H.W. Hong

Bio: H.W. Hong is an academic researcher. The author has contributed to research in topics: Instrumentation (computer programming) & Fault (power engineering). The author has an hindex of 1, co-authored 1 publications receiving 5 citations.

Intelligent system identifies and locates transmission faults

Abstract: When faults occur on major transmission lines, the bulk power system is immediately exposed to potentially costly outages. During peak loading conditions, when line availability is most critical, a lengthy outage can be detrimental to system operations. In an age of open access and retail wheeling, power exchanges are bound by contract, and any unnecessary delay of energy restoration compromises a utility's competitive position. It is necessary for protection and operation personnel to locate the fault as accurately and as expeditiously as possible. Within large utility systems, major transmission lines are located in remote or inaccessible areas, which makes fault finding difficult. The combination of using a two-ended algorithm along with the advent of improved communication technology, mathematical techniques, and PC applications made possible the development of an automatic fault location system. IFL is a PC/Windows-based operator's tool that uses two-ended loop fault analysis to identify fault types and location. The system is innovative in its integration of mature, low cost, and reliable instrumentation, combined with advanced applied mathematics to achieve a mission-critical system for electric system maintenance and operation.

K-means Clustering and Linear Regression Based Protection Scheme for Transmission Line

Abstract: In this paper, shunt fault is detected, classified and located in power transmission line. The synchronized Current signals at both the bus of the transmission line are used for obtaining approximate coefficients using wavelet transform. The proposed algorithm is used K-means clustering to detect and classify the faults. Fault location is estimated using linear regression method. The approximate wavelet coefficients of the both the buses are added to get resultant approximate coefficients. K-means clustering is applied on these resultant approximate coefficients to computes two centroid in a half cycle. The centroid difference (C.D) is computed, and the basis of the centroid difference the fault is detected and classified. Various case studies such as vary fault location, fault incidence angle and fault impedance to verify the robustness of the algorithm.

Modelos neurais autônomos para classificação e localização de defeitos em linhas de transmissão

Abstract: The problem of fault diagnosis in transmission lines is one of the main challenges for the technical management of transmission facilities. The assertiveness on this activity is crucial to support decision making, reducing unavailability rates and promoting rapid reinstatement of the transmission function, contributing to the improvement of service quality and reducing the financial impacts arising from reductions in the variable portion. This document presents a proposal of intelligent system for classification and location of faults in transmission lines. The algorithms used are based on the so-called autonomous neural models which include analytical techniques for input selection and automatic structure specification without the need for an independent set of data for validation. Using Bayesian inference for specification and training of multilayer perceptrons (MLPs), the intelligent system provides probabilistic responses for classification of the type of fault and also for the distance of the fault from the monitored substation. Thus for the development of the models, technical data are used of a transmission line that is part of the National Interconnected System (SIN) which is modeled in an electromagnetic transient simulation software, ATP, aiming to establish the various fault scenarios. Furthermore, two types of equivalent network were analyzed, one detailed and one simple, in order to specify the best model and if there were significant differences in results in terms of fault representation. The databases with voltage and current oscillographs obtained for each type of fault are used for training and testing of the intelligent system, demonstrating the potential of the algorithms used.

A real-time fault diagnosis system for high-speed power system protection based on machine learning algorithms

Abstract: This paper puts forward a real-time smart fault diagnosis system (SFDS) intended for high-speed protection of power system transmission lines. This system is based on advanced signal processing techniques, traveling wave theory results, and machine learning algorithms. The simulation results show that the SFDS can provide an accurate internal/external fault discrimination, fault inception time estimation, fault type identification, and fault location. This paper presents also the hardware requirements and software implementation of the SFDS.