Local Fault Detection Research Articles

Fault Location Estimation on Transmission Lines using Neuro-Fuzzy System

Accurate fault location on transmission line is important in ensuring consistent and reliable operation of the power deliver to long distance destination. Conventional methods for locating fault on transmission lines based on travelling wave and impendence-based methods usually suffer from large error due to the complexity of fault modeling on different type of faults. In this paper, an intelligent system for detection of fault location on transmission line using a hybrid model that integrates artificial neural network (ANN) and fuzzy expert system called Adaptive Network-Based Fuzzy Inference System (ANFIS) is proposed. First, a three phase transmission lines is modeled and various types of faults are generated using MATLAB/Simulink. Then, the faulted current signal is segmented from the faulted transmission. Next, feature extraction is performed to obtained information from the faulted current signal. In this study, the extracted features are mean, standard deviation, energy, peak-to-peak and amplitude value. Feature selection is then applied to select important features that correlate with the fault location. For single-phase-to-ground fault, peak-to-peak value and energy is used. Meanwhile, for the line-to-line and double-phase-to-ground faults, only peak-to-peak value is used. Finally, ANFIS network is trained to locate the fault occurrence. Simulation results against two regression models; Linear Regression and Gaussian Process Regression indicated that the ANFIS network is superior in locating the fault. The network achieved the lowest mean squared error (MSE) (0.0012 to 0.0022).

Adaptive protection methodology in microgrid for fault location and nature detection using q 0 components of fault current

The dynamic behaviour of microgrid system under faulty conditions makes adaptive protection a general necessity for reliable microgrid operation. In design of adaptive protection, the grid-connected and islanded modes have immense importance including grid-connected mode without distributed generators (DGs) in microgrid. In this study, a new adaptive protection scheme is proposed based on the above-mentioned modes of microgrid operation. The proposed method considers nature of DGs connected, fault location detection and fault nature identification based on quadrature and zero sequence components of fault current considering impact of X/R ratio of DGs. The proposed methodologies for adaptive protection schemes are verified in Matlab-Simulink environment and the results are found to be satisfactory while various faults are simulated at different nodes of the microgrid model. At the time of verification of effectiveness of the proposed methodologies, the time derivative of quadrature and zero-axis components of fault current are considered sufficient to instantaneously detect the fault location and fault nature in microgrid system.

СИНХРОНИЗАЦИЯ ВЕКТОРОВ ТОКОВ И НАПРЯЖЕНИЙ ПРИ ОПРЕДЕЛЕНИИ МЕСТА ПОВРЕЖДЕНИЯ НА ВОЗДУШНЫХ ЛИНИЯХ ЭЛЕКТРОПЕРЕДАЧИ

The article presents and analyzes the method of current and voltage phasors synchronization in the end points of the overhead power line when detecting fault location in order to test the efficiency of the method. Short circuit modes in the overhead transmission line are simulated using the Matlab software. The methods of fault localization in the overhead power line based on measuring the values of emergency condition parameters are applied. An algorithm implementing the proposed method of fault localization by synchronizing current and voltage phasors in the end points of the overhead power line is developed. The algorithm and the method have been tested. A series of short circuits is performed on the simulation model in the different points of the line in order to get the values of current and voltage phasors and use them for the two-end localization of the fault using the proposed method of phasor synchronization. The inaccuracy of the two-end localization of the fault resulting from asynchronized nature of current and voltage phasors reaches the highest values under short circuits in the end points of the line. It is attributable to the highest phase angle between the measured phasors of current and voltage. The maximum inaccuracy of the given model of the overhead power line is 4.5% at the phase angle of 23 degrees. The studies based on the mathematical model of the overhead power line have proved that the use of the introduced method of current and voltage phasor synchronization significantly increases the accuracy of fault localization in the overhead power lines while the error caused by asynchronized measurements is reduced to zero.

Planetary Gear Fault Detection Based on Mechanical Torque and Stator Current Signatures of a Wound Rotor Induction Generator

In this paper, an approach for tooth localized fault detection in the sun gear of a planetary gear, using the measured mechanical torque and the stator current of a wound rotor induction generator (WRIG), is proposed. A theoretical background is developed to demonstrate that the faulty sun gear produces periodic fault signatures in the mechanical torque and consequently fault-related frequencies in the stator current of the WRIG. The employed technique needs knowledge of mechanical system torsional natural frequencies, which aid in identifying the most sensitive fault characteristic harmonics in both mechanical torque and stator current spectra. It is illustrated by simulation and experimental results that the amplitude of these harmonics can be used as a fault index for diagnosis of tooth localized defect in planetary gears. A test rig based on a 5.5 kW WRIG, shaft connected to the sun gear of a planetary gear through a high-speed shaft and couplings, was utilized for validation of the proposed technique.

Novel approach using Hilbert Transform for multiple broken rotor bars fault location detection for three phase induction motor

This paper presents a new approach to detect the location of multiple broken rotor bars (MBRBs) in induction motor (IM) drive, running under no load and full load conditions using direct in and variable frequency drives. This technique is based on earlier work of location detection of one broken rotor bar. The techniques are tested for various fault severity levels so the detection of the exact location of the fault at early stage helps to reach sufficient time maintenance. In this paper, the authors used Hilbert Transform to extract the fault signature from the stator current envelope which is the low frequency component. Then statistical analysis is applied which produce a formula that is used to get the exact location of the fault in IM rotor.

Analytical investigation on geared rotor system with multi-body fault based on finite element method

With the rapid development of modern industry, the situation of high speed and heavy load is becoming even more relevant in the gear transmission system. Under the condition of high speed and heavy load, the fault type and frequency of the gear transmission system are gradually increasing. Effective and accurate detection of fault location and fault type is one of the difficult problems in today’s fault diagnosis. In the case of high speed and heavy load, the probability of multi-body fault is greatly increased. However, most of the current fault diagnosis methods are limited to the study of single fault characteristics, and do not take into account the multi body fault. In this paper, the single crack, gear coupling crack, single shaft crack, and gear and shaft coupling crack signal are analyzed by means of the short-time Fourier transform, and the corresponding fault characteristics of different fault types are found out. The modal analysis of the fault state of the gear transmission system is carried out, the structural characteristics of the gear drive are verified, and the influence of the different fault forms on the vibration characteristics of the gear is compared.

Robust Actuator‐Fault‐Tolerant Control System Based on Sliding‐Mode Observer for Thrust‐Vectoring Aircrafts

AbstractIn this paper, a robust actuator‐fault‐tolerant control (FTC) system is proposed for thrust‐vectoring aircraft (TVA) control. To this end, a TVA model with actuator fault dynamics, disturbances, and uncertain aerodynamic parameters is described, and a local fault detection and identification (FDI) mechanism is proposed to locate and identify faults, which utilizes an adaptive sliding‐mode observer (SMO) to detect actuator faults and two SMOs to identify and estimate their parameters. Finally, a fault‐tolerant controller is designed to compensate for these actuator faults, disturbances, and uncertain aerodynamic parameters; the approach combines back‐stepping control with fault parameters and a high‐order SMO. Furthermore, the stability of the entire control system is validated, and simulation results are given to demonstrate the effectiveness and potential for this robust FTC system.

Fault detection and location in a microgrid using mathematical morphology and recursive least square methods

The concept of microgrids has been proposed as an economical, reliable, and efficient way to integrate a large number of distributed energy resources (DERs) into distribution networks. To ensure the safe operation of a microgrid fast and accurate fault detection and location are crucial. This paper proposes fault detection and location in a microgrid using mathematical morphology (MM) and recursive least-square (RLS) methods. The proposed method first applies dilation and erosion median filter (DEMF) on a current signal to detect and classify the faults in microgrids. Then, the RLS method estimates the fault location using a differential equation derived from an equivalent model of the microgrid. The performance of the proposed method is evaluated on a medium voltage microgrid test system through simulations using MATLAB/SIMULINK. The simulation results depict that the proposed method provides faster fault detection and accurate fault location.

A new fault detection and fault location method for multi-terminal high voltage direct current of offshore wind farm

This paper proposes a novel protection scheme for multi-terminal High Voltage Direct Current (MTDC) systems incorporating offshore wind farm based on high-frequency components detected from the fault current signal. This method can accurately detect the fault on each line and classify the fault types. Using the post-fault current time series, both single-ended measurements (detection and classification) and double-end measurements (location), the frequency spectrum is generated to measure the gaps between the contiguous peak frequencies giving a robust and comprehensive scheme. Unlike the previous travelling wave based methods, which must identify the travelling wavefront and require a high sampling rate, the new gap-based approach is able to give accurate fault detection and fault location using any appropriate range of post-fault signals. Furthermore, the proposed method is fault resistance independent and thus even a very high fault impedance has no effect on the fault location detection. By immediately tripping the faults, the fault-caused disturbance to the offshore wind farm is minimized. A three-terminal voltage sourced converter HVDC (VSC-HVDC) system connection of offshore wind farm is modelled in PSCAD/EMTDC (Power Systems Computer Aided Design/Electro-Magnetic Transients including DC) software, which is used for obtaining the fault current data for the transmission line terminal. The algorithm is verified by studying a range of cases, by varying the fault resistance fault locations and also including external faults. The results show that the proposed method gives an accurate and reliable fault detection, classification and location on the test MTDC system.

Local outlier factor-based fault detection and evaluation of photovoltaic system

Aiming at monitoring photovoltaic (PV) systems, evaluating the degree of fault and locating the fault automatically under different outdoor conditions, this paper discusses a new procedure of fault detection and evaluation of fault degree of the PV system. For the PV array connected by PV modules in series and parallel, each string shares the same voltage. The value of current can be used to identify the underperformed strings. In addition, considering the non-stationary stochastic characteristics of current of PV strings, the local outlier factor (LOF) is applied to detect the fault in PV system by evaluating the deviation between the observed data and the whole data. Nevertheless, the LOF method is more suitable for large samples and the LOF value varies with the value of string current. Hence, the conventional LOF method is not suitable for evaluating the fault degree. In order to apply this method to different scale PV systems to detect the fault accurately and evaluate the fault degree, a modified algorithm is proposed in this study. The simulations and experiments based on the model of PV array in MATLAB/Simulink and the 10 kWp PV power plant built on the campus of Hohai University are implemented. The results of experiments reveal that the modified LOF has good performance in fault detection and fault degree evaluation in different scales of the PV systems.

Analysis of efficiency improvement in wireless power transfer system

Recently, efficiency improvement of wireless power transfer (WPT) systems has been the focus of research. Conventional methods of DC-DC converters are employed for load modulation to achieve high efficiency over large variations in mutual inductance and load. However, these additional circuits degrade the performance due to their inherent losses. To address this problem, this study presents a dual-side phase shift control method for load modulation and voltage regulation in a compact WPT system. The key features of the proposed work are: attaining load modulation and voltage regulation with reduced size of the system; elimination of communication between active bridges and simple control logic. Mathematical expression of equivalent resistance versus the phase shift angles (PSAs) is deduced and the optimal PSAs for maximum efficiency are presented. Utilisation of local control, stability analysis and fault detection method are included. MATLAB/Simulink simulation and experimental results are carried out to validate the proposed method.

Research on Leakage Fault Location Detection Technology of Urban Natural Gas Pipeline Network

For the fault diagnosis of urban natural gas pipeline network, an indoor experimental model of natural gas pipeline network was designed. By periodically testing the gas pressure flow variety of the pipe network node, and using the support vector machine (SVM) classification function which based on the minimum structure risk principle, the corresponding feature vector and kernel function parameters are determined and the samples are selected for training and testing in small quantity samples, in the case of the pipe network failure point to judge. Based on the testing data of multiple experimental models, the paper tests the fault diagnosis method of natural gas pipeline leakage which based on SVM, and compares with neural network (ANN) method under the same experimental data. The actual example shows that the diagnostic accuracy of the SVM method is better than the neural network method under the same experimental test data.

Optimal Variable Transmission for Distributed Local Fault Detection Incorporating RA and Evolutionary Optimization

Determining the variable transmission structure is the key step in designing a distributed monitoring scheme for multiunit processes. This paper proposes randomized algorithm (RA) integrated with evolutionary optimization-based data-driven distributed local fault detection scheme to achieve efficient monitoring of multiunit chemical processes. First, the RA is employed to generate faulty validation data. Second, evolutionary optimization-based variable transmission structure determination is performed to achieve the minimal non-detection rate by selecting transferred variables. Then, a principal component analysis (PCA) or kernel PCA monitoring model is established for each operation unit to identify the status of the unit. Last, a comprehensive index is developed to identify the status of the entire process. The established local monitors consider the relationship with other units but avoid introducing redundant information, thereby exhibiting superior monitoring performance. Case studies on two numerical examples, including a linear and a nonlinear case and the Tennessee Eastman benchmark process, are provided. Comparative results of traditional local or global monitoring methods verify the efficiency of the proposed monitoring scheme.

A Novel Fault Detection and Fault Location Method for VSC-HVDC Links Based on Gap Frequency Spectrum Analysis

This paper proposes a one-end gap-based fault location method for VSC-HVDC transmission line using the fault current signal. Using the post-fault current time series, the frequency spectrum is generated for measuring the gaps between the contiguous peak frequencies. This method is able to locate the fault by analyzing the single-end fault current, which guarantees a faster response than using two-end data. The new gap-based approach is able to give accurate fault detection using any appropriate range of post-fault signal. Furthermore, the proposed method is fault resistance independent. A two-terminal VSC-HVDC system is modeled in PSCAD/EMTDC. The algorithm is verified by studying different cases of different fault resistances in various fault locations. The result shows that the proposed method gives an accurate and reliable fault location detection along DC transmission line. In addition, the proposed algorithm can be potentially used in other HVDC systems.

Data-Driven Distributed Local Fault Detection for Large-Scale Processes Based on the GA-Regularized Canonical Correlation Analysis

Large-scale processes have become common, and fault detection for such processes is imperative. This work studies the data-driven distributed local fault detection problem for large-scale processes with interconnected subsystems and develops a genetic algorithm (GA)-regularized canonical correlation analysis (CCA)-based distributed local fault detection scheme. For each subsystem, the GA-regularized CCA is first performed with its all coupled systems, which aims to preserve the maximum correlation with the minimal communication cost. A CCA-based residual is then generated, and corresponding statistic is constructed to achieve optimal fault detection for the subsystem. The distributed fault detector performs local fault detection for each subsystem using its own measurements and the information provided by its coupled subsystems and therefore exhibits a superior monitoring performance. The regularized CCA-based distributed fault detection approach is tested on a numerical example and the Tennessee Eastman benchmark process. Monitoring results indicate the efficiency and feasibility of the proposed approach.

Rule-Based Data-Driven Analytics for Wide-Area Fault Detection Using Synchrophasor Data

Synchrophasor technology, also known as wide-area monitoring system technology, utilizes phasor measurement unit (PMU) to monitor real-time system data, which can provide unique insights into the operation of a power grid. In this paper, a rule-based data-driven analytics method for wide-area fault detection in a power system using synchrophasor data is proposed. As a data-driven approach, this method relies on rules created using PMU measurement data, and does not require knowledge of the power system's topology and model. It can detect fault location (bus and line) and fault type for a particular fault event. Three common types of short circuit faults in a power grid, single-line-to-ground, line-to-line, and three-phase faults, can be identified using the proposed method. Fault thresholds used in rules are determined based on theoretical values and recorded PMU data during fault events in Bonneville power administration (BPA)'s large power grid. The proposed method is validated by comparing with the recorded field data for fault events provided by BPA. It is found that it can effectively detect most faults with a great accuracy. It has been developed into a software program, and can be readily used by utility companies.

Output-Feedback Fault Detection and Accommodation of Uncertain Interconnected Systems With Time-Delayed Nonlinear Faults

This paper addresses a distributed fault detection and accommodation (DFDA) problem for uncertain nonlinear interconnected systems with faults in time-delayed nonlinearities. In contrast to existing DFDA works, we consider uncertain time-delayed interactions among subsystems and their faults, and additionally delays are assumed to be unknown. Under these conditions, a distributed memoryless local fault detection scheme consisting of a local detection estimator and its corresponding time-varying detection threshold is presented for each subsystem by using only output measurements. Then, a decentralized output-feedback control scheme for a local fault accommodation is designed based on this detection estimator and the adaptive approximation technique. The fault detectability and the stability of the proposed output-feedback DFDA scheme are thoroughly investigated in the Lyapunov sense. A simulation example is provided to illustrate the effectiveness of the proposed methodology.

Localized fault detection of sun gears based on windowed synchronous averaging in the angular domain

The vibration transmission paths between the fixed-mounted accelerometer and the meshing points of the sun gear’s local fault gear tooth is time varying in a planetary gearbox. Consequently, the conventional time synchronous averaging used in vibration-based fault detection of fixed-axis gearboxes is invalid for the localized fault detection of sun gears. To address the noise reduction, the windowed synchronous averaging originally introduced by McFadden can be employed to extract the useful components and reduce the effect of transmission paths. However, the speed fluctuation makes the above method inapplicable in real-world applications. In order to improve the robustness of windowed synchronous averaging under the speed fluctuation condition, a non-separation version of the windowed synchronous averaging in the angular domain is proposed in this article to address the speed fluctuation. In this scheme, the windowed time-domain signal is resampled at constant angular increments by the computed order tracking, which converts the non-stationary signal in the time domain into the stationary one in the angular domain. Then, the windowed angular-domain signal is processed by the synchronous averaging. In this way, the frequency blur and waveform distortion caused by rotating speed fluctuations can be eliminated. Experiments are conducted to verify the validity of the proposed method.

Induction motor broken rotor bar fault location detection through envelope analysis of start-up current using Hilbert transform

Robustness, low running cost and reduced maintenance lead Induction Motors (IMs) to pioneerly penetrate the industrial drive system fields. Broken rotor bars (BRBs) can be considered as an important fault that needs to be early assessed to minimize the maintenance cost and labor time. The majority of recent BRBs’ fault diagnostic techniques focus on differentiating between healthy and faulty rotor cage. In this paper, a new technique is proposed for detecting the location of the broken bar in the rotor. The proposed technique relies on monitoring certain statistical parameters estimated from the analysis of the start-up stator current envelope. The envelope of the signal is obtained using Hilbert Transformation (HT). The proposed technique offers non-invasive, fast computational and accurate location diagnostic process. Various simulation scenarios are presented that validate the effectiveness of the proposed technique.

LIQUEFACTION RISK ASSESSMENT BY THE USE OF GEOPHYSICAL TECHNIQUES: THE TEST AREA OF NAFPLION CITY, GREECE

An efficient and cost effective site characterization, with regard to the seismic hazard and liquefaction risk assessment, was accomplished with the aid of geophysics in the area, where the Nafplion city of Greece is expanding. The methodology adopted includes the recognition of the possible earthquake sources of the wider region, their modelling, in order to stochastically simulate the strong ground motion at the investigation area, and finally the calculation of the liquefaction risk. The investigation area was suspected of high liquefaction potential since the foundation ground consists of loose sandy silt with very shallow aquifer. The geophysical techniques considerably contributed to the detection and characterization of possible local seismic faults with the implementation of gravity and seismic methods. Special emphasis was given to the seismic depth migration and particularly to the construction of valid velocity models, in order to precisely calculate the dips of the possible faults. Additionally the geophysical techniques provided the near surface velocity structure for the calculation of the amplification of the seismic motion up to the surface, also required for the final estimation of the liquefaction risk. The seismic methods (seismic reflection, seismic refraction, seismic modelling, MASW, multichannel analysis of microtremors and crosshole investigations), if combined with geo-technical borehole testing, enhance their reliability and cover large areas in a cost-effective way in comparison with the standard borehole tests. In Nafplion area, evidence was found for a low factor of safety against liquefaction at specific sites within the study area. The results show that liquefaction probability can reach 80% at some sites depending on selected earthquake scenario, mainly at depths between 5 and 10 meters. This should be considered as highly important information for making risk-based design decision in this region.