Fault Location Identification Research Articles

Enhancement of dynamic phasor estimation‐based fault location algorithms for AC transmission lines

A new technique to improve fault location methods in AC transmission lines is introduced in this study. The identification of fault location is carried out using one-end fault location algorithms based on impedance through dynamic phasors. This timely approach relies upon the fact that a fault resistance may introduce errors in the distance estimation when a fault takes place. Therefore, active power variations caused by the fault are used to enhance the fault location algorithms, i.e. power losses increase during the fault period for non-zero fault resistances. The present approach is extensively evaluated, using ATP-EMPT software, which relies upon the computation of a compensation factor associated with the rate of change in the resistance during pre-fault and fault conditions. This is achieved through the estimated phasors for voltage and current. Furthermore, the dynamic phasor estimation technique Taylor-Kalman-Fourier Filter is compared against a static phasor method. The proposed approach is validated using three fault location algorithms which are evaluated under different fault conditions, such as fault inception, location, and fault resistance. Results show that this proposed technique improves the fault location including fault resistances by 3% with respect to existing approaches.

Rotor Angle and Voltage Stability Analysis with Fault Location Identification on the IEEE 9 Bus System

Rotor Angle and Voltage Stability Analysis with Fault Location Identification on the IEEE 9 Bus System

Rotor Angle and Voltage Stability Analysis with Fault Location Identification on the IEEE 9 Bus System

Transient stability is very imperative in multi-machine interconnected power systems in order to scrutinize and analyze the system’s performance and response. Rotor angle stability and voltage stability are studied in this paper. By applying three-phase symmetrical faults, the transient stability of the IEEE 9 bus system is studied. A characteristic double hump is analyzed in the response of the generator, which is nearer to the fault location. By analyzing the characteristic double hump, the fault location in a large interconnected power system can be determined. It is shown that, as the fault is cleared, the system takes some finite time to return to its prior state. IEEE 9 bus system is chosen as a test system, which standard parameters. MATLAB Simpower System toolbox is used for load flow and transient stability analysis.

Optimization-Based Settingless Algorithm Combining Protection and Fault Identification

The demand for faster protection algorithms is growing due to the increasingly faster dynamics in the system. The majority of existing algorithms require empirically selected set-points, which may reduce sensitivity to internal faults and cause security problems. This paper addresses these challenges by proposing a settingless time-domain unit protection algorithm for medium-voltage lines. The main idea of the algorithm is to identify which model of a protected line, i.e. healthy or with an internal fault, is more consistent with the input measurements. This is done by solving a number of small-scale convex optimization problems, which at the same time determine the characteristics of an internal fault that best fit the measurements. Thus, the proposed algorithm merges protection, fault location, and fault type identification functionalities. The algorithm's performance is extensively tested on a small grid model in MATLAB Simulink and on the IEEE 34-bus distribution grid in EMTP-RV for different types of generation and grid operating conditions. The results demonstrate that the algorithm can operate quickly and reliably, and accurately estimate fault characteristics even in the presence of noisy measurements, non-ideal communication network conditions and the mismatch in the line model assumed by the algorithm and the model utilized for grid simulations.

Fault classification and location identification in a smart DN using ANN and AMI with real‐time data

This paper presents a real-time fault classification and location identification method for a smart distribution network (DN) using artificial neural networks (ANNs) and advanced metering infrastructure (AMI). It also describes the development of a testbed for real-time testing of the proposed approach. The testbed consists of a simulated power system model [running on a digital real-time simulator (DRTS)] and AMI. The core parts of AMI are smart meters (SMs), a communication network (developed using DNP3 protocol over transfer control protocol/Internet protocol), data concentrator (DC), and a Utility Operations Centre (UOC). Event-driven data from SMs are collected in the DC and then fed to the UOC for being used as inputs for the novel ANN-based fault classification and location identification algorithm. On the basis of the data received, the algorithm can classify the fault type and locate it with high accuracy. Both balanced and unbalanced fault types are tested on different nodes and lines throughout a DN modelled in offline and on the DRTS. A comprehensive sensitivity analysis is performed to validate the effectiveness of the proposed method. Classification accuracy of over 99% is achieved when classifying all fault types, and above 95% accuracy is achieved when identifying the fault location.

Intelligent Fault Classification and Location Identification Method for Microgrids Using Discrete Orthonormal Stockwell Transform-Based Optimized Multi-Kernel Extreme Learning Machine

This paper proposes an intelligent fault classification and location identification method for microgrids using discrete orthonormal Stockwell transform (DOST)-based optimized multi-kernel extreme learning machine (MKELM). The proposed method first extracts useful statistical features from one cycle of post-fault current signals retrieved from sending-end relays of microgrids using DOST. Then, the extracted features are normalized and fed to the MKELM as an input. The MKELM, which consists of multiple kernels in the hidden nodes of an extreme learning machine, is used for the classification and location of faults in microgrids. A genetic algorithm is employed to determine the optimum parameters of the MKELM. The performance of the proposed method is tested on the standard IEC microgrid test system for various operating conditions and fault cases, including different fault locations, fault resistance, and fault inception angles using the MATLAB/Simulink software. The test results confirm the efficacy of the proposed method for classifying and locating any type of fault in a microgrid with high performance. Furthermore, the proposed method has higher performance and is more robust to measurement noise than existing intelligent methods.

Intelligent Three Tie Contactor Switch Unit-Based Fault Detection and Isolation in DC Microgrids

Faults in dc microgrids require quicker interruption than those in traditional ac power systems. This is due to the reduced stiffness of dc microgrids being powered by low inertia distributed energy resources interfaced through power electronic converters. For a high impedance fault, the limited magnitude of the fault current poses additional challenges in detection and identification of fault location in the system, with difficulty in distinguishing the fault condition from the normal operating condition. This article proposes a new cost-effective protection system for fast identification, selective isolation of short circuit and high impedance faults, and system restoration in such dc microgrids. It is achieved through proper coordination of source converters with intelligent three tie contactor switch units (ICU) and there are no solid-state circuit breakers in action. The high impedance faults are identified through multi resolution analysis of the locally measured current signals with discrete wavelet transform and K -nearest neighbor-based classifier at each source converter and contactor. The selectivity is achieved through adaptive resistance time curves set by the programmable relay units installed at each ICU node in the system.

An EWMA and region growing based control chart for monitoring image data

ABSTRACT The widespread application of machine vision system promotes the development of image-based statistical process control methodologies to improve the utilization of image data. Previous research has focused on either identification of fault size and/or location or detection of fault occurrence. There is limited research on both fault detection and identification. In this paper, an EWMA and region growing based control charting method is proposed for monitoring images of industrial products of which quality is either characterized by uniformity (e.g. LCD monitors) or a specific pattern (e.g. manufactured tiles). Simulation results show that the proposed method is not only effective in quick detection of the fault but also accurate in estimating the fault size and location. An experimental study is also provided to highlight how practitioners can implement the proposed method in applications.

Wavelet-Ann Based Fault Location Identification in Micro Gridinter Connected Transmission System

This paper presents a novel protection scheme for the protection of transmission system with microgrid (MG) having of wind energy, solar PV energy and fuel cell sources. MGs provide environmental, economical benefits for the end consumers, power usages and society. However, transmission line and MGs poses major technical challenges. Protection system must respond both MG and utility grid failures. Technical challenges of MG protection are to respond to main and MG faults. A MG model is designed and it is connected to a transmission line. Later, for detection and classification of faults wavelet Analysis (WT) is used. Faults are detected by the fault indices and compared with defined threshold value. The location of fault is done by artificial neural networks (ANN) on MG connected transmission system using detailed (D1) coefficients of energy current signals. This proposed algorithm is tested and more effective for the detection, classification and location of faults on MG interconnected transmission system. This algorithm is accurate and independent of fault inception angle (FIA), fault impedance and fault distance on line.

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>

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.

A new on-line technique to identify fault location within long transmission lines

Identification of fault location within transmission lines is essential in order to restore service rapidly, reduce unplanned outage time, minimise revenue loss, and improve network reliability. Several transmission line fault-locating techniques based on travelling waves, apparent impedance and direct circuit analysis have been introduced in the literatures. However, these methods are either impractical or too expensive to implement. In this paper, a new online technique is introduced to accurately detect the fault location within transmission lines. The technique relies on constructing a voltage-current (V-I) locus diagram that could be measured at every power frequency cycle to provide up-to-date condition state of the line. The technique does not call for special equipment as it utilises the existing metering or protection devices connected to the sending and receiving ends of the transmission line to monitor the voltage and current at both ends. To assess the robustness of the proposed technique, various types of faults have been simulated on transmission lines of various lengths and voltage levels using DIgSILENT PowerFactory software. Variations in load conditions, effect of fault resistance, different line configurations are also investigated. Results reveal the ability of the proposed technique to detect fault locations in transmission lines with high accuracy. In contrary with existing fault-locating techniques that are only employed to identify the location of faults after their occurrence, the proposed technique has the potential to detect high impedance faults. Thus an early warning signal can be issued to avoid any catastrophic consequences. The proposed technique is cost effective and easy to implement for new and existing transmission line installations.

IGBT Open-Circuit Fault Diagnosis in a Quasi-Z-Source Inverter

In this paper, a fast and practical method is proposed for open-circuit (OC) fault diagnosis (FD) in a three-phase quasi-Z-source inverter (q-ZSI). Compared with the existing fast OC FD techniques in three-phase voltage source inverters, this method is more cost-effective since no ultrafast processor or high-speed measurement is required. Additionally, the method is independent of the load condition. The proposed method is only applicable to Z-source family inverters and is based on observing the effect of shoot-through intervals on the system variables during switching periods. The proposed algorithm includes two consecutive stages: OC detection and fault location identification. When both stages of the OC FD algorithm are done, a redundant leg is activated and utilized instead of the failed leg. The accuracy of the proposed method is confirmed by the experimental results from a low-voltage q-ZSI prototype.

Analog circuit soft fault diagnosis utilizing matrix perturbation analysis

This paper proposes a novel approach of analog circuit soft fault diagnosis utilizing matrix perturbation analysis. This method establishes an output response square matrix whose elements will change if circuits fail. Fault can be diagnosed via comparing the difference between the fault-free output matrix and the faulty. According to matrix theory, matrix spectral radius and perturbation matrix m1 norm are utilized to describe the difference. Differing from artificial intelligence algorithms, it is all completely unnecessary to train samples, and can be applied to more complex circuit diagnostics with fewer test nodes. Fault diagnosis, fault location and parameter identification can be realized by quadratic curve fitting in single fault mode. Experiments confirm the feasibility and correctness of this method.

Leveraging Gaussian Process Regression and Many-Objective Optimization Through Voting Scores for Fault Identification

Using piezoelectric impedance/admittance sensing for structural health monitoring is promising, owing to the simplicity in circuitry design as well as the high-frequency interrogation capability. The actual identification of fault location and severity using impedance/admittance measurements, nevertheless, remains to be an extremely challenging task. A first-principle-based structural model using finite element discretization requires high dimensionality to characterize the high-frequency response. As such, direct inversion using the sensitivity matrix usually yields an under-determined problem. Alternatively, the identification problem may be cast into an optimization framework, in which the fault parameters are identified through the repeated forward finite element analysis that is often computationally prohibitive. This paper presents an efficient data-assisted optimization approach for fault identification without using the finite element model iteratively. We formulate a many-objective optimization problem to identify the fault parameters, where response surfaces of impedance measurements are constructed through the Gaussian process-based calibration. To balance between the solution diversity and convergence, an e-dominance-enabled many-objective simulated annealing algorithm is established. As multiple solutions are expected, a voting score calculation procedure is developed to further identify those solutions that yield better implications regarding a structural health condition. The effectiveness of the proposed approach is demonstrated by the systematic numerical and experimental case studies.

Hybrid neural network with bat approach for smart grid fault location

This paper proposes identification of fault location in smart distribution grid based on artificial intelligence using currents and voltages measured; with the help of sensor nodes in distribution system. The approach presented here is the hybrid bat algorithm with neural network, implemented on latest smart distribution system which comprises distributed generation. The fault lengths for various types of faults on distribution feeders are recognised using system parameters measured, before and after the occurrence of a fault. For verifying the performance of proposed algorithm, the MATLAB-based coding is developed and executed on sample modified IEEE test feeders. The performance of a proposed technique is compared with the simple neural network method. The proposed method founds more accurate and fast in speed.

Validation of a PMU‐based fault location identification method for smart distribution network with photovoltaics using real‐time data

A fault location (FL) identification method for smart distribution network is presented and validated using a digital real-time simulator (DRTS). The method can accurately identify the FL in a distribution network in the presence of distributed generation (DG). This method is based on state estimation (SE) algorithm which uses real-time data from simulated phasor measurement units (PMUs), placed in the distribution network. SE needs the fault currents of the generators and voltage measurements of an optimal number of nodes to perform the FL algorithm. The method was validated using the IEEE 37 node test feeder with DGs. PMUs are placed on the real-time model of the system. The real-time model was implemented on a DRTS which streams phasor data over the Internet using C37.118 protocol. OpenPDC is used to collect real-time PMU data coming from the DRTS. Microsoft SQL is used as a database management server to store data coming from OpenPDC. In the last step of the FL process, data stored in OpenPDC is fed into a FL identification algorithm to locate the fault. Both balanced and unbalanced fault types are applied to different nodes and an accurate estimation of the FL (over 90% of the cases) is achieved.

Detection and location of faults in large transmission networks using minimum number of phasor measurement units

This paper presents an algorithm for detection and location of faults in large transmission network using the minimum number of phasor measurement units (PMUs). The transfer impedance between the fault point and the respective PMU buses, may be far away from the fault point, are used to define a non-linear set of voltage and current equations, which is then transformed into linear least square estimation problem where fault location is the unknown quantity. The algorithm starts by reducing the search area using a faulted bus identification index so as to speed-up the search process. Then considering all the transmission lines within the search area, a fault location identification index together with the fault location algorithm is used to identify the faulted line along with fault location. A PMU placement scheme, to find the minimum number of PMUs and their location, required for the proposed algorithm is also presented here. The proposed algorithm is tested and validated on the IEEE 14, 30, 39, and 118-bus systems for various fault conditions. Comparative results confirm that the number of PMUs obtained by the proposed placement scheme is minimum and sufficient for detection and location of faults irrespective of fault resistance and fault type.

Transmission Line Fault Detection and Identification in an Interconnected Power Network using Phasor Measurement Units

This paper proposes a novel hybrid technique to detect and identify transmission line faults in an Interconnected Network using the measurements from Phasor Measurement Units (PMUs). The proposed fault detection and identification technique is based on Positive Sequence voltage and current measurement from PMUs. The proposed algorithm for fault detection and identification is deployed in two stages: First stage is the detection of fault using Positive Sequence Voltage Magnitude (PSVM) and second stage is the fault-location identification through Positive Sequence Current Angle Differences (PSCADs). Sometimes, both of these condition might fail to detect and identify the fault, then another condition based on Positive Sequence Current Magnitude (PSCM) is employed. The proposed hybrid technique for fault detection and fault line identification is tested on a five area interconnected transmission network that employs PMUs at its buses/nodes. During fault, the sequence of PSVM near the faulty line changes or its value drops to minimum, thus detecting the fault. If the PSVM condition fails, then fault is detected by observing sequence and magnitudes of PSCM at all the buses. Fault identification is performed by comparing the PSCAD (Positive Sequence Current Angle Difference) of corresponding node with PSCADs of remaining nodes and maximum PSCAD identifies the faulty line. Simulations are carried out in MATLAB/SIMULINK and results are given for the five area power system. The results verify the proposed fault detection and identification algorithm.

English

English