• 대한전기학회:학술대회논문집
• /
• 대한전기학회 2000년도 하계학술대회 논문집 A
• /
• pp.366-368
• /
• 2000

When faults occur in transmission lines, the classification of faults is very important. If the fault is HIF(High Impedance Fault), it cannot be detected or removed by conventional overcurrent relays (OCRs), and results in fire hazards and causes damages in electrical equipment or personal threat. The fast discrimination of fault needs to effective protection and treatment and is important problem for power system protection. This paper proposes the fault detection and discrimination algorithm for LIFs(Low Impedance Faults) and HIFs(High Impedance Faults). This algorithm uses artificial neural networks and variation of 3-phase maximum currents per period while faults. A double lines-to-ground and line-to-line faults can be detected using Neural Network. Also, the other faults can be detected using the value of variation of maximum current. Test results show that the proposed algorithms discriminate LIFs and HIFs accurately within a half cycle.

• 대한전기학회:학술대회논문집
• /
• 대한전기학회 1999년도 하계학술대회 논문집 C
• /
• pp.1440-1443
• /
• 1999

This paper proposes fault detection method using a neural network & fuzzy logic on distribution lines. Fault on distribution lines is simulated using EMTP. The pattern of high impedance fault on pebbles, ground and short-circuit fault were take as the learning model. In this paper proposed fault detection method is evaluated on various conditions. The average values after analyzing fault current by FFT of even odd harmonics and fundamental rms were used for the neural network input. Test results were verified the validity of the proposed method

• 대한전기학회논문지:전력기술부문A
• /
• 제50권9호
• /
• pp.417-423
• /
• 2001

This paper proposes a technique for fault detection and classification for both LIF(Low Impedance Fault)s and HIF(High Impedance Fault)s using Adaptive Network-based Fuzzy Inference System(ANFIS). The inputs into ANFIS are current signals only based on Root-Mean-Square(RMS) values of 3-phase currents and zero sequence current. The performance of the proposed technique is tested on a typical 154 kV Korean transmission line system under various fault conditions. Test results show that the ANFIS can detect and classily faults including (LIFs and HIFs) accurately within half a cycle.

• 대한전기학회:학술대회논문집
• /
• 대한전기학회 2001년도 하계학술대회 논문집 D
• /
• pp.2184-2186
• /
• 2001

The research presented in this paper focuses on a method for the detection of High Impedance Fault(HIF). The method will use the fast wavelet transform and neural network system. HIF on the multi-grounded three-phase four-wires primary distribution power system cannot be detected effectively by existing over current sensing devices. These paper describes the application of fast wavelet transform to the various HIF data. These data were measured in actual 22.9kV distribution system. Wavelet transform analysis gives the frequency and time-scale information. The neural network system as a fault detector was trained to discriminate HIF from the normal status by a gradient descent method. The proposed method performed very well by proving the right state when it was applied staged fault data and normal load mimics HIF, such as arc-welder.

• 대한전기학회논문지:전력기술부문A
• /
• 제52권12호
• /
• pp.675-683
• /
• 2003

This paper describes the correction strategy of an overcurrent relay applied in the linked line for interconnecting wind farm with utility power networks in order to improve the capability of a fault detection. The fault current measured in a relaying point might vary according to the fault conditions. Generally, the current of the line to line fault or the line to ground fault in the linked line is much higher than the set value of protective relay due to the large fault level. However, when the high impedance fault occurs in the linked line, we can't detect it by conventional set value because its fault level may be lower than the generating capacity of wind farm. And, the protective relay with conventional set value may generate a trip signal for the insertion of wind turbine generators due to the large transient characteristics. In order to solve above problems and improve protective relaying algorithms applied in the linked line, we propose a new correction strategy of the protective relay in the linked line. The presented method can detect the high impedance fault which can't be detected by conventional relay set value and may prevent the mis-operation of protective relay caused by the insertion of wind farm.

• 대한전기학회:학술대회논문집
• /
• 대한전기학회 2007년도 심포지엄 논문집 정보 및 제어부문
• /
• pp.98-100
• /
• 2007

DC Arc Fault Current is an electric discharge which is occurred in two opposite electrode. In this paper, DC arc detection device is designed for the display of DC arc fault current which is occurred in the local electric network with DC Power. This DC arc is one of the main causes of electric fire. Arc fault in electrical network has the characteristics of low current, high impedance and low frequency. DC Arc current detection device is designed for the display of arc fault current which has the modified arc characteristics.

• KIEE International Transactions on Power Engineering
• /
• 제3A권4호
• /
• pp.191-197
• /
• 2003

Accurate detection and classification of faults on transmission lines is vitally important. In this respect, many different types of faults occur, such as 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 undetected, 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, their modeling is difficult and numerous 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 program (EMTP). 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 (RMS) values of 3-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 demonstrate that the ANFIS can detect and classify faults including LIFs and HIFs accurately within half a cycle.

• 대한전자공학회:학술대회논문집
• /
• 대한전자공학회 2007년도 하계종합학술대회 논문집
• /
• pp.461-462
• /
• 2007

DC Arc is an electric discharge which is occurred in two oppolsite electrode when system operating with DC current appliance. In this paper, DC arc detection system is designed for the display of DC arc fault current which is occurred in the local electric network with DC Power. This DC arc is one of the main causes of electric fire of dc system. Arc fault in electrical network has the characteristics of low current, high impedance and low frequency. DC Arc current detection device is designed for the display of arc fault current which has the modified arc characteristics.

• 전기학회논문지
• /
• 제62권2호
• /
• pp.155-163
• /
• 2013

As the increasing HIF(High Impedance Fault) with the arc cannot be easily detected for the low fault current magnitude compared to actual load in distribution line. However, the arcing current shows that the magnitude varies with time and the signal is asymmetric. In addition, discontinuous changes occur at starting point of arc. Considering these characteristics, wavelet transformation of actual current data shows difference between before and after the fault. Althogh raw data(detail coefficient) of wavelet transform may not be directly applied to HIF detection logic in a device, there are several developing methods of HIF monitoring data using the original wavelet coefficients. In this paper, a simple and effective developing methods of HIF monitoring data were analized by using the signal data through an actual HIF experiment to apply them to economic devices. The methods using the sumation of the wavelet coefficient squares in one cycle of the fundamental frequency as the energies of the wavelet coefficeits and the sumation of the absolute values were compared. Besides, the improved method which less occupies H/W resouces and can be applied to field detection devices was proposed. and also Verification of this HIF detection method through field test on distribution system in KEPCO power testing center was performed.

• Journal of Electrical Engineering and Technology
• /
• 제13권1호
• /
• pp.1-10
• /
• 2018

Wide-area monitoring of tree-related high impedance fault (THIF) efficiently contributes to increase reliability of large-scaled network, since the failure to early location of them may results in critical lines tripping and consequently large blackouts. In the first place, this wide-area monitoring of THIF requires managing the placement of sensors across large power grid network according to THIF detection objective. For this purpose, current paper presents a framework in which sensors are distributed according to a predetermined risk map. The proposed risk map determines the possibility of THIF occurrence on every branch in a power network, based on electrical conductivity of trees and their positions to power lines which extracted from spectral data. The obtained possibility value can be considered as a weight coefficient assigned to each branch in sensor placement problem. The next step after sensors deployment is to on-line monitor based on moving data window. In this on-line process, the received data window is evaluated for obtaining a correlation between low frequency and high frequency components of signal. If obtained correlation follows a specified pattern, received signal is considered as a THIF. Thereafter, if several faulted section candidates are found by deployed sensors, the most likely location is chosen from the list of candidates based on predetermined THIF risk map.