Types Of Short Circuit Faults Research Articles

Electromagnetic Signal Analysis for Electrical Fault Diagnosis in Synchronous Generators

This study investigates the enhancement of fault diagnostics in synchronous generators by incorporating electromagnetic signal analysis with conventional diagnostic methodologies. The critical role of synchronous generators in maintaining power system stability and efficiency in industrial and power plant environments is underscored. Finite element modeling (FEM) is employed to simulate various fault conditions, such as stator and rotor winding faults. This study proposes a method that integrates stator current and stray magnetic flux analysis to identify five distinct types of short-circuit faults in stator and rotor windings, thereby enhancing the diagnostic capabilities for electrical faults in synchronous generators. This approach successfully identifies these electrical faults using non-invasive methods, offering a cost-effective solution that enhances fault detection. These findings are based on simulation results and serve as a preliminary stage for further validation through experimental studies. This integration is crucial for the development of efficient diagnostic systems that are capable of adapting to complex fault patterns, reducing human intervention, and streamlining maintenance operations, thus improving the reliability of synchronous generators globally.

Risk analysis of lithium battery energy storage systems under typical failures

Abstract With the rapid increase in the proportion of new energy installed capacity, to solve the problem of new energy output volatility, lithium-ion battery energy storage has developed rapidly by its electrical characteristics and economic advantages and has become a hot spot for the large-scale application of electrochemical energy storage, but it is also accompanied by safety problems such as battery short-circuit and thermal runaway. Based on the typical structure of the lithium battery energy storage system, this paper establishes a complete simulation model of the lithium battery energy storage system, calculates the change rule of battery system electrical parameters inside the battery module under different types of short-circuit faults, and summarizes the fault characteristics and risks of lithium battery energy storage system under different faults. The study shows that short-circuit faults inside the battery module will cause significant fluctuations in the terminal voltage and current of the battery system. The internal short-circuit faults will increase the inter-cluster circulating current. In contrast, the larger short-circuit current will not only cause the local battery cells to overcharge and over-discharge but also trigger the thermal runaway of the battery system.

Research on fault detection and remote monitoring system of variable speed constant frequency wind turbine based on Internet of Things

In order to study the operating characteristics of variable speed constant frequency wind turbine under different working conditions and the monitoring system of wind turbine. In this paper, the simulation model of each component system of wind turbine is established by MATLAB/Simulink module, and the influence law of different wind speed and ground fault types on the output power of wind turbine is studied. The active power of wind turbines under different short-circuit fault types is compared. At the same time, in order to realize real-time monitoring of wind turbine speed and output power, an online monitoring system for wind turbine operation based on industrial Internet of Things is proposed, and the composition and operation characteristics of this remote monitoring system are given. The practical application shows that the on-line monitoring system can accurately and remotely monitor the running status of the wind turbine and avoid the unstable running of the wind turbine. The research conclusions of this paper can provide reference for the design and construction of wind turbines and the operation of connecting to the power grid.

Transparent Grid Visualization Surveillance and Fault Warning in High Density Distributed Power Access Areas

Abstract Transparent grid, as an advanced form of “Internet + smart energy”, is of great value in supporting energy transformation and promoting the development of new power systems. Based on the grid connection requirements of high-density distributed power supply, this paper provides specific grid connection methods and trend calculations and explores the impact of grid connection on high-density distributed power supply. Subsequently, the technical framework and blueprint of the transparent grid are proposed, combined with the grid visualization system to monitor the grid state visually, and based on the random matrix single-loop theory and ARIMA algorithm to realize fault identification and early warning of the transparent grid. The analysis of the algorithm shows that the fault identification has good rapidity, feasibility, and practicability, and while ensuring that no fault alarm is issued in the case of normal disturbance of distributed power supply, it is able to issue fault alarms for all locations and types of short-circuit faults in the system and determine the fault moment within 0.1s. It is “fast”, “accurate,” and “stable” in the research and judgment of active distribution network faults, and it is more intuitive in the assignment of risk levels, and it can identify the categories and levels of faults and realize the following Visualization of early warning.

Learning the geometry of short‐circuit faults in power systems for real‐time fault detection and classification

AbstractGiven the short time intervals in which short‐circuit faults occur in a power system, a certain time delay between the moment of a fault's inception in the system to the moment in which the fault is actually detected is introduced. In this small time margin, the high amplitudes of the fault current can deal significant damage to the power system. A technique to characterise different types of short circuit faults in a power system for real‐time detection, namely AB, BC, CA, ABC, AG, BG and CG faults (and normal operation), is presented based on the geometry of the curve generated by the Clarke transform of the three‐phase voltages of the power system. The process was conducted in real time using the HIL402 system and a Raspberry Pi 3, and all programming done in the Python programming language. It was concluded that the tested types of faults can be accurately characterised using the eigenvalues and eigenvectors of the matrix that characterises an ellipse associated with each fault: eigenvalues can be used to determine the fault inception distance and eigenvectors can be used to determine the type of fault that occurred. Next, the design of a machine learning model was done based on the previously mentioned characterisation technique. The model was embedded into a Raspberry Pi 3, thus enabling fault detection and classification in a base power system in real time. Finally, the accuracy of the model was tested under different measurement conditions, yielding satisfactory results for a selected set of conditions and overcoming the shortcomings presented in the current research, which do not perform detection and classification in real time.

Broadband Modelling of Power Transformers for Sweep Frequency Impedance Studies on Winding Short-Circuit Faults

To study sweep frequency impedance (SFI) features of short-circuit (SC) faults easily, this paper proposes a broadband electric circuit model of a transformer winding and solves its three key problems. The first problem is the calculation of lumped-circuit parameters considering frequency-dependent complex anisotropic permeabilities (FDCAPs), which are caused by the physical characteristics, such as skin, proximity, and geometrical effects and anisotropic properties, of the transformer core and winding materials. The other issue is the establishment of the electric circuit model based on the SFI measurement connection mode, the transformer winding parameters, and a double-ladder network (DLN). Another issue is the construction of the state-space model of the electric circuit toward different SFI values to obtain all network branch voltages and currents. The accuracy of the proposed model is assessed by comparing its SFI signatures with those of the simulation model, without considering FDCAPs under healthy winding, and the corresponding physical transformer model during healthy winding and SC faults. It is observed that the SFI results of the proposed model are closer to the experimental measurements, and the model can be effectively used to study the SFI features of SC faults. Moreover, the impacts of different types of SC faults on the SFI data are concluded in this paper.

Deep learning techniques for transmission line fault classification – A comparative study

Despite advancements in technology, power system faults leading to electric power interruption remain a significant issue. Efficient restoration of the power system relies on the swift classification and clearance of faults. Among the various types of faults in transmission lines, open circuit and short circuit faults are commonly encountered. This study specifically focuses on the analysis of five types of short circuit faults: line-to-line, line-to-ground, double line-to-ground, triple line, and triple line-to-ground faults. Faults can cause both power failure and power loss in transmission lines. Once a fault occurs, it is crucial to restore electricity supply promptly to prevent further losses. Therefore, the development of a system capable of accurately and swiftly detecting and removing faults is essential. Traditionally, categorizing transmission line faults required sophisticated mathematical modeling, intricate signal processing techniques, and expert knowledge to interpret the output signals. In this paper, an alternative approach is proposed, utilizing deep learning techniques for transmission line fault classification. Specifically, the paper employs techniques such as artificial neural network (ANN), long short-term memory (LSTM), with and without window regression (WR). By implementing these deep learning techniques, automatic feature extraction and signal processing are achieved, streamlining the fault classification process. The experimental results obtained from this study demonstrate an accuracy of 42.98% for ANN, 99.98% for LSTM, and 99.99% for LSTM-WR. These outcomes underscore the effectiveness of the deep learning techniques employed in accurately classifying transmission line faults, with LSTM and LSTM-WR outperforming ANN in terms of accuracy.

A Novel Error-Correcting Particle Swarm Optimization Back Propagation Fault Diagnosis Method for Microgrid

Compared to traditional power grids, microgrids have a more flexible operating mode. There are various distributed power sources within the microgrid, and different types of distributed power sources have different control methods. Once a short-circuit fault occurs in the microgrid, these characteristics will increase the difficulty of microgrid fault diagnosis and reduce the accuracy of microgrid fault diagnosis. This paper proposes an error-correcting particle swarm optimization back propagation microgrid fault diagnosis method for the diagnosis of short-circuit faults in microgrids that identifies the accuracy of alarm signals, corrects unreasonable signals, and obtains the correct fault set of the microgrid through the temporal logic relationship between each protection. Using the particle swarm optimization back propagation (PSO-BP) neural network algorithm to train fault alarm signals, fast convergence can be achieved, and accurate diagnostic results can be obtained after the sixth generation training is completed. As this fault diagnosis algorithm is applied to line protection equipment, it can be used to diagnose all types of short-circuit faults. This algorithm is easy to implement and has a small data scale, which is conducive to efficient and concise fault diagnoses in microgrids.

Single fuzzy inference based fault detection and classification protection scheme for different types of short circuit faults on double circuit transmission lines

ABSTRACT An intelligent protection scheme based on the single fuzzy inference system and discrete Fourier transform is proposed for the faulty phase detection and classification of faults on the mutually coupled double circuit transmission lines. This proposed protection technique uses the magnitude of the pre-processed current information of the sending end bus only. This is implemented in the MATLAB/Simulink environment on a 400 kV, 50 Hz, and 300 km double circuit power system model. The efficacy of the proposed scheme has been tested by performing a wide range of simulation studies concerning different types of faults (common short circuit faults, cross-country and evolving faults, and high impedance faults) and typical fault scenarios (current transformer saturation, noisy environment, and fault during power swing conditions) with variations in different fault parameters and operating conditions. The results presented confirm that the proposed method detects/classifies all the faults within one cycle time and is reliable with a detection/classification accuracy of 99.75%. It is immune to variations in fault parameters, and different operating conditions, and is not affected by the zero sequence mutual impedance. It does not require any training and communication link. Further, the performance is also appraised with other training-based protection schemes.

The Impact of the Power Transformer Connections on the Flow of Zero Sequence Current

The effect of the various winding connections of three phase two windings transformer on the fault current at the line to ground (SLG-fault) and the double line to ground (DLG-fault) is studied. The ground faults are unbalanced ; so, the analysis of these types of short circuit faults is divided into three balanced circuits which are known as symmetrical networks. The zero sequence impedance appears in some types of transformer connections and its effect is reflected in the fault current value. This paper examines with help of MATLAB software by simulating a generating station and different transformer connections, the impact of three-phase two windings transformer connections which occurs on a transformer secondary side to determine which the connections type of the transformer gives the maximum and minimum zero sequence component of ground fault current.

Waveform-Similarity-Based Protection Scheme for AC Transmission Lines of MMC-HVDC System with Offshore Wind Farms

The modular multilevel converter (MMC) has become a very promising technology for long-distance and large-capacity transmission of offshore wind power. However, both sides of the AC transmission line at the sending end are controllable electronic power devices, resulting in difficulty in fault identification and inapplicability of traditional differential protection schemes. In order to solve this problem, a wave-similarity-based protection scheme is proposed for AC transmission line faults. Firstly, the symmetrical and asymmetrical fault current characteristics of the double-fed induction generator (DFIG) and MMC are studied, indicating that the fault current characteristics are obviously different from the synchronous units. Secondly, the wave-similarity-based protection scheme is proposed based on the different wave forms of the fault currents of the MMC and DFIG. When the similarity coefficient is less than the margin coefficient, there is a fault in this phase. Moreover, the proposed wave-similarity-based protection scheme can identify all types of short-circuit faults correctly and is not affected by the transition resistance. Finally, simulations of an MMC-HVDC system with offshore wind farms are conducted to validate the effectiveness and correctness of the proposed protection scheme.

Short circuit fault identification and diagnosis analysis of rotor winding for variable speed pumped storage unit

Rotor winding short circuit is a frequent fault type of generator unit. Compared with the traditional fixed-speed unit using DC excitation, the three-phase AC excitation structure of the variable speed pumped storage unit is more complicated, and the fault diagnosis of the rotor winding short circuit is more difficult. Therefore, this paper proposes an offline diagnosis method for variable speed pumped storage units based on the principle of Repetitive Surge Oscillograph (RSO). Firstly, the application scheme of the repeated pulse method in the variable speed pumped storage unit is designed. By injecting low-voltage pulses into three phases simultaneously and detecting the response curve to determine whether the unit has a short-circuit fault. Then, based on the response characteristics of different rotor winding short-circuit fault types, an identification scheme of rotor winding short-circuit fault types and faulty phases is proposed. Subsequently, the variational mode decomposition (VMD)-teager energy operator (TEO) method is used to estimate the injected pulse and characteristic curve’s initial sudden change position and then the fault position is determined by the time difference and the measured wave velocity. The simulation results show that the fault identification and fault location method proposed in this paper can be effectively applied to various rotor winding short-circuit faults of variable speed pumped storage units.

Flexible DC distribution line protection principle based on distance metric of clustering centres

Aiming at the issue of unsatisfactory selectivity of single-terminal protection, and poor speed and high requirements for data synchronization of double-terminal protection in flexible DC distribution networks, an artificial intelligence (AI)-based method is proposed to obtain the best clustering centres through unsupervised clustering analysis of historical data to realize the non-deterministic flexible DC distribution line protection principle. The method forms historical data samples by simulating different types of short-circuit faults at different locations of the line in advance, combining them with the actual faults, and collecting and processing the post-fault currents. The K-means clustering algorithm is then used to find the best clustering centres corresponding to different fault types, and fault identification and pole selection are realized by comparing the distance between real-time data and each clustering centre. The process relies merely on single-terminal current as the characteristic quantity, and it does not need complicated feature extraction and calculation. Thus, the cumbersome threshold setting in conventional current protections can be avoided. Finally, the case studies are carried out in PSCAD/EMTDC, and the results show that the proposed protection has good selectivity and rapidity, and the tolerance to fault resistance is improved compared with the conventional local-current-based protections.

Application of Wavelet Packet and Fuzzy Algorithm in Power System Short Circuit Fault Classification

Short-circuit fault, also known as a lateral fault, is one of the common types of faults in power transmission lines. This paper proposes an accurate method for identifying short-circuit fault types for power transmission line systems. According to the three-phase A, B, and C values, short-circuit faults can be subdivided into ten classes. An MDL power system with controllable short-circuit fault types is designed and tested. The method of using wavelet packet to analyze short-circuit fault waveforms and using the fuzzy controller to analyze eigenvectors is also proposed. Wavelet packet analysis makes up for the defect that wavelet transform usually only decomposes low frequencies and retains high frequency. Therefore, more accurate frequency band information and eigenvectors are obtained through wavelet packet transform, and then, the fuzzy controller is used to identify the eigenvectors, which can effectively detect and discriminate short-circuit fault types. Accurate identification of short-circuit fault types can improve transmission lines’ overall operational efficiency and promote the power system’s development.

Voltage-Assisted Sequence Current-Based Pilot Relaying for Lines With/Without TCSC

Several schemes are available in the literature to protect transmission lines (TLs). These schemes are either transmission network-specific or require modifications in case of any changes in the TL structure. FACTS devices are often incorporated in TLs to increase power transfer and improve controllability and stability. There have been many developments in the field of protection of FACTS-TL. However, the search for a scheme that can successfully adapt to the system changes and can be applied to almost any transmission network to identify the line faults is still ongoing. With advancements in communication technology and global positioning system-enabled time-tagged data, the voltage and current data can be synchronized and, therefore, the pilot relaying may become more viable. Utilizing these technological advances, a digital pilot relaying scheme, for the protection of TLs with or without series compensation and any type of short-circuit fault under any loading condition, is proposed in this paper.

Intelligent fault diagnosis for power distribution system-comparative studies

Short circuit is one of the most popular types of permanent fault in power distribution system. Thus, fast and accuracy diagnosis of short circuit failure is very important so that the power system works more effectively. In this paper, a newly enhanced support vector machine (SVM) classifier has been investigated to identify ten short-circuit fault types, including single line-to-ground faults (XG, YG, ZG), line-to-line faults (XY, XZ, YZ), double line-to-ground faults (XYG, XZG, YZG) and three-line faults (XYZ). The performance of this enhanced SVM model has been improved by using three different versions of particle swarm optimization (PSO), namely: classical PSO (C-PSO), time varying acceleration coefficients PSO (T-PSO) and constriction factor PSO (K-PSO). Further, utilizing pseudo-random binary sequence (PRBS)-based time domain reflectometry (TDR) method allows to obtain a reliable dataset for SVM classifier. The experimental results performed on a two-branch distribution line show the most optimal variant of PSO for short fault diagnosis.

Classification of Transmission Line Ground Short Circuit Fault Based on Convolutional Neural Network

Ground short circuit faults in current transmission lines are common in the power systems. In order to prevent the power system from aggravating the accident caused by short-circuit faults of transmission lines, a novel convolutional neural network (CNN) model is constructed to identify the short-circuit fault of the transmission line in the power system. The CNN model is mainly consisted of five convolutional layers, three max-pooling layers, one concatenate layer, one dropout layer, one fully connected layer, and a Softmax classifier. This method uses a fixed time window to intercept system short-circuit fault data, extracts the deep features of these data from the training samples, and then corresponds the extracted features to labels one-to-one. Finally, in PSCAD/EMTDC, the new England 10 machine 39 nodes are taken as an example to realize the simulation. The experimental results show that the CNN model can quickly and accurately identify the short-circuit fault types, and the optimal model accuracy rate reaches 99.95%. The results of this manuscript -have positive effect on enhancing the disaster prevention capability and the operation stability of transmission lines.

Local feeder automation under mode of customer power supply priority

The local feeder automation method (LFA) is applied in the single side power distribution system. The protection and re-closing of substation outgoing line are used to coordinate with the time limit characteristics of voltage-time type switch to isolate fault and recover power supply automatically. The access of small hydro-power (SH) makes the distribution system present multi power pattern. In this paper, based on LFA considering SH access, in order to achieve the goal that SH can operate in isolated network with load after fault, LFA under the mode of customer power supply priority (LFAUP) with adaptive opening time for switch is proposed. When the fault point is located on the grid side of the quasi power balance switch (QPBS) and the isolated network is on its load side, the switch can automatically isolate all types of short circuit fault from the load side without residual voltage locking (also known as X locking). When the power of the isolated network is balanced, the loads in which will not get power failure. Simulation results show LFAUP advanced in the paper is correct and feasible.

Research on the wear and life of copper tungsten alloy contacts of high voltage SF6 circuit breakers

Three phase short circuit and two phase short circuit grounding fault of power system have a significant influence on the electrical life of circuit breaker contact. In this paper, the mathematical model of each component is established by Power System Analysis Software Package (PSASP), and the simulation model is built to simulate the dynamic state of the circuit breaker after different short-circuit faults. The simulated result confirms that contact electrical wear quantity related to the type of short circuit fault and the position of short circuit point. The size of short circuit current will directly impact electrical wear quantity and electrical life of circuit breaker contact.

An Evaluation Method for Bus and Grid Structure Based on Voltage Sags/Swells Using Voltage Ellipse Parameters

Voltage sags and swells are significant issues of power quality. In order to alleviate voltage sags and swells on sensitive loads, a method is proposed to evaluate the bus and grid structure based on voltage sags/swells using voltage ellipse parameters. The voltage matrix is established for voltage sags/swells caused by four types of short-circuit faults. The voltage matrix can be converted into a minor axis matrix and a major axis matrix using the Clarke transformation, and accordingly a voltage sag matrix and a voltage swell matrix can be obtained with the interaction between voltage sags/swells and voltage ellipse parameters. The key indicator and the vulnerability indicator of bus as well as the indicator of grid structure are defined according to the voltage sag matrix and swell matrix. The buses are sorted using the indicators proposed, and the result of bus sorting provides a basis for selecting the buses that need to be managed to alleviate voltage sags/swells. The experiment results in IEEE 30-bus system and IEEE 57-bus system show that these indicators facilitate the selection of the access point for newly-added sensitive loads, and provide a theoretical guidance to the mitigation of voltage sags and swells, and help to accomplish the planning, operation and transformation of power grid.