P.V. Ramana Rao

Bio: P.V. Ramana Rao is an academic researcher from National Institute of Technology, Warangal. The author has contributed to research in topics: Wavelet transform & Wavelet. The author has an hindex of 4, co-authored 5 publications receiving 54 citations.

Wavelet Based Fault Detection, Classification and Location in Transmission Lines

Abstract: This paper deals with the application of wavelet transforms for the detection, classification and location of faults on transmission lines. A global positioning system synchronizing clock is used to sample three phase voltage and current signals at both the ends of the transmission line over a moving window length of half cycle. The current signals are analyzed with Bior2.2 wavelet to obtain detail coefficients of single decompositions. Fault indices are calculated based on the sum of local and remote end detail coefficients, and compared with threshold values to detect and classify the faults. For estimation of fault location feed forward artificial neural networks are employed, which make use of third level approximate decompositions of the voltages and currents of local end obtained with Bior4.4 wavelet. Two types of neural networks are proposed, one for locating phase faults and the other for ground faults. The proposed algorithm is tested for different locations and types of faults as well as for various incidence angles and fault impedances. The algorithm is proved to be efficient and effective in detecting, classifying and locating faults.

Wavelet ANN based stator internal faults protection scheme for 3-phase induction motor

Abstract: This paper proposes a protection scheme based on Wavelet Multi Resolution Analysis and Artificial neural network which detects and classifies various possible stator winding fault of a three-phase induction motor such as inter turn faults, line to ground faults and line to line faults. The wavelet decomposition of three-phase stator currents is carried out with Bior5.5. The maximum value of absolute peak d1 coefficients of three-phase currents is defined as fault index which is compared with a predefined threshold to detect the fault. The normalized peak d1 coefficients of these currents are fed to a Feedforward neural network to classify various faults. The algorithm has been tested for various incidence angles and proved to be simple, reliable and effective in detecting and classifying the various stator winding faults.

A transient current based bus zone protection scheme using wavelet transform

Abstract: This paper deals with the application of wavelet transform for the detection of busbar faults and providing backup for external faults In the event of internal faults the transient behavior of feeder and zone-2 currents is opposite to that of zone-1 source current In the event of feeder or zone-2 faults, the corresponding currents exhibit similar transient behavior as that of source current The transients of these current signals are analyzed using wavelet based multi resolution analysis to obtain detailed coefficients over a narrow moving window The fault indexes of each current signal, defined based on these detailed coefficients, are utilized to detect the internal faults as well as external faults The scheme is tested for different types of internal and external faults with various incidence angles and fault impedance The proposed scheme discriminates internal faults from external faults even in the presence of current transformer saturation

A New Wavelet Transform Based Busbar Protection

Abstract: This paper deals with application of Wavelet Transform for detection of busbar faults and to discriminate them from external faults. The detail coefficients of differential current and those of a source CT current are obtained over a narrow moving window. The Fault indexes of both current signals obtained are compared with their respective threshold values to detect the internal faults. In the event of external faults the d-coefficients of differential current have a time shift compared to that of source current due to saturation of CT and this is used to discriminate the external faults from internal faults. The scheme is tested for different types of external and internal faults with variations in incidence angles and fault impedances. The proposed scheme is proved to be fast, stable and reliable in detecting the internal faults and discriminating them from external faults.

Wavelet ANN Based Stator Ground Fault Protection Scheme for Turbo Generators

Abstract: This article proposes a protection scheme for turbo generators to detect stator ground faults in particular closer to neutral. The scheme considers total generated voltage of the machine and makes use of wavelet multi-resolution analysis. A fault index is defined in terms of peak and valley values of the highest-level wavelet decomposition, which is compared with a threshold value to discriminate from other transients, like sudden unbalance, in load. To identify the faulty coil, an ANN employing back propagation algorithm is used with the lowest level wavelet coefficients as inputs. The results of digital simulation are presented for different fault impedances and fault locations.

Incipient Fault Detection in Stator Windings of an Induction Motor Using Stockwell Transform and SVM

Abstract: In this article, Stockwell transform (ST) is used to analyze the stator current signals for diagnosis of various motor conditions such as healthy, stator winding interturn shorts, and phase to ground faults. ST decomposes the current signals into complex ST matrix whose magnitude has been utilized for the fault detection. The nature of the fault, that is, ground or interturn is identified using the zero sequence currents followed by postfault detection. Two separate frequency bands are defined to extract the features which are fed to two different support vector machine (SVM) models for faulty phase detection for both types of faults. Under both cases, a heuristic feature selection approach is utilized to find the optimal features for classification purposes. Average classification accuracy of 96% has been achieved for both types of faults.

Fault Detection and Classification on a Transmission Line using Wavelet Multi Resolution Analysis and Neural Network

Abstract: and distribution lines are vital links between generating units and consumers. They are exposed to atmosphere, hence chances of occurrence of fault in transmission line is very high, which has to be immediately taken care of in order to minimize damage caused by it. In this paper discrete wavelet transform of voltage signals at the two ends of the transmission lines have been analyzed. Transient energies of detail information for two consecutive data windows at fault are used for analysis. Four layer feed forward back propagation neural networks are designed to classify and locate the fault at different single line to ground fault conditions.

Fault classification and faulted phase selection for transmission line using morphological edge detection filter

Abstract: In this study, a novel algorithm for detecting and classifying faults in transmission lines is proposed. The algorithm is based on mathematical morphology and initial current travelling waves. A new morphological edge detection (MED) filter to extract the transient features from the original fault signal is designed. This MED filter can fast and accurately detect the arrival time and polarity of travelling waves in all conditions. The appropriate criteria of fault classification and faulted-phase selection are introduced based on polarity of initial current travelling waves. The simulations based on the electromagnetic transients program and MATLAB have been done to evaluate the validity of the proposed algorithm.

Early detection and localization of stator inter-turn faults based on discrete wavelet energy ratio and neural networks in induction motor

Abstract: This paper proposes an improved diagnosis method for early detection and localization of Inter-Turn Short Circuit (ITSC) faults in the stator winding of the induction motor (IM). The main advantages of the method are the simplicity, low cost, and accurate diagnosis of these types of faults such that it can detect and localize even a low number of shorted turns faults in the stator winding of the motor. This is achieved by using a novel indicator that is based on the Discrete Wavelet Energy Ratio (DWER) of three stator currents, with Artificial Neural Network (ANN). Three different models of typical neural networks, namely, Multi-Layer perceptron (MLP), radial basis function (RBF), and Elman Neural Network (ENN) based on Bayesian Regularized (BR) training algorithm are proposed for ITSC classification based on fault feature extraction using discrete wavelet transform. To test the effectiveness of the proposed method, several experimental tests were carried out under different operating conditions of the IM, which contains the healthy and the ITSC faults cases that have experimented under various loads and different numbers of shorted turns in the three phases of the motor. The obtained results proved that the DWER is an accurate and robust indicator to diagnose the ITSC fault, this is confirmed by ANN results which gave the best results with the Bayesian regularized Elman network model that has the highest performance with minimal error rate is 10−9.Consequently, the combination DWER-ENN has assured its ability to accurately detect high and even low numbers of the shorted turns and localize the defective phase even within various loads in the IM.

Real-time fault analysis of transmission lines using wavelet multi-resolution analysis based frequency-domain approach

Abstract: In a power system, transmission lines are prone to faults of different nature, which challenge the system stability and reliability. Thus system performance analysis under such fault conditions has drawn attention of researchers. Particularly, the advent of fast and efficient data acquisition using higher sampling rates combined with high speed digital signal processors has paved the way for efficient digital real-time simulations. Though sustained efforts have been made by different researchers to develop some good real-time digital simulators, this study is an attempt to implement a laboratory prototype model of a 20 V, 200 km transmission line, representing a 400 kV extra-high voltage transmission line, so as to improve the real time performance. In addition, efficient National Instruments based data acquisition system in conjunction with LabVIEW has been incorporated to acquire the best possible representative data with commensurate characterisation and transmission with fidelity. The unique contributions of this real-time fault analysis laboratory hybrid model are accurate fault detection and classification using a frequency-domain approach having immunity to fault impedance and fault inception angle, which affect the time-domain analyses severely. It is also equipped with visual displays so that even non-experts can use it for planning and decision-making purposes.