Fault Zone Identification Research Articles

Fault identification, classification, and localization in microgrids using superimposed components and Wigner distribution function

The integration of Distributed Energy Resources (DERs) into distribution grids has become increasingly feasible and sustainable due to the development of microgrids. However, the development of an effective protection strategy remains a challenge in the implementation of microgrids. To address this challenge, this paper presents a simple and novel microgrid protection method based on superimposed components, Wigner distribution function (WDF) and alienation index-based. The proposed method develops a new fault detection index (FDI) by applying the alienation coefficient and WDF on a superimposed current signal to detect faulty events in the microgrid. The scheme is inherently phase segregated because the FDI is obtained for each phase individually. In addition, the proposed strategy introduces a new fault zone identification method based on the superimposed positive sequence reactive power (SPSQ). After obtaining the complete fault information, a relevant trip signal is generated to isolate the faulty section from the rest of the grid. The proposed methodology is evaluated through simulations using MATLAB/SIMULINK software. Various fault types, with varying parameters are simulated to validate the proposed approach. The results indicate that the proposed methodology is capable of recognizing, classifying, and locating all fault types in both grid-connected and islanded modes of operation.

APLIKASI DOUBLE DIFFERENCE UNTUK IDENTIFIKASI ZONA PATAHAN MIKRO WILAYAH SULAWESI BARAT

West Sulawesi is an area in South Sulawesi province that is experiencing a sudden increase in seismic activity. There have been 264 earthquake events recorded during the 2021-2022 period. The increase in seismic activity occurred around the west Sulawesi segment and the Mamuju segment, which are local faults located in west Sulawesi. Sulawesi Island itself is the result of fragments due to larger complex reactions and has quite high seismic activity. Therefore, it is necessary to calculate the earthquake hypocenter relocation to obtain more accurate hypocenter parameters for fault zone identification and micro fault orientation. This study aims to relocate the earthquake hypocenter based on mathematical measurement data using the Double-Difference method for identification of fault zones and micro-fault orientation. The data used is BMKG earthquake catalog data for the western Sulawesi region with a range of 118.49 East - 119.59 East and 3.67 LS -2.45 LS in the 2021-2022 period. The Double-Difference method is a method based on the Geiger method using residual travel time data from each pair of hypocenters to the earthquake recording station. The principle is to compare two earthquake hypocenters that are close to the earthquake recording station with the assumption that the distance between the two hypocenters must be closer than the distance between the hypocenter and the earthquake recording station. This is done so that the raypath of the two hypocenters can be considered close to the same value. From the calculation of the earthquake hypocenter relocation, it was found that the hypocenter after relocation experienced changes in distribution that accumulated in the Mamasa fault segment area. From the residual histogram, it is found that the RMS residual is close to 0, indicating an improvement in data quality as evidenced by the observation travel time and the calculated travel time are almost close to the same value. In the cross-section results, it is obtained that the depth changes vary in the subsurface depth from the initial depth of 10 km before being relocated, indicating that the earthquake that occurred in West Sulawesi was included in the shallow crustal earthquake type.

Relocation of the Hypocenter of an Earthquake with the Double Difference Method in the Regional Study Area of Yogyakarta

The relocation of the earthquake hypocentre is necessary in order to determine the position of the earthquake centre with higher accuracy. An accurate hypocentre position is important for earthquake-prone areas mapping, seismicity analysis, and fault zone identification. The double difference algorithm technique using the hypoDD program can be used for hypocentre relocation. This article reports the earthquake relocation of 23 earthquakes in the Yogyakarta region recorded at four observation stations. The result shows that the hypocentre shift spreads randomly with a shift distance of less than 20 km, with the most shifting direction of the epicentre to the northeast. The Earthquake’s hypocentre after relocation in the land area is estimated to be triggered by a fault under the Gamping Wungkal Formation, while the earthquake around Mount Merapi is estimated to be triggered by volcanic activity. The Relocation result in the sea area show that the hypocentre leads to the subduction line.

Fault Detection and Classification in Interconnected System with Wind Generation Using ANN and SVM

Protective relays are installed in generation, transmission, and distribution system for detection, classification, and estimation of faults. To match the future load demand and to get uninterrupted power supply, use of renewable energy sources are increasing day by day. Faults can occur in transmission lines, transformers, generators, and busbars but the nature of these faults may change many times when renewable energy sources are considered. This research paper introduce techniques to detect and classify different faults on transmission line in the presence of wind energy sources using efficient tools of artificial intelligence. The main challenges of the system fault detection, in presence of wind turbine lie in their non-linearity, uncertainty and unknown disturbances. PSCAD/EMTDC software tool is used to simulate the power system model with RES which is implemented in MATLAB and Python software. Artificial Neural Network (ANN) and Support Vector Machine (SVM) algorithms have been used to classify and detect faults on transmission lines connected with wind energy source. The proposed technique has been validated for internal faults on transmission line and external faults on power system. In total of 4320 internal and external fault cases with wide variation in system parameters have been used for validation of the proposed model. The proposed model gives an overall fault zone identification accuracy of more than 99% in presence of wind energy source. The results obtained from validation show that the performance of SVM classifier is better than ANN in term of efficacy and classification time.

Fault Zone Identification for Groundwater Flow Assessment Based On Seismic Reflection Survey Data at the Area of Felda Lepar Utara, Pahang, Malaysia

Geological structures such as faults and fractures have an important influence in the process of fluid movement below the surface. The hydraulic behavior in aquifers can be determined by proper characterization of fractures, fault zones and their connectivity. In this study, we concern on detection and identification of fault zones in the groundwater basin to verify whether faults in the basin area connect to the surface, and whether the fault zones occurring serve as conduits or barriers for groundwater to flow. The seismic reflection method with Common Depth Point (CDP) profiling technique has been applied in this study. Through this study, we have identified that several large and small-scale faults were found in the study area. Generally, these large-scale faults cut the bedrock (granodiorite) up to impermeable layer. This large-scale fault group can be a barrier that block the groundwater flow. The fault zone is connected to the surface as evidenced by the presence of normal fault that is clearly observed at the surface. This seismic method is good to apply in this study because it can be used to record deeper subsurface conditions, especially for fault zone detection purposes.

A Fault Location Identification Technique for Active Distribution System

Impedance based fault location schemes using substation measurements are becoming absolute due to the introduction of distributed generation and islanding-mode operating capability of distribution system (DS). In this article, a new fault location algorithm is presented for active distribution systems using microphasor measurement units (μPMU) and smart meter (SM) data. The line current data obtained from the μPMUs in the DS are exploited to divide the system into different zones. To identify the faulted zone, a fault zone identification parameter is devised based on the pre and during fault positive sequence current injection data recorded in the μPMUs. Next, fault location analysis is limited to the identified zone to reduce the computational complexity. To locate the fault within the identified zone, the work proposes two parameters for the identification of bus nearest to fault (BNF) and faulted line section connected to the identified bus. The scheme does not require the information about the type of ground faults and is applicable to balanced, unbalanced, grid connected and as well as islanded distribution systems.

Transmission Line Protection Using High-Speed Decision Tree and Artificial Neural Network: A Hardware Co-simulation Approach

In this paper, a protection scheme of 500 kV, 50 Hz, and 220 km double end transmission system based on wavelet transform (WT), high-speed decision tree (DT), and artificial neural network (ANN) are proposed. The scheme is implemented using a microcontroller co-simulated with MATLAB/Simulink and it is validated in real-time. The proposed scheme performs fault classification, location estimation, and fault zone identification using both DT and ANN in a comparative study. The scheme calculates the wavelet transform approximation coefficients of the current and voltage signals and then sends its energy to a specific decision tree/neural network to recognize the fault type, its zone, and its location. It was found that the accuracy of the decision tree classifier outperforms the ANN classifier, in addition, achieves a significantly higher classification speed of about 0.4 msec inference time and it is easier to be applied in real-time. In contrast, the neural network is more accurate in calculating fault zone and locations. DT and ANN are trained and tested using different sets of data obtained from simulated faults that differ in type, resistance, and location. Modeling fault scenarios and data processing are carried out using the MATLAB/Simulink software package. The hardware implementation uses an 8-bit ATmega microcontroller that is interfaced with the simulated model using MATLAB support package for Arduino. Simulation results confirm that the proposed scheme using DT for fault classification and ANN for zone and location estimation is accurate and reliable even with varying fault resistance, type, and distance, and is therefore applicable in practice on a digital platform.

Fault zone identification and phase selection for microgrids using decision trees ensemble

Microgrid protection is one of the main challenges that hinders the growth in the number and variety of microgrids being developed. Fault zone identification and phase selection are among protection challenges that existing protection schemes fail to address. This paper proposes a voltage-restrained classifier-based relaying scheme to address the two protection selectivity problems. While fault detection is achieved via an undervoltage function to ensure the sensitivity of the proposed method, Decision Trees (DTs) ensemble functions are used for fault zone identification and phase selection to guarantee the selectivity of the protection scheme. The proposed approach can identify three zones, forward internal, forward external, and reverse external, which (i) facilitates the operation of a relay in primary and remote backup modes, and (ii) avoids nuisance operation under reverse faults, i.e., works as a directional element. The proposed phase selection function can identify all ten types of faults, which enables the isolation of the faulted phase(s) only. The proposed relaying logic does not require any form of communication, instead it relies on readily available sequence component measurements. Transient studies in PSCAD/EMTDC verify the performance of the proposed scheme under various faults and different microgrid operation modes.

A Strategy for Refining Fault Zone Identification of Distance Relays (Dept.E)

A Strategy for Refining Fault Zone Identification of Distance Relays (Dept.E)

Fault Classification and Fault Zone Identification in a Thyristor Controlled Series Compensator Based Transmission Line by using Decision Tree

A new method has been introduced for classification of fault and to identify zone of fault in Thyristor Controlled Series Capacitor based line by utilizing Decision Tree method. PSACD/EMTDC software is used in this paper for the simulation of TCSC. Voltage and current samples after fault are used in this method as input against predicted output vectors for zone identification of fault. Decision Tree based classification algorithm also used to classify all ten types of faults in the TCSC based line. This method is being tested on simulated data and the results indicate that this method can classify different types of faults and also identify zone of fault more accurately than any neural network systems in a TCSC based line.

Earthquake Relocation in Northern Bali Island in 2015-2017 using Double Difference Method (DD)

A research about earthquake relocation in Northern Bali Island in 2015 – 2017 using the Double Difference (DD) method has been carried out. Earthquake relocation is important to get some informations about earthquake locations with high accuracy, fault zone identification, earthquake distribution and seismic analysis. The results of the relocation indicated that there was a displacement of the location of earthquake that formed moving closer to the earthquake source namely a fault. Relocation using HypoDD program gave high accurate result indicated by the RMS residual time value close to zero e.g. 0,000 - 0.01 which mean the results of calculations and observations are closed to the real value.

Development of a new fault zone identification scheme for busbar using logistic regression classifier

This study presents the development of a new fault zone identification scheme for busbar using logistic regression binary classifier by utilising one cycle post-fault current signals of all the bays connected to the busbar. Practicability of the presented scheme has been verified by modelling an existing 400 kV Indian power generating station in power systems computer-aided design/electro-magnetic transient design and control software package. The presented scheme has been tested on enormous cases (38,400) which were generated by varying system and fault parameters. The proposed scheme provides effective discrimination between internal and external faults with a very high (99.69%) overall accuracy. Moreover, it remains stable in case of heavy through fault conditions particularly with current transformer saturation during which the conventional differential protection scheme mal-operates. Furthermore, it provides equally compatible accuracy for unknown system/unseen data set as well as for double/one-and-half breaker busbar arrangement. In addition, performance of the proposed scheme has been verified on the laboratory prototype and results are found to be satisfactory. The average tripping time is of the order of 23 ms in case of internal faults. At last, comparative evaluation of the proposed scheme with recently presented schemes in the literature indicates its superiority.

Transmission Line Fault Classification and Fault Zone Identification using Back-Algorithm based Neural NetworkPropagation

Transmission Line Fault Classification and Fault Zone Identification using Back-Algorithm based Neural NetworkPropagation

Data-mining model based adaptive protection scheme to enhance distance relay performance during power swing

The paper presents a data-mining model based adaptive protection scheme enhancing distance relay performance during power swing for both compensated and uncompensated power transmission networks. In the power transmission network, the distance relays are sensitive to certain system event such as power swings, which drive the apparent impedance trajectories into the protection zones of the distance relay (zone-3) causing mal-operation of the distance relay, leading to subsequent blackouts. Further, three-phase balanced symmetrical fault detection during power swing is one of the serious concerns for the distance relay operation. This paper proposed a new adaptive protection scheme method based on data-mining models such as DT (decision tree) and RF (random forests) for providing supervisory control to the operation of the conventional distance relays. The proposed scheme is able to distinguish power swings and faults during power swing including fault zone identification for series compensated power transmission network during stress condition like power swing. The proposed scheme has been validated on a 39-bus New England system which is developed on Dig-Silent power factory commercial software (PF4C) platform and the performance indicate that the proposed scheme can reliably enhance the distance relay operation during power swing.

An improved scheme for identifying fault zone in a series compensated transmission line using undecimated wavelet transform and Chebyshev Neural Network

Identification of fault position with respect to the compensator is an important component for a series compensated transmission line protection scheme. Based on Undecimated Discrete Wavelet Transform (UDWT) and Chebyshev Neural Network (ChNN), a new fault zone identification scheme for series compensated transmission line is proposed in this paper.The proposed technique uses only half cycle post fault data samples of three phase currents to identify the fault zone with a two-stage methodology. The performance of the proposed scheme has been compared with Discrete Wavelet Transform (DWT) and ChNN based scheme with identical fault cases comprising of all possible ten types of faults under different fault resistances, source impedances, fault inception angles and percentage compensation levels. The result demonstrates that the proposed scheme gives better performance as compared to the other methods proposed in the literature, and the DWT based technique.

Fault zone identification in the eastern part of the Persian Gulf based on combined seismic attributes

Faults, as main pathways for fluids, play a critical role in creating regions of high porosity and permeability, in cutting cap rock and in the migration of hydrocarbons into the reservoir. Therefore, accurate identification of fault zones is very important in maximizing production from petroleum traps. Image processing and modern visualization techniques are provided for better mapping of objects of interest. In this study, the application of fault mapping in the identification of fault zones within the Mishan and Aghajari formations above the Guri base unconformity surface in the eastern part of Persian Gulf is investigated. Seismic single- and multi-trace attribute analyses are employed separately to determine faults in a vertical section, but different kinds of geological objects cannot be identified using individual attributes only. A mapping model is utilized to improve the identification of the faults, giving more accurate results. This method is based on combinations of all individual relevant attributes using a neural network system to create combined attributes, which gives an optimal view of the object of interest. Firstly, a set of relevant attributes were separately calculated on the vertical section. Then, at interpreted positions, some example training locations were manually selected in each fault and non-fault class by an interpreter. A neural network was trained on combinations of the attributes extracted at the example training locations to generate an optimized fault cube. Finally, the results of the fault and nonfault probability cube were estimated, which the neural network applied to the entire data set. The fault probability cube was obtained with higher mapping accuracy and greater contrast, and with fewer disturbances in comparison with individual attributes. The computed results of this study can support better understanding of the data, providing fault zone mapping with reliable results.

Development of a new bus zone identification algorithm using support vector machine

This study presents a new support vector machine (SVM)-based fault zone identification scheme, which correctly identifies faults occurring inside and outside the protection zone of a busbar. A 220 kV substation with double busbar scheme present in an existing Indian system is considered for validation of the proposed fault zone identification algorithm for busbar protection. Fault simulations are performed using PSCAD/EMTDC software package and the outcome is then interfaced with MATLAB, where fault zone identification algorithm is implemented. A total of 44 650 internal and external fault cases with varying fault resistances, fault inception angles, fault locations, types of faults and source impedances have been used for validation of the proposed scheme. Even though the proposed scheme utilises only 29.63% of the total simulation cases (63 450) for the training of SVMs, it gives an overall fault zone identification accuracy of more than 90% when tested with using remaining 70.37% simulation cases for validation. The proposed scheme successfully discriminates between in-zone and out-of-zone faults with high fault zone identification accuracy for different fault and system conditions. Moreover, the proposed scheme remains stable during cross-country faults and severe current transformer (CT) saturation condition.

Unsymmetrical High-impedance Earth Fault Central Relay for Transmission Networks

This article presents a central relay based on wavelet transform for high-impedance earth fault detection, zone identification, location, and classification in part of the Egyptian 500-kV transmission network. The scheme recognizes the distortion of the voltage and current waveforms caused by the arcs usually associated with high-impedance earth faults for unsymmetrical faults, whether single line to ground fault The proposed discrete wavelet transform based analysis yields three phase voltages in the high-frequency range and zero-sequence root mean square current in the low-frequency range that are fed to fault detection and location algorithms, respectively, while phase currents in the high-frequency range are fed to the classification algorithm. The fault detection algorithm is based on the recursive method to sum the absolute values of the high-frequency signal generated over one voltage cycle, while the zone identification and fault location algorithms use unsynchronized zero-sequence root mean square currents. On the other hand, the fault classification algorithm is based on the currents in the high-frequency range for one-side data of the faulted line at the local relay after the detection and location process. Characteristics of the proposed central relay are analyzed by extensive simulation studies that clearly reveal that the proposed relay can accurately determine the network faulted line and can calculate fault distance with an acceptable error that does not exceed 5%. All simulation studies are carried out using a high-impedance earth fault model of a distribution system that is modified for transmission systems. An available real high-impedance earth fault case study is used to check the performance of the fault classification algorithm to classify phase and earth faults.

New fault zone identification scheme for busbar using support vector machine

This study presents a new support vector machine (SVM)-based fault zone identification scheme for busbar which correctly identifies faults occurring inside and outside the protection zone of busbar. The proposed scheme utilises one cycle post-fault current signals of all the lines as an input to SVM. In order to achieve the most optimised classifier, Gaussian radial basis function has been used for training of SVM. Feasibility of the proposed scheme has been tested by modelling an existing 400 kV Indian busbar system in PSCAD/EMTDC software package. More than 28 800 fault cases with varying fault resistances, fault inception angles, fault locations, types of faults and source impedances have been generated and used for validation of the proposed scheme. The proposed scheme effectively discriminates between in-zone and out-of-zone faults with very high fault classification accuracy for different fault and system conditions. Moreover, the proposed scheme remains stable during an early and severe current transformer (CT) saturation condition giving an accuracy of 99% for all the fault cases.

Decision tree-based fault zone identification and fault classification in flexible AC transmissions-based transmission line

Transmission line distance relaying for flexible AC transmission lines (FACTS) including thyristor controlled series compensator (TCSC), STATCOM, SVC and unified power flow controller (UPFC) has been a very challenging task. A new approach for fault zone identification and fault classification for TCSC and UPFC line using decision tree (DT) is presented. One cycle post fault current and voltage samples from the fault inception are used as input vectors against target output ‘1’ for fault after TCSC/UPFC and ‘0’ for fault before TCSC/UPFC for fault zone identification. Similarly, the DT-based classification algorithm takes one cycle data from fault inception of three phase currents along with zero-sequence current and voltage, and constructs the optimal DT for classifying all ten types of shunt faults in the transmission line fault process. The algorithm is tested on simulated fault data with wide variations in operating parameters of the power system network including noisy environment. The results indicate that the proposed method can reliably identify the fault zone and classify faults in the FACTs-based transmission line in large power network.