Fault Location Error Research Articles

Integrated fault location and isolation strategy in distribution networks using Markov decision process

The data gathered from various intelligent sensors installed throughout the network could be utilized for the fault localization, helping the system restoration, reducing the outage time and improving system reliability. However, due to the distribution network characteristics and particularities, the precise fault location is very hard to determine, especially in isolated neutral networks. Consequently, the restoration process is affected by the fault location error and the probability of fault in the exact location. In this paper, Markov Decision Process is used as a tool for the determination of the faulted feeder section and its isolation from the grid. The algorithm is based on the optimization of several criteria, while the transition probabilities among states are obtained from fault passage indicators status. The posterior probability that the particular section is faulted is obtained using the Bayes probability theory. The methodology is tested on the IEEE 123 distribution test network.

Hybrid Pattern Recognition and Multi-resolution Analysis (MRA) Based Fault Location in Power Transmission Lines

This paper proposes a fault (line-to-line) location on Ikeja West – Benin 330kV electric power transmission lines using wavelet multi-resolution analysis and neural networks pattern recognition abilities. Three-phase line-to-line current and voltage waveforms measured during the occurrence of a fault in the power transmission-line were pre-processed first and then decomposed using wavelet multi-resolution analysis to obtain the high-frequency details and low-frequency approximations. The patterns formed based on high-frequency signal components were arranged as inputs of the neural network, whose task is to indicate the occurrence of a fault on the lines. The patterns formed using low-frequency approximations were arranged as inputs of the second neural network, whose task is to indicate the exact fault type. The new method uses both low and high-frequency information of the fault signal to achieve an exact location of the fault. The neural network was trained to recognize patterns, classify data and forecast future events. Feed forward networks have been employed along with back propagation algorithm for each of the three phases in the Fault location process. An analysis of the learning and generalization characteristics of elements in power system was carried using Neural Network toolbox in MATLAB/SIMULINK environment. Simulation results obtained demonstrate that neural network pattern recognition and wavelet multi-resolution analysis approach are efficient in identifying and locating faults on transmission lines as the average percentage error in fault location was just 0.1386%. This showed that satisfactory performance was achieved especially when compared to the conventional methods such as impedance and travelling wave methods.

Study on Short-Circuit Impedance Characteristics in DN Traction Electric Lines

The fault location accuracy of traction electric lines protection device based on impedance characteristics is usually over thousands of meters. To find out the reasons that affect the accuracy of fault location, this paper studies some the key factors including rail potential, feedback current through ground and fault point impedance characteristics of traction electric lines. Then the calculation method of impedance increment and the reason of the location error are found. It is of great significance for the setting calculation and fault location error analysis of the protection system.

A new single end wideband impedance based fault location scheme for distribution systems

This paper proposes an improved impedance based fault location scheme based on system analysis at non-fundamental frequencies. The fault is treated as a voltage source that injects high frequency components into the system and the analysis is carried out using these injected components. The proposed method only requires local measurements at the substation and therefore is classified as a single end method. The new contribution is that the proposed method uses the distributed parameter line model to account for inductive and capacitive effects of the line. It has been evaluated on the IEEE 34-bus feeder which is based on an actual distribution system which has the typical features such as non-homogeneous feeder sections, asymmetrical line configurations, unbalanced loads and single and three-phase laterals. The fault point, fault resistance and fault inception angle have been varied to check their influence on the accuracy of the method. The simulation results demonstrate the accuracy of the proposed method where for most cases, the error in fault location is less than 50m.

Dynamic state estimation for double-end traveling wave arrival identification in transmission lines

A dynamic state estimation (DSE) approach is proposed to improve the accuracy to identify the front-wave arrival times of a two-terminal traveling wave protection scheme for a transmission line with low sampling rates. The solution is implemented as an offline method aiming to use existing infrastructure measurements. The work presents the principles of traveling wave (TW) theory and legacy protection schemes with this methodology. The proposed method requires GPS synchronized measurements at both ends of the line and a high fidelity model of the protected line. The paper presents comparisons between the differentiator smoother for arrival time identification, traditional impedance based fault location using 1 or 2 terminals and the proposed DSE-TW. The performance is also assessed with the Bergeron model and the frequency dependent parameter model of the transmission line. Numerical simulations and laboratory tests demonstrate that the method can correctly identify the front-wave arrival time at each end with better accuracy and different sampling rates. This work illustrates the advantages of DSE-TW such as high accuracy of arrival time identification, the minimum error of fault location and better performance with different (lower) sampling rates.

An Adaptive Fault-Location Method for a Power-Transmission Line Using Pulse Registration on a High-Frequency Wave Path

An adaptive traveling-wave method to improve the accuracy of fault location has been developed. The suggested method can compensate for an error of the two-sided method caused by the dispersive nature of a phase wire along which a high-frequency electromagnetic pulse propagates from the point of the fault. The error in fault location has been determined depending on the means of registration of a high-frequency signal. Some recommendations on connection of facilities to register electromagnetic pulses used in the devices of traveling-wave fault location have been provided. The suggested method allows one to substantially reduce errors of estimation of distance to the point of a fault and is less affected by external factors. The developed method can be applied in both in existing and future devices for fault location.

A threshold free synchrophasor measurement based multi-terminal fault location algorithm

A new impedance based fault location algorithm is proposed for a generic multi-terminal transmission network consisting of a main line and tapped branches with no direct measurements available at the intermediate tapping nodes. The core of the algorithm is a threshold free faulted branch search algorithm that systematically compares the multiple estimates of voltages for the tapping nodes, computed using synchrophasor measurements at the terminals. Once the faulted branch is identified, multi-terminal fault location problem is reduced into a two-terminal fault location problem to determine the exact location of fault within the branch. The proposed algorithm is simple and does not involve resource intensive mathematical operations such as matrix inversion. The proposed algorithm was implemented as a module in an in-house developed real-time synchrophasor application software program, and its performance was evaluated using a test setup consisting of a real-time simulator, an actual synchrophasor network, and a phasor data concentrator. The tests showed that the proposed algorithm is capable of correctly determining the faulted branch, except for few faults that are very close to tapping nodes. When the algorithm is used with practical synchrophasor measurements, the fault location error was within a typical tower span for most cases.

New fault location scheme for three-terminal untransposed parallel transmission lines

This paper proposes a new fault location scheme in the phase-domain for three-terminal untransposed double-circuit transmission lines utilizing synchronized voltage and current measurements obtained by GPS technique. The proposed scheme is derived taking into consideration the distributed line model and the mutual couplings effect between the parallel lines to obtain accurate results. The proposed scheme is derived based on the transmission line theory and Taylor series expansion of the distributed line model parameters. All fault types including normal shunt faults, evolving faults, and cross-country faults can be discriminated from each other and the fault location can be obtained for all fault types. The evolving faults include earth faults occurring at the same location in two phases of one circuit or two phases of different circuits at different fault inception time. While cross-country faults include earth faults occurring at different locations in two phases of one circuit or two phases of different circuits at same or different fault inception time. The proposed scheme is tested under different fault locations, different fault resistances, different fault inception angles, and all fault types including cross-country and evolving faults. Also, the effect of different sampling rate, measurement and synchronization errors, earth resistivity variations, and transmission line parameters errors on the proposed scheme is considered. Simulations studies conducted by MATLAB software demonstrate that the maximum estimation error in fault location does not exceed 3.06%.

Support vector machine based fault classification and location of a long transmission line

This paper investigates support vector machine based fault type and distance estimation scheme in a long transmission line. The planned technique uses post fault single cycle current waveform and pre-processing of the samples is done by wavelet packet transform. Energy and entropy are obtained from the decomposed coefficients and feature matrix is prepared. Then the redundant features from the matrix are taken out by the forward feature selection method and normalized. Test and train data are developed by taking into consideration variables of a simulation situation like fault type, resistance path, inception angle, and distance. In this paper 10 different types of short circuit fault are analyzed. The test data are examined by support vector machine whose parameters are optimized by particle swarm optimization method. The anticipated method is checked on a 400kV, 300km long transmission line with voltage source at both the ends. Two cases were examined with the proposed method. The first one is fault very near to both the source end (front and rear) and the second one is support vector machine with and without optimized parameter. Simulation result indicates that the anticipated method for fault classification gives high accuracy (99.21%) and least fault distance estimation error (<0.21%) for all discussed cases. In order to verify the accuracy of the proposed method, a comparison is carried out with methods published by other researchers. Separate investigation is also carried out with the transmission line placing thyristor controlled series capacitor in the middle and applying the same proposed method. It is observed from the test results of the thyristor controlled series capacitor based transmission line model that fault classification gives a high accuracy of 98.36% and absolute fault location error is >0.29%.

Improved fault location algorithm for multi‐location faults, transforming faults and shunt faults in thyristor controlled series capacitor compensated transmission line

Fault location estimation in series compensated transmission lines is quite difficult because a non-linear current dependent circuit appears between the substation and fault point. In particular, the faults which occur at different locations at the same time in different phases known as multi-location faults has not been addressed by researchers. Other types of fault that may occur in transmission lines are transforming faults where one type of fault transforms to another type fault after some time. In this study, a fault location estimation scheme using artificial neural network (ANN) is presented for multi-location faults, transforming faults as well as for commonly occurring shunt faults in thyristor controlled series capacitor (TCSC) compensated transmission line. DB-4 wavelet is used for pre-processing of the three-phase current and voltage signals. The shunt capacitance of the line is considered based on distributed parameter line model. Feasibility of the ANN-based fault location algorithm is tested under a wide variation of parameters, such as fault type, location, fault resistance and fault inception angle. Fault location errors are within 0.001–1% range.

Enhancing the performance of transmission line directional relaying, fault classification and fault location schemes using fuzzy inference system

This study aims to improve the performance of transmission line directional relaying, fault classification and fault location schemes using fuzzy system. Three separate fuzzy inference system are designed for complete protection scheme for transmission line. The proposed technique is able to accurately detect the fault (both forward and reverse), locate and also identify the faulty phase(s) involved in all ten types of shunt faults that may occur in a transmission line under different fault inception angle, fault resistances and fault location. The proposed method needs current and voltage measurements available at the relay location and can perform the fault detection and classification in about a half-cycle time. The proposed fuzzy logic based relay has less computation complexity and is better than other AI based methods such as artificial neural network, support vector machine, and decision tree (DT) etc. which require training. The percentage error in fault location is within 1 km for most of the cases. Fault location scheme has been validated using χ2 test with 5% level of significance. Proposed scheme is a setting free method and is suitable for wide range of parameters, fault detection time is less than half cycle and relay does not show any reaching mal-operation so it is reliable, accurate and secure.

Universal Wavefront Positioning Correction Method on Traveling-Wave-Based Fault-Location Algorithms

A universal wavefront positioning correction method on traveling-wave-based fault-location algorithms is proposed. According to the location accuracy analysis for the faults of which the fault positions change successively, it is noted that the real fault distance generally cannot be divided exactly by the distance corresponding to an integer sampling interval. It will lead to unavoidable truncated error when the discrete sampling points of traveling-wave-based fault-location algorithms are applied. Furthermore, it is disclosed that the fault-location error can be limited within a single sampling interval by the simple wavefront identification method in the event of low sampling rate. In view of the aforementioned two scenarios, an appropriate correction strategy in the event of the appropriate sampling rate is put forward. The fault-location accuracy can be improved greatly with the aid of the proposed correction strategy. The effectiveness of the proposed correction strategy is verified with various typical EMTDC-based simulation test results.

Support Vector Machines for classification and locating faults on transmission lines

This paper presents a new approach to classify fault types and predict the fault location in the high-voltage power transmission lines, by using Support Vector Machines (SVM) and Wavelet Transform (WT) of the measured one-terminal voltage and current transient signals. Wavelet entropy criterion is applied to wavelet detail coefficients to reduce the size of feature vector before classification and prediction stages. The experiments performed for different kinds of faults occurred on the transmission line have proved very good accuracy of the proposed fault location algorithm. The fault classification error is below 1% for all tested fault conditions. The average error of fault location in a 380kV–360-km transmission line is below 0.26% and the maximum error did not exceed 0.95km.

Application of a distance relaying scheme to compensate fault location errors due to fault resistance

This paper proposes a distance relaying scheme based on the current phase jump behavior during fault conditions to improve the apparent impedance estimated by the distance relay. For a nonpilot protection scheme, the measured impedance is affected by error due to the combined effects of fault resistance and prefault load. An experimental relation between the current phase jump introduced with fault inception and the X/R ratio seen by the distance protection is deduced. The phase jump correction factor obtained is an exponential function of the X/ R ratio of the line. This factor is applied to the apparent impedance measured by the relay and it allows mitigating the adverse effect of prefault power. The relaying scheme improves significantly the accuracy in estimation of the resistive fault location. The application of this scheme does not require communication links from the remote end of line and is applicable to all types of fault.

Accurate fault location on EHV lines using both RBF based support vector machine and SCALCG based neural network

An appropriate method for fault location on Extra High Voltage (EHV) transmission line using Support Vector Machine (SVM) is proposed in this paper. It relies on the application of SVM and frequency characteristics of the measured single end positive sequence voltage and current measurement of transient signals of the system. This paper is proposing a new hybrid approach for fault location on EHV lines using Radial Basis Function (RBF) basis SVM and Scaled Conjugate Gradient (SCALCG) basis neural network method. Sample inputs are determined by MATLAB. The average error of fault location in 400 kV and 150 km line is tested and the results prove that the proposed method is effective and reduce the error within a short duration of time using both RBF based SVM and SCALCG based neural network.

Intelligent approaches using support vector machine and extreme learning machine for transmission line protection

This paper proposes two approaches based on wavelet transform-support vector machine (WT-SVM) and wavelet transform-extreme learning machine (WT-ELM) for transmission line protection. These methods uses fault current samples for half cycle from the inception of fault. The features of the line currents are extracted by first level decomposition of the current samples using discrete wavelet transform (DWT) and extracted features are applied as inputs to SVM and ELM for faulted phase detection, fault classification, location and discrimination between fault and switching transient condition. The feasibility of the proposed methods have been tested on a 240-kV, 225-km transmission line for all the 10 types of fault using MATLAB Simulink. Upon testing on 9600 fault cases with varying fault resistance, fault inception angle, fault distance, pre-fault power level, and source impedances, the performance of the proposed methods are quite promising. The performance of the proposed methods is compared in terms of classification accuracy and fault location error. The results indicate that SVM based approach is accurate compared to ELM based approach for fault classification. For fault location, the maximum error is less with SVM than ELM and the mean error of SVM is slightly higher than ELM.

Research on fault location technology based on BP neural network in DWDM optical network

BP neural network is introduced to the fault location field of DWDM optical network in this paper. The alarm characteristics of the optical network equipments are discussed, and alarm vector and fault vector diagrams are generated by analyzing some typical instances. A 17 × 14 × 18 BP neural network structure is constructed and trained by using MATLAB. By comparing the training performances, the best training algorithm of fault location among the three training algorithms is chosen. Numerical simulation results indicate that the sum squared error (SSE) of fault location is less than 0.01, and the processing time is less than 100 ms. This method not only well deals with the missing alarms or false alarms, but also improves the fault location accuracy and real-time ability.

A Universal Fault Location Technique for N-Terminal $({N}\geqq 3)$ Transmission Lines

This paper presents a universal fault location technique for N-terminal transmission lines based on synchronized phasor measurement units. The development of the technique is based on two-terminal fault location technique. The proposed algorithm is different from traditional multiterminal fault location techniques. We apply two-terminal fault location technique to N-terminal transmission lines and propose a novel fault section selector/fault locator. The proposed method has a very good tolerance. The proposed approach provides an analytical solution and its computational burden is very low since it does not require iterative operations. An extensive series of simulations were conducted to verify the accuracy of the proposed algorithm. The average fault location error under various fault conditions is well below 1%.

Accurate Fault Location in the Power Transmission Line Using Support Vector Machine Approach

The paper presents a new approach to the location of fault in the high-voltage power transmission line, relying on the application of the support vector machine and frequency characteristics of the measured one-terminal voltage and current transient signals of the system. The extensive numerical experiments performed for location of different kinds of faults of the transmission line have proved very good accuracy of fault location algorithm. The average error of fault location in a 200-km transmission line is below 100 m and the maximum error did not exceed 2 km.

Earthquake Locations in the Inner Continental Borderland, Offshore Southern California

The inner Continental Borderland region, offshore southern California, is tectonically active and contains several faults that are potential seismic hazards to nearby cities. However, fault geometries in this complex region are often poorly constrained due to a lack of surface observations and uncertainties in earthquake locations and focal mechanisms. To improve the accuracy of event locations in this area, we apply new location methods to 4312 offshore seismic events that occurred between 1981 and 1997 in seven different regions within the Borderland. The regions are defined by either temporal or spatial clustering of seismic activity in the Southern California Seismic Network (SCSN) catalog. Obtaining accurate locations for these events is difficult, due to the lack of nearby stations, the limited azimuthal coverage, and uncertainties in the velocity structure for this area. Our location procedure is based on the L-1 norm, grid search, waveform cross-correlation method of Shearer (1997), except that we use a nearest neighbor approach (Astiz et al., 2000) to identify suitable event pairs for waveform cross-correlation and we explore the effect of different velocity models on the locations and associated station terms. In general, our relocated events have small estimated relative location errors and the events are more clustered than the SCSN catalog locations. A quarry on the south tip of Catalina Island provides a test of our location accuracy and suggests that, under ideal conditions, offshore events can be located to within 1 to 2 km of their true locations. Our final locations for most clusters are well correlated with known local tectonic features. We relate the 1981 Santa Barbara Island ( M L = 5.3) earthquake with the Santa Cruz fault, the 13 July 1986 Oceanside ( M L = 5.3) sequence with the San Diego Trough fault zone, and events near San Clemente Island with the known trace of the San Clemente fault zone. Over 3000 of the offshore events during this time period are associated with the 1986 Oceanside earthquake and its extended aftershock sequence. Our locations define a northeast-dipping fault plane for the Oceanside sequence, but in cross-section the events are scattered over a broad zone (about 4-km thick). This could either be an expression of fault complexity or location errors due to unaccounted for variations in the velocity structure. Events that occur near Coronado Bank in the SCSN catalog are relocated closer to the San Diego coast and suggest a shallow-angle, northeast-dipping fault plane at 10 to 15 km depth. Manuscript received 12 February 1999.