DOI•
Identification of fault zone in distribution system in the presence of pv module and esd by dtcwt–state vector machine algorithm using optimally placed measuring devices
TL;DR: A novel scheme for identifying the location of short-circuit faults that occur in distribution system is suggested and genetic algorithm– based heuristic search method is used to place measuring devices at optimal location, and it is carried out in MATLAB.
Abstract: In this paper, a novel scheme is suggested for identifying the location of short-circuit faults that occur in distribution system. The proposed genetic algorithm and graph theory–based method is designed such a way that it splits electrical distribution system into protection zones containing buses, protection relays with measuring devices. Proposed methodology also decreases the calculation burden in dealing with large number of data sets. Genetic algorithm– based heuristic search method is used to place measuring devices at optimal location, and it is carried out in MATLAB. A new signal processing technique named dual tree complex wavelets transform is used for feature extraction, and support vector machine–based machine learning classifier is used for pattern recognition. IEEE33 bus radial distribution system and IEEE13 bus feeder test systems are tested for validating the proposed methodology and all the simulation work carried out in MATLAB Simulink.
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TL;DR: There are several arguments which support the observed high accuracy of SVMs, which are reviewed and numerous examples and proofs of most of the key theorems are given.
Abstract: The tutorial starts with an overview of the concepts of VC dimension and structural risk minimization. We then describe linear Support Vector Machines (SVMs) for separable and non-separable data, working through a non-trivial example in detail. We describe a mechanical analogy, and discuss when SVM solutions are unique and when they are global. We describe how support vector training can be practically implemented, and discuss in detail the kernel mapping technique which is used to construct SVM solutions which are nonlinear in the data. We show how Support Vector machines can have very large (even infinite) VC dimension by computing the VC dimension for homogeneous polynomial and Gaussian radial basis function kernels. While very high VC dimension would normally bode ill for generalization performance, and while at present there exists no theory which shows that good generalization performance is guaranteed for SVMs, there are several arguments which support the observed high accuracy of SVMs, which we review. Results of some experiments which were inspired by these arguments are also presented. We give numerous examples and proofs of most of the key theorems. There is new material, and I hope that the reader will find that even old material is cast in a fresh light.
15,696 citations
TL;DR: The genetic algorithm is introduced as an emerging optimization algorithm for signal processing and a number of applications, such as IIR adaptive filtering, time delay estimation, active noise control, and speech processing, that are being successfully implemented are described.
Abstract: This article introduces the genetic algorithm (GA) as an emerging optimization algorithm for signal processing. After a discussion of traditional optimization techniques, it reviews the fundamental operations of a simple GA and discusses procedures to improve its functionality. The properties of the GA that relate to signal processing are summarized, and a number of applications, such as IIR adaptive filtering, time delay estimation, active noise control, and speech processing, that are being successfully implemented are described.
1,093 citations
01 Jan 2011
TL;DR: A new technique for feature selection that uses information from a confusion matrix and evaluates one attribute at a time, creating subsets of attributes that are complementary that is, they misclassify different classes.
Abstract: This paper introduces a new technique for feature selection and illustrates it on a real data set Namely, the proposed approach creates subsets of attributes based on two criteria: (1) individual attributes have high discrimination (classification) power; and (2) the attributes in the subset are complementary that is, they misclassify different classes The method uses information from a confusion matrix and evaluates one attribute at a time
265 citations
TL;DR: The proposed approach is able to improve the fault detection and isolation performance significantly with respect to three performance indicators, namely fault detection rate, detection delay and correct isolation rate, in comparison with the conventional method, which only uses the voltage measurements of DC-link.
Abstract: Due to the complex and harsh operation conditions, like corrosion, aging cable and static electricity, of electrical traction drive system, ground fault will generate large short circuit current to harm the key components. Effective fault diagnosis is important, but also challenging. The conventional method used for ground fault detection only takes advantage of voltage measurements of DC-link. Other measurements onboard are also available, which are correlated with the voltage measurements. Taking the correlation into account will improve the detection performance. To this end, this paper presents a data-driven solution, which makes full use of the correlation between the voltage measurements with other measurements onboard. The proposed method consists of two components: (1) a canonical correlation analysis-based fault detection method, which takes into account the correlation within measurements; (2) a fault isolation method by means of the fault direction, which can be obtained with the available faulty data stored in the long-term operation. The developed method is applied to a traction drive system. It is shown that the proposed approach is able to improve the fault detection and isolation performance significantly with respect to three performance indicators, namely fault detection rate, detection delay and correct isolation rate, in comparison with the conventional method, which only uses the voltage measurements of DC-link.
58 citations
TL;DR: A recognized method of distribution line fault type was proposed based on the analysis of time-frequency features of fault waveform, and results indicated that recognition success rate reached 90%, which verified the feasibility of using time- Frequency characteristics of faultWaveform to realize recognition of Distribution line fault types.
Abstract: Accurate recognition of distribution line fault types can provide directional guidance for line operation and maintenance personnel. Based on the analysis of time-frequency features of fault waveform, a recognized method of distribution line fault type was proposed in this paper. Through modeling and theoretical analysis of waveforms of different fault types, characteristic parameters, which could characterize waveforms of different fault types from three aspects, time domain, frequency domain, and electric arc, were put forward. Calculation formula for extracting characteristic parameters according to fault waveform data was proposed, recognition logic was established by taking multi-parameter fusion as a basis, and then,automatic recognition of distribution line fault types caused by different factors was realized through detection and classification of characteristic parameters of input waveform data. Finally, 136 groups of field fault waveform data provided by the Electric Power Research Institute were used to do closed-loop control and verification of the algorithm, and results indicated that recognition success rate reached 90%, which verified the feasibility of using time-frequency characteristics of fault waveform to realize recognition of distribution line fault types.
37 citations