The majority of power system faults occur in transmission lines. The classification of these faults in power systems is an important issue. In this paper, the real parameters of a 28 km, 154 kV transmission line between Simav and Demirci in Turkey's electricity transmission network is simulated in MATLAB/Simulink. Wavelet packet transform (WPT) is applied to instantaneous voltage signals. Instantaneous active power components are obtained by multiplying instantaneous currents obtained from a voltage source side with these WPT-based voltage signal components. A new feature vector extraction scheme is employed by calculating the energies of instantaneous active power components.Constructed feature vectors are treated with a classifier for short-circuit faults that occurred in high-voltage energy transmission lines; this is known as the common vector approach (CVA). This is the first implementation of CVA in the classification of short-circuit faults that occurred in high-voltage energy transmission lines. Furthermore, the same feature vector is applied to a support vector machine and artificial neural network for a comparison with the CVA method regarding classification performance and testing duration issues. Additionally, a graphical user interface is designed in MATLAB/GUI. Various noise levels, source frequencies, fault distances, fault inception angles, and fault exposure durations can be investigated with this interface. Classification of short-circuit faults in high-voltage transmission line is achieved by using an offline monitoring methodology. It is concluded that a combination of the proposed feature extraction scheme with the CVA classifier gives substantially high performance for the classification of short circuit faults in transmission line.
Development of technology increased the attention of the research community on power quality (PQ) disturbance classification problem. This paper presents wavelet based effective feature extraction method and support vector machines (SVM) for PQ disturbance classification problem. Two common kinds of power quality disturbances, voltage sag and swell, are considered in this paper. After multi-resolution signal decomposition of PQ disturbances, feature vector can be obtained. Multi-resolution analysis (MRA) technique of discrete wavelet technique (DWT) and Parseval's theorem are employed to extract the energy distribution features of sag and swell signals. SVM are used to classify these feature vectors of PQ disturbances. Performance of two kinds of method used in SVM is compared aspect of training time and training error.
The detection and classification of power quality events that disturb the voltage and/or current waveforms in the electrical power distribution networks is very important to generate electrical energy and to deliver this energy to the end-user equipment at an acceptable voltage. Various property extraction methods are used to determine the type of disturbances in the electrical signal. In this study, seven power distortions including voltage sag, voltage swell, voltage harmonics, voltage sag with harmonics, voltage swell with harmonics, flicker, transient signals and pure sine as a reference signal is used. Synthetic data are produced in MATLAB using parametric equations based on TS EN 50160 standard. Four kinds of feature extraction as frequency-amplitude, time-amplitude, geometric mean and standard deviation is made with Stockwell Transform (ST), which is one of the methods used for the feature extraction of the determined GKB. Detection of voltage distortions is interpreted through these properties. 640 simulation data is entered into the classifier by using Support Vector Machines (SVM) and Artificial Neural Networks (ANN) and their classification performance is compared.
In this paper, energy of wavelet coefficients are used for determining power quality disturbances (PQD) which are important for power systems. These power quality disturbances are voltage with harmonics, transient and flicker. After analyzing energy coeffients based on wavelet transform, these PQD are compared with healty condition. 50 Hz pure sine is chosen as reference. These signals are generated by using MATLAB. Sampling frequency is 25.6 kHz. Energy distribution of detail coefficients are obtained by using 12 level Daubechies-4 discrete wavelet filter. Parseval theorem is applied to wavelet coefficients for obtaining energy distribution of detail coefficients of these relevant disturbances in different resolution levels. Satisfactory results are taken visually when examining energy distrubutions of these PQD. Also it is observed that these PQD can be distinguished visually when their amplitudes are increased. Energy coefficients of PQD and amplitude of PQD are in direct propotion.
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