Classification of Stockwell Transform Based Power Quality Disturbance with Support Vector Machine and Artificial Neural Networks (original) (raw)
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The classification of power quality (PQ) disturbances to improve the PQ is an important issue in utilities and industrial factories. In this paper, an approach to classify PQ disturbances is presented. First, a signal containing one of the PQ disturbances, like sag, swell, flicker, interruption, transient, or harmonics, is evaluated using the proposed approach. Afterwards, S-transform and TT-transform are applied to the signal and an artificial neural network is used to recognize the disturbance using S-transform and TT-transform data, like the variance and mean values of S-transform and TT-transform matrices. The main features of the proposed approach are the real-time and very fast recognition of the PQ disturbances. Finally, the proposed method’s results are compared with the support vector machine and k-nearest neighbor classification methods to verify the results. The results show the effectiveness of this state-of-the-art approach.
Neural network for Power Quality disturbances recognition and classification using S-transform
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Energies, 2020
The detection, mitigation, and classification of power quality (PQ) disturbances have been issues of interest in the power system field. This paper proposes an approach to detect and classify various types of PQ disturbances based on the Stockwell transform (ST) and decision tree (DT) methods. At first, the ST is developed based on the moving, localizing, and scalable Gaussian window to detect five statistical features of PQ disturbances such as the high frequency of oscillatory transient, distinction between stationary and non-stationary, the voltage amplitude oscillation around an average value, the existence of harmonics in a disturbance signal, and the root mean square voltage at the internal period of sag, swell or interruption. Then, these features are classified into nine types, such as normal, sag, swell, interruption, harmonic, flicker, oscillatory transient, harmonic voltage sag, and harmonic voltage swell by using the DT algorithm that is based on a set of rules with the ...
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This paper presents a simple and effective method for detection of complex power quality disturbances using S-transform amplitude matrix. In this work, classification of complex power quality disturbances has been implemented using a rule-based decision tree for different noise levels, such as with no noise, 30-dB noise, and 45-dB noise. The S-transform is distinct, which provides a frequency-dependent resolution with direct relationship to the Fourier spectrum. The features obtained from S-transform amplitude matrix are dissimilar, clear, and immune to noise. According to a rule-based decision tree, 7 types of single power disturbance and 16 types of complex power disturbance are well identified in this work. The proposed work is simulated using MATLAB simulation, and the various results are found, which detect the single and complex power quality disturbances; and it proves that the proposed method is effective and unaffected against noise.
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This paper presents a method of power quality classification using support vector machines (SVMs). In SVM training, the kernel parameters, and feature selection have very important roles for SVM classification accuracy. Therefore, most appropriates of these kernel types, kernel parameters and features should be used for the SVM training. In this paper to get optimal features for the classifier two stage of feature selection has been used. In first stage mutual information feature selection (MIFS) and in the second stage correlation feature selection (CFS) techniques are used for feature extraction from signals to build distinguished patterns for classifiers. MIFS can reduce the dimensionality of inputs, speed up the training of the network and get better performance and with CFS can get optimal features. In order to create training and testing vectors, different disturbance classes were simulated using parametric equations i.e., pure sinusoid, sag, swell, harmonic, outage, sag and harmonics and swell and harmonics. Finally, the investigation results of this novel approach are shown. The test results show that the classifier has an excellent performance on training speed, reliability and accuracy.
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In recent years Power Quality has become an important issue for both utilities and customers. The increasing use of power quality sensitive equipment forced the distribution utilities to adopt a new method for continuously monitors the power quality of grid. Poor power quality may cause overheating of lines, inaccurate metering, and reduced efficiency of appliances. This dissertation proposes a new method to classify certain power quality disturbances. The power quality disturbances such as voltage sags, voltage swells, and voltage interruptions will be considered under study. In this dissertation work simulation of an electrical power system with power quality disturbances is done in MATLAB Simulink. Capturing of voltage signal is done for analyzing the power quality disturbances. The simulation results are further analyzed for the classification of power quality disturbances using MATLAB programming. The captured voltage signals are decomposed by using the signal processing techniques namely Wavelet transform. The estimation of statistical parameters is done from the decomposed signals for feature extraction. The extracted features are further used to classify the disturbances using soft computing techniques namely KNN. The proposed Wavelet-KNN approach classify these frequently occurring power quality disturbances accurately and with less computational complexity.
IEEE Transactions on Industry Applications, 2015
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.
IET Conference Publications, 2009
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Power quality disturbances (PQDs) result serious problems in the reliability, safety and economy of power system network. In order to improve electric power quality events, the detection and classification of PQDs must be made type of transient fault. Software analysis of wavelet transform with multiresolution analysis (MRA) algorithm and feed forward neural network (probabilistic and multilayer feed forward neural network )based methodology for automatic classification of eight types of PQ signals (flicker, harmonics, sag, swell, impulse, fluctuation, notch and oscillatory) will be presented. The wavelet family, Db4 is chosen in this system to calculate the values of detailed energy distributions as input features for classification because it can perform well in detecting and localizing various types of PQ disturbances. This technique classifies the types of PQDs problem sevents.The classifiers classify and identify the disturbance type according to the energy distribution. The re...