Unbalanced load flow with hybrid wavelet transform and support vector machine based Error-Correcting Output Codes for power quality disturbances classification including wind energy

Purpose. The most common methods to designa multiclass classification consist to determine a set of binary classifiers and to combine them. In this paper support vector machine with Error-Correcting Output Codes (ECOC-SVM) classifier is proposed to classify and characterize the power qualitydisturbances such as harmonic distortion,voltage sag, and voltage swell include wind farms generator in power transmission systems. Firstly three phases unbalanced load flow analysis is executed to calculate difference electric network characteristics, levels of voltage, active and reactive power. After, discrete wavelet transform is combined with the probabilistic ECOC-SVM model to construct the classifier. Finally, the ECOC-SVM classifies and identifies the disturbance type according tothe energy deviation of the discrete wavelet transform. The proposedmethod gives satisfactory accuracy with 99.2% compared with well known methods and shows that each power quality disturbances has specific deviations from the pure sinusoidal waveform,this is good at recognizing and specifies the type of disturbance generated from the wind power generator.Наиболее распространенные методы построения мультиклассовой классификации заключаются в определении набора двоичных классификаторов и их объединении. В данной статье предложена машина опорных векторов с классификатором выходных кодов исправления ошибок(ECOC-SVM) с целью классифицировать и характеризовать такие нарушения качества электроэнергии, как гармонические искажения, падение напряжения и скачок напряжения, включая генератор ветровых электростанций в системах передачи электроэнергии. Сначала выполняется анализ потока несимметричной нагрузки трех фаз для расчета разностных характеристик электрической сети, уровней напряжения, активной и реактивной мощности. После этого дискретное вейвлет-преобразование объединяется с вероятностной моделью ECOC-SVM для построения классификатора. Наконец, ECOC-SVM классифицирует и идентифицирует тип возмущения в соответствии с отклонением энергии дискретного вейвлет-преобразования. Предложенный метод дает удовлетворительную точность 99,2% по сравнению с хорошо известными методами и показывает, что каждое нарушение качества электроэнергии имеет определенные отклонения от чисто синусоидальной формы волны, что способствует распознаванию и определению типа возмущения, генерируемого ветровым генератором

Multivariate Techniques for Identifying Diffractive Interactions at the LHC

31 pages, 14 figures, 11 tablesClose to one half of the LHC events are expected to be due to elastic or inelastic diffractive scattering. Still, predictions based on extrapolations of experimental data at lower energies differ by large factors in estimating the relative rate of diffractive event categories at the LHC energies. By identifying diffractive events, detailed studies on proton structure can be carried out. The combined forward physics objects: rapidity gaps, forward multiplicity and transverse energy flows can be used to efficiently classify proton-proton collisions. Data samples recorded by the forward detectors, with a simple extension, will allow first estimates of the single diffractive (SD), double diffractive (DD), central diffractive (CD), and non-diffractive (ND) cross sections. The approach, which uses the measurement of inelastic activity in forward and central detector systems, is complementary to the detection and measurement of leading beam-like protons. In this investigation, three different multivariate analysis approaches are assessed in classifying forward physics processes at the LHC. It is shown that with gene expression programming, neural networks and support vector machines, diffraction can be efficiently identified within a large sample of simulated proton-proton scattering events. The event characteristics are visualized by using the self-organizing map algorithm.Peer reviewe

Optimized complex power quality classifier using one vs. rest support vector machine

Nowadays, power quality issues are becoming a significant research topic because of the increasing inclusion of very sensitive devices and considerable renewable energy sources. In general, most of the previous power quality classification techniques focused on single power quality events and did not include an optimal feature selection process. This paper presents a classification system that employs Wavelet Transform and the RMS profile to extract the main features of the measured waveforms containing either single or complex disturbances. A data mining process is designed to select the optimal set of features that better describes each disturbance present in the waveform. Support Vector Machine binary classifiers organized in a ?One Vs Rest? architecture are individually optimized to classify single and complex disturbances. The parameters that rule the performance of each binary classifier are also individually adjusted using a grid search algorithm that helps them achieve optimal performance. This specialized process significantly improves the total classification accuracy. Several single and complex disturbances were simulated in order to train and test the algorithm. The results show that the classifier is capable of identifying >99% of single disturbances and >97% of complex disturbances.Fil: de Yong, David Marcelo. Universidad Nacional de Río Cuarto. Facultad de Ingeniería. Departamento de Electricidad y Electrónica; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba; ArgentinaFil: Bhowmik, Sudipto. Nexant Inc; Estados UnidosFil: Magnago, Fernando. Universidad Nacional de Río Cuarto. Facultad de Ingeniería. Departamento de Electricidad y Electrónica; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba; Argentin