Fault Section Identification Research Articles

Fault Section Identification Protection Algorithm for Modular Multilevel Converter-Based High Voltage DC With a Hybrid Transmission Corridor

This paper addresses the protection of a high voltage direct current (HVDC) transmission system, utilizing the modular multilevel converter (MMC) topology in addition to incorporating a hybrid transmission corridor (transmission line including overhead line and cable sections). A solution is proposed for identifying the section within which a dc fault is located for the purpose of maintaining power delivery. A detailed model of the MMC-HVDC system is simulated using PSCAD and an in depth fault analysis is performed. A characteristic signal is discovered and then implemented into a novel solution. The end result is a fault section identification protection algorithm, implementing protective relay coordination to protect the system from false circuit breaker reclose as well as enabling fast system restart for nonpermanent faults. This restart protection algorithm is implemented without the use of a communications channel between converter stations, introducing novelty and quick restart response.

A New Fault-Location Algorithm for Extra-High-Voltage Mixed Lines Based on Phase Characteristics of the Hyperbolic Tangent Function

A new fault-location algorithm for extra-high-voltage mixed lines based on phase characteristics of the hyperbolic tangent function is proposed. This paper takes overhead-cable-overhead mixed lines for example. The faulted section identification functions for overhead cable connections described by the hyperbolic tangent are developed separately when faults occur on the first overhead line, the middle cable, and the last overhead line. When a fault occurs on different sections, the faulted section identification functions appear as obviously different phase characteristics according to the phase characteristics of the hyperbolic tangent function. Thus, the faulted section can be identified. The fault location can be therefore accurately determined with the voltage and current positive-sequence fault components on both ends of the faulted section. Simulations and calculations with PSCAD and MATALB show that the fault-location algorithm is correct and accurate.

Distribution network fault section identification and fault location using wavelet entropy and neural networks

Fault location in power system distribution networks is especially difficult because of the existence of several laterals/tap-offs in distribution networks. This implies that the calculated fault point can be wrongly estimated to be in any of the laterals. This paper proposes a new hybrid method combining Discrete Wavelet Transform (DWT) and artificial neural network (ANN) for fault section identification (FSI) and fault location (FL) in power system distribution networks. DWT was used in the analysis and extraction of the characteristic features from fault transient signals of the three phase line current measurements obtained at a single substation relaying point, rather than the double-ended approach used in the existing literature. Entropy Per Unit (EPU) indices are afterwards computed from the DWT decomposition, and are used as input to multi-layer ANN models serving as FSI classifiers and FL predictors respectively. The proposed hybrid method is tested using a benchmark IEEE 34-node test feeder. Comparisons, verification, and analysis made using the experimental results obtained from the application of the method showed very good performance for different fault types, fault locations, fault inception angles, and fault resistances. The proposed hybrid method is unique because of the pre-processing stage done with the DWT-EPU indices, the use of only line current measurements from a single relaying point, and the division of the FSI and FL tasks into sub-problems with respective ANN models.

Time domain complete protection scheme for parallel transmission lines

Abstract In this paper, time domain relaying schemes for complete protection of parallel transmission lines using wavelet and artificial neural network (ANN) are presented. Four different ANN networks are designed for detection of the fault, fault section identification, classification of fault and location of fault in time domain. The 3rd level approximate discrete wavelet transform (DWT) coefficients of signals of one end are used as input to ANN network. Proposed method is tested with varying fault location, inception angle, fault type and fault resistance. The test results show that the fault is detected and located within 5 ms time accurately. This scheme offers primary protection as well as backup protection to the lines.

A fuzzy Petri net based approach for fault diagnosis in power systems considering temporal constraints

Abstract The fuzzy Petri net is a promising and efficient approach that can tackle the complexities of power system fault diagnosis. In this work, the temporal constraint between event occurrences in power systems is investigated. Then, it is introduced to a fuzzy Petri net (FPN) for fault diagnosis. The temporal attributes are assigned to the propositions in the Petri net, so that temporal information can be taken into account, which makes the true hypothesis distinguishable from the false ones. The modified matrix execution algorithm can enhance computational efficiency, with a “weighted average” operation included to improve the fault-tolerance. The developed model possesses a modular structure, which is easy to adapt to topology changes, and to accommodate modern protection schemes. A preliminary evaluation of the operating performance of protective devices is also carried out after fault section identification. The testing results on the IEEE 14-bus power system and Zhejiang provincial power system in China demonstrate that the developed model is correct and efficient. Compared with three existing fault diagnosis methods, the proposed one has stronger fault-tolerance with lower computational cost, and is suitable for on-line fault diagnosis in large-scale power systems.

The Use of Artificial Neural Networks in the Theoretical Investigation of Faults in Transmission Lines

This paper describes the development of a fast, efficient, artificial neural network (ANN) based fault diagnostic system (FDS) for the location of fault on transmission lines. The principal functions of this diagnostic system are: detection of fault occurrence, identification of faulted sections and classification of faults into types. This has been achieved through a cascaded, multilayer ANN structure using the back-propagation (BP) learning algorithm. This paper shows that the FDS accurately identifies High Impedance Faults, which are relatively difficult to identify with other methods. Test results are simulated and generated in MATLAB using Apo 132KV transmission line in Apo transmission substation, Abuja. These results amply demonstrate the capability of the FDS in terms of accuracy and speed with respect to detection, localization, and classification of faults in transmission lines. http://dx.doi.org/10.4314/njt.v34i4.26

A single ended directional fault section identifier and fault locator for double circuit transmission lines using combined wavelet and ANN approach

Abstract Fault section identification and determining its location are important aspects to reduce down/repair time, speed up restoration of power supply and to improve the reliability. In this paper combined wavelet and artificial neural network based directional protection scheme is proposed for double circuit transmission lines using single end data to identify the faulty section and its location with reach setting up to 99% of line length. The proposed method requires the three phase currents and voltage to be measured at one end of the double circuit transmission line modelled using distributed parameter line model which also considers the effect of shunt capacitance. Approximate coefficients feature vector of the three phase voltage and current are extracted using discrete wavelet transform to train the ANN with Levenberg Marquardt algorithm. The proposed scheme involves two stages. The first stage identifies the zone/section of the fault and the second stage calculates the fault location from the relaying point. The proposed combined Wavelet and ANN based fault location scheme is also compared with ANFIS based fault location scheme. The test results of the proposed scheme show that the fault section identification and location estimation is very accurate and the average percentage error in fault location estimation is within 0.001%. This method is adaptive to the variation of fault type (both forward and reverse), fault inception angle, fault location and fault resistance. The main advantage of the proposed scheme is that it offers primary protection to 99% of line length using single end data only and also backup protection to the adjacent forward and reverse line sections.

Faulted Section Identification Method Based on the Transient Current Distribution for the NUGS

The faulted feeder selection and fault identification under the condition of single-phase earth fault in the neutral un-effectual grounded system (NUGS) has long been a problem. As a matter of fact, existing methods that have been proposed may face some difficulty in the case of some scenarios, as the impact of the Petersen coil must be taken into consideration, or when the accurate location of the faulted point is required and the zero-sequence current cannot be measured. To against this, a novel faulted section identification method based on the transient current distribution is put forward by means of theoretical analysis and simulation study. It is shown by the results that this methods can give accurate location of the fault by comparing the transient RMS value of the upstream and the downstream of the fault point, even though the Petersen coil is switched in.

신재생 에너지 전원을 고려한 배전선로 고장구간 판단 기법

Faulted section identification is one of the most important function in distribution automation systems. Conventional over current detection based fault indicators frequently mal-operate due to the reverse fault current from the renewable energy resources. This paper present a new faulted section identification method based on the fuzzy decision making technique. In order to establish feasibility of the proposed method, case studies using Matlab Simulink has been performed.

A Earth Fault Section Identification Method Based on ANN

The method to discriminate the fault within or beyond the protective zone according to the signals with high frequency detected in protection has problem with low reliability when the resistance of earth fault is high. The transient voltage with low frequency detected in protection can reflect the size of fault intensity approximately, and the transient voltage with high frequency detected in protection in internal fault is bigger than that in external fault when the size of fault intensity is same. The high frequency transient voltage and low frequency transient voltage detected in protection are chosen as inputs and fault section as outputs for training and testing the neural network for fault identification in the UHVDC transmission line, fault section can be identified when input fault characteristic data to the trained model. A variety of transmission line earth fault situation are simulated by PSCAD, and this earth fault section identification method based ANN shows satisfactory performance, even the fault resistance is high.

An integrated technique for fault location and section identification in distribution systems

This paper proposes an integrated impedance and transient-based formulation for fault location in distribution systems. The technique uses an extended apparent impedance analysis in order to estimate the fault distance, taking into account the unbalanced operation, intermediate loads, laterals, and time-varying load profile of distribution systems. The fault distance information is used in calculating characteristic frequencies associated to each possible fault location. A transient analysis is proposed to evaluate the spectral content of the fault-generated traveling wave, thus identifying its most significant frequency components. Based on the time–frequency correlation it is possible to eliminate the multiple estimates obtained from the impedance-based method, thus resulting in a single fault location. Aiming to verify the application of the proposed methodology, simulations were carried out in the ATP/EMTP Software considering a real distribution feeder with 3472 buses.

Evaluation of Artificial Intelligence Techniques for Fault Type Identification in Advanced Series Compensated Transmission Lines

ABSTRACTIn this paper, two well-known artificial intelligence techniques, artificial neural network (ANN) and upport vector machine (SVM) have been modelled for fault type classification for a transmission line equipped with thyristor controlled series compensator (TCSC) to compare their performance. Due to the non-linear changes introduced by the compensation devices, protection of the series compensated transmission line has been a difficult task. The problem is still complex for advanced series compensated transmission lines. The relaying procedure for a compensated transmission line usually involves fault type identification and fault section identification to conclude for the final decision. The algorithm modelled in this paper has been devised on the analysis of a half cycle post-fault current signals, with the help of discrete wavelet transform as a feature extraction tool. The training and testing of ANN and SVM have been performed with an identical fault data set for comparison. The accuracy of the classifiers had been tested with a large number of fault cases encircling different system conditions and fault conditions. These system conditions are simulated on Power System Computer Aided Design/ElectroMagnetic Transients Program (PSCAD/EMTP) with variation in system parameters such as change in firing angle of TCSC, fault resistance, fault inception angle, loading angle of generator, and source impedance variation for all 10 types of faults. The classifiers are compared in detail with their performance at various firing angles of TCSC and for different fault types.

Fault diagnosis system for series compensated transmission line based on wavelet transform and adaptive neuro-fuzzy inference system

This paper proposes a new fault diagnosis approach based on combined wavelet transform and adaptive neuro-fuzzy inference system for fault section identification, classification and location in a series compensated transmission line. It performs an effective feature extraction approach based on norm entropy in order to obtain the features represented main frequency, harmonic and transient characteristics of the fault signals. The proposed method uses the samples of fault voltages and currents for one cycle duration from the inception of fault. The feasibility of the proposed method has been tested on a 400kV, 300km series compensated transmission line for all the ten types of faults using MATLAB/Simulink for a large data set of 23,436 fault cases comprising of all the 10 types of faults. Fault signals varying with fault resistance, fault inception angle, fault distance, load angle, percentage compensation level and source impedance are applied to the proposed algorithm. The results also indicate that the proposed method is robust to wide variation in system conditions and has higher fault diagnosis accuracy with regard to the other approaches in the literature for this problem.

Accurate Fault Classification and Section Identification Scheme in TCSC Compensated Transmission Line using SVM

Accurate Fault Classification and Section Identification Scheme in TCSC Compensated Transmission Line using SVM

Application of extreme learning machine for series compensated transmission line protection

This paper proposes a new approach based on combined Wavelet Transform-Extreme Learning Machine (WT-ELM) technique for fault section identification (whether the fault is before or after the series capacitor as observed from the relay point), classification and location in a series compensated transmission line. This method uses the samples of fault currents for half cycle duration from the inception of fault. The features of fault currents are extracted by first level decomposition of the current samples using discrete wavelet transform (DWT) and the extracted features are applied as inputs to ELMs for fault section identification, classification and location. The feasibility of the proposed method has been tested on a 400 kV, 300 km series compensated transmission line for all the ten types of faults using MATLAB simulink. On testing 28,800 fault cases with varying fault resistance, fault inception angle, fault distance, load angle, percentage compensation level and source impedance, the performance of the proposed method has been found to be quite promising. The results also indicate that the proposed method is robust to wide variation in system and operating conditions.

A new approach for fault classification and section detection in compensated transmission line with TCSC

This paper presents a new combined S-Transform and Logistic Model Tree (ST-LMT) techniques for fault classification and fault section identification in advanced series compensated transmission system. The Thyristor Controlled Series Capacitor (TCSC) is employed at the midpoint of a line. Initially, ST is utilized to extract some useful features of post fault three line currents for one cycle duration to accomplish fault diagnosis task. A key attribute of ST is that it uniquely combines a frequency dependent resolution of the time–frequency space. The features extracted are used to train a single LMT model for fault type classification and determining fault section (whether the fault occurs before or after TCSC) simultaneously. The feasibility of the proposed algorithm has been tested on transmission line with TCSC for 11 types of fault using PSCAD/EMTDC. The result indicates that the ST-LMT method reliably classifies all types of fault and detects fault section with high accuracy on a 300 km, 400 kV TCSC-based transmission line. Copyright © 2010 John Wiley & Sons, Ltd.

Differential equation-based fault locator for unified power flow controller-based transmission line using synchronised phasor measurements

The fault location algorithm based on a differential equation-based approach for a transmission line employing a unified power flow controller (UPFC) using synchronised phasor measurements is presented. First, a detailed model of the UPFC and its control is proposed and then, it is integrated into the transmission system for accurately simulating fault transients. The method includes the identification of fault section for a transmission line with a UPFC using a wavelet-fuzzy discriminator. Features are extracted using a wavelet transform and the normalised features are fed to the fuzzy logic systems for the identification of fault section. After the identification of the fault section, the control shifts to the differential equation-based fault locator that estimates the fault location in terms of the line inductance up to the fault point from the relaying end. Shunt faults are simulated with wide variations in operating conditions and a pre-fault parameter setting. The instantaneous fault current and voltage samples at the sending and receiving ends are fed to the designed algorithm sample by sample, which results in the fault location in terms of the line inductance. The proposed method is tested for different fault situations with wide variations in operating conditions in the presence of a UPFC.

Fault Classification and Section Identification of an Advanced Series-Compensated Transmission Line Using Support Vector Machine

Distance protection of flexible ac transmission lines, including the thyristor-controlled series compensator (TCSC), static synchronous compensator, and static var compensator has been a very challenging task. This paper presents a new approach for the protection of TCSC line using a support vector machine (SVM). The proposed method uses postfault current samples for half cycle (ten samples) from the inception of the fault and firing angle as inputs to the SVM. Three SVMs are trained to provide fault classification, ground detection, and section identification, respectively, for the line using TCSC. The SVMs are trained with polynomial kernel and Gaussian kernel with different parameter values to get the most optimized classifier. The proposed method converges very fast with fewer numbers of training samples compared to neural-network and neuro-fuzzy systems which indicates fastness and accuracy of the proposed method for protection of the transmission line with TCSC

05/00676 Phase selection and fault section identification in thyristor controlled series compensated line using discrete wavelet transform

05/00676 Phase selection and fault section identification in thyristor controlled series compensated line using discrete wavelet transform

Phase selection and fault section identification in thyristor controlled series compensated line using discrete wavelet transform

Discrete wavelet transform is used to analyze faulty signals for a transmission system employing a thyristor controlled series capacitor at the midpoint of a line. Different frequency components of current signals are considered to select the phase(s) involved with a fault. To identify the fault section the new approach utilizes the difference in high frequency components of the current signals. For both phase selection and section identification tasks new energy and standard deviation-based indices are also calculated for the transmission system.