Recognition of power quality disturbances using S-transform and Fuzzy C-means clustering
01 Sep 2016-pp 1-6
TL;DR: In this article, a method for detection and classification of power quality (PQ) disturbances using Stockwell's transform is presented, in which various features of signals are extracted from the multi-resolution analysis based on Stockwell transform and used to classify PQ disturbances using the decision tree initialized Fuzzy C-means clustering.
Abstract: This paper presents a method for detection and classification of power quality (PQ) disturbances using Stock-well's transform. PQ disturbances are generated using MATLAB as per IEEE-1159 standard. Various features of signals are extracted from the multi-resolution analysis based on Stockwell's transform. These features are used to classify PQ disturbances using the decision tree initialized Fuzzy C-means clustering. It is observed that the Fuzzy C-means clustering based classification yields satisfactory accuracy even under noisy conditions. The investigated PQ disturbances include voltage sag, swell, interruption, harmonics, notch, flicker, oscillatory transient, impulsive transient and spike. Effectiveness of proposed algorithm has been established by satisfactory results of various case studies.
Citations
More filters
TL;DR: A novel method for assessing PQ associated with wind energy integration is proposed that is effective to recognize PQ issues in power systems with high penetration of wind energy with a low computational burden and detects different operational issues in the distribution network.
Abstract: Power quality (PQ) is a vital issue in the present power systems integrated with large renewable energy sources since more power electronics devices are incorporated in the system. This article proposes a novel method for assessing PQ associated with wind energy integration. This method is effective to recognize PQ issues in power systems with high penetration of wind energy with a low computational burden. Furthermore, it detects different operational issues in the distribution network. Stockwell transform (S-transform) is utilized to decompose the voltage signal and calculate the S-matrix. To assess the PQ, a plot is developed from this matrix. The features of this matrix such as mean, standard deviation, and maximum deviation are further utilized for detecting the operational issues such as wind speed variation, islanding, synchronization, and outage of the wind generation by using clustering with fuzzy C-means. A modified IEEE 13-bus test system is utilized to validate the proposed method, which is also supported by hardware and real-time digital simulator results. The quality of power is graded with the help of a proposed PQ index under various operational events with different levels of wind energy penetration. The proposed method is effective for the identification and grading of different operational events in terms of PQ and recognizing a wide range of PQ issues with a high share of wind energy. The performance of the proposed scheme is established by comparing its results with other approaches.
96 citations
Cites result from "Recognition of power quality distur..."
...These curves are compared to the corresponding curves of standard PQ disturbances [23]....
[...]
TL;DR: A method based on Stockwell's transform (S-transform) is presented in this paper for power quality (PQ) assessment and detection of islanding, outage and grid synchronization of renewable energy sources.
Abstract: Renewable energy (RE) sources when integrated to utility network to form hybrid power system pose challenges in terms of stability and power quality. A method based on Stockwell's transform (S-transform) is presented in this paper for power quality (PQ) assessment and detection of islanding, outage and grid synchronization of renewable energy sources. Voltage signals are decomposed using multi-resolution analysis (MRA) of S-transform and features extracted are realized to assess the power quality disturbances associated with various events. Operational events are detected with the help of features extracted from the S-transform based decomposition of negative sequence component of voltage. A power quality index is proposed to rank the various operational events based on power quality. Proposed algorithm has been tested successfully on IEEE-13 bus test system with necessary modifications to integrate wind and solar PV generators to form the hybrid power system. Simulation results are validated in real time environment with the help of real time digital simulation (RTDS) system.
88 citations
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
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..."
...In [20], authors introduced a technique to classify the single stage PQ disturbances using FCM based on features computed using ST from time-frequency representation of the disturbances....
[...]
TL;DR: The proposed algorithm proved to be successful for detecting different PQ disturbances under all the investigated operating conditions and will help to enhance the SE integration level into the utility grid.
Abstract: Enhancement in solar energy (SE) injection into the power system network creates power quality (PQ) issues in the supply. This article presents an approach supported by Stockwell transform ( $S$ -transform) for assessment of PQ issues related with the grid interfaced solar photovoltaic (SPV) system under various operating conditions. This will help to enhance the SE integration level into the utility grid. The set up, to perform assessment of the PQ issues includes an emulated SPV system interfaced with the utility at the point of common coupling (PCC). Measurements of voltage and current signals are performed by utilizing power network analyzer. The captured voltage signals are analyzed using $S$ -transform for the detection of a variety of PQ problems associated with the grid interfacing and outage of the SPV system. Effects on PQ due to presence of the various types of loads at PCC have also been investigated under the same operating conditions. Effect of partial shading of SPV plates on the PQ is also investigated. Harmonic analysis is performed for all the investigated events. The proposed algorithm proved to be successful for detecting different PQ disturbances under all the investigated operating conditions.
26 citations
References
More filters
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
"Recognition of power quality distur..." refers methods in this paper
...The dyadic-orthonormal WT has been used to detect, classify and localize the PQ disturbances by the authors in [4]....
[...]
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
"Recognition of power quality distur..." refers methods in this paper
...PQ disturbances detection and classification techniques are reported in [3]....
[...]
TL;DR: The modified Gaussian window is found to provide excellent normalized frequency contours of the power signal disturbances suitable for accurate detection, localization, and classification of various nonstationary power signals.
Abstract: This paper presents a new approach for the visual localization, detection, and classification of various nonstationary power signals using a variety of windowing techniques. Among the various windows used earlier like sine, cosine, tangent, hyperbolic tangent, Gaussian, bi-Gaussian, and complex, the modified Gaussian window is found to provide excellent normalized frequency contours of the power signal disturbances suitable for accurate detection, localization, and classification. Various nonstationary power signals are processed through the generalized S-transform with modified Gaussian window to generate time-frequency contours for extracting relevant features for pattern classification. The extracted features are clustered using fuzzy C-means algorithm, and finally, the algorithm is extended using either particle swarm optimization or genetic algorithm to refine the cluster centers.
256 citations
"Recognition of power quality distur..." refers methods in this paper
...A hybrid Fuzzy C-means particle swarm optimization (PSO) technique to cluster the features into distinct groups for classification of PQ disturbances is reported in [12]....
[...]
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
"Recognition of power quality distur..." refers background or methods or result in this paper
...From, the Table III it is also evident that the proposed algorithm presents higher efficiency in comparison with the algorithms proposed in the references [2], [6], [8] and [9]....
[...]
...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 [2]....
[...]
...Performance of the algorithm has also been studied in the noisy environment with 20 dB SNR (signal to noise ratio)....
[...]
...A noise of 20 dB SNR is added to establish the performance under noisy conditions....
[...]
...Noise level considered in this paper is 20 dB SNR which is higher than that considered in [2] and [8]....
[...]
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
"Recognition of power quality distur..." refers background in this paper
...The S-transform uses a window whose width decreases with the frequency and provides a frequency dependent resolution This gives high time resolution at high frequency and high frequency resolution at low frequency [5]....
[...]