Fault Waveforms Research Articles

Fault Diagnosis of an Excitation System Using a Fuzzy Neural Network Optimized by a Novel Adaptive Grey Wolf Optimizer

As the excitation system is the core control component of a synchronous condenser system, its fault diagnosis is crucial for maximizing the reactive power compensation capability of the synchronous condenser. To achieve accurate diagnosis of excitation system faults, this paper proposes a novel adaptive grey wolf optimizer (AGWO) to optimize the initial weights and biases of the fuzzy neural network (FNN), thereby enhancing the diagnostic performance of the FNN model. Firstly, an improved nonlinear convergence factor is introduced to balance the algorithm’s global exploration and local exploitation capabilities. Secondly, a new adaptive position update strategy that enhances the interaction capability of the position information is proposed to improve the algorithm’s ability to jump out of the local optimum and accelerate the convergence speed. In addition, it is demonstrated that the proposed AGWO algorithm has global convergence. By selecting real fault waveforms of the excitation system for case validation, the results show that the proposed AGWO has a better convergence performance compared to the grey wolf optimizer (GWO), particle swarm optimization (PSO), whale optimization algorithm (WOA), and marine predator algorithm (MPA). Specifically, compared to the FNN and GWO-FNN models, the AGWO-FNN model improves average diagnostic accuracy on the test set by 4.2% and 2.5%, respectively. Therefore, the proposed AGWO-FNN effectively enhances the accuracy of fault diagnosis in the excitation system and exhibits stronger diagnostic capability.

High impedance fault detection method based on improved Mayr Arc model and fitting curve coefficients

To solve the problem of weak transient fault currents and significant nonlinear characteristics caused by arcs in resonant grounding systems, proposing a high-impedance fault (HIF) detection method based on an improved Mayr arc model and fitting curve coefficients. Firstly, to address the issue of low accuracy in simulating fault waveforms in existing HIF models, construct an improved arc model based on Mayr and control the parameter values under different feature dimensions, achieving accurate simulation of arc waveforms. Secondly, based on the impedance characteristics of the feeder, analyze the phase frequency characteristics, determine the first capacitive frequency band, and then determine the cutoff frequency of the Chebyshev filter to improve the ability of waveform feature extraction and fault recognition. Finally, by fitting the voltage and current geometric characteristic curve within the characteristic frequency band, using the least squares method and the quadratic coefficient values of the fitting curve to construct detection criteria, accurate detection of HIF is possible. Research shows that the improved arc model can fully characterize the current arc waveform under various working conditions. Simulations and real-world measurements verify that HIF can be accurately detected using the quadratic coefficient value of the fitting curve.

A fault diagnosis method of hot spots for photovoltaic clusters based on model parameters

AbstractUtilizing the direct current‐side electrical data resources of the photovoltaic power generation system on the FusionSolar platform, this study investigates the impact of hot spot faults on the output characteristics of photovoltaic strings and proposes a hot spot fault diagnosis method based on time series waveform characteristics. By analyzing the mechanisms of hot spot generation and evolution, as well as the characteristic differences in I–V curves and time series compared to other types of faults, the waveform variation patterns of hot spots in current and voltage time series are obtained. A function form suitable for hot spot fault waveform characteristics in time series graphs is constructed, and fault diagnosis feature vectors are extracted. Combining field operation and maintenance experience, a fuzzy reasoning fault diagnosis system is established to determine the causes and estimate the severity of hot spot faults. Experimental results indicate that hot spot faults have unique and corresponding variations in the current/voltage time series waveforms of the string output. The constructed function form can clearly represent the waveform variation patterns, and the established fuzzy reasoning system can achieve effective and reliable diagnosis of hot spot faults.

New energy transmission line fault location method based on Pearson correlation coefficient

The access of new energy power source makes the traditional transmission line structure become complex, and due to the influence of power electronic device control strategy, the fault characteristics have been fundamentally changed, resulting in the traditional transmission line fault localization method can not be applied to the new energy sending line. To address the above problems, this paper analyzes the transient current characteristics of different power supply faults, and learns that there are obvious differences in the transient currents on both sides of the fault point inside and outside the transmission line area, and then proposes a new energy transmission line fault localization method based on the Pearson correlation coefficient, which determines the fault location by calculating the correlation coefficients of the fault waveforms of the neighboring monitoring points. Finally, comparative experiments are carried out under different fault types and fault locations to further verify the applicability of the proposed method.

Study on the algorithm of fault recording analysis combining its time-domain waveforms with phase-domain trajectories

The untimely handling of faults in a power system has a negative impact on its operation and even the national economy, and this requires coordination in the functions of protective relaying as well as supervisory & control devices, where digital fault recorders are used to record fault waveforms of electrical physical quantities. The fault recording of a simulated current is taken as the research object in this article, and it is transformed from the time-domain waveform into a phase-domain trajectory, which is used to analyze fault feature parameters and then reformulate the waveform. The original waveform of the current will be substituted by the reformulated one with fault features to realize functions in the power system. The algorithm of reformulating fault recording, the correlativity of the reformulated waveform and its original one, and errors produced in the research process are researched. The high correlation coefficient between the reformulated waveform and its original one shows that the algorithm studied in the article offers a simple and convenient option for fault recording analysis.

Flashover pattern analysis for 275 kV double circuit transmission lines during direct lightning strikes

Lightning overvoltage causes outages of transmission lines (TLs) in tropical countries. Identifying and analyzing the key factors responsible for tripping the lines can improve the performance of the lines. In this work, the flashover patterns due to direct lightning strikes on 275 kV double circuit TLs and towers in Malaysia was evaluated with Electromagnetic Transient Program (EMTP-RV). Three parameters that include lightning current magnitude, power frequency angle and tower footing resistance were analyzed on the line performance during direct lightning strikes. The simulated results found that two key parameters, lightning current at 270 kA and footing resistance at 50 Ω, reveal the highest impact on the insulator flashover. This finding was also verified from multivariate analysis results which is rarely found in the previous works in this field. As a key contribution in this work and in validation to the model, the simulated fault waveform patterns and voltage magnitudes were compared with the actual site data obtained from six different locations in Peninsular Malaysia. The analyzed results show a similar waveform pattern with an overall average voltage magnitude difference of 6.0 % before and after fault. The model is useful to electric utilities for analyzing transient performance of TLs.

Research on intelligent identification algorithm for preventing external damage behavior of power cable based on machine learning

Abstract As the key carrier of power distribution line power transmission and the supporting artery of urban functioning, power cables are widely used in electric power engineering construction. The number of users is increasing, and the distribution of locations is becoming more complex. However, if a power cable failure occurs, if not eliminated in time, it will threaten the safety of the power grid, seriously affecting the production of enterprises and the lives of residents. The article first on the power cable vibration signal recognition algorithm design, extraction of power cable external damage fault signal characteristics, proposed based on PSO-SVM power cable external damage fault waveform recognition algorithm, and the design of power cable anti-external damage intelligent early warning system, to achieve a certain period according to the trend of signal changes in the vibration event to discriminate. Research results show that under the same computer operating conditions, the average running time of the algorithm proposed in this paper is only 3.43. Compared to the other two algorithms, the algorithm has the highest recognition accuracy, the fastest convergence speed, and the optimal comprehensive performance. In the external damage signal recognition and warning test, the recognition rate of the excavator passing through defense zone one and two at a distance of 3 meters is 100%, which is greater than 80%, in line with the expected effect.

The numerical simulation of a four-stage linear transformer driver module based on the Preisach magnetic core model.

The use of linear transformer drivers (LTDs) is widely considered the most promising technological approach for the development of future pulsed-power accelerators. In large-scale pulsed-power accelerators, abnormal conditions like switch prefire can occur frequently during tests and normal operations due to the presence of a large number of switches. The diagnosis of such faults based on signature waveforms requires further investigation. According to previous research, the characteristics of the magnetic cores greatly influence the fault waveforms. In this paper, a full-cycle mathematical model of the magnetic core is established utilizing a classical Preisach model based on experimental results. This model is coupled with the LTD circuit model in simulations, and simulation results are obtained under the condition of switch prefire. The simulation results are in good agreement with the experimental results from a four-stage LTD module with a sharing shell and de-ionized water insulated transmission line. The magnetization process of the cores is also determined under prefire conditions. Analyses of the magnetization process indicate that the completely demagnetized core shows high permeability under positive excitation and that the permeability abruptly decreases as the excitation is reversed. The hysteresis characteristics result in a phenomenon in which the output voltage in the prefired stage is almost unipolar. Finally, the features of the fault voltages captured in the experiments are also explained.

New pilot protection method based on the current fault component waveform similarity

AbstractIn order to improve the ability of the modular multi‐level converter to quickly clear and isolate faults in the multi‐terminal flexible DC grid, this paper analyzes the characteristics of current faults in the multi‐terminal flexible direct current grid, introduces the comprehensive distance of current fault waveform, and proposes a line protection method based on the similarity of current fault waveform. This protection method firstly uses Euclid Dynamic Time Warping (DTW) distance and entropy weight method to process electrical quantity signals, calculates the comprehensive distance of current fault waveform of two converter stations, and builds the protection criterion based on the similarity of current fault waveform. Then, the fault identification is realized by using the calculated comprehensive distance, and the fault pole identification is realized by using the ratio of comprehensive distance between positive and negative poles. Finally, PSCAD was used to build a simulation model to output the fault data under different fault conditions, and MATLAB was used to process the fault data to verify the protection algorithm. The simulation results show that the proposed method has good resistance to high resistance, and its rapidity and adaptability are further improved. Compared with other similarity methods, this method combines Euclidean DTW distance and entropy method to process the signal, realizes the fault pole identification, and can still operate correctly under the interference of 500 Ω transition resistance and 20 dB noise ratio. The research results will provide a theoretical basis for the safe and reliable operation of flexible DC power grid.

A fault diagnosis method for active power factor correction power supply based on seagull algorithm optimized kernel‐based extreme learning machine

AbstractTo address the issue of diagnosing hard and soft faults in active power factor correction (APFC) power supply, this study analyzes failure modes resulting from aging and malfunction of various sensitive components. The power fault waveform patterns are initially analyzed based on the circuit's THD, current ripple value, and RMS value. The inductor current signals in different fault modes are then utilized to extract and construct time–frequency fusion fault features of the APFC power supply. Finally, these feature quantities are downscaled and optimized using the RF algorithm. The SOA‐KELM model of the APFC converter is proposed, and the feature vectors under different fault modes are used to classify and diagnose faults, achieving hard and soft fault detection of the converter. The experiments show that the method achieves 100% accuracy for hard fault diagnosis and 96.36% accuracy for soft fault diagnosis of the converter, demonstrating high diagnostic accuracy.

Non-unit ultra-high-speed DC line protection method for HVDC grids using dynamic fitting algorithm with data reconstruction

Existing non-unit protection schemes are inevitably influenced by the high fault-resistance in MMC-HVDC grid, which is a serious problem affecting the rapidity and reliability of fault recognition. This study proposes a dynamic fitting of data reconstruction-based protection method to improve the accuracy and rapidity of HVDC grid protection. Due to the propagation characteristics of the initial voltage traveling wave being unaffected by the transition resistance, the self-adapting exponential fitting algorithm is used to recognize the fault waveform. Then, sliding data window with the voltage traveling wave fit by the optimal basis exponential fitting algorithm could avoid the influence of initial value selection on fitting convergence. Meanwhile, the original fault traveling wave is reconstructed by “Bayesian compressed sensing algorithm (BCSA)” in a low sampling rate. This method could reduce the operation response time of the proposed method to satisfy the ultra-high-speed requirement of MMC-HVDC grid and also adapt the limitation of sampling rate in existing protection devices. Practical case studies in PSCAD/EMTDC and protection prototype testing have demonstrated that the proposed method is effective under different fault locations, fault types, and high fault impedance.

Transmission line fault cause identification method based on transient waveform image and MCNN-LSTM

Power system operators commonly employ fault recorders to record and analyze transmission line faults. However, the length of transmission lines, the number and location of power sources and the sampling frequency of fault recorder will influence the characteristics of the fault waveform leading to fault identification error. This paper proposed a multi-head convolutional neural network with long short-term memory (MCNN-LSTM) using transient waveform image as input for transmission line fault type and fault cause classification. First, the characteristics of transmission line faults were analyzed. Second, MCNN-LSTM is designed by considering interaction between electrical signal. Finally, the modified IEEE-39 node power network was adopted to test the proposed method. The test results show that: this method can improve the identification accuracy of transmission line faults even if there is class unbalance in fault sample set, and shows strong adaptability when transferred to small-size fault samples of transmission lines.

Deep Learning-Based Composite Fault Diagnosis

As the core component of many large machinery, rotating equipment occupies a high proportion in industrial manufacturing. Intelligent fault diagnosis for them is becoming the core focus of Industry 4.0 and its evolution. To address the problem of poor fault classification accuracy of rotating equipment, this paper proposes a new solution to deploy a composite fault diagnosis model at the front end to achieve low power consumption, continuous automatic identification and monitoring of equipment faults. We creatively combined the idea of speech recognition to solve the problem that traditional feature extraction methods are insufficient in handling composite fault diagnosis when dealing with fault waveform files, and used Fbank speech feature extraction idea to extract fault features, thus simplifying the complex fault diagnosis problem into an image classification problem. The generated composite fault Fbank feature data set is significantly different in visual observation. Therefore, we further designed the LeNet-F network based on the LeNet network for composite fault diagnosis. The model was validated on the K210 development board to achieve an accuracy of 97.41% for hybrid fault classification at a model size of 2.4MB. This makes it possible to apply the deep learning method to the front end to directly complete the composite fault diagnosis.

Long Short-Term Memory Network-Based HVDC Systems Fault Diagnosis under Knowledge Graph

To enhance the precision of fault diagnosis for high-voltage direct-current (HVDC) systems by effectively extracting various types of fault characteristics, a fault diagnosis method based on the long short-term memory network (LSTM) is proposed in this paper. The method relies on a knowledge graph platform and is developed using measured data from four fault types in an HVDC substation located in southwest China. Firstly, a knowledge graph for the HVDC systems is constructed, then the fault waveform data is preprocessed and divided into a training set and a test set. Various optimizers are employed to train and test the LSTM. The proposed strategy’s accuracy is calculated and compared with recurrent neural network (RNN), eXtreme Gradient Boosting (XGBoost), support vector machine (SVM), Naive Bayes classifier, probabilistic neural networks (PNN), and classification learner (CL), which are commonly used in fault diagnosis. Results indicate that the proposed method achieves an accuracy of over 95%, which is 30% higher than RNN, 8% higher than XGBoost, 4% higher than SVM, 7% higher than Naive Bayes, 40% higher than PNN, and 42% higher than classification learner (CL), respectively; the method also has the minimum time cost, fully demonstrating its superiority and effectiveness compared to other methods.

Study on fault waveform library of PT high voltage side fuse in hydropower station

In the arc-suppression coil grounding system of a medium voltage distribution network, high voltage side voltage transformer insurance fusing faults frequently occur, including fusing during startup and grid connection, fusing during operation, fusing during the shutdown, etc., which will directly affect the accuracy of measurement, metering, protection, and other secondary equipment. In this paper, we used the calculation of electromagnetic transient EMTP-ATP software, theoretical analysis of the typical hydropower station wiring and parameters of the medium voltage system, set up the improved model of neutral point grounding system, simulation of single-phase earth fault line and disappeared in the transient process, calculation of PT transient current, to verify that the way to study the mechanism of frequent fusing of fuse in the PT. Typical fault waveform databases are established under different bad working conditions (ferromagnetic resonance, low-frequency nonlinear oscillation, lightning wave invasion, etc.).

Fault Location and Classification for Distribution Systems Based on Deep Graph Learning Methods

Accurate and timely fault diagnosis is of great significance for the safe operation and power supply reliability of distribution systems. However, traditional intelligent methods limit the use of the physical structures and data information of power networks. To this end, this study proposes a fault diagnostic model for distribution systems based on deep graph learning. This model considers the physical structure of the power network as a significant constraint during model training, which endows the model with stronger information perception to resist abnormal data input and unknown application conditions. In addition, a special spatiotemporal convolutional block is utilized to enhance the waveform feature extraction ability. This enables the proposed fault diagnostic model to be more effective in dealing with both fault waveform changes and the spatial effects of faults. In addition, a multi-task learning framework is constructed for fault location and fault type analysis, which improves the performance and generalization ability of the model. The IEEE 33-bus and IEEE 37-bus test systems are modeled to verify the effectiveness of the proposed fault diagnostic model. Finally, different fault conditions, topological changes, and interference factors are considered to evaluate the anti-interference and generalization performance of the proposed model. Experimental results demonstrate that the proposed model outperforms other state-of-the-art methods.

Power cable fired by transient arcing below the action value of relay protection: An analysis of a medium-voltage cable joint breakdown fault

Cable joint breakdown accidents have occurred frequently in recent years and have gradually become an important concern of the power maintenance department. The study of the fault development process of the cable joint is helpful to detect and diagnose in advance, so as to avoid the expansion of the accident. In this paper, a breakdown fault of 20 kV damp three-core cable joint was investigated. Firstly, the recorded fault waveform was analyzed, and the fault cable joint sample was disassembled. Then, the circuit simulation model was established to calculate the short-circuit resistance, which aimed to reveal the dynamic characteristics of the short-circuit resistance of the composite interface for the damp joint. Finally, the effect of moisture on the electric field distortion of the interface and the thermal ablation damage effect of the transient short-circuit current on the copper mesh were discussed by the finite element method. This paper reveals the phenomenon of transient grounding faults arising from moisture at the internal interface of cable joint, which may damage the grounding protection line of the joint and affect the sensitivity of the protective device. The method for analyzing the breakdown fault case of damp cable joint presented in this paper can provide reference and suggestions for the investigation and solution in similar cases.

Comparative detection and fault location in underground cables using Fourier and modal transforms

<span>In this research, we create a single-phase to ground synthetic fault by the simulation of a three-phase cable system and identify the location using mathematical techniques of Fourier and modal transforms. Current and voltage signals are measured and analyzed for fault location by the reflection of the waves between the measured point and the fault location. By simulating the network and line modeling using alternative transient programs (ATP) and MATLAB software, two single-phase to ground faults are generated at different points of the line at times of 0.3 and 0.305 s. First, the fault waveforms are displayed in the ATP software, and then this waveform is transmitted to MATLAB and presented along with its phasor view over time. In addition to the waveforms, the detection and fault location indicators are presented in different states of fault. Fault resistances of 1, 100, and 1,000 ohms are considered for fault creation and modeling with low arch strength. The results show that the proposed method has an average fault of less than 0.25% to determine the fault location, which is perfectly correct. It is varied due to changing the conditions of time, resistance, location, and type of error but does not exceed the above value.</span>

Transmission line fault-cause identification method for large-scale new energy grid connection scenarios

The accurate fault-cause identification for overhead transmission lines supports the operation and maintenance personnel in formulating targeted maintenance strategies and shortening the time of inspecting faulty lines. With the goal of achieving “carbon peak and carbon neutrality”, the schemes for clean energy generation have rapidly developed. Moreover, new energy-consuming equipment has been widely connected to the power grid, and the operating characteristics of the power system have significantly changed. Consequently, these have impacted traditional fault identification methods. Based on the time-frequency characteristics of the fault waveform, new energy-related parameters, and deep learning model, this study proposes a fault identification method suitable for scenarios where a high proportion of new energy is connected to the power grid. Ten parameters related to the causes of transmission line fault and new energy connection scenarios are selected as model characteristic parameters. Further, a fault identification model based on adaptive deep belief networks was constructed, and its effect was verified by field data.

Multi-fault diagnosis for series-connected lithium-ion battery pack with reconstruction-based contribution based on parallel PCA-KPCA

Various faults of the lithium-ion battery threaten the safety and performance of the battery system. The early faults are difficult to detect and isolate owing to unobvious abnormality and the nonlinear time-varying characteristics of the battery. Herein, a multi-fault diagnosis strategy is proposed that focuses on detecting and isolating different types of faults, and estimating fault waveforms of the battery, including inconsistency evaluation, virtual connection fault, and external short circuit. First, the principal component analysis (PCA) model of the battery is established and the contribution is employed to detect the abnormity in the battery pack. Once the fault is detected, the parallel kernel principal component analysis (KPCA) technology is adopted to reconstruct the fault waveform of the battery parameters, including ohmic resistance, terminal voltage, and open-circuit voltage. These parameters are jointly taken as fault indexes improving the reliability of fault diagnosis. Finally, the proposed method is verified using amounts of tested data of eight cells in series. The results indicate that the contribution-based PCA method can accurately detect the fault. Furthermore, the reconstruction-based parallel PCA-KPCA can accurately estimate the fault waveform of the faulty battery, which helps investigate the fault degree and causes.