Fault Conditions Research Articles

Faulty feeder selection method for high impedance grounding fault in resonant grounding system based on discrete Fréchet distance

Abstract When a high impedance fault (HIF) occurs in a resonant grounding system, the electrical signal is weak and the current distinction between the faulty feeder and non-faulty feeder is inconspicuous. To address this issue, this paper proposes a faulty feeder selection method based on discrete Fréchet distance (DFD). Firstly, the instantaneous value of the zero-sequence current flowing through each sampling point, along with the instantaneous values at its previous sampling moment, is accumulated to enhance the fault signal, and furthermore to magnify the distinction in the amplitude of the accumulated waveforms of zero-sequence current between the faulty feeder and non-faulty feeder. Then, the DFD of the accumulated waveforms of the zero-sequence current for each feeder is calculated, and the Fréchet distance matrix is established to acquire the average DFD (ADFD) value of the accumulated waveform for each feeder. Finally, the relative DFD (RDFD) value is defined as a criterion to distinguish the bus fault from the feeder fault according to the values of the two feeders with the maximum ADFD value and the minimum ADFD value. If the RDFD value is negative, it is identified as a bus fault, otherwise, the feeder corresponding to the maximum ADFD value is identified as a faulty feeder. MATLAB/Simulink simulation results and field measurement data show that the proposed method is not affected by various fault conditions, and the accuracy and reliability in faulty feeder selection are high when detecting an HIF, with good applicability.

Wear characteristics evolution of helical gear with initial defects of bearing inner ring

This study investigated the wear characteristics and operational condition of gears with initial defects of the bearing inner ring. The working states of gears were evaluated through a combination of vibration signal analysis, wear debris concentration analysis, qualitative analysis of the wear debris, and tooth surface analysis. Based on the Hertz contact theory, a dynamic model for the gear drive system was constructed, enabling the simulation and comparison of vibration acceleration signals under normal and fault conditions. The research results revealed that bearing defects induced irregularities in vibration frequency and amplitude, adversely affecting the performance and stability of the gear system. Furthermore, these defects trigger the creation of substantial wear debris, which exacerbates gear wear and reduces the gear lifespan by about 15%. Additionally, bearing defects induced intermittent load spikes and uneven stress distribution across tooth contacts, resulting in localized high shear stresses, material flaking, and surface spalling. Consequently, initial defects of the bearing will accelerate gear wear progression and lead to abnormal wear patterns, potentially resulting in the premature failure of the gear train system. The study investigates the underlying mechanisms of gear wear evolution influenced by these defects and offers practical guidelines for the engineering, manufacturing, and maintenance of gear systems.

A novel fault detection and identification method for complex chemical processes based on OSCAE and CNN

The safety and reliability of chemical processes are of paramount importance. However, due to the complicated heat, mass, and momentum transfer processes coupled with chemical reactions, and the interrelated effects among various equipment units, it is difficult to accurately detect and identify faults occurring in the chemical system. This study proposes an innovative fault diagnosis framework specifically designed for chemical systems. A method based on orthogonal self-attention convolutional autoencoder (OSCAE) is constructed for fault detection. The convolutional autoencoder is combined with an orthogonal attention mechanism to extract time-series characteristic information of the operational state, thereby achieving precise detection of fault conditions. Additionally, a convolutional neural network (CNN) model uses both raw signals and data reconstructed by OSCAE to identify different faults, which enhances the features of the input and enables highly accurate fault identification. The effectiveness and robustness of the propose method is validated with simulation data of our self-established chemical distillation system and the publicly available Tennessee Eastman process system. The diagnosis efficacy in fault detection and identification is validated by applying fault detection metrics, high-dimensional feature visualizations, and confusion matrix analyses across two case studies. This paper not only provides an effective diagnostic framework for chemical systems but also offers a usable benchmark dataset for chemical distillation systems to the academic community, with the hope that the research content can enhance the stability and reliability of chemical processes.

Research on pressure propagation in long-distance water pipelines for fault conditions by VMD and wavelet transform

Long-distance water pipelines in complex geographical environments face potential risks such as leaks. This study uses variational mode decomposition (VMD) and continuous wavelet transform (WT) to analyze pressure fluctuation signals in pipelines for detecting and locating leaks and other anomalies. CFD simulations are conducted to obtain pressure fluctuation data under various fault conditions. The pressure signals are then decomposed into multiple Intrinsic Mode Functions (IMFs) using VMD, and WT is applied to analyze their time–frequency characteristics. The results show significant differences in amplitude and frequency of IMF components between fault and normal conditions, with reduced amplitude and altered frequency under fault conditions, providing a basis for fault detection. Additionally, the IMF characteristics differ at different fault locations, aiding in fault localization. This research offers an effective method for leak detection and localization in long-distance pipelines, crucial for enhancing safety and stability.

Analysis of temperature characteristics of high‐speed train wheelset system under complex operating conditions

AbstractWith the rapid advancements in high‐speed train technology, the importance of ensuring the safety of train operations has become paramount. Bearings, being a critical component of train bogies, have garnered significant attention for their role in maintaining safety standards. Monitoring the temperature of bearings to evaluate their motion state is a common practice in high‐speed trains, emphasizing the need for further research into temperature fluctuations. In this study, a dynamic model is developed for the bearing rotor system of high‐speed trains. By considering the contact points between raceways and rolling elements, the power loss in the bearing is obtained and a transient temperature‐field model of the system is established. The relationship between node temperature and factors such as ambient temperature, train running speed, and load is illustrated, with a detailed presentation of the influence of bearing fault type and size on node temperature. The analysis results reveal that the node temperature increases with higher values corresponding to those quantifiable factors and is most affected by rolling element fault. Additionally, it is observed that the temperature rises rapidly in the initial stage and gradually flattens out over time. The comparative analysis of temperature under different fault conditions shows that the node temperature is most affected by the rolling element fault. Experiments and actual line temperature data are used to verify the validity of the model. The comparison results show that the simulation aligns well with experimental and line data. The transient temperature‐field model of the bearing rotor system in high‐speed trains can effectively simulate and predict the temperature change process of each node of the system. The simulation results hold certain theoretical guiding significance for further research and practical applications in ensuring train operation safety.

A practical implementation of virtualized protection system with IEC61850 under Docker

Substation Automation Systems (SAS) are currently designed and operated based on IEC 61850 standard. This standard along with the evolution of the information processing technology brings the possibility of developing new centralized SAS architectures based on virtualized protection and control. However, communication latency must be evaluated as it can limit the scalability of virtualized SAS. This paper explores via a practical simulation case the effects of the number of virtual IEDs in the data traffic of a virtualized IEC61850 SAS based on Docker. Simulation results have shown that the increase in the network traffic due to a higher number of virtual IEDs produces a longer delay in tripping operation under a fault condition.

Investigation of condition monitoring system for grid connected photovoltaic (GCPV) system with power electronics converters using machine learning techniques

Power systems protection has become more vital in recent years to ensure stability, reliability, security, and power quality due to the exponential growth of grid-connected photovoltaic (GCPV) systems. As a result, several nations have set new grid codes for the grid integration of PV plant installations to overcome these concerns. Investigating Fault Ride Through (FRT) capacity is one of the primary criteria for grid codes. For 3-phase GCPV systems, fault detection and classification approaches are proposed in this study. Firstly, different faults that occurred in the GCPV system are categorized and compared, with the critical and analytical evaluation of grid codes, particularly FRT requirements such as Low Voltage Ride Through (LVRT) and High Voltage Ride Through (HVRT) for different nations. Further, a detailed classification and a comparison of the existing FRT techniques are presented for better control methods based on system complexity, detection accuracy, and other evolutionary criteria. To ensure smooth grid operation, accurate fault detection and condition monitoring of the systems are required. This paper discusses a machine learning (ML) based technique for detecting faults in 3-phase GCPV systems. Multi-peak phenomena caused by FRT capabilities and anti-islanding detection are the main problems experienced while integrating a PV system into the local grid. The fault classification technique is developed using weighted K-nearest neighbor (WKNN) and fine Gaussian support vector machine (FGSVM) based ML approaches utilizing Wavelet Transform. The proposed ML-based findings show that the fault detection algorithm-based classification accuracy has significantly improved.

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.

Vibrational Analysis on Cooling Fan with Induced Mechanical Faults

Abstract This paper reports the investigation of different mechanical faults such as unbalanced loading, broken blades, and deformed blades induced in a fan using vibration analysis. The fan used in this study was a CPU cooling fan as a representation of a basic fan system giving an idea of vibration signatures when it is subjected to faults conditions. This study assists in developing the techniques of condition monitoring for the diagnosis of failures and faults in fan systems while logging the operating status and trend in performance. The investigation is carried out in the turbine testing lab using a Tri-axial accelerometer and a commercial Intel® CPU cooling fan consisting of 7 blades. The fan was run at different RPMs with normal and fault conditions like unbalanced loading conditions induced by adding mass of different weights, breakage of blades, deformation of blades, and holes in the blades. These faults are induced to simulate various mechanical faults that frequently occur in fan-based systems. The vibration data obtained from the accelerometer are filtered and the time series of acceleration values are plotted using MATLAB. For further analysis of data, the Fast Fourier Transform is done to evaluate the peaks of vibration signals in normal and fault conditions. Vibrational amplitude in fault conditions like Hole-2 has 4.5 times higher amplitude than normal and Cut-2 has 31.5 times higher amplitude.

A New Cross-Domain Motor Fault Diagnosis Method Based on Bimodal Inputs

Electric motors are indispensable electrical equipment in ships, with a wide range of applications. They can serve as auxiliary devices for propulsion, such as air compressors, anchor winches, and pumps, and are also used in propulsion systems; ensuring the safe and reliable operation of motors is crucial for ships. Existing deep learning methods typically target motors under a specific operating state and are susceptible to noise during feature extraction. To address these issues, this paper proposes a Resformer model based on bimodal input. First, vibration signals are transformed into time–frequency diagrams using continuous wavelet transform (CWT), and three-phase current signals are converted into Park vector modulus (PVM) signals through Park transformation. The time–frequency diagrams and PVM signals are then aligned in the time sequence to be used as bimodal input samples. The analysis of time–frequency images and PVM signals indicates that the same fault condition under different loads but at the same speed exhibits certain similarities. Therefore, data from the same fault condition under different loads but at the same speed are combined for cross-domain motor fault diagnosis. The proposed Resformer model combines the powerful spatial feature extraction capabilities of the Swin-t model with the excellent fine feature extraction and efficient training performance of the ResNet model. Experimental results show that the Resformer model can effectively diagnose cross-domain motor faults and maintains performance even under different noise conditions. Compared with single-modal models (VGG-11, ResNet, ResNeXt, and Swin-t), dual-modal models (MLP-Transformer and LSTM-Transformer), and other large models (Swin-s, Swin-b, and VGG-19), the Resformer model exhibits superior overall performance. This validates the method’s effectiveness and accuracy in the intelligent recognition of common cross-domain motor faults.

Real-time risk prediction of chemical processes based on attention-based Bi-LSTM

Refined risk prediction must be achieved to guarantee the safe and steady operation of chemical production processes. However, there is high nonlinearity and association coupling among massive, complicated multisource process data, resulting in a low accuracy of existing prediction technology. For that reason, a real-time risk prediction method for chemical processes based on the attention-based bidirectional long short-term memory (Attention-based Bi-LSTM) is proposed in this study. First, multisource process data, such as temperature, pressure, flow rate, and liquid level, are preprocessed for denoising. Data correlation is analyzed in time windows by setting time windows and moving step lengths to explore correlations, thus establishing a complex network model oriented to the chemical production process. Second, network structure entropy is introduced to reduce the dimensions of the multisource process data. Moreover, a 1D relative risk sequence is acquired by max–min deviation standardization to judge whether the chemical process is in a steady state. Finally, an Attention-based Bi-LSTM algorithm is established by integrating the attention mechanism and the Bi-LSTM network to fit and train 1D relative risk sequences. In that way, the proposed algorithm achieves real-time prediction and intelligent perception of risk states during chemical production. A case study based on the Tennessee Eastman process (TEP) is conducted. The validity and reasonability of the proposed method are verified by analyzing distribution laws of relative risks under normal and fault conditions. Also, the proposed algorithm importantly improves the prediction accuracy of chemical process risks relative to that of existing prediction technologies.

Seismic response of mountain tunnel induced by fault slip

With the rapid development of Chinese transportation networks, such as the Sichuan-Tibet railway, numerous tunnels are under construction or planned in mountainous regions. Some of these tunnels must traverse or be situated near active fault zones, which could suffer damage from fault slip. In this study, the seismic response of a mountain tunnel subjected to coseismic faulting was analyzed using a fault-structure system in a two-step process. Firstly, a nonuniform slip model was proposed to calculate the ground deformations and internal displacements induced by a specific active fault on a geological scale, considering nonuniform slips on the fault plane. The 1989 Loma Prieta and 2022 Menyuan earthquakes were chosen as case studies to validate the proposed slip model. Secondly, the calculated displacement of the Menyuan earthquake was used as the input load for the discrete–continuous coupling analysis of the Daliang tunnel on an engineering scale. The simulated deformation of the Daliang tunnel aligned with the on-site damage observations following the Menyuan earthquake. Lastly, the effects of different fault conditions on the tunnel seismic response were investigated. The results indicate that the distribution of the peak longitudinal strain of the lining is governed by fault mechanisms, and the degree of fault slip significantly influences the response of the tunnel. A tunnel passing through an active fault with a wider fault fracture zone and smaller dip angle experience less damage.

FAULT DETECTION AND CLASSIFICATION USING DENSENET, SURF-BASED ANN, AND INFRARED THERMOGRAPHY

The reliability and efficiency of electric motors are critical in industrial applications, where unexpected faults can lead to costly downtime and safety hazards. Traditional fault detection methods often require extensive manual inspection and may not capture subtle anomalies in motor behavior. Infrared thermography has emerged as a non-invasive technique to detect temperature variations in motor components, which can indicate potential faults. However, the challenge lies in accurately classifying these faults to prevent failures. Current methods lack the precision needed to classify various motor faults accurately and quickly, especially when dealing with complex thermal patterns. The integration of advanced deep learning architectures with feature extraction techniques presents an opportunity to enhance the detection and classification of motor faults. This study proposes a hybrid model combining DenseNet, a deep learning architecture known for its high performance in image analysis, with a Speeded-Up Robust Features (SURF)-based Artificial Neural Network (ANN) for feature extraction and classification. Infrared thermography images of motors were first processed through DenseNet for initial feature extraction. The SURF algorithm further refined these features, which were then classified using ANN. The model was trained and validated on a dataset of infrared thermography images, representing various motor fault conditions, including bearing wear, misalignment, and insulation failure. The proposed model achieved an overall accuracy of 98.7% in detecting and classifying motor faults, outperforming traditional methods by 5.2%. The model also demonstrated high sensitivity (97.8%) and specificity (99.1%) in identifying subtle temperature variations indicative of early-stage faults. These results highlight the effectiveness of the DenseNet and SURF-based ANN approach in enhancing the reliability of motor fault detection using infrared thermography.

Failure analysis of arch dam under fault action based on inter-generational coordination

Complex geological faults play a crucial role in the site selection and construction of dams. In this paper, considering the intergenerational coordination theory and addressing the complex geological fault conditions of the LCGX hydropower project, the entire life cycle of the arch dam is divided into four stages: initial state, dam foundation excavation, arch dam casting, and reservoir operation. Improved multiple linear regression with modified boundary conditions is employed for the inversion of initial stress, considering the unloading effects caused by dam foundation excavation and the temperature differentials arising from concrete hydration during arch dam casting, and considering the effective stress principle during the water storage operation stage. Based on the above considerations, the whole life cycle of LCGX arch dam from initial state to excavation and casting operation and maintenance is analyzed, including the impact of faults in the LCGX dam site area on the arch dam. The water pressure overload method is employed to evaluate the safety of the arch dam. And the concrete grid backfill replacement is carried out by the combination of open excavation and hole excavation. The results indicate that faults have varying degrees of influence across different stages of intergenerational coordination, with f119 and F115 showing more pronounced effects. In the overload calculation, the f119 fault, along with the hazardous rock mass formed by cutting the foundation surface, and the f124 fault, cause the arch dam to undergo displacement, thus the arch dam occurs shear failure, the reinforcement greatly improves the safety of arch dam. This study systematically investigates the arch dam and slope displacement caused by complex geological faults in the LCGX hydroelectric project dam site area. It explores the specific impact of faults on the arch dam, providing essential references for structural safety during dam maintenance.

Robust Secondary Controller for Islanded Microgrids with Unexpected Electrical Partitions under Fault Conditions

Robust Secondary Controller for Islanded Microgrids with Unexpected Electrical Partitions under Fault Conditions

Honey Badger Algorithm Based DVR Controllers for Improved Power Quality‏ in a Microgrid Combined of (Wind System\ Grid\Nonlinear Load)

Power quality (PQ) is crucial in today's energy supply networks, where even little voltage fluctuations can have a big impact on how well household appliances and technologies operate. The suggested dynamic voltage regulator (DVR) approach helps to create a new generation power grid that is more dependable and effective. In this study, the honey badger optimizer (HBO) is used to optimize the controllers of the DVR for improving PQ via voltage control. The efficacy of the optimized DVR is further increased by its integration with a microgrid (MG) wind supply. The suggested technique makes use of a low-complexity control approach for voltage regulation to adjust for harmonic distortion, swells, and voltage dips in the addressed system. The technique accomplishes voltage improvement, bus stabilization, energy-efficient utilization, and harmonic distortion reduction by using the DVR in conjunction with an MG wind supply. Various voltage disturbances, such as balanced and unbalanced swell and sag, voltage imbalance, notching, various fault states, and power system harmonic distortion, are taken into consideration to show the approach's usefulness. The findings indicate PQ enhancement, demonstrating that the load voltage roughly matches the nominal value.

Modelling of electromechanical coupling dynamics for high-speed EHT system used in HEV and characteristics analysis

As the development of hybrid electric vehicle (HEV) to high-speed, heavy-load, the electromechanical coupling vibration becomes the bottleneck in high-speed electromechanical hybrid transmission (EHT) system. To explore the dynamics characteristics and optimization direction for high-speed EHT system, an electromechanical coupling dynamics model under multi-source excitations is established with lumped-distributed parameter method and verified with experiment data. The dynamics model shows higher accuracy in both time and frequency domain analysis. On this basis, firstly, the comparison between lumped-shaft and distributed-shaft model is studied under time and frequency domain. Comparing with the lumped-shaft model, the distributed-shaft model shows higher accuracy, and can better reflect the coupling vibration of multi-stage planetary gears (PGs). Secondly, the inherent vibration model is derived, and the effect of electromechanical coupling and high-speed working conditions on inherent vibration characteristics are studied. Thirdly, a new method called ‘machine-electricity-magnet coupling interface’ is proposed to reveal the coupling vibration phenomenon. In addition, the signal of stator current and electromagnetic torque includes the frequency of PGs, which is a basis on the fault diagnosis and state monitor of EHT system. Last but not the least, the vibration acceleration of EHT system is analysed under variable speed and load working conditions. Rotation speed and gear meshing force is found as the main influencing factor of series EHT system, and the specific optimization direction is also given.

An integrated Petri net-pseudo bond graph model for nuclear hazard assessment

A pseudo-bond graph is presented to model the heat transferred from the fuel rods to the coolant via its cladding in a generic nuclear reactor case study. Simulations performed using this model are used to explore the temperatures of the core components under ordinary and emergency scenarios, considering various conditions of coolant supply and reactor power output. The model is combined with a timed stochastic Petri net to produce a hybrid model, in which the reactor operation and fault status is determined by the Petri net and fed into the bond graph to examine the resulting impact on core temperatures, which in turn are fed back into the Petri net process. The results predict the distribution of the reactor operational durations before a disruption occurs. The model provides the temperature profiles attained by the cladding and fuel components, indicating a low probability of dangerous temperatures.

Toward understandable semi-supervised learning fault diagnosis of chemical processes based on long short-term memory ladder autoencoder (LSTM-LAE) and self-attention (SA)

Fault diagnosis and localization play vital role in chemical process monitoring. Finding the root cause of fault accurately and timely is the key to avoid serious accidents and ensure process safety. Unfortunately, most deep learning-based models only involves in predicting fault state and interpretability is still not fully explored. Besides, lack of labeled samples in practical situations makes supervised learning difficult to implement. To circumvent the obstacle, semi-supervised learning based on long short-term memory ladder autoencoder is combined with self-attention mechanism, which is intended to establish interpretable model by explicitly clarifying the corresponding relationship between abnormal variables and faults. Using Tennessee Eastman process and practical high-purity carbonate production process as benchmark, fault diagnosis and identification performance of proposed LSTM-LAE-SA is validated, and interpretability analysis is performed to demonstrate its capabilities in abnormal variable locations. The developed understandable model could improve operator's trust and industrial application in fault diagnosis system.

AC fault ride-through control strategy of MMC-UHVDC system with hierarchical connection mode

The ultra-high voltage direct current (UHVDC) transmission composed of modular multilevel converters (MMC) is an important technology for large-scale centralized transmission of renewable energy. In UHVDC system, temporary faults in the AC power grid system are a high probability fault, and the fault control strategy affects the safety and reliability of system operation. This paper studies on the condition of AC power grid faults occurring at the receiving converter station. Firstly, study the system characteristics after the occurrence of AC faults, and use theoretical analysis to derive the trend of DC voltage changes of each converter valve. Then, an AC fault ride-through control strategy with high power transmission capability is proposed with hierarchical connection structure, the strategy controls the system to synchronously reduce DC voltage and AC active power after a fault, maximizing the retention of the system’s transmission capacity during the occurrence of faults, thereby reduce power shock in the system. Finally, a simulation model of the dual ended system has built based on the PSCAD simulation platform. The simulation results show that when a single-phase ground fault and a three-phase ground fault occur in the high valve group at the receiving station, the system can retain about 83% and 50% of the transmission capacity during the fault period, respectively. Meanwhile, there is no serious overvoltage or overcurrent phenomenon in the system. The simulation results verified the effectiveness of the proposed control strategy.