundary components for MMC based HVDC grids

For Modular Multilevel Converter (MMC) based DC grids, current-limiting reactors (CLRs) are mainly employed to suppress the fault current and provide boundary effects to detect internal faults. Thus, most existing protection schemes are highly dependent on the larger CLRs to guarantee high selectivity. However, in existing MMC based HVDC projects, the size of CLRs is restrained by the cost, weight and system stability under normal state. Thus, boundary protections may fail to detect high-resistance faults and pole-to-ground faults. To overcome these shortcomings, this paper proposes a fast and selective DC fault detection algorithm independent of boundary components. The propagation characteristics of line-mode backward traveling-waves (TW) are analyzed to identify external and internal faults. The polarities of zero-mode backward TWs are employed to select faulted poles. To detect remote faults, a pilot protection scheme based on the directional overcurrent is adopted as the complementary criterion. The detection speed of the proposed protection is fast, with a delay less than 1.1ms. Besides, it is robust to fault resistance and immune to noise. Various simulation results in PSCAD/EMTDC demonstrate that the proposed method is not affected by AC faults, fault distances and fault type

AN INTELLIGENT PASSIVE ISLANDING DETECTION AND CLASSIFICATION SCHEME FOR A RADIAL DISTRIBUTION SYSTEM

Distributed generation (DG) provides users with a dependable and cost-effective source of electricity. These are directly connected to the distribution system at customer load locations. Integration of DG units into an existing system has significantly high importance due to its innumerable advantages. The high penetration level of distributed generation (DG) provides vast techno-economic and environmental benefits, such as high reliability, reduced total system losses, efficiency, low capital cost, abundant in nature, and low carbon emissions. However, one of the most challenges in microgrids (MG) is the island mode operations of DGs. the effective detection of islanding and rapid DG disconnection is essential to prevent safety problems and equipment damage. The most prevalent islanding protection scheme is based on passive techniques that cause no disruption to the system but have extensive non-detection zones. As a result, the thesis tries to design a simple and effective intelligent passive islanding detection approach using a CatBoost classifier, as well as features collected from three-phase voltages and instantaneous power per phase visible at the DG terminal. This approach enables initial features to be extracted using the Gabor transform (GT) technique. This signal processing (SP) technique illustrates the time-frequency representation of the signal, revealing several hidden features of the processed signals to be the input of the intelligent classifier. A radial distribution system with two DG units was utilized to evaluate the effectiveness of the proposed islanding detection method. The effectiveness of the proposed islanding detection method was verified by comparing its results to those of other methods that use a random forest (RF) or a basic artificial neural network (ANN) as a classifier. This was accomplished through extensive simulations using the DigSILENT Power Factory® software. Several measures are available, including accuracy (F1 Score), the area under the curve (AUC), and training time. The suggested technique has a classification accuracy of 97.1 per cent for both islanded and non-islanded events. However, the RF and ANN classifiers\u27 accuracies for islanding and non-islanding events, respectively, are proven to be 94.23 and 54.8 per cent, respectively. In terms of the training time, the ANN, RF, and CatBoost classifiers have training times of 1.4 seconds, 1.21 seconds, and 0.88 seconds, respectively. The detection time for all methods was less than one cycle. These metrics demonstrate that the suggested strategy is robust and capable of distinguishing between the islanding event and other system disruptions

Signal Processing and Classification Tools for Intelligent Distributed Monitoring and Analysis of the Smart Grid

This paper proposes a novel framework for an intelligent monitoring system that supervises the performance of the future power system. The increased complexity of the power system could endanger the reliability, voltage quality, operational security or resilience of the power system. A distributed structure for such a monitoring system is described and some of the advanced signal processing techniques or tools that could be used in such a monitoring system are given. Several examples for seeking the spatial locations and finding the underlying causes of disturbances are included