Showing papers on "Fault indicator published in 2023"

Fault Location in Distribution Network by Solving the Optimization Problem Based on Power System Status Estimation Using the PMU

Abstract: Fault location is one of the main challenges in the distribution network due to its expanse and complexity. Today, with the advent of phasor measurement units (PMU), various techniques for fault location using these devices have been proposed. In this research, distribution network fault location is defined as an optimization problem, and the network fault location is determined by solving it. This is done by combining PMU data before and after the fault with the power system status estimation (PSSE) problem. Two new objective functions are designed to identify the faulty section and fault location based on calculating the voltage difference between the two ends of the grid lines. In the proposed algorithm, the purpose of combining the PMU in the PSSE problem is to estimate the voltage and current quantities at the branch point and the total network nodes after the fault occurs. Branch point quantities are calculated using the PMU and the governing equations of the π line model for each network section, and the faulty section is identified based on a comparison of the resulting values. The advantages of the proposed algorithm include simplicity, step-by-step implementation, efficiency in conditions of different branch specifications, application for various types of faults including short-circuit and series, and its optimal accuracy compared to other methods. Finally, the proposed algorithm has been implemented on the IEEE 123-node distribution feeder and its performance has been evaluated for changes in various factors including fault resistance, type of fault, angle of occurrence of a fault, uncertainty in loading states, and PMU measurement error. The results show the appropriate accuracy of the proposed algorithm showing that it was able to determine the location of the fault with a maximum error of 1.21% at a maximum time of 23.87 s.

Two Terminal Instantaneous Power-Based Fault Classification and Location Techniques for Transmission Lines

Abstract: Transmission lines are an important part of the power system, as they are the carriers of power from one end to another. In the event of a fault, the power transferring process is disturbed and can even damage the equipment, which is attached to the generation end as well as the user end. Most of the power systems are connected to the transmission lines, so it is very important to make the transmission lines secure. For protection purposes, relays are used, but relays only trip in the event of a fault and do not tell us about the location of the fault. The power system requires a speedy protection system. For a speedy protection system, quick and fast fault analysis and classification are required. An effective approach for the analysis of the transmission line with three sources is proposed. This method is quite effective and accurate for locating the fault and classifying its types. This technique needs power measurement from both ends simultaneously for fault diagnosis. Instantaneous power and sign power values are used for fault detection and classification. A voltage profile is used to identify the fault location. For three-phase transmission lines, voltage profiles are built up at different segment points to locate the fault. The IEEE-9 bus system is simulated for this technique. MATLAB is employed for simulation purposes. The test system is simulated with different types of faults at different locations. Relay operation has not affected the accuracy of the system. This technique has an accuracy of more than 97%. This method is quite effective for the analysis of power transmission lines. It can discriminate the fault type, identify the faulty phase of the line, and locate the point of the fault. Faults are located with errors not more than 0.45%. Moreover, the time difference between the actual fault and the calculated fault obtained from the estimated location is not more than 0.004 s. Simulations are claimed to be executed in less computational time, ensuring effective and rapid protection against faults.

Estimating the Error of Fault Location on Overhead Power Lines by Emergency State Parameters Using an Analytical Technique

Abstract: Fault location on overhead power lines achieved with the highest possible accuracy can reduce the time to locate faults. This contributes to ensuring the stability of power systems, as well as the reliability of power supply to consumers. There are a number of known mathematical techniques based on different physical principles that are used in fault location on overhead power lines and whose errors vary. Fault location on overhead power lines uses techniques based on the estimation of emergency state parameters, which are referred to as distance-to-fault techniques and are widely used. They are employed in digital protection relay terminals and power-line fault locators. Factors that have a significant impact on the error of fault location on overhead power lines by emergency state parameters are design, manufacturing, and operation. The aim of this article is to analyze the existing techniques and to present a new analytical technique for estimating errors of fault location on overhead power lines by using emergency state parameters. The technique developed by the authors makes it possible to properly take into account a set of random factors, including various measurement errors of currents and voltages in the emergency state, which have a significant impact on the fault location on overhead power lines error. The technique allows one to determine more accurately the fault location and the size of the inspection area, which is necessary to reduce the time it takes to carry out emergency recovery operations. The proposed technique can be applied in fault locators and digital protection relay terminals that use both single-end, double- and multi-end sensing of currents and voltages in the emergency state.

Advanced fault location scheme for superconducting cables based on deep learning algorithms

Abstract: This paper addresses the challenge of fault localization in power grids which incorporate Superconducting Cables (SCs) and presence of inverter-connected generation, by proposing a novel data-driven fault location scheme. The developed fault location algorithm utilizes the transformation of time domain fault current and voltage signatures, to time–frequency domain and exploits the advantages of a Convolutional Neural Network (CNN) to estimate the fault position along SCs. The proposed algorithm has been tested using a verified model of SC, and the results revealed that it can provide precise fault localization for a wide range of fault scenarios, including different fault types, fault resistance values and fault inception angles. Furthermore, the proposed scheme robustness has been verified against different influencing factors accounting for very small increments of fault location, additive noise and different value of sampling frequency. For validation purposes, the effectiveness of the CNN-based algorithm has been compared with other data-driven algorithms and the relevant advantages have been highlighted.

An Online Metric Learning-Based Open-Switch and Current Sensor Fault Diagnosis for MSPMSM Systems

Abstract: Rapid and accurate removal of faulty sectors is the conventional fault tolerance method for highly secure multi-sector permanent magnet synchronous motors (MSPMSMs). In this paper, a new fault diagnosis method for MSPMSMs based on metric learning (ML) is proposed. Its advantage is that it can complete fault detection and faulty sector location within 0.1 current fundamental cycle, which is faster than previous methods. Meanwhile, for the faulty sector, 28 types of open switch faults and current sensor faults can be accurately identified. In addition, since the fault diagnosis is based on structural characteristics, the proposed method is independent of system parameters and naturally robust to the speed and current variation. Particularly, using the consistent structural characteristics of MSPMSM sectors, an amplitude-trend distance (ATD) is presented to obviously reflect the difference of the same phase currents between healthy and faulty sectors. Subsequently, to ensure the speed of fault detection and high false alarm immunity, the ATD value under different fault states is theoretically analyzed, and a reasonable threshold is set. Compared to traditional ML methods, which requires complex computational classification methods, four characteristic parameters sector, polar, close to zero (CTZ), and sensor flag (SF) are developed to locate the faulty sector and the faulty switch or current sensor. The experiment results on four-sector PMSM verify the effectiveness of the proposed fault diagnosis method.

SILOS: An Intelligent Fault Detection Scheme for Solar Insecticidal Lamp IoT With Improved Energy Efficiency

Abstract: Solar Insecticidal Lamp Internet of Things (SIL-IoT) nodes are susceptible to failures due to the harsh environment, burn-in, theft, and vandalism in the agricultural setting. Most state-of-the-art research mainly focuses on fault detection without considering hardware and communication performance in terms of potential fault modes, energy consumption, and network loads. This study presents a completely decentralized solution, namely a SIL-oriented binary sliding window-based fault self-detection scheme (SILOS), which can be performed on each SIL-IoT node. The problem we are trying to answer is how to detect faults as accurately as possible while keeping the communication overhead, memory, and computational costs low. Specifically, we develop a fault dictionary concept to 1) model the faults of SIL-IoT nodes; 2) construct the fault dictionary according to the characteristics of measurements; and 3) detect faults via the fault dictionary. In addition, a binary-based sliding window (BSW) fault self-detection approach is proposed to save detection costs and reduce the false alarm rate (with only 92 B system caches). A series of experiments are performed to evaluate the performance of the proposed method. The result demonstrates that the BSW method can detect faults with an average accuracy of 99.14% with less than 1% energy consumption. By only sending the fault code, 71-B data (i.e., data transmitting and forwarding) can be reduced, saving energy consumption, and decreasing network congestion.

Differential equation fault location algorithm with harmonic effects in power system

Abstract: About 80% of faults in the power system distribution are earth faults. Studies to find effective methods to identify and locate faults in distribution networks are still relevant, in addition to the presence of harmonic signals that distort waves and create deviations in the power system that can cause many problems to the protection relay. This study focuses on a single line-to-ground (SLG) fault location algorithm in a power system distribution network based on fundamental frequency measured using the differential equation method. The developed algorithm considers the presence of harmonics components in the simulation network. In this study, several filters were tested to obtain the lowest fault location error to reduce the effect of harmonic components on the developed fault location algorithm. The network model is simulated using the alternate transients program (ATP)Draw simulation program. Several fault scenarios have been implemented during the simulation, such as fault resistance, fault distance, and fault inception angle. The final results show that the proposed algorithm can estimate the fault distance successfully with an acceptable fault location error. Based on the simulation results, the differential equation continuous wavelet technique (CWT) filter-based algorithm produced an accurate fault location result with a mean average error (MAE) of less than 5%.

Fault-Tolerant and Self-Reliant Characteristic in Series Resonant Converter for Semiconductor Open/Short-Circuit Faults

Abstract: With the increased popularity of series resonant converter (SRC) in grids and microgrids, a robust converter with fault-tolerant (FT) feature becomes supremely important. A single-switch primary-side failure results in half of the pre-fault voltage and lesser power at the output. While a secondary-side short-circuit (SC) fault results in negligible voltage and power, leading to discontinuous converter operation. For more than one switch fault on any side, the SRC behavior in the post-fault condition is different. This article analyses the self-reliant characteristic in SRC for single/two-switch open-circuit (OC) and SC fault. A post-fault correction is proposed, which maintains the continuity of operation and the rated output voltage at the load for different fault conditions. An FT capacitor is used along with the control parameter variation in the post-fault correction for a single-switch and different combinations of two-switch faults. The proposed method is also applicable for single-diode SC or OC fault. The analysis of the self-reliant feature and the post-fault correction is discussed and tested on a 1 kW, 250 V SRC prototype for different fault conditions.

Power Grid Fault Localtion Method Based on Graph Neural Network

Abstract: With the improvement of the interconnection degree of the power grid and the massive grid-connected operation of new energy, the fault characteristics of power grid have changed. After the fault occurs, quickly locating the fault components will help improve the fault handling efficiency of the dispatchers. In this paper, a graph neural network used for fault component identification is designed. The fault label is classified and marked in multiple levels to facilitate the focus identification of fault components. The empirical constraints of dispatchers are integrated into the loss function to further improve the accuracy of fault identification. An IEEE30 example is used for simulation verification. The results show that the fault element identification method based on graph neural network proposed in this paper has the advantages of high accuracy and strong resistance to interference.

Review on Fault Detection and Classification in Transmission Line using Machine Learning Methods

Abstract: Fast and precise fault categorization, location estimate, and fault detection are crucial because persistent faults can interrupt the power supply. The power outage zone will extend to nearby areas after the fault incident. Accurate and prompt fault identification is required for a power system to return to a healthy state. Protection, fault detection, diagnosis, identification, and localization are essential for efficient working of power system. Transmission line(TL) extensions are necessary due to rising industrialization and power demand, which greatly increases the complexity of the power system network. Analysis of faults in this intricate network becomes challenging. This paper reviews the latest machine-learning methods used for the identification and classification of faults in power systems.

A Fault Tolerance Method for Multiple Current Sensor Offset Faults in Grid-Connected Inverters

Abstract: Three-phase grid-connected inverters have been widely used in the distributed generation system, and the current sensor has been applied in closed-loop control in inverters. When the current sensor offset faults occurs, partial fault features of multiple current sensors disappear from the closed-loop control grid-connected system, which leads to difficulties for fault diagnostics and fault-tolerant control. This paper proposes a fault tolerance method based on average current compensation mode to eliminate these adverse effects of fault features. The average current compensation mode compensates the average of the three-phase current to the αβ axis current to realize the fault feature reconstruction of the current sensor. The mode does not affect the normal condition of the system. Then, the data-driven method is used for fault diagnosis, and the corresponding fault tolerant control model is selected according to the diagnosis results. Finally, the experimental results show that the proposed strategy has a good fault tolerance control performance and can improve the fault feature discrimination and diagnostic accuracy.

Study of influence of transient resistance on distance fault location based on one-sided method

Abstract: Fault location on 110–220 kV overhead power lines is one of the main functions of modern relay protection devices. Currently, the actual errors of fault location based on emergency mode (EM) parameters in most cases are about 5 %. However, there are cases when they exceed 10–20 %. One-sided fault location based on EM parameters and improvement of its accuracy are important issues to study since there isn’t a communication channel on power lines for the transmission of emergency information everywhere. The main parameter that has a significant impact on the accuracy of fault location method is the transient resistance. The aim of the research is to study the effect of transient resistance on the one-sided fault location method based on EM parameters proposed by A.E. Arzhannikov. In the course of the study, the following tasks are set: assessment of the effect of transient resistance on the accuracy of the specified fault location method; determination of the polarizing value for fault location, which ensures greater accuracy of the method, including the presence of transient resistance at the place of a short circuit; defining a criterion to recognize the type of short circuit that does not depend on the transient resistance; development of a method to determine the value of the transient resistance at the fault location. To study and evaluate the errors of distance fault location based on EM parameters, a series of calculations of short circuit currents has been made for various transient resistances at the fault location and at various distances of the fault. The calculation of currents and data processing has been carried out in the ARM SRZA software package. A single overhead line with a voltage of 110 kV with a two-sided supply and a length of 70 km is chosen as the object under study. To perform the research, the primary converters are taken as ideal. The authors have obtained the estimation of the errors of the one-sided fault location method. It is proposed to use the zero-sequence current as a polarizing quantity in the indicated fault location method, which provides greater accuracy than the use of the negative sequence current. To ensure the stable operation of one-sided fault location based on EM parameters, especially in case of short circuit with significant transient resistances, the use of methods that are insensitive to transient resistances is justified. A parameter to identify the type of short circuit is proposed, the coefficient of the image Kobr. Its values are determined for each type of short circuit, a small dependence on transient resistances is found. A calculation substantiation is given for the value of the identification coefficient of two-phase faults (Phase-to-Phase and Phases-to-Earth) according to the ratio of negative and zero sequence currents. The authors have estimated the transient resistance at the fault site by the ratio of symmetrical components for the simulated power transmission line. The results of the study can be used to improve the existing methods of fault location based on EM parameters, namely: to improve their accuracy considering the transient resistance at the fault site; to improve their accuracy using a suitable polarizing value; to determine the type of short circuit more accurately by using the proposed identification parameter.

An intelligent technique for fault detection and localization of three-level ANPC inverter with NP connection for electric vehicles

Abstract: An intelligent technique for detecting and localizing an inverter switch fault or phase fault of a Three-Level Active Neutral Point Clamped (ANPC) inverter is proposed in this research. Moreover, a 3L-ANPC inverter can gain the controllability of EV's power train and not need to be stalled even after the occurrence of the fault. Hence, an efficient fault diagnosis methodology is required to identify the type of phase fault by a Support Vector Machine (SVM), a machine learning model consisting of sets of labeled training data with regression and classification challenges. Finally, when the fault occurs, the location of the switch fault can be identified by a Deep Neural Network (DNN), which consists of layers of neurons between the input and output layers which fuses the feature extraction process with increased accuracy. Thus, the detection and localization of the open-circuit fault of the switches in the ANPC inverter help overcome all single faults, hence gaining its current controllability without stopping the vehicle. The accuracy of fault detection is improved in a precise manner. Finally, the performance of the proposed work is evaluated over other conventional models concerning varied metrics like the accuracy of identification and localization.

A Sensor Fault Diagnosis Method for Rectifier Used in Traction Systems

Abstract: Reliable fault diagnosis methods are necessary to ensure the safety of traction systems. Sensor faults are common in rectifiers, they can cause the feedback signal deviation and lead to security risks. This paper proposes a fault diagnosis method for grid-side current and DC-link voltage sensor faults in rectifiers, which can not only locate the faults but also identify fault types. First, the topology is introduced, the model is built. Then, the grid current residual is calculated and analysed with different sensor faults. Next, the residual is applied to fault detection. Diagnosis functions based on characteristics of the current and the residual are constructed, faults can be located and fault types can be identified. Finally, a simulation testing platform based on MATLAB is built, the correctness and effectiveness of this method can be verified by simulation results.

Transmission line fault analysis using actuating relays and Atmega 328P controller

Abstract: Since transmission lines account for 80–86% of power system problems, protecting transmission lines is a critical concern. This research proposes a method to identify and categorize various short circuit defects on transmission lines. Various operational and fault circumstances on the transmission of high voltage lines, including L-L fault, single phase to earth fault, and double line fault, double line Three fault scenarios triple line fault, triple line to ground fault, and triple line are simulated using MATLAB software. These flaws damage the linked devices to the power grid. The main motto of this work is to analyze various kinds of faults and also to detect the type of the fault in transmission line. In order to simulate and evaluate the various faults, a 100 km transmission line model was created. A sim power system block library had a fault block, and the harmonic content of the various faults was studied using the FFT tool, the effect of faults was observed. Based on the analysis, a hardware model is designed to identify the faults in transmission line using actuating relays and Atmega 328P microcontroller and display the fault type and parameters like fault impedance, and distance at which fault occurred.

Fault Sensing of the Distribution Cable Feeders by Time-domain Measurements

Abstract: Power distribution cable feeders are prone to faults due to the poor working environment and internal defects. Considering the structure and electrical parameters of the typical distribution three-core cable, a fault sensing method is proposed. The fault sensing in this paper includes three aspects: fault cable detection, fault phase identification and fault resistance estimation. The grounding line currents of cable feeders are firstly analyzed in different neutral grounding modes. Based on the amplitudes and directions of the grounding line currents, a fault feeder detection criterion is then presented. Finally, based on the equivalent circuit of the fault cable, an algorithm for identifying the fault phase is developed by estimating the fault resistance. The experiment model of the distribution cable feeders was created by RTDS. Various fault experiments were carried out. The results show that the neutral grounding modes and fault conditions have little effect on the proposed method.

Real-time open-circuit fault diagnosis method for T-type rectifiers based on median current analysis

Abstract: T-type rectifiers are widely used in DC fast chargers, super uninterrupted power supplies, renewable energy, and other low-voltage and medium-voltage rectifier applications. High equipment reliability and high-quality power are often required in these applications but are damaged by the transistor open-circuit fault. This paper proposes a novel real-time open-circuit fault diagnosis method for the T-type rectifier based on median current analysis. The method only uses the three-phase currents to obtain the median current and realize the diagnosis. In the rectifier, partial open-circuit faults result in weak fault signals, making the diagnosis challenging. To solve this, the median current obtained by the currents performs fault frequency modulation to enhance the fault signals at first. Then, the ratio of the fundamental frequency to the dc component is used to realize fault detection. The inner/outer fault is identified at the same time. At last, the fault transistor is located by simple change point detection. The change points determine the fault phase and transistor fault. The effectiveness and robustness of the proposed method are verified by the experiment results.

Research on fault diagnosis and comprehensive suppression function of airborne system

Abstract: At present, the aircraft fault decision-making function only deals with a single fault, but the aircraft fault has concurrency. The existing aircraft fault decision-making function lacks the ability to deal with multiple fault concurrence situations. How to trace the source of multiple fault alarm information and excavate the original fault is of great significance for simplifying the alarm display and improving the pilot’s fault handling efficiency. In this paper, a fault diagnosis and comprehensive suppression function is designed, which consists of a fast fault location method based on prior knowledge and a comprehensive diagnosis and suppression method based on fault tree knowledge. The fast fault location method based on prior knowledge is based on case reasoning, which writes the past troubleshooting cases and many elements into the fault case base. When new faults occur, the matching degree of similar cases in the case base is obtained through retrieval model, so as to quickly obtain the current fault processing method. The comprehensive diagnosis method based on fault tree knowledge converts the fault tree into a binary decision diagram, and uses Huffman coding to realize computer programming. The probability of each cut set event in the binary decision graph is the probability product of its contained bottom event, so as to determine the risk degree of the failure to locate the cause of the failure. The fault sup-pression method classifies and processes the alarm information when multiple faults occur in a single system and multiple faults occur in multiple systems. The original fault and derivative fault are filtered by using the fault correlation value, the original fault is displayed, and the corresponding derivative fault is suppressed. The fault diagnosis and comprehensive suppression function of the aircraft airborne system designed in this paper provides sup-port for the development of the large aircraft alarm system.

Fault Diagnosis for Power Converters Based on Incremental Learning

Abstract: In practical fault diagnosis, the monitoring fault data is accumulated incrementally, it is necessary to detect the newly added fault data. To this end, this paper proposed a broad residual network (BRES) fault diagnosis method with incremental learning capability. Firstly, the deep feature representation of the raw data is obtained by the residual network, and the obtained features and corresponding labels are then updated to the BLS. For the newly collected data, the incremental learning of new fault modes is achieved by automatic feature extraction of the ResNet and the node expansion of the BLS. The effectiveness of the proposed method is verified by motor-driven converters fault diagnosis. Experimental results indicate that the method can effectively update the diagnosis model to incrementally learn new fault categories and new fault modes.

Robust Fault Location Method for Transmission Lines Using PMUs

Abstract: The random noise and abnormally greater numbers may affect the accuracy of traditional transmission line fault location. A robust fault location method for transmission lines based on PMUs (Phasor Measurement Unit) is proposed in this paper. The node voltage equations based on voltage fault components are adopted to establish the quadratic nonlinear overdetermined equations of fault location, only using limited PMUs adjacent to the faulted line. In order to quantify the impact of measurement errors, the residual errors are introduced into the fault location equations. Levenberg-Marquardt (L-M) algorithm has been used to solve the equations. Then the original fault location data at each time after the occurrence of one cycle is obtained. A filtering method is proposed to filter the possible interference such as noise and abnormally greater numbers in fault location sequence. The abnormal data that deviating from the mean can be detected by the probability distribution and replaced by smooth filtering data. The simulation results on IEEE 39-bus systems show that proposed algorithm can effectively improve the location accuracy, is not affected by the fault location, transition resistance, fault type, and has strong anti-interference ability and good robustness.

Research on Open Circuit Fault Location and Fault Tolerant Control of Inverter Power Transistor

Abstract: As the key equipment of power conversion, the stability of the inverter directly affects the working state of the equipment. Aiming at the open circuit fault of the power tube, this paper designs a fault diagnosis and fault tolerance scheme of a three-phase bridge inverter based on the current signal. Firstly, the amplitude and phase characteristics of the current vector are used to judge the fault and fault location of the power tube. Then the output fault characteristic signal is converted into a fault-tolerant control signal. The fault leg is switched into a fault-tolerant leg under fault-tolerant control of the inverter bridge. The simulation results show that the diagnosis method proposed in this paper can judge the health and fault status of the inverter with high accuracy and achieve fault tolerant control, improving the stability of the inverter.

Researched on Fault Location of Aircraft Distribution Lines Based on the Petri Net and Precise Integration

Abstract: Aiming at the problem that the distributed power grid is embedded in the power distribution system, which leads to the difficulty of system fault location, this paper proposes a line fault analysis mode based on petri nets. preprocessing. On this basis, the double-ended electrical signal of the faulty line is collected as the input signal of the fine integration method. Through the four-level Longo-Kuta iterative calculation, some and only the voltage values of the line at the fault point are equal, so as to realize the voltage value of the fault point. accurate locating. The fine integral fault location method based on petri net is feasible. In order to verify the fault location capability of this method, this paper establishes a single-phase line model with distributed power sources on the MATLAB platform, sets up ground faults, and conducts simulations. The simulation results show that the distribution network fault location model can accurately locate the line when the ground fault occurs in the system; at the same time, when the fault information is incomplete, the fault line can also be determined by the petri network fault model, and the precise integration method can achieve accurate location.