Fault Recorder Data Research Articles

Research on the analysis method of power system relay protection action characteristics based on fault recording data

The action characteristics of power system relay protection devices can well analyze whether the relevant actions are correct. An analysis method of relay protection action characteristics in power system based on fault recording data is designed. After analyzing the relay device structure and protection action characteristics and requirements, the power system relay protection action record data recorded by the fault recorder; The second order RC low-pass filter is used to preprocess the recorded data; The filtered data is input into the analysis model of relay protection action characteristics based on SVM algorithm. The SVM algorithm classifies whether the relay protection action characteristics recorded by the filtered fault recording data meet the expectations, and completes the analysis of power system relay protection action characteristics. The experimental results show that this method can effectively analyze the operation characteristics of power system relay protection, and can accurately check whether the relay protection device can operate correctly.

Multi‐view synergistic enhanced fault recording data for transmission line fault classification

AbstractFault recorded data has been proven to be effective for fault diagnosis of overhead transmission lines. Utilizing deep learning to mine potential fault patterns in fault recording data is an inevitable trend. However, it is usually difficult to obtain massive labeled fault recording data, which results in deep learning‐based fault diagnosis models not being adequately trained. Although data augmentation methods provide ideas for expanding the training data, existing data augmentation algorithms (e.g. random perturbation‐based augmentation) may lead to distortion of multi‐view data, that is, time domain data and frequency domain data of the fault recorded data, which results in the inconsistency of physical properties and statistical distributions of the generated data and the actual recording data, and misguides the training of the models. Hence, this study proposes a transmission line fault classification method via the multi‐view synergistic enhancement of fault recording data. The methodology proposes to start with a synergistic enhancement of multi‐view data such as time and frequency domains of fault recording data, and utilizes contrastive learning to further improve the performance of the fault classification model while ensuring that the generated data is not distorted. Experimental results on three real‐world datasets validate the effectiveness of the proposed method.

A Method for Identifying External Short-Circuit Faults in Power Transformers Based on Support Vector Machines

Being a vital component of electrical power systems, transformers significantly influence the system stability and reliability of power supplies. Damage to transformers may lead to significant economic losses. The efficient identification of transformer faults holds paramount importance for the stability and security of power grids. The existing methods for identifying transformer faults include oil chromatography analysis, temperature assessment, frequency response analysis, vibration characteristic examination, and leakage magnetic field analysis. These methods suffer from limitations such as limited sensitivity, complexity in operation, and a high demand for specialized skills. In this paper, we propose a method to identify external short-circuit faults of power transformers based on fault recording data on short-circuit currents. It involves analyzing the current signals of various windings during faults, extracting appropriate features, and utilizing a classification algorithm based on a support vector machine (SVM) to determine fault types and locations. The influence of different kernel functions on the classification accuracy of SVM is discussed. The results indicate that this method can proficiently identify the type and location of external short-circuit faults in transformers, achieving an accuracy rate of 98.3%.

Resolution and Analysis of Transmission Line Fault Types Based on Recording Type Data and Deep Learning

The reliable identification of fault types in transmission lines is essential for restoring power supply swiftly and minimizing economic losses during outages, thereby ensuring the safe and efficient functioning of the power system. This paper addresses the challenge of low recognition accuracy in existing transmission line fault diagnosis methods and presents a novel approach based on fault recording data collected from both ends of the line. This method distinguishes between lightning-strike and non-lightning-strike faults, utilizing a deep learning network architecture to analyze time-domain information from recorded data, using the initial and terminal waveforms as inputs. The proposed fault identification model integrates fault current phase mode transformation, Local Mean Decomposition (LMD) decomposition, and spectral entropy analysis, applying deep learning principles to enhance fault detection precision. This comprehensive approach enables the effective identification of various fault types on transmission lines. Extensive simulation tests were conducted using a sophisticated fault simulation model developed within simulation software to validate the proposed algorithm’s efficacy. The results demonstrate the algorithm’s high accuracy and efficiency in recognizing various fault types on transmission lines.

A Fault Location Algorithm for Multi-Section Combined Transmission Lines Considering Unsynchronized Sampling

This paper introduces a fault location technique for multi-section combined transmission lines, utilizing unsynchronized fault record data. The approach determines the intersection points of the voltage magnitudes computed using the fault current and voltage records of each terminal and the constant line parameters of each section. Finally, the real fault location is identified based on the calculated inverse angle of equivalent impedance. To evaluate the performance of the proposed algorithm, a three-section combined transmission line is constructed using the PSCAD/EMTDC software version 5.0.0, and various fault scenarios are simulated and tested employing the proposed algorithm. The results indicate that the proposed algorithm is applicable to various types of faults in a multi-section combined transmission line; the estimations are accurate.

Digital Fault Locator for Double End Fed Transmission Lines

The paper presents a digital fault locator by dynamic system parameter estimation for a double end fed transmission line. The method uses about 1/6 cycle of recorded fault data and doesn‟t require filtering of dc offset and high-frequency components. The system differential equations are based on a lumped parameter line model, Thevenin equivalents at both ends of the line and an unknown fault resistance. The accuracy is demonstrated by a representative set of tests results obtained with computer simulation

Improved distance protection for wind farm transmission line based on dynamic frequency estimation

The frequency of the fault current fed by the wind farm may deviate from the fundamental frequency after the line fault. This characteristic seriously affects the performance of the traditional distance protection. This paper proposes an improved distance protection method based on dynamic frequency estimation. After the protection is started, the dynamic frequency is estimated by multiple signal classification algorithm firstly. The distance criterion is not activated until the estimated frequency is close to the fundamental frequency. Accordingly, the influence of the frequency deviation of the fault current on the traditional distance protection is prevented. The delay time of the protection can be significantly shorted compared with the fixed-delay method due to the fast estimation of the transient frequency. Finally, the RTDS simulation model is built to validate the correctness of theoretical analysis and superiority of the proposed method. The fault recording data of practical engineering is also discussed to further verify the feasibility of the proposed method.

Evolution of grounding failure‐insulation failure of 10 kV cable joints: Prerequisites of an explosion in enclosed cable trench

AbstractAn explosion accident in an enclosed cable trench caused by the discharge of 10 kV three‐phase cable joints is discussed. Combined with the disassembly analysis, the fault recording data analysis, and the validation of experiment, the evolution of the cable joint fault is deduced and discussed. The results show that the trigger of the cable joint fault is the creepage discharge at the cross‐linked polyethylene–silicon rubber insulation interface. This results in the partial breakdown and the grounding failure of three‐phase cable joints. Under the long‐term floating potential and current thermal effect, the insulations are gradually ablated and decomposed into a large amount of combustible gas. Finally, the accumulated combustible gas is ignited by the arc caused by a three‐phase short circuit at the moment of the reclosing operation. The analytical method and conclusions proposed in this paper can provide suggestions and guidance to prevent analogous distribution network cable joint fault accidents.

An Improved Coupled FE–Circuit Model for Turn-to-Turn Short-Circuit Faults Inside Converter Transformers

Turn-to-turn short-circuit (TTSC) faults, which are among the most common and potentially threatening faults inside large-capacity converter transformers, can lead to catastrophic accidents and causalities. Due to the lack of a proper model, the reproduction and analysis of actual TTSC faults remain difficult challenges. In this paper, converter transformer TTSC faults are numerically investigated through directly coupling an internal finite element (FE) model with a model of the external electric circuit. In every calculation step, data are automatically transferred between the FE and circuit domains, and the simulations for the winding magnetic leakage flux and fault current are executed simultaneously. Furthermore, the Hochrainer arc model is used to simulate the nonlinear conductance of the low-impedance arc shorting adjacent turns; this model is directly integrated into the short-circuit loop by means of user-defined code. A realistic 405.2 MVA/500 kV single-phase three-core-limb converter transformer within an ultra-high voltage (UHV) converter system is modeled as a simulation case and subjected to various TTSC faults. Direct comparisons reveal agreement between the calculated results and the recorded fault data, indicating that the approach can reproduce actual faults, thus offering advantages as a replacement for dangerous and costly on-site short-circuit tests in converter transformers.

Data Mining Technology for Equipment Machinery and Information Network Data Resources

In order to solve the problem of aviation equipment system maintenance, it is very difficult to judge the faulty finished product according to the fault phenomenon, the author proposes a data mining-based prediction model for aviation equipment failure finished products. The model takes historical fault record data as input, clusters a large number of fault descriptions through text clustering to obtain fault phenomenon clusters, and establishes a many-to-many relationship between “fault phenomenon” and “fault finished product.” A probability distribution algorithm for faulty finished products is proposed, and by matching new fault phenomena and fault phenomenon clusters, the probability distribution of faulty finished products is calculated. The experimental results show that after calling the model to complete the clustering of the fault information database, 18966 fault phenomenon clusters are obtained, and each fault phenomenon cluster contains 2.9 fault records on average, the many-to-many relationship between the fault phenomenon and the faulty finished product of the fault information database is successfully constructed. The model can effectively predict the probability distribution of products that may fail according to the fault description, and the prediction accuracy can be improved with the increase of the amount of data to meet the actual security needs.

Research on Fault Diagnosis of PST Electro-Hydraulic Control System of Heavy Tractor Based on Support Vector Machine

Due to the harsh working environment of the tractor, the transmission can often be faulty. In order to ensure the reliability of its operation, it must be monitored and the fault discovered. In this paper, the support vector machine (SVM) method is used. The eigenvector conversion of the original data uses the following eigenvectors: Three fault modes (leakage fault of shift clutch hydraulic cylinder, blockage fault of oil passage, and blockage fault of proportional valve spool) are identified in matrix and laboratory (MATLAB) with the help of the library for support vector machines (LibSVM) toolkit, and the classification accuracy of test samples is 90%. The normal mode of the PST electro-hydraulic system and the three kinds of fault modes mentioned above are discriminated against, and the correct rate of fault diagnosis reaches 95%, which meets the needs of practical engineering. Analysis of the fault recording data of the power shifting transmission shift solenoid valve shows that the difference between fault pressure data and normal data is small, and the value of traffic data is greater. This method can realize the fault mode online recognition based on controller area network (CAN) communication, and the research results provide a theoretical basis for the fault diagnosis of the PST electro-hydraulic control system.

Research and implementation of a highly reliable transient and same source data comparison method based on fault recorder documents

Abstract The existing fault recording analysis system has powerful functions in the analysis of fault characteristic factors such as waveform analysis, vector analysis, and harmonic analysis. However, it uses redundant fault recording data to analyze, compare and locate faults in the power system. Rarely involved, far from reaching the level it should be, and even wasting fault recording data with large amounts of data and rich information content. A highly reliable transient homology data comparison based on recording files is given. Analytical method. Introduced the comparison method of the recording data from the same source, the content involves how to match the channel waveform of fault recorder documents, how to use the zero-crossing algorithm to align, how to find the starting point of the protection action through the mutation amount algorithm, and how to pass the cubic spline The interpolation algorithm obtains valid data, how to compare the valid data, and finally obtain the comparison result, and give advice on operation and maintenance.

An Unsupervised Mutual Information Feature Selection Method Based on SVM for Main Transformer Condition Diagnosis in Nuclear Power Plants

Dissolved gas in oil (DGA) is a common means of monitoring the condition of an oil-immersed transformer. The concentration of dissolved gas and the ratio of different gases are important indexes to judge the condition of power transformers. Monitoring devices for dissolved gas in oil are widely installed in main transformers, but there are few recorded fault data of main transformers. The special operation and maintenance modes of main transformers leads to the fault modes particularity of main transformers. In order to solve the problem of insufficient samples and the feature uncertainty, this paper puts forward an unsupervised mutual information method to select the feature verified by the optimized support vector machine (SVM) model of particle swarm optimization (PSO) method and tries to find the feature sequence with better performance. The methos is validated by data from nuclear power transformers.

Layered Lossless Compression Method of Massive Fault Recording Data

In order to overcome the problems of large error and low precision in traditional power fault record data compression, a new layered lossless compression method for massive fault record data is proposed in this paper. The algorithm applies LZW (Lempel Ziv Welch) algorithm, analyzes the LZW algorithm and existing problems, and improves the LZW algorithm. Use the index value of the dictionary to replace the input string sequence, and dynamically add unknown strings to the dictionary. The parallel search method is to divide the dictionary into several small dictionaries with different bit widths to realize the parallel search of the dictionary. According to the compression and decompression of LZW, the optimal compression effect of LZW algorithm hardware is obtained. The multi tree structure of the improved LZW algorithm is used to construct the dictionary, and the multi character parallel search method is used to query the dictionary. The multi character parallel search method is used to query the dictionary globally. At the same time, the dictionary size and update strategy of LZW algorithm are analyzed, and the optimization parameters are designed to construct and update the dictionary. Through the calculation of lossless dictionary compression, the hierarchical lossless compression of large-scale fault record data is completed. Select the optimal parameters, design the dictionary size and update strategy, and complete the lossless compression of recorded data. The experimental results show that compared with the traditional compression method, under this compression method, the mean square error percentage is effectively reduced, and the compression error and compression rate are eliminated, so as to ensure the integrity of fault record data, achieve the compression effect in a short time, and achieve the expected goal.

An Unmanned Aerial Vehicle Troubleshooting Mode Selection Method Based on SIF-SVM with Fault Phenomena Text Record

At present, the research on fault analysis based on text data focuses on fault diagnosis and classification, but it rarely suggests how to use that information to troubleshoot faults reported in unmanned aerial vehicles (UAVs). Selecting the exact troubleshooting procedure to address faults reported by UAVs generally requires experienced technicians with professional equipment. To improve the efficiency of UAV troubleshooting, this paper proposed a troubleshooting mode selection method based on SIF-SVM (Serial information fusion and support vector machine) using the text feature data from fault description records. First, Word2Vec was used in text data feature extraction. Second, in order to increase the amount of information in the modeling data, we used the information fusion method. SVM was then used to construct the classification model for troubleshooting mode selection. Finally, the effectiveness of the proposed model was verified by using the fault record data of a new fixed-wing UAV.

Analysis of current characteristics of transformer bias caused by subway stray current based on measured data

The metro stray current, as the driving source which causes the magnetic bias of the urban network transformer, has the characteristics of rapid change, complex propagation path, and many influencing factors.It’s unknown where the stray current leaks from, and its specific value cannot be obtained through measurement methods.Therefore, this paper studies the characteristics of transformer neutral magnetic current bias based on the measured data and fault recording data in the process of measurement, and analyzes the influence of stray current on transformer in urban network.

Research on Fault Diagnosis of Launch Vehicle’s Power Transformation and Transmission System Based on Big Data

The on-board power supply system provides power for the launch vehicle. The power transmission and transformation system plays an irreplaceable role to ensure that the on-board power supply system receives the normal working voltage of the launch vehicle. There are many types of faults in power transmission and transformation systems. The traditional faulty diagnosis method of power transmission and transformation equipment has the disadvantages of being susceptible to experts’ subjectivity and model’s ossification. In this paper, a new method of equipment fault diagnosis based on big data is proposed. On the basis of big data, this paper introduces the failure mode clustering algorithm, the state parameter correlation analysis algorithm, the fault diagnosis method based on the correlation matrix, and other key fault diagnosis technologies. The fault record data of the 400 kV voltage grade oil-immersed transformer bushing in the past ten years by a Chinese combat unit is used as a case for demonstration. The results show that the accuracy rate of SC-LSTM-K-means clustering model exceeds 95%. And the fault classification mode can be accurately obtained. A priori correlation algorithm with TA coefficient can be used to evaluate the strong and weak relationship between the state parameters; the fault diagnosis matrix based on Pearson’s correlation coefficient can accurately determine the fault mode consistent with the actual operation and maintenance test results. Therefore, the fault diagnosis method of power transmission and transformation system based on big data can both effectively obtain the inherent laws of historical data and realize more accurate fault diagnosis with data adaptability.

Faulty Line Identification Method Based on Bayesian Optimization for Distribution Network

Selective ground-fault protection is greatly valued for the safe and reliable operation of power systems. With the wide adoption of fault indicator in distribution network, the amount of available fault data increases dramatically. The in-depth investigation of fault recording data helps improve the accuracy of faulty line identification. To perform fault data analysis with higher efficiency, a single-phase-to-ground fault identification model based on the k-Nearest Neighbor (kNN) classification algorithm is proposed in the paper. In this model, the eigenvectors consist of wavelet energy ratio, wavelet coefficients variance and wavelet power obtained by the decomposition of transient components. Furthermore, through the theoretical analysis and experimental comparison of three parameter adjustment algorithms, Bayesian Optimization algorithm is selected to find the optimal parameters of fault identification model, and realize the adaptive adjustment of model parameters. Finally, the validity and feasibility of the model are verified by the experimental data, and the accuracy and efficiency of fault identification are improved by using Bayesian Optimization algorithm.

The study on the Mal-operation of the transformer current differential protection during external faults cause by CT saturation

This paper analyses the unusual mal-operation of the differential protection for transformer current differential protection during the external fault. The mal-operation reason is researched in detail, based on the mathematical analysis, the simulation, and the field recorded fault data analysis. It is disclosed that the CT saturation leads to the maloperation. Based on this, this paper analyses the improvement for the CT saturation identification method, and discusses the CT selection principles. This paper is aimed at attracting the attention on the problem of the CT saturation and proposing the reference of the analysis method the countermeasure for this problem.

Fault Diagnosis Scheme Based on Grid Fault Degree Sample Data and AI Classification Method

Considering the differences of fault characteristics in the process of fault diagnosis, loss and false positives of protection and circuit breaker information, power grid fault diagnosis method based on integrated fault degree is proposed. Firstly , this method uses wavelet transform to analyze the amplitude characteristics and energy characteristics in the electrical quantity information on the fault recording data, and extracts the key indicators; Then, according to the binary information from remote communication system, directed bipartite graph is used to update fault symptoms, determine the fault boundary, and to calculate suspected degree; finally, integrated fault degree is determined by the improved D-S evidence theory, and final fault diagnosis result is determined by improved support vector machine method. Simulation results show that the proposed scheme improves the accuracy of diagnosis, and has good application prospects.