R.K. Aggarwal

A Novel Approach to the Classification of the Transient Phenomena in Power Transformers Using Combined Wavelet Transform and Neural Network

The wavelet transform is a powerful tool in the analysis of the power transformer transient phenomena because of its ability to extract information from the transient signals simultaneously in both the time and frequency domain. This paper presents a novel technique for accurate discrimination between an intemal fault and a magnetizing inrush current in the power transformer by combining wavelet transforms with neural networks. The wavelet transform is first applied to decompose the differential current signals of the power transformer into a series of detailed wavelet components. The spectral energies of the wavelet components are calculated and then employed to train a neural network to discriminate an intemal fault from the magnetizing inrush current. The simulated results presented clearly show that the proposed technique can accurately discriminate between an intemal fault and a magnetizing inrush current in power transformer protection.

Performance evaluation of a distance relay as applied to a transmission system with UPFC

This paper presents analytical and simulation results of the application of distance relays for the protection of transmission systems employing flexible alternating current transmission controllers such as the unified power flow controller (UPFC). Firstly a detailed model of the UPFC and its control is proposed and then it is integrated into the transmission system for the purposes of accurately simulating the fault transients. An apparent impedance calculation procedure for a transmission line with UPFC based on the power frequency sequence component is then investigated. The simulation results show the impact of UPFC on the performance of a distance protection relay for different fault conditions; the studies also include the influence of the setting of UPFC control parameters and the operational mode of UPFC.

An Alternative Approach to Adaptive Single-Pole Auto Reclosing in High-Voltage Transmission Systems Based on Variable Dead Time Control

This paper presents a new concept, based on variable dead time control, in adaptive single-pole auto reclosing (SPAR). The proposed scheme can give rise to a high rate of successful reclosing by adapting to variable dead times. The significance of this algorithm is that it uses the waveform patterns of the voltage transients following initial breaker opening. The performance of this proposed method is tested under a variety of fault locations on the Korean 765 kV system, and the outcome of the study clearly indicates that variable dead time auto-reclosing scheme can be used as an attractive and effective means of better management and operation of a high-voltage transmission system.

An Algorithm for Compensating Secondary Currents of Current Transformers

Current transformer (CT) saturation may cause power system relays to malfunction. The conventional method used to deal with the problem is overdimensioning of the transformer core so that CTs can carry up to 20 times the rated current without exceeding 10% ratio correction. However, this not only reduces the sensitivity of power system relays, but also increases the CT core size. This paper presents a technique of estimating the secondary current corresponding to the CT ratio under CT saturation. The proposed algorithm can improve the sensitivity of relays to low level internal faults, minimize the instability of relays for external faults, and might ultimately assist in reducing the dimension of the required CT core cross-section.

A practical approach to accurate fault location on extra high voltage teed feeders

The basis of an alternative approach for accurately locating faults on teed feeders is described. The technique developed uses fault voltages and currents at all three ends. The method is virtually independent of fault resistance and largely insensitive to variations in source impedance, teed and line configurations, including line untransposition. The basic theory of the technique is presented. It is extensively tested using simulated primary system voltage and current waveforms, which include the transducer/hardware errors encountered in practice. The performance clearly shows a high degree of accuracy. >

A novel fault classification technique for double-circuit lines based on a combined unsupervised/supervised neural network

Summary form only as given. The work described in this paper addresses the problems encountered by conventional techniques in fault type classification in double-circuit transmission lines; these arise principally due to the mutual coupling between the two circuits under fault conditions, and this mutual coupling is highly variable in nature. It is shown that a neural network based on a combined unsupervised/supervised training methodology provides the ability to accurately classify the fault type by identifying different patterns of the associated voltages and currents. The technique is compared with that based solely on a supervised training algorithm (i.e., bad-propagation network classifier). It is then tested under differed fault types, location resistance and inception angle; different source capacities and load angles are also considered. All the test results show that the proposed fault classifier is very well suited for classifying fault types in double-circuit lines.

Design and implementation of an adapative single pole autoreclosure technique for transmission lines using artificial neural networks

Adaptive single pole autoreclosure (SPAR) offers many advantages over conventional techniques. In the case of transient faults, the secondary arc extinction time can be accurately determined and in the case of a permanent fault, breaker reclosure can be avoided. This paper describes, in some detail, the design and implementation of a SPAR technique using artificial neural networks (ANNs). The design described includes special methods for extracting features from post-circuit breaker opening fault data, which is a prerequisite for setting up training data sets. The technique is then implemented in hardware based on a high performance T800 transputer system and some results obtained from laboratory tests of this equipment are presented.

Analysis of power transformer dissolved gas data using the self-organizing map

Incipient faults in power transformers can degrade the oil and cellulose insulation, leading to the formation of dissolved gases. Even though established approaches that relate these dissolved gas information to the condition of power transformers are already developed, it is discussed in this paper that they still contain some limitations. In view of that, this paper introduces an alternative approach for the analysis of dissolved gas data, which can produce more convincing interpretation and fault diagnosis. The proposed approach, which is based on the data mining methodology and the self-organizing map, has been compared and validated using conventional interpretation schemes and real fault-cases, thereby proven to be capable of enhancing the condition monitoring of power transformers.

A New Approach to Phase Selection Using Fault Generated High Frequency Noise and Neural Networks

Single-pole autoreclosure is quite extensively used in long-line applications and involves tripping only the faulted phase for single-phase earth faults. Reliable and fast phase selection is thus imperative in order to avoid potential problems of system insecurity and instability. Conventional phase selectors, primarily based on power frequency measurands, can suffer some impairment in performance because of their heavy dependency on varying system and fault conditions. However, the advent of artificial neural networks (ANNs), with their ability to map complex and highly nonlinear input/output patterns, provides an attractive potential solution to the long-standing problems of accurate and fast phase selection. This paper describes the design of a novel phase selector using ANNs. The technique is based on utilising fault generated high frequency noise (captured through the high voltage coupling capacitor of a conventional capacitor voltage transformer) to essentially recognise the various patterns generated within the frequency spectra of the fault generated noise signals on the three phases, for the purposes of accurately deducing the faulted phase. The paper demonstrates a new concept and methodology in phase selection which will facilitate single-pole autoreclosure applications in power systems.

A wavelet transform based decision making logic method for discrimination between internal faults and inrush currents in power transformers

This paper describes a decision making logic method for discrimination between internal faults and inrush currents in power transformers using the wavelet transform based feature extraction technique. It is shown that the features extracted by the wavelet transform have a more distinctive property than those extracted by the fast Fourier transform due to the good time and frequency localisation characteristics of the wavelet transform. As a result, by quantifying the extracted features, the decision for distinguishing an internal fault from an inrush current in different power transformer systems can be accurately made. The extensive simulation studies have verified that the proposed method is more reliable and simpler, and is suitable for different power transformer systems.