Control energy consumption and system performance in vector control of voltage source converters

Power electronics plays an important role in the control and conversion of modern electric power systems. In particular, to integrate various renewable energies using DC transmissions and to provide more flexible power control in AC systems, significant efforts have been made in the modulation and control of power electronics devices. Pulse width modulation (PWM) is a well developed technology in the conversion between AC and DC power sources, especially for the purpose of harmonics reduction and energy optimization. As a fundamental decoupled control method, vector control with PI controllers has been widely used in power systems. However, significant power loss occurs during the operation of these devices, and the loss is often dissipated in the form of heat, leading to significant maintenance effort. Though much work has been done to improve the power electronics design, little has focused so far on the investigation of the controller design to reduce the controller energy consumption (leading to power loss in power electronics) while maintaining acceptable system performance. This paper aims to bridge the gap and investigates their correlations. It is shown a more thoughtful controller design can achieve better balance between energy consumption in power electronics control and system performance, which potentially leads to significant energy saving for integration of renewable power sources.

Electron-scale dissipative electrostatic solitons in multi-species plasmas

The linear and nonlinear properties of small-amplitude electron-acoustic solitary waves are investigated via the fluid dynamical approach. A three-component plasma is considered, composed of hot electrons, cold electrons, and ions (considered stationary at the scale of interest). A dissipative (wave damping) effect is assumed due to electron-neutral collisions. The background (hot) electrons are characterized by an energetic (excessively superthermal) population and are thus modeled via a κ-type nonthermal distribution. The linear characteristics of electron-acoustic excitations are discussed, for different values of the plasma parameters (superthermality index κ and cold versus hot electron population concentration β). Large wavelengths (beyond a threshold value) are shown to be overdamped. The reductive perturbation technique is used to derive a dissipative Korteweg de-Vries (KdV) equation for small-amplitude electrostatic potential disturbances. These are expressed by exact solutions in the form of dissipative solitary waves, whose dynamics is investigated analytically and numerically. Our results should be useful in elucidating the behavior of space and experimental plasmas characterized by a coexistence of electron populations at different temperatures, where electron-neutral collisions are of relevance.

Identification of Peptide Inhibitors of Enveloped Viruses Using Support Vector Machine

The peptides derived from envelope proteins have been shown to inhibit the protein-protein interactions in the virus membrane fusion process and thus have a great potential to be developed into effective antiviral therapies. There are three types of envelope proteins each exhibiting distinct structure folds. Although the exact fusion mechanism remains elusive, it was suggested that the three classes of viral fusion proteins share a similar mechanism of membrane fusion. The common mechanism of action makes it possible to correlate the properties of self-derived peptide inhibitors with their activities. Here we developed a support vector machine model using sequence-based statistical scores of self-derived peptide inhibitors as input features to correlate with their activities. The model displayed 92% prediction accuracy with the Matthew’s correlation coefficient of 0.84, obviously superior to those using physicochemical properties and amino acid decomposition as input. The predictive support vector machine model for self- derived peptides of envelope proteins would be useful in development of antiviral peptide inhibitors targeting the virus fusion process.

A Fast Algorithm for Sparse Support Vector Machines for Mobile Computing Applications

This research presents a fast algorithm for projected support vector machines (PSVM) by selecting a basis vector set (BVS) for the kernel-induced feature space, the training points are projected onto the subspace spanned by the selected BVS. A standard linear support vector machine (SVM) is then produced in the subspace with the projected training points. As the dimension of the subspace is determined by the size of the selected basis vector set, the size of the produced SVM expansion can be specified. A two-stage algorithm is derived which selects and refines the basis vector set achieving a locally optimal model. The model expansion coefficients and bias are updated recursively for increase and decrease in the basis set and support vector set. The condition for a point to be classed as outside the current basis vector and selected as a new basis vector is derived and embedded in the recursive procedure. This guarantees the linear independence of the produced basis set. The proposed algorithm is tested and compared with an existing sparse primal SVM (SpSVM) and a standard SVM (LibSVM) on seven public benchmark classification problems. Our new algorithm is designed for use in the application area of human activity recognition using smart devices and embedded sensors where their sometimes limited memory and processing resources must be exploited to the full and the more robust and accurate the classification the more satisfied the user. Experimental results demonstrate the effectiveness and efficiency of the proposed algorithm. This work builds upon a previously published algorithm specifically created for activity recognition within mobile applications for the EU Haptimap project [1]. The algorithms detailed in this paper are more memory and resource efficient making them suitable for use with bigger data sets and more easily trained SVMs.

Spatio-Temporal Rich Model For Motion Vector Steganalysis

We propose a spatio-temporal rich model of motion vector planes as a part of a full steganalytic system against motion vector based steganography. Superior detection accuracy of the rich model over the previous methods has been lately demonstrated for digital images in both spatial and DCT domain. It has not been heretofore used for detection of motion vector steganography. We also introduced a transformation so as to extend the feature set with temporal residuals. We carried out the tests along with most recent motion vector steganalysis and steganography methods. Test results show that the proposed model delivers an outstanding performance compared to the previous methods.

Modelling the fresh properties of self-compacting concrete using support vector machine approach

The main objective of the study presented in this paper was to investigate the feasibility using support vector machines (SVM) for the prediction of the fresh properties of self-compacting concrete. The radial basis function (RBF) and polynomial kernels were used to predict these properties as a function of the content of mix components. The fresh properties were assessed with the slump flow, T50, T60, V-funnel time, Orimet time, and blocking ratio (L-box). The retention of these tests was also measured at 30 and 60 min after adding the first water. The water dosage varied from 188 to 208 L/m3, the dosage of superplasticiser (SP) from 3.8 to 5.8 kg/m3, and the volume of coarse aggregates from 220 to 360 L/m3. In total, twenty mixes were used to measure the fresh state properties with different mixture compositions. RBF kernel was more accurate compared to polynomial kernel based support vector machines with a root mean square error (RMSE) of 26.9 (correlation coefficient of R2 = 0.974) for slump flow prediction, a RMSE of 0.55 (R2 = 0.910) for T50 (s) prediction, a RMSE of 1.71 (R2 = 0.812) for T60 (s) prediction, a RMSE of 0.1517 (R2 = 0.990) for V-funnel time prediction, a RMSE of 3.99 (R2 = 0.976) for Orimet time prediction, and a RMSE of 0.042 (R2 = 0.988) for L-box ratio prediction, respectively. A sensitivity analysis was performed to evaluate the effects of the dosage of cement and limestone powder, the water content, the volumes of coarse aggregate and sand, the dosage of SP and the testing time on the predicted test responses. The analysis indicates that the proposed SVM RBF model can gain a high precision, which provides an alternative method for predicting the fresh properties of SCC.

Spatio-temporal Rich Model Based Video Steganalysis on Cross Sections of Motion Vector Planes

A rich model based motion vector steganalysis benefiting from both temporal and spatial correlations of motion vectors is proposed in this work. The proposed steganalysis method has a substantially superior detection accuracy than the previous methods, even the targeted ones. The improvement in detection accuracy lies in several novel approaches introduced in this work. Firstly, it is shown that there is a strong correlation, not only spatially but also temporally, among neighbouring motion vectors for longer distances. Therefore, temporal motion vector dependency along side the spatial dependency is utilized for rigorous motion vector steganalysis. Secondly, unlike the filters previously used, which were heuristically designed against a specific motion vector steganography, a diverse set of many filters which can capture aberrations introduced by various motion vector steganography methods is used. The variety and also the number of the filter kernels are substantially more than that of used in previous ones. Besides that, filters up to fifth order are employed whereas the previous methods use at most second order filters. As a result of these, the proposed system captures various decorrelations in a wide spatio-temporal range and provides a better cover model. The proposed method is tested against the most prominent motion vector steganalysis and steganography methods. To the best knowledge of the authors, the experiments section has the most comprehensive tests in motion vector steganalysis field including five stego and seven steganalysis methods. Test results show that the proposed method yields around 20% detection accuracy increase in low payloads and 5% in higher payloads.

Review of the genus Bursaphelenchus Fuchs, 1937 (Nematoda, Aphelenchida) with some conclusions on the host-parasite and vector-parasite evolution.

INTRODUCTION AND GOALS: Genus Bursaphelenchus includes several pests of the world importance for the rural economy, the most dangerous are the Bursaphelenchus xylophilus (the pinewood nematode caused decline of the pine trees in south Asia and in one spot area in Europe, Portugal, Peninsula de Setubal) and the Bursaphelenchus cocophilus, causing the decline of coco-palm plantations in Carribean and Latin American regions. The peculiarity of the host-parasite association of the genus that the nematode life cycle includes three trophic components: plant (mostly a tree), insect vector and a fungus. Goals of the presentation is to list all species of the world fauna and all efficient diagnostic characters, then create the identification tool and analyze the similarity of species and possible ways and causes of the host-parasite evolution of the group. RESULTS: Complete list of species with synonymy and a catalogue of all efficient diagnostic characters with their states, selected from papers of the most experienced taxonomists of the genus, are given for the genus Bursaphelenchus. List of known records of Bursaphelenchus species with names of natural vectors and plants and their families is given (for world pests the most important groups of trees and insects are listed). The tabular, traditional and computer-aided keys are presented. Dendrograms of species relationships (UPGMA, standard distance: mean character difference) based on all efficient taxonomic characters and separately on the spicule characters only, are given. Discussion whether the species groups are natural or purely diagnostic ones is based on the relationships dendrograms and the vector and associated plant ranges of Bursaphelenchus species; the xylophilus species group (B. xylophilus, B. abruptus, B. baujardi, B. conicaudatus, B. eroshenkii, B. fraudulentus, B. kolymensis, B. luxuriosae; B. mucronatus), the hunti group (B. hunti, B. seani, B. kevini and B. fungivorus) are probably the natural ones. CONCLUSIONS: The parasitic nematode association includes three trophic components: plant, insect vector and fungus. The initial insect-plant complex Scolytidae-Pinaceae is changeable and only in rare occasions the change of the preferred vector to Cerambycidae (the xylophilus group), Hymenoptera (the hunti group) led to formation of the natural species-groups. From the analysis it is clear that although the vector range is changeable it is comparatively more important for the evolution of the genus Bursaphelenchus than associations with plants at the family level. Data on the fungi species (3rd component in natural Bursaphelenchus associations) are insufficient for the detailed comparative analysis.

On-line diagnostics of transformer winding insulation failures, by Park`s vector approach

This paper presents a non-invasive approach for diagnosing winding insulation failures in three-phase transformers, which is based on the on-line monitoring of the primary and secondary current Park's Vector. Experimental and simulated results demonstrate the effectiveness of the proposed technique, for detecting winding inter-turn insulation faults in operating three-phase transformers.

Incipient turn-to-turn winding fault diagnosis of power transformers by the on-load exciting current Extended Park`s Vector Approach

This paper presents the application of the on-load exciting current Extended Park's Vector Approach for diagnosing incipient turn-to-turn winding faults in operating power transformers. Experimental and simulated test results demonstrate the effectiveness of the proposed technique, which is based on the spectral analysis of the AC component of the on-load exciting current Park's Vector modulus.

Transformers on-load exciting current Park`s vector approach as a tool for winding faults diagnostics

This paper presents the development of a new approach for diagnosing the occurrence of inter-turn short-circuits in the windings of three-phase transformers, which is based on the on-line monitoring of the on-load exciting current Park's Vector patterns. Experimental and simulated results demonstrate the effectiveness of the proposed technique for detecting winding inter-turn insulation faults in operating three-phase transformers.

Diagnóstico de avarias em transformadores trifásicos em serviço através da análise do Vector de Park das correntes de excitação em carga

Devido ao seu custo e importância estratégica, os transformadores de potência constituem um componente vital dos sistemas de produção, transmissão e distribuição de energia eléctrica. A sua fiabilidade constitui assim um factor crucial no funcionamento dos sistemas eléctricos de energia. Não admira, pois, que sobre estes equipamentos recaiam grandes preocupações relativamente à sua manutenção e, consequentemente, ao desenvolvimento de métodos capazes de fornecerem um diagnóstico completo e fiável do seu estado de funcionamento. Para o efeito, torna-se porém indispensável possuir um conhecimento detalhado acerca das avarias susceptíveis de ocorrerem nos transformadores, bem como dos mecanismos específicos que lhe estão subjacentes.

Estimating bottom properties with a vector sensor array during MakaiEx 2005

Nowadays, vector sensors which measure both acoustic pressure and particle velocity begin to be available in underwater acoustic systems, normally configured as vector sensor arrays (VSA). The spatial filtering capabilities of a VSA can be used, with advantage over traditional pressure only hydrophone arrays, for estimating acoustic field directionality as well as arrival times and spectral content, which could open up the possibility for its use in bottom properties' estimation. An additional motivation for this work is to test the possibility of using high frequency probe signals (say above 2 kHz) for reducing size and cost of actual sub bottom profilers and current geoacoustic inversion methods. This work studies the bottom related structure of the VSA acquired signals, regarding the emitted signal waveform, frequency band and source-receiver geometry in order to estimate bottom properties, specially bottom reflection coefficient characteristics. Such a system was used during the Makai 2005 experiment, off Kauai I., Hawai (USA) to receive precoded signals in a broad frequency band from 8 up to 14 kHz. The agreement between the observed and the modelled acoustic data is discussed and preliminary results on the bottom reflection estimation are presented.

Detection of trasformer intermittent winding faults by the on-load exciting current Park`s vector approach

This paper presents the application of the on-load exciting current Park's Vector Approach for diagnosing permanent and intermittent turn-to-turn winding faults in operating power transformers. First, an experimental investigation of the behaviour of the transformer under the occurrence of both permanent and intermittent winding faults is presented. Finally, experimental test results demonstrate the effectiveness of the proposed diagnostic technique, which is based on the on-line monitoring of the on-load exciting current Park's Vector patterns.