A Novel Clarke Wavelet Transform Method to Classify Power System Disturbances
نویسندگان
چکیده
With wide spread use of sensitive nonlinear electronic devices the switching transients are capable of degrading the quality of power. Utilities often switch the shunt capacitor banks to cope up with sagging voltage levels, thereby generating transients, which travel into the network of end users. Capacitor switching can cause over voltage, resonance and in advert tripping of Adjustable Speed Drives (ASD) and many other sensitive electronics devices. This paper presents a method to distinguish between transients arising out of capacitor switching, , load switching and line to ground fault. The three phase voltages are first transformed to alpha, beta, and zero sequence components using Clarke transform. The DWT of the alpha, beta, and zero sequence components is obtained and the magnitude of the detail coefficients is used to extract distinguishing features. A real power system has been simulated in PSCAD/EMTDC with lines modelled using frequency dependant phase model
منابع مشابه
Detection and Localization of Power Quality Disturbances Using Space Vector Wavelet Transform: A New Three Phase Approach
This paper presents a new three phase approach based on space vector discrete wavelet transform to detect and localize power quality disturbances (PQD). This approach provides high resolution time frequency representation used to detect and localize the disturbances. Supplementary information about detected disturbances (duration and frequency spectrum) extracted in order to characterize them. ...
متن کاملA Machine Intelligence Approach for Classification of Power Quality Disturbances
This paper presents the combination of advanced signal processing techniques and the machine intelligence approach to classify the power quality events. The Wavelet Transform (WT) and the S – Transform (ST) are utilized to extract the important useful features of the disturbance signal. The features extracted by using the above approaches are used to train a PNN classifier for automatic classif...
متن کاملA novel method based on a combination of deep learning algorithm and fuzzy intelligent functions in order to classification of power quality disturbances in power systems
Automatic classification of power quality disturbances is the foundation to deal with power quality problem. From the traditional point of view, the identification process of power quality disturbances should be divided into three independent stages: signal analysis, feature selection and classification. However, there are some inherent defects in signal analysis and the procedure of manual fe...
متن کاملWavelet - Support Vector Machine Approach for classification of Power Quality Disturbances
This paper presents a wavelet transform and Support Vector Machine (SVM) based algorithm for classification of power quality (PQ) disturbances. The features extracted through the wavelet transform are trained by a SVM for classification of power quality disturbances. Five types of disturbances are considered for the classification problem. The simulation results reveal that the combination of w...
متن کاملWavelet-Based Signal Processing Techniques For Disturbance Classification and Measurement
This paper presents the wavelet domain theoretical basis for rms value and total harmonic distortion measurements. It provides a systematic method for analyzing power system disturbances in the wavelet domain. The wavelet domain capabilities in detection, classification, and measurements of different power system disturbances are presented. The distortion event is mapped into the wavelet domain...
متن کامل