Myeon-Song Choi

An intelligent and efficient fault location and diagnosis scheme for radial distribution systems

In this paper, an effective fault location algorithm and intelligent fault diagnosis scheme are proposed. The proposed scheme first identifies fault locations using an iterative estimation of load and fault current at each line section. Then an actual location is identified, applying the current pattern matching rules. If necessary, comparison of the interrupted load with the actual load follows and generates the final diagnosis decision. Effect of load uncertainty and fault resistance has been carefully investigated through simulation results that turns out to be very satisfactory.

A new fault location algorithm using direct circuit analysis for distribution systems

The unbalanced nature of distribution systems due to single-phase laterals and loads gives difficulty in the fault location. This paper proposes a new fault location algorithm developed by the direct three-phase circuit analysis for unbalanced distribution systems, which has not been investigated due to high complexity. The proposed algorithm overcomes the limit of the conventional algorithm, which requires the balanced system. It is applicable to any power system, but especially useful for the unbalanced distribution systems. Its effectiveness has been proved through many EMTP simulations.

An optimal tracking neuro-controller for nonlinear dynamic systems

Multilayer neural networks are used to design an optimal tracking neuro-controller (OTNC) for discrete-time nonlinear dynamic systems with quadratic cost function. The OTNC is made of two controllers: feedforward neuro-controller (FFNC) and feedback neuro-controller (FBNC). The FFNC controls the steady-state output of the plant, while the FBNC controls the transient-state output of the plant. The FFNC is designed using a novel inverse mapping concept by using a neuro-identifier. A generalized backpropagation-through-time (GBTT) algorithm is developed to minimize the general quadratic cost function for the FBNC training. The proposed methodology is useful as an off-line control method where the plant is first identified and then a controller is designed for it. A case study for a typical plant with nonlinear dynamics shows good performance of the proposed OTNC.

Fault Location for Underground Power Cable Using Distributed Parameter Approach

This paper proposes an extensive fault location model for underground power cable in distribution system using voltage and current measurements at the sending-end. First, an equivalent circuit that models a faulted underground cable system is analyzed using distributed parameter approach. Then, the analysis of sequence networks in three-phase network is obtained by applying the boundary conditions. This analysis is used to calculate a fault distance in single section using voltage and current equations. The extension to multi-section is further analyzed based on Korean distribution systems. This method is an iterative process to determine a faulted section from the network. Finally, the case studies are evaluated with variations of fault distance and resistance, which also includes the evaluation of its robustness to load uncertainty.

A Direct Three-Phase Circuit Analysis-Based Fault Location for Line-to-Line Fault

From a direct three-phase circuit analysis, an accurate fault-location algorithm has been obtained for the line-to-line fault as an extension of the authors previous work for line-to-ground fault location. Robustness of the proposed algorithm to load impedance uncertainty is enhanced by the introduction of impedance compensation using voltage and current measurements. Simulation results show a high degree of accuracy and robustness to load uncertainty.

Service Restoration Methodology for Multiple Fault Case in Distribution Systems

Current KEPCOs distribution automation system (DAS) provides a very effective restoration solution for the single fault case but cannot handle multiple faults. This paper proposes a two-step restoration scheme-sequential and simultaneous restoration-for multiple fault cases. Efficiency has been achieved by introduction of restoration performance index (RPI) and load-balancing algorithm. Test results to show effectiveness of the proposed scheme are presented, and field experience of DAS in Korea is described as well

A neural network-based power system stabilizer using power flow characteristics

A neural network-based power system stabilizer (neuro-PSS) is designed for a generator connected to a multi-machine power system utilizing the nonlinear power flow dynamics. The use of power flow dynamics provides a PSS for a wide range of operation with reduced size neural networks. The neuro-PSS consists of two neural networks: neuro-identifier and neuro-controller. The low-frequency oscillation is modeled by the neuro-identifier using the power flow dynamics, then a generalized backpropagation-through-time (GBTT) algorithm is developed to train the neuro-controller. The simulation results show that the neuro-PSS designed in this paper performs well with good damping in a wide operation range compared with the conventional PSS.

Blocking of Zone 3 Relays to Prevent Cascaded Events

Defense systems are needed to prevent catastrophic failures of a power grid due to cascaded events. Cascaded events can be attributed to improper operations of protective relays. The most challenging problem for the design and implementation of a defense system is the performance in accuracy and speed in a real-time environment. Protective devices are normally designed to operate fast in order to isolate the fault(s). This paper proposes a new methodology to distinguish line overloads from actual faults for distance relays. In order to distinguish between line flow transfers from a line outage and an actual fault, the line outage distribution factor (LODF) and generation shift factor (GSF)-based power flow estimation method, and a secure peer-to-peer (P2P) communication structure are adopted. Computer simulations of cascaded events for a six-bus system and the Korean power grid have been performed to establish the feasibility of the proposed scheme.

An accurate fault location algorithm for double-circuit transmission systems

This paper describes an accurate fault location algorithm based on sequence current distribution meters for a double-circuit transmission system. The proposed method uses the voltage and current collected at only the local end of a single-circuit. This method is virtually independent of the fault resistance and the mutual coupling effect caused by the zero-sequence current of the adjacent parallel circuit and insensitive to the variation of source impedance. The fault distance is determined by solving the forth-order KVL (Kirchhoffs voltage law) based distance equation. The zero-sequence current of adjacent circuit is estimated by using a zero-sequence current distribution factor and the zero-sequence current of the self-circuit. Thousands of fault simulations by EMTP have proved the accuracy and effectiveness of the proposed algorithm.

A novel algorithm for fault classification in transmission lines using a combined adaptive network and fuzzy inference system

Accurate detection and classification of faults on transmission lines is vitally important. In this respect, many different types of faults occur, inter alia low impedance faults (LIF) and high impedance faults (HIF). The latter in particular pose difficulties for the commonly employed conventional overcurrent and distance relays, and if not detected, can cause damage to expensive equipment, threaten life and cause fire hazards. Although HIFs are far less common than LIFs, it is imperative that any protection device should be able to satisfactorily deal with both HIFs and LIFs. Because of the randomness and asymmetric characteristics of HIFs, the modelling of HIF is difficult and many papers relating to various HIF models have been published. In this paper, the model of HIFs in transmission lines is accomplished using the characteristics of a ZnO arrester, which is then implemented within the overall transmission system model based on the electromagnetic transients programme. This paper proposes an algorithm for fault detection and classification for both LIFs and HIFs using Adaptive Network-based Fuzzy Inference System (ANFIS). The inputs into ANFIS are current signals only based on Root-Mean-Square values of three-phase currents and zero sequence current. The performance of the proposed algorithm is tested on a typical 154 kV Korean transmission line system under various fault conditions. Test results show that the ANFIS can detect and classify faults including (LIFs and HIFs) accurately within half a cycle.