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Proceedings ArticleDOI

Recognition of power quality disturbances using S-transform and rule-based decision tree

TL;DR: In this article, a method for recognition of power quality disturbances using Stockwell's transform has been presented, which includes voltage sag, swell, interruption, harmonics, notch, flicker, oscillatory transient, impulsive transient and spike.
Abstract: This paper presents a method for recognition of power quality disturbances using Stockwell's transform. Power quality disturbances are generated using MATLAB as per IEEE standards. Various features of signals are extracted from the multi-resolution analysis based on Stockwell's transform. These features are used to classify various power quality disturbances using the rule-based decision tree. It is observed that high efficiency of classification is achieved using S-transform based ruled decision tree. The investigated power quality disturbances include voltage sag, swell, interruption, harmonics, notch, flicker, oscillatory transient, impulsive transient and spike. Effectiveness of the proposed algorithm has been established by satisfactory results of various case studies.
Citations
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Journal ArticleDOI
01 Oct 2017
TL;DR: A method based on Stockwell's transform and Fuzzy C-means clustering initialized by decision tree has been proposed for detection and classification of power quality (PQ) disturbances and is established effectively by results of high accuracy.
Abstract: Display Omitted The S-transform based decision tree initialized Fuzzy C-means clustering technique is proposed for recognition of PQ disturbances.Sum absolute values curve is introduced to increase efficiency of algorithm.Results of FCM technique are more efficient compared with rule based decision tree.Validation of results is carried out with 100 data sets of each PQ disturbance with and without noise and comparing with real time results.Classification accuracy more than 99% is achieved even in the noisy environment. A method based on Stockwell's transform and Fuzzy C-means (FCM) clustering initialized by decision tree has been proposed in this paper for detection and classification of power quality (PQ) disturbances. Performance of this method is compared with S-transform based ruled decision tree. PQ disturbances are simulated in conformity with standard IEEE-1159 using MATLAB software. Different statistical features of PQ disturbance signals are obtained using Stockwell's transform based multi-resolution analysis of signals. These features are given as input to the proposed techniques such as rule-based decision tree and FCM clustering initialized by ruled decision tree for classification of various PQ disturbances. The PQ disturbances investigated in this study include voltage swell, voltage sag, interruption, notch, harmonics, spike, flicker, impulsive transient and oscillatory transient. It has been observed that the efficiency of classification based on ruled decision tree deteriorates in the presence of noise. However, the classification based on Fuzzy C-means clustering initialized by decision tree gives results with high accuracy even in the noisy environment. Validity of simulation results has been verified through comparisons with results in real time obtained using the Real Time Digital Simulator (RTDS) in hardware synchronization mode. The proposed algorithm is established effectively by results of high accuracy to detect and classify various electrical power quality disturbances.

88 citations

Journal ArticleDOI
TL;DR: This manuscript introduces an algorithm for identification of the complex nature PQ events in which it is supported by Stockwell’s transform (ST) and decision tree (DT) using rules and verified that the proposed approach can effectively be employed for design of the online complex PQ monitoring devices.
Abstract: Deteriorated quality of power leads to problems, such as equipment failure, automatic device resets, data errors, failure of circuit boards, loss of memory, power supply issues, uninterrupted power supply (UPS) systems generate alarm, corruption of software, and heating of wires in distribution network. These problems become more severe when complex (multiple) power quality (PQ) disturbances appear. Hence, this manuscript introduces an algorithm for identification of the complex nature PQ events in which it is supported by Stockwell’s transform (ST) and decision tree (DT) using rules. PQ events with complex nature are generated in view of IEEE-1159 standard. Eighteen different types of complex PQ issues are considered and studied which include second, third, and fourth order disturbances. These are obtained by combining the single stage PQ events such as sag & swell in voltage, momentary interruption (MI), spike, flicker, harmonics, notch, impulsive transient (IT), and oscillatory transient (OT). The ST supported frequency contour and proposed plots such as amplitude, summing absolute values, phase and frequency-amplitude obtained by multi-resolution analysis (MRA) of signals are used to identify the complex PQ events. The statistical features such as sum factor, Skewness, amplitude factor, and Kurtosis extracted from these plots are utilized to classify the complex PQ events using rule-based DT. This is established that proposed approach effectively identifies a number of complex nature PQ events with accuracy above 98%. Performance of the proposed method is tested successfully even with noise level of 20 dB signal to noise ratio (SNR). Effectiveness of the proposed algorithm is established by comparing it with the methods reported in literature such as fuzzy c-means clustering (FCM) & adaptive particle swarm optimization (APSO), Wavelet transform (WT) & neural network (NN), spline WT & ST, ST & NN, and ST & fuzzy expert system (FES). Results of simulations are validated by comparing them with real time results computed by Real Time Digital Simulator (RTDS). Different stages for design of complex PQ monitoring device using the proposed approach are also described. It is verified that the proposed approach can effectively be employed for design of the online complex PQ monitoring devices.

42 citations


Cites methods from "Recognition of power quality distur..."

  • ...A method using S-transform and DT using rules for identification and to classify the single stage (simple nature) PQ events is presented by authors in [18]....

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Journal ArticleDOI
TL;DR: A novel algorithm is proposed to detect and classify the power quality disturbances for distribution networks with distributed generation and provides an approach for online real-time detection of embedded systems.
Abstract: In this paper, a novel algorithm is proposed to detect and classify the power quality (PQ) disturbances for distribution networks with distributed generation. First, a distribution system with photovoltaic and wind power generation is built as a test platform. Then, nine types of power quality disturbances in the distribution network are decomposed by variational mode decomposition (VMD) and the noise is filtered. Meanwhile, the mode functions containing characteristic information are extracted as input signals of detrended fluctuation analysis (DFA). Power quality disturbances are classified from the view of their distributed energy operational status, and three types of windows are set up to deal with different frequency disturbances. The two-dimensional and three-dimensional scatter plots of each type under three windows are depicted, and the criteria are determined to distinguish the disturbances under the different operating conditions. The simulations show that the algorithm is simpler, more accurate and feasible. It provides an approach for online real-time detection of embedded systems.

19 citations


Cites background from "Recognition of power quality distur..."

  • ...Higher precision can be achieved even under higher level noise [13]–[19]....

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Journal ArticleDOI
TL;DR: A complete and inclusive study of power quality events, such as automatic classification and signal processing via creative techniques and the noises effect on the detection and classification of powerquality disturbances are revealed.
Abstract: Around the globe, the necessity of green supply with a dedicated standard quality thrust of consumers is increasing day by day. The advancement in technology urges the electrical power system to deliver a high-quality rated undistorted sinusoidal current, the voltage at a constant desired standard frequency to its consumers. The present paper reveals a complete and inclusive study of power quality events, such as automatic classification and signal processing via creative techniques and the noises effect on the detection and classification of power quality disturbances. It’s planned to make a possible list for quick reference to obtain an extensive variety on the condition & status of available research for detection and classification for young engineers, designers and researchers who enter in the power quality field. The current extensive study is supported by a critical review of more than 200 publications on detection and classification techniques of power quality disturbances.

11 citations


Cites methods from "Recognition of power quality distur..."

  • ...While in [55] the author presented a rule-based tree with the help of ANN and S-transform which further improved in [56] to rule based tree with the only S-transform....

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Journal ArticleDOI
TL;DR: In this article, a novel method of fault location for high-permeability active distribution network based on improved variational mode decomposition (IVMD) and S-transform is proposed.
Abstract: Aiming at the problem of the inaccurate fault location in high-permeability active distribution network, a novel method of fault location for active distribution network based on improved variational mode decomposition (IVMD) and S-transform is proposed in this paper Firstly, the judgment method of instantaneous frequency is adopted to determine the value of decomposition layers $k$ Meanwhile the algorithm of variational mode decomposition (VMD) is utilized to decompose the fault signal of traveling wave Then, the kurtosis criterion is employed to select the appropriate intrisic mode functions (IMFS) for S-transform in order to obtain the S-matrix with higher resolution The high-frequency components in the matrix are extracted to determine the arrival time for the head of the fault traveling wave Finally, the fault distance can be calculated by the special distance formula without considering the velocity of the traveling wave The simulations show that the proposed method can accurately measure the fault distance for the transmission lines in high permeability active distribution network, and the measurement error is limited within 200m, which verifies the accuracy and effectiveness of the proposed algorithm

10 citations

References
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Journal ArticleDOI
01 Apr 1996
TL;DR: In this article, the authors present a new approach to detect, localize, and investigate the feasibility of classifying various types of power quality disturbances using dyadic-orthonormal wavelet transform analysis.
Abstract: In this paper we present a new approach to detect, localize, and investigate the feasibility of classifying various types of power quality disturbances. The approach is based on wavelet transform analysis, particularly the dyadic-orthonormal wavelet transform. The key idea underlying the approach is to decompose a given disturbance signal into other signals which represent a smoothed version and a detailed version of the original signal. The decomposition is performed using multiresolution signal decomposition techniques. We demonstrate and test our proposed technique to detect and localize disturbances with actual power line disturbances. In order to enhance the detection outcomes, we utilize the squared wavelet transform coefficients of the analyzed power line signal. Based on the results of the detection and localization, we carry out an initial investigation of the ability to uniquely characterize various types of power quality disturbances. This investigation is based on characterizing the uniqueness of the squared wavelet transform coefficients for each power quality disturbance.

908 citations


Additional excerpts

  • ...However, this method is considerably affected by the presence of electrical noise in the signal....

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Journal ArticleDOI
TL;DR: A comprehensive review of signal processing and intelligent techniques for automatic classification of the power quality (PQ) events and an effect of noise on detection and classification of disturbances is presented in this paper.
Abstract: Requirement of green supply with higher quality has been consumers’ demand around the globe The electrical power system is expected to deliver undistorted sinusoidal rated voltage and current continuously at rated frequency to the consumers This paper presents a comprehensive review of signal processing and intelligent techniques for automatic classification of the power quality (PQ) events and an effect of noise on detection and classification of disturbances It is intended to provide a wide spectrum on the status of detection and classification of PQ disturbances as well as an effect of noise on detection and classification of PQ events to the researchers, designers and engineers working on power quality More than 150 research publications on detection and classification techniques of PQ disturbances have been critically examined, classified and listed for quick reference

326 citations


Additional excerpts

  • ...The advanced signal processing and artificial intelligent techniques had been proposed for effective recognition of these disturbances....

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Journal ArticleDOI
TL;DR: In this article, a novel approach for detection and classification of power quality (PQ) disturbances is proposed, where distorted waveforms are generated based on the IEEE 1159 standard, captured with a sampling rate of 20 kHz and de-noised using discrete wavelet transform (DWT) to obtain signals with higher signal-to-noise ratio.
Abstract: A novel approach for detection and classification of power quality (PQ) disturbances is proposed. The distorted waveforms (PQ events) are generated based on the IEEE 1159 standard, captured with a sampling rate of 20 kHz and de-noised using discrete wavelet transform (DWT) to obtain signals with higher signal-to-noise ratio. The DWT is also used to decompose the signal of PQ events and to extract its useful information. Proper feature vectors are selected and applied in training the wavelet network classifier. The effectiveness of the proposed method is tested using a wide spectrum of PQ disturbances including dc offset, harmonics, flicker, interrupt, sag, swell, notching, transient and combinations of these events. Comparison of test results with those generated by other existing methods shows enhanced performance with a classification accuracy of 98.18%. The main contribution of the paper is an accurate (because of proper selection of feature vectors), fast (e.g. a new de-noising approach with proposed identification criterion) and robust (at different signal-to-noise ratios) wavelet network-based algorithm (as compared to the conventional wavelet-based algorithms) for detection/classification of individual, as well as combined PQ disturbances.

229 citations


Additional excerpts

  • ...Poor PQ may cause overheating of lines, mal-operation of protective equipments, inaccurate metering, premature ageing of equipment and appliances, motor failures, interference with communication systems and reduced efficiency of appliances [3]....

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Journal ArticleDOI
TL;DR: In this article, an algorithm based on Stockwell's transform and artificial neural network-based classifier and a rule-based decision tree is proposed for the recognition of single stage and multiple power quality (PQ) disturbances.
Abstract: This paper deals with a modified technique for the recognition of single stage and multiple power quality (PQ) disturbances. An algorithm based on Stockwell's transform and artificial neural network-based classifier and a rule-based decision tree is proposed in this paper. The analysis and classification of single stage PQ disturbances consisting of both events and variations such as sag, swell, interruption, harmonics, transients, notch, spike, and flicker are presented. Moreover, the proposed algorithm is also applied on multiple PQ disturbances such as harmonics with sag, swell, flicker, and interruption. A database of these PQ disturbances based on IEEE-1159 standard is generated in MATLAB for simulation studies. The proposed algorithm extracts significant features of various PQ disturbances using S-transform, which are used as input to this hybrid classifier for the classification of PQ disturbances. Satisfactory results of effective recognition and classification of PQ disturbances are obtained with the proposed algorithm. Finally, the proposed method is also implemented on real-time PQ events acquired in a laboratory to confirm the validity of this algorithm in practical conditions.

220 citations


Additional excerpts

  • ...The Stockwell transform (S-transform) is a time-frequency spectral localization technique proposed by Stockwell that combines the features of WT and STFT....

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Journal ArticleDOI
TL;DR: Comparison study between wavelet transform (WT) and S-transform (ST) based on extracted features for detection of islanding and power quality (PQ) disturbances in hybrid distributed generation (DG) system demonstrates the advantages of S -transform over WT in detection of Islanding and different disturbances under noise-free as well as noisy scenarios.
Abstract: In this paper, comparative study between wavelet transform (WT) and S-transform (ST) based on extracted features for detection of islanding and power quality (PQ) disturbances in hybrid distributed generation (DG) system is presented. The hybrid system consists of DG resources like photovoltaic, fuel cell, and wind energy systems connected to grid. The negative sequence component of the voltage signal is used in islanding detection of these resources from the grid. Voltage signal extracted directly at the point of common coupling is considered for detection of PQ disturbances. Further, the effect of variation of grid impedances on islanding and PQ disturbances and effect of islanding on the coherency between the energy resources is also presented in this paper. The study for different scenarios of DG system is presented in the form of time-frequency analysis. The energy content and standard deviation of ST contour and WT signal is also reported in order to validate the graphical results. The results demonstrate the advantages of S -transform over WT in detection of islanding and different disturbances under noise-free as well as noisy scenarios.

219 citations


Additional excerpts

  • ...Various disturbance signals are designated by the class symbols from C2 to C10 and the pure sine wave is designated by C1 as shown in Table I....

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