Shaik Abdul Gafoor

Bio: Shaik Abdul Gafoor is an academic researcher from National Institute of Technology, Warangal. The author has contributed to research in topics: Fault indicator & Fault (power engineering). The author has an hindex of 3, co-authored 3 publications receiving 43 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.

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.

Performance Analysis of Metaheuristic Techniques in Combined Economic Emission Dispatch considering VPL effect for Dynamic System

Abstract: In past two decades solution to the Dynamic Economic Load Dispatch (DELD) problem considering emission and transmission losses along with Valve Point Loading (VPL) effect has been investigated by researchers using several meta-heuristic techniques. Numerical techniques such as lambda iteration method, dynamic programming etc. have been unable to solve the ELD problem with VPL effect. This paper presents a systematic performance evaluation of meta-heuristic techniques named as Particle Swarm Optimization and the Genetic Algorithm for combined economic emission dispatch problem considering Valve Point Loading (VPL) to meet 24 hr. load profile on IEEE 9 bus test case with 3 generator units.

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.

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.

Comparison of fourier & wavelet transform methods for transmission line fault classification

Abstract: Nowadays, power supply has become a business asset. The quality and reliability of power system needs to be maintained in order to obtain optimum performance. Therefore, it is extremely important that transmission line faults from various sources to be identified accurately, reliably and be corrected as soon as possible. In this paper, a new technique is discussed by which avoiding noise in fault detection in high voltage transmission lines is achieved. Later, a comparative study of the performance of Fourier transform and wavelet transform based methods combined with protective relaying pattern classifier algorithm Neural Network for classification of faults is presented. A new classification method is proposed for decreasing training time and dimensions of NN. The proposed algorithms are based on Fourier transform analysis of fundamental frequency of current signals in the event of a short circuit. Similar analysis is performed on transient current signals using multi-resolution Haar wavelet transform, and comparative characteristics of the two methods are discussed.

Transmission line fault location estimation by Fourier & wavelet transforms using ANN

Abstract: Nowadays, power supply has become a business commodity. The quality and reliability of power needs to be maintained in order to obtain optimum performance. Therefore, it is extremely important that transmission line faults from various sources be identified accurately, reliably and be corrected as soon as possible. In this paper, a comparative study of the performance of Fourier transform and wavelet transform based methods combined with Neural Network (NN) for location estimation of faults on high voltage transmission lines is presented. A new location method is proposed for decreasing training time and dimensions of NN. The proposed algorithms are based on Fourier transform analysis of fundamental frequency of current and voltage signals in the event of a short circuit on a transmission line. Similar analysis is performed on transient current and voltage signals using multi-resolution Daubchies-9 wavelet transform, and comparative characteristics of the two methods are discussed.