Showing papers on "Fault indicator published in 2022"
TL;DR: In this paper , the performance of the impedance-based fault location methods is compared in which the impedance parameter of the faulted line section is calculated as a measure of the distance to the fault.
Abstract: Electric transmission lines play a very essential role in transmitting power energy from generation centers to consumption regions. They can be exposed to fault occurrences due to various reasons, such as lightning strikes, malfunction of components, and human errors. Since fault is unpredictable, a fast fault location method is required to minimize the impact of fault in power systems. This paper presents a research work for comparing the performance of the impedance-based fault location methods, in which the impedance parameter of the faulted line section is calculated as a measure of the distance to the fault. To evaluate the capability of the methods for correctly detecting and locating the fault locations, comprehensive simulation results are carried out. This computation is based on modeling and simulating a three-phase 220kV overhead transmission line in the Matlab/Simulink software. Short circuits which occur in various fault resistances and locations along the transmission line are emulated to investigate several case studies and the accuracy of fault location determination is calculated to compare the performance among these fault location methods.
35 citations
TL;DR: This work introduces a machine learning based technique that relies on satellite weather data and low-frequency inverter measurements for accurate fault diagnosis of PV systems, and demonstrates that this approach is sensitive to faults with a severity as small as 5 %.
29 citations
TL;DR: Wang et al. as mentioned in this paper proposed a novel method for fault diagnosis based on memory spiking neural P systems, which can distinguish false faults caused by measurement tampering attacks, which could potentially lead circuit breakers to trip creating a false fault in the absence of any faulty section.
19 citations
TL;DR: The proposed method can be utilized for different multiterminal dc systems and is effective under different fault locations, different fault types, and high fault impedances, and does not require high sampling frequency and has good robustness against measuring noise.
Abstract: Existing nonunit protection schemes inevitably require setting, which is a serious problem in practical engineering. Faults occurred at different fault zones will result in different equivalent models, therefore, the fault zone can be determined by recognizing which equivalent model the fault fits well with. In this article, this “model recognition” idea is introduced in fault identification and a “setting-less” protection method is proposed. First, the Peterson equivalent circuits when faults occur at backward external zone, internal zone, and forward external zone are presented, respectively. Accordingly, the corresponding three fault voltage expressions are derived, which are defined as three fault modes. Then, the three fault modes are used to approximate the measured fault voltage using Levenberg-Marquardt optimal approximation method. The fault mode that best fits the measured fault voltage is recognized as the final determined fault mode, which is used for fault identification without setting threshold value. Numerous test studies carried out in Power Systems Computer Aided Design/Electromagnetic Transients including DC (PSCAD/EMTDC) and real-time digital simulator have demonstrated that the proposed method can be utilized for different multiterminal dc systems and is effective under different fault locations, different fault types, and high fault impedances. The proposed method does not require high sampling frequency and has good robustness against measuring noise.
17 citations
TL;DR: In this paper , a recurrent neural network is used to identify six relevant types of faults, based on the past 24 hours of measurements, as opposed to only taking into account the most recent measurement.
16 citations
TL;DR: In this article , a fault location scheme based on support vector machines (SVM) was proposed for DC microgrid clusters, which is also without any communication link in methodology or implementation in the system.
16 citations
TL;DR: A random forest regressor (RFR)-based model is used to detect real-time fault location and its duration simultaneously and consistently outperformed the other models in detection accuracy, prediction error, and processing time.
Abstract: Power system failures or outages due to short-circuits or “faults” can result in long service interruptions leading to significant socio-economic consequences. It is critical for electrical utilities to quickly ascertain fault characteristics, including location, type, and duration, to reduce the service time of an outage. Existing fault detection mechanisms (relays and digital fault recorders) are slow to communicate the fault characteristics upstream to the substations and control centers for action to be taken quickly. Fortunately, due to availability of high-resolution phasor measurement units (PMUs), more event-driven solutions can be captured in real time. In this paper, we propose a data-driven approach for determining fault characteristics using samples of fault trajectories. A random forest regressor (RFR)-based model is used to detect real-time fault location and its duration simultaneously. This model is based on combining multiple uncorrelated trees with state-of-the-art boosting and aggregating techniques in order to obtain robust generalizations and greater accuracy without overfitting or underfitting. Four cases were studied to evaluate the performance of RFR: 1. Detecting fault location (case 1), 2. Predicting fault duration (case 2), 3. Handling missing data (case 3), and 4. Identifying fault location and length in a real-time streaming environment (case 4). A comparative analysis was conducted between the RFR algorithm and state-of-the-art models, including deep neural network, Hoeffding tree, neural network, support vector machine, decision tree, naive Bayesian, and K-nearest neighborhood. Experiments revealed that RFR consistently outperformed the other models in detection accuracy, prediction error, and processing time.
14 citations
TL;DR: In this paper , the authors considered the design and analysis of a distributed sensor fault accommodation scheme for a class of nonlinear systems, modeled as interconnected subsystems, where each subsystem is subject to possible local sensor faults or may be impacted by a sensor fault in a neighboring subsystem due to the exchange of information between neighboring controllers.
Abstract: This article considers the design and analysis of a distributed sensor fault accommodation scheme for a class of nonlinear systems, modeled as interconnected subsystems. Each subsystem is subject to possible local sensor faults or may be impacted by a sensor fault in a neighboring subsystem due to the exchange of information between neighboring controllers. The proposed adaptive approximation-based distributed fault accommodation scheme is triggered in the event that a sensor fault is detected. The stability of the closed-loop system in the presence of sensor faults is rigorously analyzed both for the period prior to the detection of the fault, as well as after the detection of the fault when the fault accommodation scheme is activated. The effectiveness of the proposed scheme is illustrated by a simulation example.
13 citations
TL;DR: In this article , the authors proposed a model recognition approach for fault identification and a setting-less protection method, which does not require high sampling frequency and has good robustness against measuring noise.
Abstract: Existing nonunit protection schemes inevitably require setting, which is a serious problem in practical engineering. Faults occurred at different fault zones will result in different equivalent models, therefore, the fault zone can be determined by recognizing which equivalent model the fault fits well with. In this article, this “model recognition” idea is introduced in fault identification and a “setting-less” protection method is proposed. First, the Peterson equivalent circuits when faults occur at backward external zone, internal zone, and forward external zone are presented, respectively. Accordingly, the corresponding three fault voltage expressions are derived, which are defined as three fault modes. Then, the three fault modes are used to approximate the measured fault voltage using Levenberg-Marquardt optimal approximation method. The fault mode that best fits the measured fault voltage is recognized as the final determined fault mode, which is used for fault identification without setting threshold value. Numerous test studies carried out in Power Systems Computer Aided Design/Electromagnetic Transients including DC (PSCAD/EMTDC) and real-time digital simulator have demonstrated that the proposed method can be utilized for different multiterminal dc systems and is effective under different fault locations, different fault types, and high fault impedances. The proposed method does not require high sampling frequency and has good robustness against measuring noise.
13 citations
TL;DR: In this article , the authors proposed a line parameter-based fault detection technique for dc microgrid systems, which is based on estimating the resistance and detecting the fault even during high-resistance fault case.
Abstract: Evaluation toward the line parameter-based fault detection technique is an attractive option due to the limitations associated with the conventional differential and overcurrent protection schemes, mostly during a high-resistance fault situation. The fault detection strategy based on the line parameter can be exploited using the resistance instead of directly taking the decision from voltage and current. In this article, the proposed technique is focused on estimating the resistance and detecting the fault even during high-resistance fault case. Resistance is estimated using the local measurements available at the bus. After that, the sign of the estimated resistance is compared at both ends of the line segment to detect the fault and the use of the sign of resistance eliminates the time synchronization issues. The proposed method is capable of detecting the fault in radial as well as ring configuration dc microgrid. A ring main dc microgrid system is simulated using the EMTDC software to validate the proposed technique. The performance of the proposed approach is also validated using a dc microgrid hardware setup and tested for numerous situations. The results obtained from simulation and experiment verify that the proposed method accurately detects the faults (close in fault, high-resistance fault) in the dc microgrid.
13 citations
TL;DR: In this article , an adaptive fault diagnosis network framework is proposed to solve the equipment fault diagnosis problems with new fault types under multiple working conditions, which consists of a multi-scale feature extractor, adaptive fault discriminator, and a new fault cluster.
TL;DR: An adaptive order-band energy ratio method, an enhanced version of the SER, to quantitatively and intelligently diagnose gear faults under different operational conditions and enables an improved ability to monitor a PG’s health in the actual complex environment is proposed.
TL;DR: In this paper , the authors proposed an open-circuit fault diagnosis method in power semiconductors of three-level neutral-point-clamped (3L-NPC) rectifiers by using a fault injection strategy.
Abstract: This article proposes an open-circuit fault diagnosis method in power semiconductors of three-level neutral-point-clamped (3L-NPC) rectifiers by using a fault injection strategy. To enhance the anti-interference ability of the diagnosis method, an improvement is made in the traditional calculation method of phase-to-phase pole voltage residuals by selectively adopting the quantifying operation according to the current level. Then, the normalized and quantified residuals are obtained as the diagnosis variables. All signals for calculations are derived from the controller and pre-existing sensors, avoiding the requirement of extra hardware. To improve the diagnosis accuracy and speed, a fault injection strategy is designed. A preidentification method provides guidance for choosing the type of fault to inject. On this basis, the fault injection is implemented by revising the switching signals sent from the modulation module for a short time. In this way, inner-switch faults and outer-switch faults are easier to be differentiated, and the misdiagnosis of simultaneous multiple-switch faults is avoided. Therefore, the proposed diagnosis method is accessible to detect single-switch faults as well as multiple-switch faults in 3L-NPC rectifiers more accurately and fast. The experiments are carried out to confirm the effectiveness and the robustness of the proposed fault diagnosis method.
TL;DR: This work proposes a novel algorithm equipped with a fault indicator module stemming from the introduction of an automatic threshold and both deterministic and probabilistic fault indicators, thus offering a complete, valuable tool for supporting decision making with limited human intervention.
TL;DR: In this article , the authors proposed a scheme to detect broken bar faults and discriminate the severity of faults under starting conditions, where successive variable mode decomposition (SVMD) is applied to analyze the stator starting current to extract the fault component, and the signal reconstruction is proposed to maximize the energy of the faulty component.
Abstract: When an induction motor is running at stable speed and low slip, the fault signal of the induction motor’s broken bar faults are easily submerged by the power frequency (50 Hz) signal. Thus, it is difficult to extract fault characteristics. The left-side harmonic component representing the fault characteristics can be distinguished from power frequency owing to V-shaped trajectory of the fault component in time-frequency (t-f) domain during motor startup. This article proposed a scheme to detect broken bar faults and discriminate the severity of faults under starting conditions. In this scheme, successive variable mode decomposition (SVMD) is applied to analyze the stator starting current to extract the fault component, and the signal reconstruction is proposed to maximize the energy of the fault component. Then, the quadratic regression curve method of instantaneous frequency square value of the fault component is utilized to discriminate whether the fault occurs. In addition, according to the feature that the energy of the fault component increases with the fault severity, the energy of the right part of the fault component is proposed to detect the severity of the fault. In this paper, experiments are carried out based on a 5.5 kW three-pole induction motor. The results show that the scheme proposed in this paper can diagnose the broken bar faults and determine the severity of the fault.
TL;DR: Three intelligent approaches for fault detection, classification and location in a three-terminal VSC-HVDC system based on ANNs, although each one has a specific pre-processing step to process the DC current samples.
TL;DR: In this article , a faulty feeder detection method based on transient energy and cosine similarity was proposed, and the fault possibility of each feeder can be quantitatively estimated by calculating the cumulative density function.
Abstract: When a single-phase-to-ground (SPG) fault occurs in distribution networks, the existing faulty feeder detection techniques do not present reliable detection results. This paper introduces a novel faulty feeder detection method based on transient energy and cosine similarity. First, the fault characteristics of transient zero-sequence current (TZSC) are analyzed. It is found that the fault transient characteristics of transient components decrease as the grounding resistance increases. Second, to fully maintain the fault transient features, a fundamental frequency component (FFC) shifting method is introduced to remove FFCs. Next, the transient currents without containing FFCs are utilized to generate two fault indicators: transient energy within the first half power cycle and cosine similarity after the first half power cycle. After that step, to effectively merge the two fault indicators, the Laplace distribution is utilized. The fault possibility of each feeder can be quantitatively estimated by calculating the cumulative density function (CDF). Finally, the feeder with the maximum fault possibility is determined as the faulty feeder. The effectiveness and applicability of the proposed detection method are verified in the radial distribution network and the modified IEEE 34-bus distribution network. The results of the field test also indicate the feasibility of the proposed SPG detection method.
TL;DR: In this article , a new machine learning-based fault location method is proposed for use regardless of fault characteristics and DG performance using recorded data of micro-PMUs during a fault.
Abstract: Faults in distribution networks occur unpredictably, causing a threat to public safety and resulting in power outages. Automated, efficient, and precise detection of faulty sections could be a major element in immediately restoring networks and avoiding further financial losses. Distributed generations (DGs) are used in smart distribution networks and have varied current levels and internal impedances. However, fault characteristics are completely unknown because of their stochastic nature. Therefore, in these circumstances, locating the fault might be difficult. However, as technology advances, micro-phasor measurement units (micro-PMU) are becoming more extensively employed in smart distribution networks, and might be a useful tool for reducing protection uncertainties. In this paper, a new machine learning-based fault location method is proposed for use regardless of fault characteristics and DG performance using recorded data of micro-PMUs during a fault. This method only uses the recorded voltage at the sub-station and DGs. The frequency component of the voltage signals is selected as a feature vector. The neighborhood component feature selection (NCFS) algorithm is utilized to extract more informative features and lower the feature vector dimension. A support vector machine (SVM) classifier is then applied to the decreased dimension training data. The simulations of various fault types are performed on the 11-node IEEE standard feeder equipped with three DGs. Results reveal that the accuracy of the proposed fault section identification algorithm is notable.
TL;DR: In this paper , three intelligent approaches for fault detection, classification and location in a three-terminal VSC-HVDC system are compared, and the proposed approaches are all based on ANNs, although each one has a specific pre-processing step to process the DC current samples.
TL;DR: In this paper , the authors proposed an online fault diagnostic method to detect high-resistance connection (HRC) and open-phase faults (OPF) in six-phase permanent magnet machine drives.
Abstract: Even though multiphase machines have inherent fault-tolerant capabilities due to their additional degrees of freedom, fault diagnosis still plays an important role to continuously monitor these machines and, thus, prevent critical failures. High-resistance connection (HRC) is one of the most common electric faults in electrical machines and is caused by damaged power connections. These faults create a current unbalance, increasing both the machine losses and torque pulsations, and can eventually lead to open-phase faults (OPFs). This article proposes an online fault diagnostic method to detect HRCs and OPFs in six-phase permanent magnet machine drives. The proposed method is intended for application in electric drives controlled by a predictive current control strategy and does not require the installation of extra hardware. Two variants of the fault diagnostic algorithm are presented in this work: one based on the analysis of the secondary components of the reference current errors and the other variant based on the prediction current errors. Experimental results demonstrate the effectiveness and sensitivity of the proposed diagnostic method for the detection of these types of fault.
TL;DR: In this article , an improved fault location method with exact distributed parameter line model and sparse estimation is proposed for power networks, which works for faults within two or three-terminal lines of a power network, and only requires limited PMU placement.
TL;DR: In this article , an online ITF diagnosis method of induction motors is proposed by utilizing the negative sequence current as a fault signal based on the fault model of the previous study in part I.
Abstract: This paper (Part II) is a follow-up paper to our previous work on developing induction motor inter-turn fault (ITF) models (Part I). In this paper, an online ITF diagnosis method of induction motors is proposed by utilizing the negative sequence current as a fault signal based on the fault model of the previous study in part I. The relationships among fault parameters, negative sequence current, and fault copper loss are analyzed with the ITF model. The analyses show that the fault severity index, a function of fault parameters, is directly related to the negative sequence and the copper loss. Therefore, the proposed model-based fault diagnosis method estimates the fault severity index from the negative sequence current and recognizes the ITF. With the estimated fault severity index, the fault copper loss by the ITF, causing thermal degradation, can be calculated. Finally, experiments were performed in various fault conditions to verify the proposed fault diagnosis method.
TL;DR: In this paper , a single-ended fault identification algorithm using a closed-form parametric model of the fault transient behavior is presented. But the model is not suited due to the transmission delays.
Abstract: The protection of meshed HVDC grids requires the fast identification of faults affecting the transmission lines. Communication-based methods are thus not suited due to the transmission delays. Many approaches involving a model of the transient behavior of the faulty line have recently been proposed. Nevertheless, an accurate description of the traveling wave phenomenon in multi-conductor lines such as overhead lines requires complex computations ill-suited for fast fault identification. This paper presents a single-ended fault identification algorithm using a closed-form parametric model of the fault transient behavior. The model combines physical and behavioral parts and depends explicitly on the parameters that characterize the fault, namely the fault distance and impedance. When a fault is suspected, the fault parameters are estimated so that the model fits best the received measurements. The confidence region of the estimated fault parameters is used to decide whether the protected line is actually faulty or not. The proposed algorithm is tested on a 4 station grid simulated with EMTP-RV software. The method is able to identify the faulty line using a measurement window of less than 0.5 ms. This allows ultra-fast fault clearing and can hence improve the overall reliability of future HVDC grids.
TL;DR: In this article , two methods for fault detection and isolation of the faulty segment through the line and bus voltage measurement were discussed, and two algorithms with their corresponding MATLAB/SIMULINK platforms were developed.
Abstract: Fault detection and isolation are important tasks to improve the protection system of low voltage direct current (LVDC) networks. Nowadays, there are challenges related to the protection strategies in the LVDC systems. In this paper, two proposed methods for fault detection and isolation of the faulty segment through the line and bus voltage measurement were discussed. The impacts of grid fault current and the characteristics of protective devices under pre-fault normal, under-fault, and post-fault conditions were also discussed. It was found that within a short time after fault occurrence in the network, this fault was quickly detected and the faulty line segment was efficiently isolated from the grid, where this grid was restored to its normal operating conditions. For analysing the fault occurrence and its isolation, two algorithms with their corresponding MATLAB/SIMULINK platforms were developed. The findings of this paper showed that the proposed methods would be used for microgrid protection by successfully resolving the fault detection and grid restoration problems in the LVDC microgrids, especially in rural villages.
TL;DR: In this paper , the authors proposed a fault detection and location estimation method with the help of extreme gradient boost (XGBoost) algorithm for IEEE 14 bus transmission network, which reduced the relaying time associated with detection and classification of fault along with accurate fault location estimation.
TL;DR: In this article , an OC fault diagnostic method of inverters in closed-loop controlled PMSM drive systems based on current behavior was presented, which has the ability to detect the fault within one eighth of a current cycle.
TL;DR: Wang et al. as mentioned in this paper proposed a method of single-phase grounding fault feeder detection based on improved K-means power angle clustering analysis, which collected various fault characteristic quantities of each feeder under different operation and fault conditions, and established a historical sample database.
TL;DR: In this paper , a fault protection diagnostic scheme for a power distribution system is proposed, which comprises a wavelet packet decomposition (WPD) for signal processing and analysis and a support vector machine (SMV) for fault classification and location.
Abstract: In this paper, a fault protection diagnostic scheme for a power distribution system is proposed. The scheme comprises a wavelet packet decomposition (WPD) for signal processing and analysis and a support vector machine (SMV) for fault classification and location. The scheme is tested on a reduced Eskom 132 kV power line. The WPD is used to extract fault signatures of interest and the SVM is subsequently used for fault classification and locating various fault conditions. Furthermore, we investigate the effectiveness of the SVM scheme using different samples of the cycles for fault classification and location. The results show that the fault classification and location on a distribution line can be determined rapidly and efficiently irrespective of the fault impedance and incipient angle with minimum estimation error. Lastly, the proposed scheme is tested on a grid-integrated system with renewable energy sources.
TL;DR: In this paper , an arc fault detection and identification method via supervised non-intrusive current disaggregation for lowvoltage consumers in the service entrance is proposed, where a NILM module initially identifies the appliances in operation in the residential household while an arc detection module screens the simultaneous total current signal for an arc defect.
TL;DR: In this article , a fault location algorithm is presented for active distribution systems using microphasor measurement units (μPMU) and smart meter (SM) data, where the line current data obtained from the μPMUs in the DS are exploited to divide the system into different zones.
Abstract: 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.