Fault Location Estimation Research Articles

Intelligent Damage Classification and Estimation in Power Distribution Poles Using Unmanned Aerial Vehicles and Convolutional Neural Networks

Damage estimation is part of daily operation of power utilities, often requiring a manual process of crew deployment and damage report to quantify and locate damages. Advancement in unmanned aerial vehicles (UAVs) as well as real-time communication and learning technologies could be harnessed towards efficient and accurate automation of this process. This paper develops a model to automate the process of estimating and localizing damages in power distribution poles, which utilizes the images taken by UAVs transferred in real-time to an intelligent damage classification and estimation (IDCE) unit. The IDCE unit integrates four convolutional neural networks to learn the states of poles from images, extract the image characteristics, and train an automated intelligent tool to replace manual fault location and damage estimation. The proposed model first determines the type of pole damages, including falling and burning, and then estimates the percentage of damage in each type. The IDCE unit also localizes damages in the poles by locating possible burning or arcing parts. A data set of 1615 images is utilized to train, validate and test the proposed model, which demonstrates high accuracy of the model in classifying and estimating damages in distribution poles.

A Reliable Protection Scheme for Fast DC Fault Clearance in a VSC-Based Meshed MTDC Grid

A multi-terminal high voltage DC (MTDC) grid, is an optimum and cost-effective transmission network to minimize the energy crisis worldwide largely. However, the core demand for a fast DC protection scheme with an extraordinary strict fault clearance time of a few milliseconds, is a key research gap in this network, holding back its development and scalability. To bridge this gap, this paper proposes a novel protection scheme for a meshed MTDC grid. The main goals of the scheme include accurate discrimination of a faulty line, rapid fault detection, fault location estimation, significant fault current reduction, and fully selective isolation of only the faulted line, while continuing normal power flow in the healthy grid zones. Reliability of the scheme for long and extra-long-distance power transmission is increased by aiding the differential protection and Type-D traveling wave (TW)-based algorithms utilizing the distributed optical current sensing technology with the other auxiliary methods and backup plans. These auxiliary methods include independent discrete wavelet transform (DWT), current derivative polarity principles with a minimum sample (short time) window, overcurrent relays, and AC circuit breakers (ACCBs). A faulty segment of a transmission line is accurately discriminated from the healthy ones by measuring a series of multi-point differential currents on it. A faulty line at a particular DC node is accurately discriminated using the differential protection by measuring the current flowing into or out of each line from each side at every node to obtain the algebraic sum. The current sum of a real-time local transient data and a delayed remote data is compared to a preset threshold level. DC fault current is significantly reduced below the breakable levels by coordinating bidirectional hybrid DC circuit breakers (HDCCBs) with the active and passive fault current limiters (FCLs) and the half bridge-VSC-based modular multilevel converters (MMCs). The proposed concepts are successfully verified by the simulation results under a variety of fault scenarios and are found to be accurate.

Combination effects of fault resistance and remote in feed current on simple impedance based fault location

There are many types of fault location algorithms and one of it is simple impedance based fault location algorithm which is still widely used by power system utilities around the world mainly because of no communication channel exists between local and remote substations. The aim of this research is to study the combination effects of remote in feed current and fault resistance on the accuracy of simple impedance based fault location algorithm which is used to estimate fault location when the transmission line is connected to equivalent Thevenin sources from both ends. Simulation has been carried out using Matlab Simulink software for single line-to-ground fault and line-to-line fault. For each type of fault, fault location and fault resistance were varied for two conditions which were when remote breaker in open and remote breaker in close conditions. From the results, it can be concluded that remote in feed current will add additional error to the error due to the existence of fault resistance when fault resistance not equal to zero.

Time-domain based fault location for series compensated transmission lines without requiring fault type

Accurate fault location algorithms enable a quicker return to operation of faulted lines. The previously published fault location algorithms for series compensated transmission lines need the fault type and/or the compensator model to be known before fault location estimation. Thus, the accuracy of them is highly related to the fault type classification method and/or modelling of the compensator. This article suggests a fault location scheme for transmission lines compensated by series devices without requiring fault type based on synchronized instantaneous values collected from both ends of the line along with taking advantage of the distributed parameter line model in the time domain. The main advantages of this method are: (1) It not only determines faulty phases, but also estimates the fault location, simultaneously; (2) It is also independent on models of the series devices, so that the scheme is valid for transmission lines compensated by any series compensator. This paper examines the accuracy of the new algorithm for different fault situations with wide variations in system operating conditions in the presence of a Thyristor-Controlled Switched Capacitor (TCSC) and a Static Synchronous Series Compensator (SSSC). The results show that the fault type-classification and location accuracy of the method is high.

Sequence component based approach for fault discrimination and fault location estimation in UPFC compensated transmission line

This study presents an integrated approach for discrimination of internal and external fault, and to obtain pin-point location of the fault in a transmission line connected to unified power flow controller (UPFC). To detect internal fault, this method uses positive sequence component of voltage and current at the local and remote end bus. Fault location estimation technique requires both positive and negative sequence voltage and current components measured from both ends of the line. The performance of the proposed technique has been evaluated by simulating a 400 kV double circuit UPFC compensated transmission line. This technique can detect internal fault under stressed conditions, viz. high impedance fault (HIF), change in source strength, change in load angle, and power swing. Precise fault location has been achieved under wide variation of system and fault parameters. The comparative assessment has been provided to prove the efficacy of the proposed approach.

타선흡상현상을 고려한 교류철도 급전계통 고장점 표정기법

타선흡상현상을 고려한 교류철도 급전계통 고장점 표정기법

A novel approach to estimate fault location in current source converter–based HVDC transmission line by Gaussian process regression

A novel approach to estimate fault location in current source converter–based HVDC transmission line by Gaussian process regression

Fault location estimation for series-compensated double-circuit transmission line using EWT and weighted RVFLN

In this paper, empirical Wavelet transform (EWT), Hilbert transform (HT) and weighted random vector functional link network (WRVFLN) are integrated for fault detection, classification, and location estimation in a series capacitor compensated double circuit transmission line (SCCDCTL). The full cycle current signals from the point of fault inception are decomposed using EWT to extract three band-limited modes (BLMs). The four efficacious instantaneous features namely energy, Shannon entropy, the standard deviation of the magnitude, and crest factor are computed from the Hilbert transformed array of the BLMs to construct the feature vector. A diagonal matrix W is computed from the zero sequence current of original six current signals as a weighting factor to categorize the ground fault accurately. Numerous faults are generated with a wide variation of the system conditions such as fault resistance, fault inception angle, fault distance, percentage compensation level, source impedance, line parameters, load angle, and inter-circuit fault in MATLAB/Simulink environments. An efficient WRVFLN computational intelligence technique is proposed to recognize and estimate the location of the faults by taking the extracted suitable feature vector with weight factor as an input. The performances of WRVFLN are compared with the recently developed advanced classifiers such as least-square support vector machine (LSSVM) and extreme learning machine (ELM) in the MATLAB interface. The lesser computational complexity, faster learning speed, superior classification accuracy, accurate fault location estimation, and short event detection time prove that the proposed EWTHT–WRVFLN method can be implemented in the real power system for online fault diagnosis. Finally, the developed system architecture is implemented on the reconfigurable digital field programmable gate array (FPGA) in ISE design suite 14.5 environments to verify the cogency of the proposed method in real-time. The feasibility of the proposed method is tested and validated by using the fast FPGA digital circuitry in a loop.

An approach for fault detection and location in solar PV systems

The task of fault detection and diagnosis in large-scale photovoltaic (PV) plants is expected to be a major challenge as more and more plants with increasingly large capacities continue to come into existence. To maintain safety, reliability, and productivity of large-scale PV plants it is essential to develop approaches that allow automatic detection and location of any mal-operation among thousands of PV modules. This paper proposes an approach to detect PV plant faults through the generation of fault indicator signals called ‘residuals’ for each string and the comparison of residuals with a threshold value. Furthermore, a regression-based approach is proposed to estimate fault location as a function of fault current and irradiance level measurements. The proposed approach is demonstrated by specifically focusing on intra-string line-line faults. Various line-line fault case studies are verified through simulations and validated on an experimental setup in a solar PV plant. Initially the approach is developed for a fixed size PV array, but subsequently extensions of the proposed approach to any array size are developed and presented. The generalisation potential of the approach is demonstrated through successful validation on multiple PV array configurations.

MT–HVdc Systems Fault Classification and Location Methods Based on Traveling and Non-Traveling Waves—A Comprehensive Review

Estimation of fault classification and location in a multi-terminal high voltage direct current (MT–HVdc) transmission system is a challenging problem and is considered to be a fundamental maneuver of dc grid protection. This research paper critically reviews traveling and non-travelling wave methods of classification and location of dc faults in multi-terminal HVdc transmission systems. Detailed mathematical analysis of MT–HVdc systems composed of high grounding resistance, cable and overhead line segments, and bipolar coupled transmission network under healthy and faulty conditions, are evaluated. The gravity of this research paper addresses benefits and shortcomings of traveling and non-traveling wave methods and futuristic techniques of fault classification and location.

Fault location estimation for series-compensated double-circuit transmission line using parameter optimized variational mode decomposition and weighted P-norm random vector functional link network

In this paper, both the proposed parameter optimized variational mode decomposition (POVMD) and weighted P-norm random vector functional link network (WPRVFLN) are integrated for fault detection, location estimation, and classification in a series capacitor compensated double circuit transmission line (SCCDCTL). The required number of decomposition is determined from the magnitude spectrum of full-cycle current signals from the point of fault inception. An entropy index is used to obtain the optimum value of data-fidelity factor for extracting the most suitable band-limited intrinsic mode functions (BLIMFs). The four efficacious features namely standard deviation of the magnitude, energy, Renyi entropy and crest factor are computed from the Hilbert transformed array of the BLIMFs to construct the feature vector. A diagonal matrix W is computed based on zero sequence current of the original current signals as a weighting factor to categorize the ground fault accurately. Numerous faults are generated with a wide variation of the system conditions in MATLAB/Simulink simulation environment. An efficient WPRVFLN computational intelligence technique is proposed to recognize the fault by taking the extracted feature vector with weight factor, and its performances are compared with the recently developed classifiers in the MATLAB interface. The lesser computational complexity, faster learning speed, superior fault location estimation accuracy, and short event detection time prove that the proposed POVMD–WPRVFLN method can be implemented in the real power system for online fault recognition. Finally, the feasibility of the proposed method is tested and validated by using the fast FPGA digital circuitry in a loop.

Comparison of different regression models to estimate fault location on hybrid power systems

Various pattern recognition methods have been suggested for estimating high-voltage alternating current transmission line fault location. However, insufficient studies have been conducted on the transmission lines connected to hybrid power generation systems such as wind and solar plants. In this study, the performance of different regression methods was investigated on a hybrid power system. Different faults with random distances on the transmission line were simulated and a fault database created by recording the current and voltage signals of these faults. After normalising this data in the pre-processing phase, it was passed to the digital signal processing stage. By repeating the experiments, 497 different faults were created. Fault types, fault resistances, and fault inception angles were changed randomly in order to obtain similar fault occurrence conditions as in real life by writing a Matlab code. In order to obtain distinctive features, the discrete wavelet transform was used. For training and validation of the dataset, Matlab Regression Learner App (RLA) was employed and the obtained results compared to select the best model. After significant fault simulation, Matern 5/2, a type of Gaussian progress regression model, showed more promising results compared to other RLA models.

Novel Fault Detection and Localization Algorithm for Low-Voltage DC Microgrid

Accurate estimation of fault location is highly crucial for swift maintenance and early power restoration. Since faults in dc networks are time critical, this article proposes a new fault detection and localization scheme for a low-voltage direct current (LVdc) microgrid network. Low-resistance faults are detected by observing voltage across the inductor whereas high-resistance ground faults are identified by measuring ground current at the relay location. Thereafter, based on iterative method, fault location is estimated by comparing analytically derived fault current with the measured value of fault current. Genuineness of the suggested technique has been assessed by simulating various internal, external, and simultaneous faults on a typical LVdc microgrid network modeled in power system computer aided design/electromagnetic transients including dc (PSCAD/EMTDC) environment. The proposed method is capable of detecting and locating both low- and high-resistance dc faults without utilizing remote-end quantities. Subsequently, initial guess has minimal impact on the convergence and accuracy of the proposed algorithm. Comparative evaluation of the proposed technique with other techniques clearly proves its superiority in terms of better discrimination against external faults, rapid detection during internal faults, independency on the network topology, and higher accuracy for fault distance estimation against all types of internal faults.

Fault location identification of double circuit transmission line using discrete wavelet transform

<span lang="EN-GB">The importance of supplying undisturbed electricity keep increasing due to modernization and lifestyle. Any disturbance in the power system may lead to discontinuation and degradation in the power quality. Therefore, detecting fault, fault type and fault location is a major issue in power transmission system in order to ensure reliable power delivery system. This paper will compare two prominent methods to estimate the fault location of double circuit transmission line. Those methods are Discrete Wavelet Transform algorithm and Fast Fourier Transform algorithm. Simulations has been carried out in MATLAB/Simulink and a variety of fault has been imposed in order to analyse the capability and accuracy of the fault location detection algorithm. Results obtained portrayed that both algorithms provide good performance in estimating the fault location. However, the maximum percentage error produced by the Discrete Wavelet Transform is only 0.25%, 0.6% lower than maximum error produces by Fast Fourier Transform algorithm. As a conclusion, Discrete Wavelet Transform possesses better capability to estimate fault location as compared to Fast Fourier Transform algorithm.</span>

Cloud computing in the smart grid context: an application to aid fault location in distribution systems concerning the multiple estimation problem

Cloud computing has been envisioned as the main technology capable of integrating and managing many systems in a smart grid (SG). Thus, this research aims to develop a cloud computing infrastructure to store and manipulate smart distribution system data. To do this, extensible architecture with essential services was proposed to be integrated into an advanced metering infrastructure to host new applications in smart distribution systems. Based on this proposition, a cloud computing platform was developed using open source tools. A new application to reduce multiple estimation for fault location in radial distribution systems using data mining techniques over smart meter data was implemented. An optimised version of the data mining tool known as DAMICORE (DAta MIning of COde REpositories) was implemented as an extension to the proposed architecture basic services. The new cloud application was tested using hundreds of fault simulations on a test feeder and was able to reduce multiple estimation by more than 90% in the simulated fault cases. The results show that the proposed cloud computing architecture and infrastructure enable new smart distribution system applications, contributing to the development of SGs and diffusion of cloud computing in this context.

Data Mining-Based Method to Reduce Multiple Estimation for Fault Location in Radial Distribution Systems

This paper presents an approach to reduce the multiple estimation effect of fault location algorithms. This effect occurs in fault location techniques based on fault distance estimation concerning radial distribution feeders. This approach is based on a data mining technique called data mining of code repositories (DAMICORE). This tool is executed from the perspective of cloud computing in the context of smart grids. It is noteworthy that the voltage and current signals are received by a cloud using smart meters and disturbance recorders. Thus, this cloud receives a feature vector that is extracted by them from the signals acquired. Considering this, the cloud becomes responsible for executing the DAMICORE which, in turn, defines relations among the faulty events. The IEEE 34-Bus Test Feeder was simulated as the test case system. Moreover, the data mining process was able to reduce errors due to the multiple estimation of faulty branches.

A Two-Stage Fault Location Identification Method in Multiarea Power Grids Using Heterogeneous Types of Data

This paper proposes a two-stage fault location method for multiarea power systems. It utilizes available heterogeneous types of data consisting of status data (i.e., discrete data) and analog data (i.e., continues data). A distributed casual model-based diagnosis method using P-invariant Petri nets is proposed that utilizes status data to find faulted sections. Each subsystem/area of multiarea power systems has its own diagnostic model and determines the diagnosis solution based on its local net, the local manifestations, and limited information exchange with the neighboring subsystems/areas. Once a short list of possible fault sections is determined, the second stage further improves the estimated fault location using analog data. The actual fault location is estimated by comparing the measured analog data with their associated calculated values in computer programs using short-circuit analysis algorithms. Simulation results demonstrate that the proposed distributed fault location method can diagnose faults in multiarea power systems accurately.

Effective algorithm for fault discrimination and estimation of fault location in transmission lines

Sensitivity, selectivity, speed of operation and reliability of the conventional current differential protection schemes are affected and limited by the distributed shunt capacitance of high-voltage long transmission lines. In this study, a new pilot protection algorithm has been proposed based on the computation of change in shunt line admittance ‘Y’ during dynamic conditions using synchronised data. Therefore, it does not require a separate compensating technique to nullify the effect of line charging current in the fault detection. This scheme can quickly discriminate the internal faults and also detects the faulty phase. Also, it has an advantage of estimation of an accurate fault location. The performance of the proposed algorithm is evaluated on 400 kV, 50 Hz two-bus system as well as IEEE-39 bus 345 kV system using EMTDC/PSCAD and MATLAB simulation tools. The simulation results confirm the proposed algorithm can effectively discriminate the faults, independent of fault location, fault resistance, type of fault, series compensation lines and source impedance. Finally, a hardware experiment using National Instruments (NI) based LabVIEW platform is carried out to check the practical feasibility of the proposed algorithm.

Negative-Sequence Network Based Fault Location Scheme for Double-Circuit Multi-Terminal Transmission Lines

The fault resistance and infeeded currents from tapped transmission lines are the two main challenges to establish correct fault location estimation in multi-terminal transmission lines. Also, fault location estimation in double-circuit lines is more complicated than single-circuit lines due to the effect of mutual coupling and inter-circuit faults. This paper proposes a negative-sequence network-based method to estimate the fault location of double-circuit multi-terminal transmission lines. In the presented method, signals are measured in the relay location and then, communication links are not required as it uses only one end measurements. Also, in conventional distance relays, six elements are needed to calculate the fault location in all types of faults. Furthermore, in this method, by using only one element for each transmission line, accurate fault location can be estimated. Also, high fault resistance, mutual coupling, and infeeded currents from tapped transmission lines do not have impact on the performance of the presented scheme. To investigate the effects of the line shunt capacitances on the proposed method, a PI model of the transmission lines is used in all the investigations. Simulation results carried out using MATLAB software indicate that the proposed scheme is able to produce excellent performance for the fault location estimation.

High-speed fault detection and location in DC microgrids systems using Multi-Criterion System and neural network

This paper presents a new protection method for LVDC ring-bus microgrid systems based on Multi-Criterion System (MCS) and Neural Network (NN). The proposed method aimed at high-speed detecting line-to-ground (LG) and line-to-line (LL) low impedance faults without using a definite threshold of differential current by using specific rules and multi-criterion system. MCS protection showed speed and accuracy compared to differential protection. Also, NN estimated fault location in percent of line length acceptably as a secondary controller. In order to evaluate the reliability and the enforceability of fault detection and location schemes, simulated network and protection algorithms are implemented and tested in laboratory-scale. The implementation results indicate that the MCS and NN protection scheme can consistently detect and estimate fault locations in the order of a few milliseconds. To reach this goal, a loop type LVDC microgrid with proper power electronic equipment like solid-state bidirectional breakers and the multi-level inverter is fulfilled.