Fault classification in power systems using EMD and SVM

Babu, N. Ramesh; Mohan, Bhadragiri Jagan

doi:10.1016/j.asej.2015.08.005

Cited by 97 publications

(14 citation statements)

References 15 publications

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“…Various indices such as energy, the spread of the instantaneous temporal energy density and deviation of the instantaneous temporal energy density are computed in order to identify a healthy condition and a line-to-ground or line to line to line faults. In [35], power system faults classification using EMD and support vector machines is proposed. EMD is used for decomposing voltages of transmission line into IMF.…”

Section: Introductionmentioning

confidence: 99%

EMD-Based Feature Extraction for Power Quality Disturbance Classification Using Moments

et al. 2016

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Abstract:In electric power systems, there are always power quality disturbances (PQDs). Usually, noise contamination interferes with their detection and classification. Common methods perform frequency or time-frequency analyses on the power distribution signal for detecting and classifying a limited number of PQDs with some difficulties at low signal-to-noise ratio (SNR). In this regard, recently proposed methodologies for PQD detection estimate several parameters and apply distinct signal processing techniques to improve the detection of PQD. In this work, a novel methodology that merges empirical mode decomposition (EMD), the moments of a random variable, and an artificial neural network (ANN) is proposed for detecting and classifying different PQD. The proposed method estimates skewness, kurtosis, and Shannon entropy from the EMD of one-phase voltage/current signal. Then, an ANN is in charge of classifying the input signal into one of nine different classes for PQD, receiving these parameters as inputs. The effectiveness of the proposed method was verified through computer simulations and experimentation with real data. Obtained results demonstrate its high effectiveness reaching an outstanding 100% of accuracy in detecting and classifying all treated PQD through a few number of parameters, outperforming most of previously proposed approaches.

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Section: Introductionmentioning

confidence: 99%

EMD-Based Feature Extraction for Power Quality Disturbance Classification Using Moments

et al. 2016

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“…For high impedance fault detection in electrical distribution networks, the WT extracts dynamic characteristics to feed a decision-making system based on SVM [107]. The SVM is also used along with the Hilbert Huang transform to decompose the voltages of transmission lines into intrinsic mode functions [108] for fault classification in power systems. The main contribution of the proposed algorithm is the possibility of its application to any transmission line, no matter the line configuration, with no need for re-training at different load values, voltage levels, and fault resistances.…”

Section: Hybrid Techniquesmentioning

confidence: 99%

Predictive Data Mining Techniques for Fault Diagnosis of Electric Equipment: A Review

et al. 2020

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Data mining is a technological and scientific field that, over the years, has been gaining more importance in many areas, attracting scientists, developers, and researchers around the world. The reason for this enthusiasm derives from the remarkable benefits of its usefulness, such as the exploitation of large databases and the use of the information extracted from them in an intelligent way through the analysis and discovery of knowledge. This document provides a review of the predictive data mining techniques used for the diagnosis and detection of faults in electric equipment, which constitutes the pillar of any industrialized country. Starting from the year 2000 to the present, a revision of the methods used in the tasks of classification and regression for the diagnosis of electric equipment is carried out. Current research on data mining techniques is also listed and discussed according to the results obtained by different authors.

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“…However, different kernel functions can be implemented, and the Gaussian Radial Basis Function (GRBF) kernel has proven to be reliable. It is described in detail elsewhere [27].…”

Section: Support Vector Machinementioning

confidence: 99%

Electric Vehicle Battery Lifetime Extension through an Intelligent Double-Layer Control Scheme

et al. 2019

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Electric vehicles (EVs) are recognized as promising options, not only for the decarbonization of urban areas and greening of the transportation sector, but also for increasing power system flexibility through demand-side management. Large-scale uncoordinated charging of EVs can impose negative impacts on the existing power system infrastructure regarding stability and security of power system operation. One solution to the severe grid overload issues derived from high penetration of EVs is to integrate local renewable power generation units as distributed generation units to the power system or to the charging infrastructure. To reduce the uncertainties associated with renewable power generation and load as well as to improve the process of tracking Pareto front in each time sequence, a predictive double-layer optimal power flow based on support vector regression and one-step prediction is presented in this study. The results demonstrate that, through the proposed control approach, the rate of battery degradation is reduced by lowering the number of cycles in which EVs contribute to the services that can be offered to the grid via EVs. Moreover, vehicle to grid services are found to be profitable for electricity providers but not for plug-in electric vehicle owners, with the existing battery technology and its normal degradation.

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Fault classification in power systems using EMD and SVM

Cited by 97 publications

References 15 publications

EMD-Based Feature Extraction for Power Quality Disturbance Classification Using Moments

EMD-Based Feature Extraction for Power Quality Disturbance Classification Using Moments

Predictive Data Mining Techniques for Fault Diagnosis of Electric Equipment: A Review

Electric Vehicle Battery Lifetime Extension through an Intelligent Double-Layer Control Scheme

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