Fault Location In Power Systems Research Articles

Review on Artificial Intelligence-Based Fault Location Methods in Power Distribution Networks

This paper provides a comprehensive and systematic review of fault localization methods based on artificial intelligence (AI) in power distribution networks described in the literature. The review is organized into several sections that cover different aspects of the methods proposed. It first discusses the advantages and disadvantages of various techniques used, including neural networks, fuzzy logic, and reinforcement learning. The paper then compares the types of input and output data generated by these algorithms. The review also analyzes the data-gathering systems, including the sensors and measurement equipment used to collect data for fault diagnosis. In addition, it discusses fault type and DG considerations, which, together with the data-gathering systems, determine the applicability range of the methods. Finally, the paper concludes with a discussion of future trends and research gaps in the field of AI-based fault location methods. Highlighting the advantages, limitations, and requirements of current AI-based methods, this review can serve the researchers working in the field of fault location in power systems to select the most appropriate method based on their distribution system and requirements, and to identify the key areas for future research.

A method of grounding fault location in power system based on adaptive filtering

A method of grounding fault location in power system based on adaptive filtering

A method of grounding fault location in power system based on adaptive filtering

A method of grounding fault location in power system based on adaptive filtering

Kung’s component extraction in power system fault location

This paper presents a method for the accurate estimation of phasors in fault location applications. One of the essential tasks of power system digital relays is to decrease the consequences that follow faults by fast detection and localization. A fault signal (current and voltage) is typically composed of the fundamental component in addition to a damped DC signal, together with damped harmonics and inter-harmonics. A significant challenge in impedance-based fault analysis is the accurate estimation of the fundamental frequency component. While different variations of the Fourier-based algorithm are the most commonly used in the estimation of the fundamental component, their accuracy tends to be sufficient for protection relaying schemes but not for fault location. This paper proposes a novel fault feature extraction method that is highly accurate; it combines Kung’s method for signal decomposition followed by a nonlinear least-squares estimation. The proposed technique has been successfully evaluated in the context of a fault location method; the numerical tests show that the proposed two-stage approach can accurately estimate phasor information in the presence of transients and noise.

Distribution Systems High-Impedance Fault Location: A Parameter Estimation Approach

High-impedance fault location has always been a challenge for protection engineering. On the other hand, if this task is successfully realized, maintenance action could be performed in order to avoid potential injuries. For an effective protection scheme, high-impedance fault location should be performed, but a lack of research on this area is noted. This paper proposes a new analytical formulation for high-impedance fault location in power systems. The approach is developed in the time domain, considering a high-impedance fault model consisting of two antiparallel diodes. Using this model, the fault distance and parameters are estimated as a minimization problem. First, a linear least square-based estimator is applied without consideration of line capacitance. Second, a steepest descent-based estimator is proposed in order to consider the line capacitance. Studies were carried out with the IEEE 13 bus modeled in the Alternate Transients Program. Encouraging test results are found indicating the method's potential for real-life applications.

A fast fault location method based on detecting the minimum number of phasor measurement units using a novel adaptive binary differential evolution optimization algorithm

SUMMARY This paper proposes a novel adaptive binary differential evolution optimization algorithm based on optimal placement of phasor measurement units (PMUs) and an intelligent strategy for fault location in power systems. The novelties of the proposed adaptive binary differential evolution optimization algorithm are that the proposed approach presents two new factors, namely, “weight factor” and “reduction factor”, in order to improve convergence speed and enhance the capability of the novel modified binary differential evolution optimization algorithm for detecting optimal placement of PMUs in large-scale problems. To investigate the capability of the proposed approach, it is applied to several IEEE test systems (IEEE 14 bus, IEEE 24 bus, IEEE 30 bus, IEEE 39 bus, and IEEE 57 bus test systems). In addition, a comparison with conventional optimization algorithms has been carried out. Then, according to the PMUs data, an intelligent fault location strategy will be proposed. The main advantages of the proposed intelligent fault location strategy are higher accuracy and speed than conventional fault location approaches. Copyright © 2014 John Wiley & Sons, Ltd.

Fault-location Observability Analysis on Power Distribution Systems

Most of the fault-location methods developed in the past employed measurements obtained from a limited number of meters installed in a power system. Optimal meter placement in power systems is to make the best use of a limited number of meters available to keep the entire network observable. This article presents the fault-location observability analysis for distribution systems and proposes a novel optimal meter placement algorithm to keep the system observable in terms of fault-location determination. First, the observability of fault location in power systems is defined. Then analysis of the whole system is performed to determine the fewest number of meters needed and the best locations to place those meters to achieve fault-location observability. Case studies based on a 16-bus distribution system have been carried out to illustrate the proposed algorithms.

Wavelet and Neural Network-Based Fault Location in Power Systems Using Statistical Analysis of Traveling Wave

This paper describes a traveling wave-based fault location in power systems without using global positioning system (GPS) timing. To extract the transient wave from the recorded waves at the bus bars, wavelet denoising is used. The residual signal in this procedure has a large amount of information about the fault. The proposed algorithm uses the statistical analysis parameters of the traveling wave as the inputs of a defined artificial neural network. All the possible fault types are generated using the ATP-EMTP and results are discussed. Extensive simulation studies indicate that proposed network has a reliable response. 0.92 % Average error shows the power of this algorithm against the reactance-based techniques. In contrary, this is a higher error than the traveling wave-based fault location using GPS timing. Nevertheless, in proposed method because of omitting complicated utilities such as GPS receiver, the costs will be decreased and a higher degree of performance in an efficient manner is achieved.

Fault location in power networks with mixed feeders using the complex space-phasor and Hilbert–Huang transform

The paper introduces a practical approach to power system fault location in power networks using advanced fault signal processing. The three-phase fault voltages are converted to the vector of absolute values of its complex space-phasor. This vector represents fault traveling wave and it is further processed for fault location finding with the Hilbert–Huang transform. The simulation results, including single line to ground faults, faults in mixed feeders and high-impedance arcing faults, confirm the accuracy and practical applicability of the proposed approach.

Recurrent Polynomial Neural Networks for Enhancing Performance of GPS in Electric Systems

Global Positioning System (GPS) is a worldwide satellite system that provides navigation, positioning, and timing for both military and civilian applications. GPS based time reference provides inexpensive but highly-accurate timing and synchronization capability and meets requirements in power system fault location, monitoring, and control. In the present era of restructuring and modernization of electric power utilities, the applications of GIS/GPS technology in power industry are growing and covering several technical and man-agement activities. Because of GPS receiver’s error sources are time variant, it is necessary to remove the GPS measurement noise. This paper presents novel recurrent neural networks called the Recurrent Pi-Sigma Neural Network (RPSNN) and Recurrent Sigma-Pi Neural Network (RSPNN). The proposed NNs have been used as predictor in GPS receivers timing errors. The NNs were trained using the dynamic Back Propagation (BP) algorithm. The actual data collection was used to test the performance of the proposed NNs. The ex-perimental results obtained from a Coarse Acquisition (C/A)-code single-frequency GPS receiver strongly support the potential of the method using RPSNN to give high accurate timing. The GPS timing RMS error reduces from 200 to less than 40 nanoseconds.

Utilização de um modelo neuro-fuzzy para a localização de defeitos em sistemas de potência

Este trabalho apresenta a aplicação de um modelo neuro-fuzzy para o processamento de alarmes e localização de defeitos em sistemas de potência. Diferentes técnicas são investigadas para o estabelecimento de relações fuzzy entre padrões de alarme e defeitos no sistema. Uma base de dados contendo as relações fuzzy é empregada para o treinamento de redes neurais artificiais. Os vetores de entrada das redes neurais são constituídos por padrões de alarmes associados a diferentes eventos e cada neurônio de saída é responsável por estimar o grau de pertinência de um componente específico na classe de componentes defeituosos. Testes são realizados utilizando um sistema de 7 barras e também um sistema real.

Unsynchronized two-terminal fault location estimation

A technique for power system fault location estimation which uses data from both ends of a transmission line and which does not require the data to be synchronized is described. The technique fully utilizes the advantages of digital technology and numerical relaying which are available today and can easily be applied for offline analysis. This technique allows for accurate estimation of the fault location irrespective of the fault type, fault resistance, load currents, and source impedances. Use of two-terminal data allows the algorithm to eliminate previous assumptions in fault location estimation, thus increasing the accuracy of the estimate. The described scheme does not require real-time communications, only offline post-fault analysis. The paper also presents fault analysis techniques utilizing the additional communicated information.