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Benemar Alencar de Souza

Bio: Benemar Alencar de Souza is an academic researcher from Federal University of Campina Grande. The author has contributed to research in topics: Fault (power engineering) & High impedance. The author has an hindex of 2, co-authored 2 publications receiving 101 citations.

Papers
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Journal ArticleDOI
TL;DR: A transient-based algorithm that 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.
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.

155 citations


Cited by
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Journal ArticleDOI
TL;DR: In this article, a fault-location scheme for ungrounded photovoltaic (PV) systems is proposed, in which high frequency noise patterns are used to identify the fault location.
Abstract: Identifying ground faults is a significant problem in ungrounded photovoltaic (PV) systems because such earth faults do not provide sufficient fault currents for their detection and location during system operation. If such ground faults are not cleared quickly, a subsequent ground fault on the healthy phase will create a complete short circuit in the system. This paper proposes a novel fault-location scheme in which high frequency noise patterns are used to identify the fault location. The high-frequency noise is generated due to the switching transients of converters combined with the parasitic capacitance of PV panels and cables. Discrete wavelet transform is used for the decomposition of the monitored signal (midpoint voltage of the converters) and features are extracted. Norm values of the measured waveform at different frequency bands give unique features at different fault locations and are used as the feature vectors for pattern recognition. Then, a three-layer feedforward artificial neural networks classifier, which can automatically classify the fault locations according to the extracted features, is investigated. The proposed fault-location scheme has been primarily developed for fault location in the PV farm (PV panels and dc cables). The method is tested for ground faults as well as line–line faults. These faults are simulated with a real-time digital simulator and the data are then analyzed with wavelets. Finally, the effectiveness of the designed fault locator is tested with varying system parameters. The results demonstrate that the proposed approach has accurate and robust performance even with noisy measurements and changes in operating conditions.

113 citations

Journal ArticleDOI
TL;DR: A novel HIF detection method, which combines variational mode decomposition (VMD) and Teager–Kaiser energy operators (TKEOs) and has higher feature extraction accuracy, less calculation time, and better judgment accuracy.
Abstract: The focus of the paper is the difficulty of high impedance fault (HIF) detection in distribution network, and its ease to be confused with capacitor switching (CS) and load switching (LS). Based on the intermittent reignition and extinction characteristics of HIF current, this paper proposes a novel HIF detection method, which combines variational mode decomposition (VMD) and Teager–Kaiser energy operators (TKEOs). The HIF detection method is as follows: First, perform the VMD on transient zero sequence currents to obtain the intrinsic mode functions (IMFs) and select the IMFs with the largest kurtosis value as the characteristic IMFs. Second, calculate the characteristic IMFs to obtain TKEOs and divide into subintervals of TKEOs waveform to calculate the time entropy values. Finally, construct HIF detection criterion as follows: when time entropy value is 0, it is judged as CS or LS. When the entropy value is not 0, it is judged as HIF. A large number of simulations and field data tests show that the method is accurate and stable, and under the interference of 1 dB strong noise, it can accurately judge. Compared with other methods, the method has higher feature extraction accuracy, less calculation time, and better judgment accuracy.

98 citations

Journal ArticleDOI
TL;DR: The use of an evolving neural network was shown to be a quite appropriate approach to fault detection since high impedance faults is a time-varying problem and the performance of the proposed evolving system for detecting and classifying faults was compared with well-established computational intelligence methods.
Abstract: This paper concerns how to apply an incremental learning algorithm based on data streams to detect high impedance faults in power distribution systems. A feature extraction method, based on a discrete wavelet transform that is combined with an evolving neural network, is used to recognize spatial–temporal patterns of electrical current data. Different wavelet families, such as Haar, Symlet, Daubechie, Coiflet and Biorthogonal, and different decomposition levels, were investigated in order to provide the most discriminative features for fault detection. The use of an evolving neural network was shown to be a quite appropriate approach to fault detection since high impedance faults is a time-varying problem. The performance of the proposed evolving system for detecting and classifying faults was compared with those of well-established computational intelligence methods: multilayer perceptron neural network, probabilistic neural network, and support vector machine. The results showed that the proposed system is efficient and robust to changes. A classification performance in the order of 99% is exhibited by all classifiers in situations where the fault patterns do not significantly change during tests. However, a performance drop of about 13–24% is exhibited by non-evolving classifiers when fault patterns suffer from gradual or abrupt change in their behavior. The evolving system is capable, after incremental learning, of maintaining its detection and classification performance even in such situations.

88 citations

Journal ArticleDOI
TL;DR: A novel application of smart meters (SMs) in the high impedance fault (HIF) detection in distribution systems that detects HIF depending on the amount of even harmonics present in the voltage waveforms measured by SMs.
Abstract: This paper presents a novel application of smart meters (SMs) in the high impedance fault (HIF) detection in distribution systems. Detection of HIF on distribution systems poses a significant challenge to the protection engineers. Existing HIF detection methods primarily use measurements available at distribution substations. Lack of availability of measurements near the fault points often makes HIF detection further difficult. The proposed method utilizes the voltage measurements of SMs to address this issue. HIF detection in the presence of nonlinear loads is another challenge. The proposed method detects HIF depending on the amount of even harmonics present in the voltage waveforms measured by SMs. The performance of the proposed method has been evaluated using both PSCAD simulations and experimental set-up. The proposed method has also been implemented on a commercial energy meter to show its viability. The performance of the proposed method has also been evaluated considering HIF, voltage sag-swells, capacitor/load switching, transformer energization, feeder energization, power electronic loads, arc furnace loads, and distributed generators. The proposed method performs satisfactorily in the investigated scenarios. The performance of the proposed method has also been compared with the existing HIF detection methods.

78 citations

Journal ArticleDOI
TL;DR: The approach validation is performed using a real dataset comprising a large number of experiments, sampled in a functioning network in the presence of noise, and the classification is performed by boosted decision trees, which showed high dependability and security in the classification of small phase-to-earth and phase- to-phase HIFs.
Abstract: High-impedance faults (HIFs) are linked to enduring unaddressed knowledge gaps due to their diverse and complex behavior, despite being extensively researched disturbances. Vegetation HIFs, for instance, are a particular type of fault that can lead to great fire hazards and life risks. They have unique fault signatures and should receive special attention if fire risk mitigation is desired. This paper focuses on the detection of these distinct, very small current faults. As the main correlational features, the proposed methodology uses the vegetation fault signatures’ high-frequency content. Different from many previous works that rely on HIF models, the approach validation is performed using a real dataset comprising a large number of experiments, sampled in a functioning network in the presence of noise. The classification is performed by boosted decision trees, which showed high dependability and security in the classification of small phase-to-earth and phase-to-phase HIFs.

77 citations