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Proceedings ArticleDOI

Fast Protection Scheme For Distribution System using Hilbert-Huang Transform

TL;DR: HHT is applied for fast detection and location of faults using quarter cycle post fault current information, measured at the secondary of the substation transformer, and it has been established that the protection algorithm is not affected by non faulty transients such as switching of compensating equipment.
Abstract: In this work, Hilbert-Huang Transform (HHT) is applied for fast detection and location of faults using quarter cycle post fault current information, measured at the secondary of the substation transformer. Empirical Mode Decomposition (EMD) is carried out over a moving window to obtain first level residue, on which Hilbert Transform (HT) is applied in order to obtain the instantaneous attributes. Standard deviations of instantaneous frequency (SDIF), amplitude (SDIA) and average value of residue (AVR) are obtained for computing fault location indices. A tree like logic flow is implemented by comparing these indices with thresholds to identify the zone of fault and subsequently the exact fault location. The proposed algorithm has been implemented for IEEE 13 bus distribution system using MATLAB. Various case studies involving changes in fault type, fault incidence angle, bus location, fault resistance have been carried out in order to establish the proposed algorithm. It has also been established that the protection algorithm is not affected by non faulty transients such as switching of compensating equipment.
Citations
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Proceedings ArticleDOI
27 Jul 2014
TL;DR: In this paper, the authors developed a method based on combination of empirical mode decomposition (EMD) and Hilbert transform for assessment of power quality events, which can be conceived as superimposition of various oscillating modes and EMD is used to separate out these intrinsic modes known as intrinsic mode functions (IMF).
Abstract: The aim of this paper is to develop a method based on combination of Empirical Mode Decomposition (EMD) and Hilbert Transform for assessment of power quality events. A distorted waveform can be conceived as superimposition of various oscillating modes and EMD is used to separate out these intrinsic modes known as intrinsic mode functions (IMF). Hilbert transform is applied to first three IMF to obtain instantaneous amplitude and phase which are then used for constructing feature vector. The work evaluates the detection capability of the methodology and a comparison with S-Transform is made to show the superiority of the technique in detecting the PQ disturbance like voltage spike and notch. A Probabilistic Neural Network is used as a mapping function for identifying the various disturbance classes. Results show a better classification accuracy of the methodology.

40 citations

Journal ArticleDOI
TL;DR: The proposed fault identification technique makes use of the empirical mode decomposition of three-phase current signals of a distribution network to detect, classifying, and locating faults with DG penetration in the presence of noise within half cycle.
Abstract: This article presents a fault identification technique that makes use of the empirical mode decomposition of three-phase current signals of a distribution network. The current signals measured at the substation bus over a moving window are decomposed to obtain the first-level intrinsic mode function and residue. The standard deviation of the first-level residue is calculated as a fault index for each phase and compared with a threshold to detect and categorize the type of fault. A ground fault index based on the average value of the first-level residue of the neutral current is proposed to discriminate the phase–phase and phase–phase–ground faults. The proposed algorithm has been successfully tested on the IEEE 13 bus and 34 bus distribution systems in the presence of the solar photovoltaic power plant by varying the type of fault, fault incidence angle, and fault location in the presence of noise. The selectivity of the proposed algorithm has been established by testing the algorithm with nonfaulty transients such as transformer excitation and deexcitation, feeder energization and deenergization, load switching, capacitor switching, and also those associated with distributed generation (DG) penetration like islanding and tripping of DG. Subsequently, the residue-based features are fed to a decision tree to locate the faults on the distribution system. Thus, the proposed algorithm has been successful in detecting, classifying, and locating faults with DG penetration in the presence of noise within half cycle by utilizing only current signals.

13 citations


Cites methods from "Fast Protection Scheme For Distribu..."

  • ...Residue-based features using the EMD provide fast fault detection and classification as reported in [13] and [22]....

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  • ...A tree-structured fault location algorithm is proposed using thresholds to locate the faulty zone in [22]....

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Journal ArticleDOI
TL;DR: A fast protection algorithm based on Hilbert-Huang transform (HHT) is proposed in this article for islanding and fault detection, classification, and location in a distribution system penetrated by a solar renewable energy source.

9 citations

Journal ArticleDOI
TL;DR: In this paper, the impact of fault circumstances on distribution grid parameters in the presence of wind power generation is explored in a study conducted on the IEEE-13 bus test feeder, which was connected to wind power plants.

9 citations

References
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Journal ArticleDOI
TL;DR: Test results indicate that the proposed relaying scheme can effectively protect the microgrid against faulty situations, including wide variations in operating conditions.
Abstract: This paper presents an intelligent protection scheme for microgrid using combined wavelet transform and decision tree. The process starts at retrieving current signals at the relaying point and preprocessing through wavelet transform to derive effective features such as change in energy, entropy, and standard deviation using wavelet coefficients. Once the features are extracted against faulted and unfaulted situations for each-phase, the data set is built to train the decision tree (DT), which is validated on the unseen data set for fault detection in the microgrid. Further, the fault classification task is carried out by including the wavelet based features derived from sequence components along with the features derived from the current signals. The new data set is used to build the DT for fault detection and classification. Both the DTs are extensively tested on a large data set of 3860 samples and the test results indicate that the proposed relaying scheme can effectively protect the microgrid against faulty situations, including wide variations in operating conditions.

258 citations


"Fast Protection Scheme For Distribu..." refers background in this paper

  • ...84KHz [17] 1 cycle Not considered 6 CERTS 6....

    [...]

Journal ArticleDOI
TL;DR: In this article, a novel method for high impedance fault (HIF) detection based on pattern recognition systems is presented, using this method, HIFs can be discriminated from insulator leakage current (ILC) and transients such as capacitor switching, load switching (high/low voltage), ground fault, inrush current and no load line switching.
Abstract: A novel method for high impedance fault (HIF) detection based on pattern recognition systems is presented in this paper. Using this method, HIFs can be discriminated from insulator leakage current (ILC) and transients such as capacitor switching, load switching (high/low voltage), ground fault, inrush current and no load line switching. Wavelet transform is used for the decomposition of signals and feature extraction, feature selection is done by principal component analysis and Bayes classifier is used for classification. HIF and ILC data was acquired from experimental tests and the data for transients was obtained by simulation using EMTP program. Results show that the proposed procedure is efficient in identifying HIFs from other events.

202 citations


"Fast Protection Scheme For Distribu..." refers background in this paper

  • ...Wavelet transform (WT) [1] and Stockwell Transform (ST) [2] are reported extensively for fault diagnosis at both transmission and distribution levels due to their multi resolution capabilities....

    [...]

Journal ArticleDOI
TL;DR: In this paper, most of the techniques that have been developed since the past and commonly used to locate and detect faults in distribution systems with distributed generation are reviewed, the working principles, advantages and disadvantages of past works related to each fault location technique are highlighted in this paper.
Abstract: Distribution systems are continuously exposed to fault occurrences due to various reasons, such as lightning strike, failure of power system components due to aging of equipment and human errors. These phenomena affect the system reliability and results in expensive repairs, lost of productivity and power loss to customers. Since fault is unpredictable, a fast fault location and isolation is required to minimize the impact of fault in distribution systems. Therefore, many methods have been developed since the past to locate and detect faults in distribution systems with distributed generation. The methods can be divided into two categories, conventional and artificial intelligence techniques. Conventional techniques include travelling wave method and impedance based method while artificial intelligence techniques include Artificial Neural Network (ANN), Support Vector Machine (SVM), Fuzzy Logic, Genetic Algorithm (GA) and matching approach. However, fault location using intelligent methods are challenging since they require training data for processing and are time consuming. In this paper, most of the techniques that have been developed since the past and commonly used to locate and detect faults in distribution systems with distributed generation are reviewed. Research works in fault location area, the working principles, advantages and disadvantages of past works related to each fault location technique are highlighted in this paper. Hence, from this review, the opportunities in fault location research area in power distribution system can be explored further.

188 citations


"Fast Protection Scheme For Distribu..." refers background in this paper

  • ...Fault location in distribution system is further challenging due to shorter distribution lines, complex topology, various laterals, unbalance operation and time varying load profile [5]....

    [...]

Journal ArticleDOI
TL;DR: In this paper, a novel fault detection and classification technique called wavelet singular entropy (WSE) is proposed for extremely high-voltage transmission line using the fault transients, which incorporates the advantages of the wavelet transform, singular value decomposition, and Shannon entropy.
Abstract: A novel technique for fault detection and classification in the extremely high-voltage transmission line using the fault transients is proposed in this paper. The novel technique, called wavelet singular entropy (WSE), incorporates the advantages of the wavelet transform, singular value decomposition, and Shannon entropy. WSE is capable of being immune to the noise in the fault transient and not being affected by the transient magnitude so it can be used to extract features automatically from fault transients and express the fault features intuitively and quantitatively even in the case of high-noise and low-magnitude fault transients. The WSE-based fault detection is performed in this paper, which proves the availability and superiority of WSE technique in fault detection. A novel algorithm based on WSE is put forward for fault classification and it is verified to be effective and reliable under various fault conditions, such as fault type, fault inception time, fault resistance, and fault location. Therefore, the proposed WSE-based fault detection and classification is feasible and has great potential in practical applications.

164 citations


"Fast Protection Scheme For Distribu..." refers methods in this paper

  • ...It is applied for classification of faults in EHV transmission lines using WT features in [9]....

    [...]

Journal ArticleDOI
TL;DR: In this article, the authors developed a method based on combination of empirical-mode decomposition (EMD) and Hilbert transform for assessment of power quality events, which can be conceived as superimposition of various oscillating modes and EMD is used to separate out these intrinsic modes known as intrinsic mode functions.
Abstract: The aim of this paper is to develop a method based on combination of empirical-mode decomposition (EMD) and Hilbert transform for assessment of power quality events. A distorted waveform can be conceived as superimposition of various oscillating modes and EMD is used to separate out these intrinsic modes known as intrinsic mode functions (IMF). Hilbert transform is applied to first three IMF to obtain instantaneous amplitude and phase which are then used for constructing feature vector. The work evaluates the detection capability of the methodolpogy and a comparison with S-transform is made to show the superiority of the technique in detecting the PQ disturbance like voltage spike and notch. A probabilistic neural network is used as a mapping function for identifying the various disturbance classes. Results show a better classification accuracy of the methodology.

161 citations