Núbia Silva Dantas Brito

Bio: Núbia Silva Dantas Brito is an academic researcher from Federal University of Campina Grande. The author has contributed to research in topics: Fault (power engineering) & Wavelet. The author has an hindex of 15, co-authored 62 publications receiving 1105 citations. Previous affiliations of Núbia Silva Dantas Brito include Federal University of Paraíba & Federal University of Rio Grande do Norte.

Fault detection and classification in transmission lines based on wavelet transform and ANN

Abstract: This paper proposes a novel method for transmission-line fault detection and classification using oscillographic data. The fault detection and its clearing time are determined based on a set of rules obtained from the current waveform analysis in time and wavelet domains. The method is able to single out faults from other power-quality disturbances, such as voltage sags and oscillatory transients, which are common in power systems operation. An artificial neural network classifies the fault from the voltage and current waveforms pattern recognition in the time domain. The method has been used for fault detection and classification from real oscillographic data of a Brazilian utility company with excellent results

High Impedance Fault Identification on Distribution Networks

Abstract: This paper presents a transient-based algorithm for high-impedance fault identification on distribution networks. It uses the discrete wavelet transform to monitor high- and low-frequency voltage components at several points of the power system, being able to indicate the most likely area within which the disturbance has occurred, without requiring data synchronization nor the knowledge of feeder or load parameters. The proposed algorithm is evaluated through electromagnetic transients program simulations of high-impedance faults in a 13.8 kV system modeled from actual Brazilian distribution grid data. Solid faults, capacitor bank switching, and feeder energization are also simulated, considering the system with and without distributed generation. Obtained results show that the algorithm significantly reduces the search field of the high-impedance fault, reliably distinguishing it from other disturbances.

Real-Time Detection of Transients Induced by High-Impedance Faults Based on the Boundary Wavelet Transform

Abstract: The development of modern protection functions is a challenge in the emerging environment of smart grids because the current protection system technology still has several limitations, such as the reliable high-impedance fault (HIF) detection in multigrounded distribution networks, which poses a danger to the public when the protection system fails. This paper presents the wavelet coefficient energy with border distortions of a one-cycle sliding window designed for the real-time detection of transients induced by HIFs. By using the border distortions, the proposed wavelet-based methodology presents a reliable detection of transients generated by HIFs with no time delay and energy peaks scarcely affected by the choice of the mother wavelet. The signatures of different HIFs are presented in both time and wavelet domains. The performance of the proposed wavelet-based method was assessed with compact and long mother wavelets by using data from staged HIFs on an actual energized power system, taking into account different fault surfaces, as well as simulated HIFs. The proposed method presented a more reliable and accurate performance than other evaluated wavelet-based algorithms.

Daubechies wavelets in quality of electrical power

Abstract: Wavelet analysis is a new method for electrical power quality analysis. A brief theoretical background on the wavelet theory and suggestions of some applications in the area of power quality are presented. Investigations on the use of some Daubechies wavelets, namely Daub4, Daub12 and Daub20, are carried out.

Effects of the fault inception angle in fault-induced transients

Abstract: The analysis of fault-induced transients in three-phase overhead transmission lines can provide extensive information about the fault type, detection, location, direction and sustained time in satisfactory agreement with real application in protective relays These transients depend on the system topology, load condition and the fault parameters, such as the fault type, resistance, inception angle and location This study addresses the fault inception angle effects in the energies of the fault-induced transients in both voltages and currents by means of the wavelet coefficient energy analysis at the first three wavelet scales, in which a generic energy equation regarding the fault-induced transients as a function of the fault inception angle in all kinds of faults was established

Fault detection and classification in transmission lines based on wavelet transform and ANN

Abstract: This paper proposes a novel method for transmission-line fault detection and classification using oscillographic data. The fault detection and its clearing time are determined based on a set of rules obtained from the current waveform analysis in time and wavelet domains. The method is able to single out faults from other power-quality disturbances, such as voltage sags and oscillatory transients, which are common in power systems operation. An artificial neural network classifies the fault from the voltage and current waveforms pattern recognition in the time domain. The method has been used for fault detection and classification from real oscillographic data of a Brazilian utility company with excellent results

A critical review of detection and classification of power quality events

Abstract: Requirement of green supply with higher quality has been consumers’ demand around the globe The electrical power system is expected to deliver undistorted sinusoidal rated voltage and current continuously at rated frequency to the consumers This paper presents a comprehensive review of signal processing and intelligent techniques for automatic classification of the power quality (PQ) events and an effect of noise on detection and classification of disturbances It is intended to provide a wide spectrum on the status of detection and classification of PQ disturbances as well as an effect of noise on detection and classification of PQ events to the researchers, designers and engineers working on power quality More than 150 research publications on detection and classification techniques of PQ disturbances have been critically examined, classified and listed for quick reference

Fault detection, classification and location for transmission lines and distribution systems: a review on the methods

Abstract: A comprehensive review on the methods used for fault detection, classification and location in transmission lines and distribution systems is presented in this study. Though the three topics are highly correlated, the authors try to discuss them separately, so that one may have a more logical and comprehensive understanding of the concepts without getting confused. Great significance is also attached to the feature extraction process, without which the majority of the methods may not be implemented properly. Fault detection techniques are discussed on the basis of feature extraction. After the overall concepts and general ideas are presented, representative works as well as new progress in the techniques are covered and discussed in detail. One may find the content of this study helpful as a detailed literature review or a practical technical guidance.

High impedance fault detection: A review

Abstract: Protection from the high impedance fault (HIF) has been one of the biggest challenges in the power distribution network HIF typically occurs when the conductors in distribution network break and touch the ground surface; or lean and touch a tree branch This fault, with current magnitude close to the load current level, is not detectable by over-current relays This paper aims to review the literature related to the HIF phenomenon In this work, the HIF detection techniques are categorized, evaluated, and compared with one another Furthermore, the existing HIF models and HIF location techniques are reviewed Finally, the shortcoming of the existing perspective toward the HIF phenomenon and the possible road to the future for HIF detection is discussed

An expert system based on S-transform and neural network for automatic classification of power quality disturbances

Abstract: In this paper, an S-transform-based neural network structure is presented for automatic classification of power quality disturbances. The S-transform (ST) technique is integrated with neural network (NN) model with multi-layer perceptron to construct the classifier. Firstly, the performance of ST is shown for detecting and localizing the disturbances by visual inspection. Then, ST technique is used to extract the significant features of distorted signal. In addition, an optimum combination of the most useful features is identified for increasing the accuracy of classification. Features extracted by using the S-transform are applied as input to NN for automatic classification of the power quality (PQ) disturbances that solves a relatively complex problem. Six single disturbances and two complex disturbances as well pure sine (normal) selected as reference are considered for the classification. Sensitivity of proposed expert system under different noise conditions is investigated. The analysis and results show that the classifier can effectively classify different PQ disturbances.