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Ultra high voltage (UHV) transmission tower-lines systems (TTLSs) are responsible for the crucial function of transmitting and distributing electrical power over long distances, which inevitably traverse active tectonic faults when passing through seismic areas. This paper investigates the seismic behaviors of the fault-crossing UHV TTLSs, taking into account the traveling wave effects of fault-crossing ground motions. Furthermore, three scenarios (i.e. the fault located at the midspan, the side span, and the one side) according to the location of the faults relative to the TTLSs, are designated to investigate the effects of fault-crossing locations on the seismic behaviors of the fault-crossing TTLSs. The results indicate that the traveling wave effects may amplify or weaken the seismic responses and collapse of the near-fault and fault-crossing TTLSs, and the effects on the longitudinal displacement responses of the TTLS are significantly greater than those in the transverse direction, but they do not affect the distribution of the collapse weak position of the TTLSs across the fault. Besides, the effect of traveling waves on the seismic behaviors of the transmission tower further away from the fault is greater than that relatively closer to the fault. This study of the impact of the traveling wave effects and fault-crossing locations on the TTLSs serves as a valuable reference for seismic behaviors of the UHV TTLSs crossing faults. [ABSTRACT FROM AUTHOR]

Fault regions inferred to be slowly slipping are interpreted to accommodate much of tectonic plate motion aseismically and potentially serve as barriers to earthquake rupture. Here, we build on prior work using simulations of earthquake sequences with enhanced dynamic fault weakening to show how fault regions that exhibit decades of steady creep or transient slow‐slip events can be driven to dynamically fail by incoming earthquake ruptures. Following substantial earthquake slip, such regions can be under‐stressed and locked for centuries prior to slowly slipping again. Our simulations illustrate that slow fault slip indicates that a region is sufficiently loaded to be failing about its quasi‐static strength. Hence, if a fault region is susceptible to failing dynamically, then observations of slow slip could serve as an indication that the region is critically stressed and ready to fail in a future earthquake, posing a qualitatively different interpretation of slow slip for seismic hazard. Plain Language Summary: Earthquakes are thought to predominantly occur along sections of faults that appear stuck and actively accumulating strain under tectonic plate motion. Other fault regions observed to be slowly slipping are thought to release some of this strain without causing strong shaking, potentially limiting the location and amount of fault slip in earthquakes. Here we present numerical simulations of long‐term fault slip that add to a body of work showing how fault areas can host different styles of slow slip for several decades prior to failing destructively when pushed by an incoming earthquake rupture. Our models show how relatively short‐term observations of slow fault slip compared to the recurrence of large earthquakes over several centuries can mask fault regions that are capable of experiencing substantial slip in future earthquakes. Importantly, our simulations suggest that if a fault region is capable of failing during an earthquake, then observations of slow slip may indicate that the region is favorably stressed to fail in a future earthquake, representing a qualitatively different interpretation of slow slip for seismic hazard. Key Points: Short‐term inferences of fault coupling provide limited insight into which faults regions can undergo large slip in future earthquakesSteady and transient slow slip indicates that fault stress levels are loaded near quasi‐static failure conditionsIf a fault region is susceptible to failing dynamically, slow slip may suggest it is critically stressed to fail in a future earthquake [ABSTRACT FROM AUTHOR]

This article introduces a novel Quantum-inspired Future Search Algorithm (QFSA), an innovative amalgamation of the classical Future Search Algorithm (FSA) and principles of quantum mechanics. The QFSA was formulated to enhance both exploration and exploitation capabilities, aiming to pinpoint the optimal solution more effectively. A rigorous evaluation was conducted using seven distinct benchmark functions, and the results were juxtaposed with five renowned algorithms from existing literature. Quantitatively, the QFSA outperformed its counterparts in a majority of the tested scenarios, indicating its superior efficiency and reliability. In the subsequent phase, the utility of QFSA was explored in the realm of fault detection in underground power cables. An Artificial Neural Network (ANN) was devised to identify and categorize faults in these cables. By integrating QFSA with ANN, a hybrid model, QFSA-ANN, was developed to optimize the network's structure. The dataset, curated from MATLAB simulations, comprised diverse fault types at varying distances. The ANN structure had two primary units: one for fault location and another for detection. These units were fed with nine input parameters, including phase-currents and voltages, current and voltage values from zero sequences, and voltage angles from negative sequences. The optimal architecture of the ANN was determined by varying the number of neurons in the first and second hidden layers and fine-tuning the learning rate. To assert the efficacy of the QFSA-ANN model, it was tested under multiple fault conditions. A comparative analysis with established methods in the literature further accentuated its robustness in terms of fault detection and location accuracy. this research not only augments the field of search algorithms with QFSA but also showcases its practical application in enhancing fault detection in power distribution systems. Quantitative metrics, detailed in the main article, solidify the claim of QFSA-ANN's superiority over conventional methods. [ABSTRACT FROM AUTHOR]

In order to improve the effect and timeliness of ground fault monitoring and improve the effect of fault early warning, a DC ground fault monitoring method for electrical equipment in 110 kV smart substations based on improved rough sets is proposed. The characteristics of the DC ground fault current are analyzed, and the equivalent model of the DC system is constructed according to the analysis results. The low-frequency signal is enhanced according to the fuzzy rough set, and the low-frequency sampled transient zero-sequence current signal is enhanced preprocessing and wavelet packet decomposition according to the signal enhancement proportional coefficient, and the fault line is selected according to the principle of maximum energy. The wavelet packet transform method is used to decompose and analyze the waveform of the leakage current generated when a single-point grounding fault occurs in a branch of the DC system, and the grounding fault location of the DC system of the substation is realized by relative entropy operation. With the help of the cloud platform, a DC ground fault monitoring platform is built, and the ground fault monitoring is completed by using this platform. The experimental results show that the method can obtain the characteristic harmonics of the ground fault, accurately monitor whether the mutation occurs, and can realize the early warning of the ground fault of the electrical equipment in a faster time, indicating that it can realize the accurate and timely monitoring of the ground fault. [ABSTRACT FROM AUTHOR]

The grounding circulation current of the sheath and armour of single‐core AC submarine cables is high during operation, which can easily cause insulation damage and faults. Traditional methods that only monitor the amplitude of grounding circulation current make it difficult to detect defects in the cables, so further research is needed on the mathematical mechanism and detection methods of grounding circulation current. A mathematical model for the capacitance and impedance coupling of a single‐core submarine cable was established, and the influence of factors such as active power, length of armour stripping section, armour resistivity, armour magnetic permeability and grounding resistance on the grounding circulation current was analysed. A method for identifying abnormal grounding circulation current of submarine cables based on the combination of amplitude and phase was proposed. [ABSTRACT FROM AUTHOR]

Machine learned fault detection approaches are being increasingly used for fault detection in distribution grid. However, the performance of the models can be improved by customizing the models. In this regard, a customised artificial neural network (CANN) for fault detection in a distribution grid is proposed in this paper. The proposed work develops a CANN that combines the "up‐pyramid" and "down‐pyramid" model of ANN into a "custom‐pyramid" model. As a result, the same model can be used both for determining the types of fault as well as its location. The data needed to train the model has been taken from a reconfigured IEEE‐33 bus distribution system developed in Typhoon HIL real‐time simulator. Spectral‐kurtosis is utilized for extraction of features of the faulted transient signals which are used as input data to develop the CANN. The result showcases that the reduction of input features reduces computational complexity without compromising its accuracy. The proposed model classifies fault location with an accuracy of 95.43%. The proposed method also identifies fault type with an accuracy of 96.08%. Several test cases have been developed to test the method. The method proved to be able to perform in most of the cases. [ABSTRACT FROM AUTHOR]

The modern distribution network system is gradually becoming more complex and diverse, and traditional fault location methods have difficulty in quickly and accurately locating the fault location after a single-phase ground fault occurs. Therefore, this study proposes a new solution based on the Markov transfer field and deep learning to predict the fault location, which can accurately predict the location of a single-phase ground fault in the distribution network. First, a new phase-mode transformation matrix is used to take the fault current of the distribution network as the modulus 1 component, avoiding complex calculations in the complex field; then, the extracted modulus 1 component of the current is transformed into a Markov transfer field and converted into an image using pseudo-color coding, thereby fully exploiting the fault signal characteristics; finally, the Darknet-19 network is used to automatically extract fault features and predict the distance of the fault occurrence. Through simulations on existing models and training and testing with a large amount of data, the experimental results show that this method has good stability, high accuracy, and strong anti-interference ability. This solution can effectively predict the distance of ground faults in distribution networks. [ABSTRACT FROM AUTHOR]

Simultaneous diagnosis of multiple cables is a new research topic that focuses on reducing the weight, volume and computing time of fault-locating devices. For condition monitoring of energised cables, Spread Spectrum Reflectometry (SSR) has been extensively used. SSR includes Sequence Time Domain Reflectometry (STDR) and Spread Spectrum Time Domain Reflectometry (SSTDR) techniques. Recent research works have proposed the use of STDR for the simultaneous diagnosis of multiple cables. However, noise and interference limit the system's ability to identify small faults. In this paper, we conduct a detailed analysis of the application of the SSR method to simultaneous diagnosis of noisy cable bundles. The system is evaluated using the Detection Error Rate (DER) as a function of Signal-to-Noise Ratio (SNR), false alarm threshold, and fault location. To select the optimal spreading sequences' set for SSR, the performances of various sequences with good correlation properties were compared. [ABSTRACT FROM AUTHOR]

With the advancement in sensor and communication technology, autonomous systems have been incrementally reshaping the execution of tasks in commercial and military sectors. Since the systems are designed to complete tasks without or with minimal human intervention, fault diagnosis based on sensor data has been crucial to preventing accidents and fatalities. In this paper, fault diagnosis for autonomous systems is designed based on nonlinear adaptive observers, tested in numerical simulations, and implemented in a robotic platform. To this end, we utilize the persistence of excitation conditions on the parametric model of the faults. We derive sufficient conditions for the nonlinear adaptive observer in terms of linear matrix inequality to ensure the convergence of the estimates. Furthermore, we consider one‐sided Lipschitz conditions to obtain less conservative results. The main advantage of using the nonlinear adaptive observer is that the method converges quickly to the actual fault and requires minimum computational effort. However, solving the linear matrix inequality might not be trivial. Numerical simulations based on a single‐link flexible joint robot model and experimental tests in an autonomous quadcopter are performed to validate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

As the terminal of the power system, the distribution network is the main area where failures occur. In addition, with the integration of distributed generation, the traditional distribution network becomes more complex, rendering the conventional fault location algorithms based on a single power supply obsolete. Therefore, it is necessary to seek a new algorithm to locate the fault of the distributed power distribution network. In existing fault localization algorithms for distribution networks, since there are only two states of line faults, which can usually be represented by 0 and 1, most algorithms use discrete algorithms with this characteristic for iterative optimization. Therefore, this paper combines the advantages of the particle swarm algorithm and genetic algorithm and uses continuous real numbers for iteration to construct a successive particle swarm genetic algorithm (SPSO-GA) different from previous algorithms. The accuracy, speed, and fault tolerance of SPSO-GA, discrete particle swarm Genetic algorithm, and artificial fish swarm algorithm are compared in an IEEE33-node distribution network with the distributed power supply. The simulation results show that the SPSO-GA algorithm has high optimization accuracy and stability for single, double, or triple faults. Furthermore, SPSO-GA has a rapid convergence velocity, requires fewer particles, and can locate the fault segment accurately for the distribution network containing distorted information. [ABSTRACT FROM AUTHOR]

The complexity and the criticality of automotive electronic implanted systems are steadily advancing and that is especially the case for automotive software development. ISO 26262 describes requirements for the development process to confirm the safety of such complex systems. Among these requirements, fault injection is a reliable technique to assess the effectiveness of safety mechanisms and verify the correct implementation of the safety requirements. However, the method of injecting the fault in the system under test in many cases is still manual and depends on an expert, requiring a high level of knowledge of the system. In complex systems, it consumes time, is difficult to execute, and takes effort, because the testers limit the fault injection experiments and inject the minimum number of possible test cases. Fault injection enables testers to identify and address potential issues with a system under test before they become actual problems. In the automotive industry, failures can have serious hazards. In these systems, it is essential to ensure that the system can operate safely even in the presence of faults. We propose an approach using natural language processing (NLP) technologies to automatically derive the fault test cases from the functional safety requirements (FSRs) and execute them automatically by hardware-in-the-loop (HIL) in real time according to the black-box concept and the ISO 26262 standard. The approach demonstrates effectiveness in automatically identifying fault injection locations and conditions, simplifying the testing process, and providing a scalable solution for various safety-critical systems. [ABSTRACT FROM AUTHOR]

Due to the overlap of initial traveling wave signals, the traveling wave propagation process in hybrid distribution lines is complicated to analyze. The most significant challenge posed by the traditional passive traveling wave-locating method for hybrid distribution lines lies in identifying the fault section and distinguishing the reflected wave from the fault point or the hybrid connection points. Based on this approach, with the application of the aerial-mode component of the pulse signal generated at the fault point, a fault-section-identification and fault-locating scheme for hybrid distribution feeders with active pulse injection is proposed. When power in a line is cut after a single-to-line ground (SLG) fault occurs, the same pulse is injected into the three phases from the neutral point of the coupling capacitor bank to construct the zero-mode component, which propagates to the SLG fault three-phase asymmetrical point, producing an aerial-mode component that is reflected back to the first end of the line. With the application of the arrival time of an aerial-mode wavefront, it is simple to locate the SLG fault for arbitrary forms of hybrid lines. The simulation results confirm the feasibility of the fault-locating scheme under different feeders, different fault locations, and fault resistances. The results of the experiments confirm the high practical value of the proposed method. [ABSTRACT FROM AUTHOR]

Under a distributed information system, the scale of various operational components such as applications, operating systems, databases, servers, and networks is immense, with intricate access relationships. The silo effect of each professional is prominent, and the linkage mechanism is insufficient, making it difficult to locate the infrastructure components that cause exceptions under a particular application. Current research only plays a role in local scenarios, and its accuracy and generalization are still very limited. This paper proposes a novel fault location method based on dynamic operation maps and alarm common point analysis. During the fault period, various alarm entities are associated with dynamic operation maps, and alarm common points are obtained based on graph search addressing methods, covering deployment relationship common points, connection common points (physical and logical), and access flow common points. This method, compared with knowledge graph approaches, eliminates the complex process of knowledge graph construction, making it more concise and efficient. Furthermore, in contrast to indicator correlation analysis methods, this approach supplements with configuration correlation information, resulting in more precise positioning. Through practical validation, its fault hit rate exceeds 82%, which is significantly better than the existing main methods. [ABSTRACT FROM AUTHOR]

Renewable energy resources have increasingly been integrated into electric power grids. Moreover, new challenges constantly arise, showing the need for modernization in monitoring, control, and protection. Transmission and distribution systems have explored the use of phasor measurement units (PMUs) in an attempt to meet these requirements. The decreasing cost of hardware components of PMUs, similar to what occurred with digital relays, is an important factor in terms of their worldwide use in different applications. Low-cost variations of PMUs focused on distribution systems, such as micro- and D-PMUs, are growing in number. Thus, this survey article presents significant and recent research conducted in the field of power systems using PMUs. Firstly, it briefly describes the characteristics of PMUs concerning hardware and phasor estimation algorithms. Afterward, recent studies related to transmission and distribution system protection based on PMUs are presented, such as fault detection and location, stability, and wide-area protection. Finally, the article also provided important guidelines, which certainly can be useful for future research in the years to come. [ABSTRACT FROM AUTHOR]

To address the challenge of inaccurate fault location of variational mode decomposition (VMD) in practical engineering, due to poor choice of mode decomposition number K and quadratic penalty factor α, a traveling wave fault location method using Northern Goshawk optimization algorithm (NGO) to optimize VMD was proposed. First, the NGO algorithm is used to optimize VMD, and the optimal K and α are obtained. Secondly, the optimal parameters are inputted into VMD for fault signal decomposition, and the eigenmode components are obtained. Due to the difficulty of identification of the traveling wave head in the process of traveling wave propagation, Hilbert transform is used to determine the time of initial arrival of the traveling wave head at both ends of the line, and the fault location is precisely calculated by using the two-ended traveling wave fault detection formula. Finally, simulation experiments are carried out to verify the accuracy of the proposed location method, which shows that the proposed location method can locate the fault more accurately and has good engineering application value. [ABSTRACT FROM AUTHOR]

Power transmission lines in Ethiopia are experiencing an alarmingly high frequency of unexplained outages triggered by environmental factors, which significantly undermine the reliability of the country's power system. This paper presents investigations aiming to identify those among the unexplained fault records that have been caused by pollution induced flashovers. An identification method is developed, which associates the contextual fault features, such as information about the characteristics of the fault, fault location, and time of day, as well as month of its occurrence with local meteorological/climatic and environmental conditions. A total number of 4231 unexplained faults, recorded between 2015 and 2022, were analyzed. Among them, 1045 faults (24.7%) were identified as being most likely caused by pollution induced flashovers. The entire network suffered from more than 130 pollution-induced flashovers annually with a frequency of about 0.8 faults/100 km/year. The fault frequency strongly differed among the grid regions, being highest in the Northeast and lowest in the Southwest region. Moreover, the performed analyses also concentrated on the evaluation of the pollution performance of various insulator types employed in the network. The results indicate that porcelain insulators have the highest pollution-induced flashover intensity of 1.47 faults/year/1000 units, followed by silicone rubber polymeric composite insulators and glass insulators with the respective intensities of 1.21 and 0.83 faults/year/1000 units. These results indicate that despite the high expectations towards the pollution performance of silicone rubber polymeric insulators, their use in the Ethiopian climatic and environmental conditions appears to be unsatisfactory. [ABSTRACT FROM AUTHOR]

Three-dimensional finite-element models, which can handle the stress perturbations caused by subsurface mechanical heterogeneities and fault interactions, have been combined with the finite source inversion to estimate the coseismic slip distribution over the fault plane. However, the mesh grid for discretizing the governing equations in the finite-element model significantly affects the numerical accuracy. In this study, we performed kinematic finite source inversion with idealized (regular observation point array; M1A–M1D) and regional (GEONET, GPS Earthquake Observation Network System stations in Japan; M2A–M2H) models with different mesh sizes to quantitatively analyse the effect of the mesh grid size around the fault plane on the inverted fault slip distribution. Synthetic observation data vectors obtained from the finest models (M1A and M2A) are compared with those from the coarser models (M1B–M1D and M2B–M2H), which were adopted to construct Green's function matrix. We found that the coarser mesh models derived a smaller surface displacement, leading to a decrease in the norm of Green's function matrix, which in turn influences the fault slip magnitude from the finite source inversion. Finally, we performed the source inversion for the fault slip distribution of the 2011 M w 9.0 Tohoku–Oki earthquake using the coseismic surface displacements recorded at the GEONET and seafloor stations and finite-element modelling. By reducing the mesh size on the fault, we confirmed that the estimated magnitude of fault slip converged to approximately 80 m, which is consistent with the range of fault slip amounts from previous studies based on the Okada model. At least 0.88 million total domain elements and a 6.7 km2 mesh size on the fault plane with an area of 240 × 720 km2 are required for the convergence of the fault slip. Furthermore, we found that the location of the maximum fault slip is less sensitive to the mesh size, implying that source inversion based on a coarse mesh model (i.e. less than 0.5 million elements and > ∼60 km2 mesh size) can quickly provide the rough fault slip distribution. [ABSTRACT FROM AUTHOR]

Ensuring the safe and stable operation of high-speed trains necessitates real-time monitoring and diagnostics of their suspension systems. While machine learning technology is widely employed for industrial equipment fault diagnosis, its effective application relies on the availability of a large dataset with annotated fault data for model training. However, in practice, the availability of informational data samples is often insufficient, with most of them being unlabeled. The challenge arises when traditional machine learning methods encounter a scarcity of training data, leading to overfitting due to limited information. To address this issue, this paper proposes a novel few-shot learning method for high-speed train fault diagnosis, incorporating sensor-perturbation injection and meta-confidence learning to improve detection accuracy. Experimental results demonstrate the superior performance of the proposed method, which introduces perturbations, compared to existing methods. The impact of perturbation effects and class numbers on fault detection is analyzed, confirming the effectiveness of our learning strategy. [ABSTRACT FROM AUTHOR]

Data-driven fault diagnosis method is widely used in the field of engine health management, which uses engine sensor data as input and engine faulty components as output for component-level fault diagnosis of the engine. The application premise of the general data-driven fault diagnosis method is that all data come from the same working conditions, that is, they belong to the same distribution. However, this assumption is not valid in the actual engine fault diagnosis, because the engine state will change with the increase of running time. In the meantime, collecting engine data is usually expensive, time-consuming, and laborious. To solve these problems, extreme learning machine (ELM)-based two transfer learning methods for fault diagnosis of turboshaft engines are proposed in this paper. One is joint solving ELM (JSELM), which regards the information of the target domain and source domain as similar and different parts, respectively, and knowledge is extracted from them at the same time. The other is model transfer-based ELM (MTELM), which uses the idea of pretraining. First, a general ELM classifier is trained with the source domain data and then fine-tuned with the target domain data. Both methods have a good real-time performance as the traditional ELM. When there are a few data in the target domain, they achieve much better classification accuracy than traditional ELM. Finally, experiments are carried out with turboshaft engine simulation data. The results show that both methods are effective, especially MTELM, which has better classification accuracy. [ABSTRACT FROM AUTHOR]

Machine learning (ML) algorithms are increasingly used in power systems applications. One important application is the classification and localization of various types of transmission line faults. Using voltage and current measurements from phasor measurement units (PMUs), a number of useful features can be extracted, which can form the basis of a ML-based prediction of the fault type, line, and distance on the line. This paper proposes a technique to find the optimal number and placement of PMUs by performing thorough feature selection. The features are selected to maximize the accuracy of the ML classification and regression algorithms. The results show that for the IEEE 14 bus system, the use of only five PMUs is sufficient to obtain high levels of accuracy. For example, a testing accuracy of 99.0% and 97.1% can be achieved for the fault type and fault line location, respectively. As for the fault distance along the line, the testing MAE of 3.1% can be obtained along with an R 2 score of 94.4%. Adding more PMUs does not provide any additional value in terms of accuracy. [ABSTRACT FROM AUTHOR]

In industrial applications, three-phase induction motors (IMs) are widely used, and they are subjected to many types of faults. One such fault is the loss of a motor phase caused by a blown fuse, a broken wire, mechanical damage, etc. When this fault occurs during motor operation, it continues to rotate but experiences rapid heating, which can ultimately lead to motor failure. Therefore, various protection devices are available to protect the motor against this fault, but most traditional protection devices do not offer a comprehensive classification of such a fault. So, in this paper, an efficient neural network model is presented for detecting and classifying 12 types of phase loss faults for a three-phase induction motor (IM) based on factors such as the unhealthy phase, fault location, and motor action modes (standstill, transient, and steady-state modes). Thus, the main goal of this work is to determine the motor mode during the fault, the defective phase, and its location to help the maintenance team repair the fault quickly. The system is simulated and tested using the "MATLAB/Simulink" software, employing a feed-forward neural network. The simulation results demonstrate that the proposed network achieves correct detection and classification of phase loss faults within a short time frame from the occurrence of the fault. Therefore, the proposed network model proves to be a simple and reliable solution for integration into the protection system of a three-phase IM, enabling the detection and classification of various phase loss faults. [ABSTRACT FROM AUTHOR]

The paper presents a ground fault location algorithm (GFLA) in double‐circuit transmission lines (DCTL) with a T‐off connection to an industrial microgrid (IMG) containing electric arc furnaces (EAF), renewable distributed generations (RDG), and static VAR compensator (SVC). The design presented utilizes the information on the current and voltage phasors of one side of the transmission lines (TL) during the fault period, and there is no need for the availability of information on the type of faulty phase(s) and even the faulty section. To find the exact location of the fault in this design, the voltage phasor equations governing the fault point in positive, negative, and zero‐sequence (PNZS) domains, the zero‐sequence current (ZSC) equation of the fault point, and the boundary condition equation of the fault point have been used. The simulation system is implemented in Simulink/MATLAB using accurate modelling of all network components. The algorithm has been tested and evaluated in different fault conditions, including various locations, phase types, phase‐to‐ground connection resistance, occurrence time, and microgrid (MG) operation modes. The successful results of various tests of the proposed design as well as the analysis of various sensitivities confirm the satisfying performance of the suggested algorithm. [ABSTRACT FROM AUTHOR]

In order to address the problem that the main drive system of rolling mill is easily affected by the impact of biting steel, and considering the nonlinear friction damping and the external perturbations of the main drive system of rolling mill during the rolling process, a fault model of the main drive system of rolling mill is established, and a fault diagnosis and fault tolerance control method of the main drive system of rolling mill based on the nonlinear sliding-mode observer is proposed. In order to suppress the influence of external perturbations on fault diagnosis, a nonlinear sliding-mode observer is constructed for fault diagnosis and fault reconfiguration of the system, and the robustness of the observer to fault reconfiguration is improved by using the sliding-mode control rate v , and the stability of the designed nonlinear sliding-mode observer is proved using Lyapunov's stability theorem. In order to ensure that the system can operate normally even after a fault occurs, a reference model is designed, and a new controller is redesigned for fault-tolerant control of the system by adding a fault compensation term to the original control scheme using fault estimation information. Through the simulation study of the main drive system of stand F4 of 2030 mm cold rolling mill, it is verified that the observer can accurately track the system state with an angular velocity error of 2.45% and detect and estimate the main drive system failure of rolling mill with an estimation error of no more than 0.04% after a fault occurs; the fault-tolerant control of the main drive system of rolling mill is carried out by using the fault information to restore the system to its normal state, and the angular velocity error is 1.89%. [ABSTRACT FROM AUTHOR]

Aiming at the problem of inaccurate transformer fault diagnosis in dissolved gas analysis, this paper proposes a novel diagnostic method that integrates an enhanced honey badger algorithm (EHBA) with an ensemble learning-based deep hybrid kernel extreme learning machine (DHKELM). First, kernel principal component analysis (KPCA) was deployed for feature fusion of the gas data, thus extracting more effective features. The DHKELM, combining polynomial and RBF kernel functions, was used as a base learning to build a powerful classifier with Adaboost framework. The EHBA introduces information sharing and firefly perturbation strategies based on HBA. This EHBA was harnessed to optimize the DHKELM's critical parameters, establishing the EHBA-DHKELM-Adaboost transformer fault diagnosis model. Finally, the features garnered by KPCA were fed into the model, simulating and validating various fault diagnosis models. The findings reveal that EHBA-DHKELM-Adaboost achieves 98.75% diagnostic accuracy in transformer faults, surpassing other models. [ABSTRACT FROM AUTHOR]

This research focuses on leveraging wavelet transform for fault classification within electrical power transmission networks. This study meticulously examines the influence of various parameters, such as fault resistance, fault inception angle, fault location, and other essential components, on the accuracy of fault classification. We endeavor to explore the interplay between classification accuracy and the input data while assessing the efficacy of combining wavelet analysis with deep learning methodologies. The data, sourced from network recorders, including phase currents and voltages, undergo a scaled continuous wavelet transform (S-CWT) to generate scalogram images. These images are subsequently utilized as inputs for pretrained deep learning models. The experiments encompass various fault scenarios, spanning distinct fault types, locations, times, and resistance values. A remarkable feature of the proposed work is the attainment of 100% classification accuracy, obviating the need for additional algorithmic enhancements. The foundation of this achievement is the deliberate selection of the right input. The decision to employ an identical number of samples as the number of scales for the CWT emerges as a pivotal factor. This approach underpins the high accuracy and renders supplementary algorithms superfluous. Furthermore, this research underscores the versatility of this approach, showcasing its effectiveness across diverse networks and scenarios. Wavelet transform, after rigorous experimentation, emerges as a reliable tool for capturing transient fault characteristics with an optimal balance between time and frequency resolutions. [ABSTRACT FROM AUTHOR]

Evaluating the physical mechanisms that link hydraulic fracturing (HF) operations to induced earthquakes and the anticipated form of the resulting events is significant in informing subsurface fluid injection operations. Current understanding supports the overriding role of the effective stress magnitude in triggering earthquakes, while the impact of change rate of effective stress has not been systematically addressed. In this work, a modified critical stiffness was brought up to investigate the likelihood, impact, and mitigation of induced seismicity during and after hydraulic fracturing by developing a poroelastic model based on rate-and-state fraction law and linear stability analysis. In the new criterion, the change rate of effective stress was considered a key variable to explore the evolution of this criterion and hence the likelihood of instability slip of fault. A coupled fluid flowedeformation model was used to represent the entire hydraulic fracturing process in COMSOL Multiphysics. The possibility of triggering an earthquake throughout the entire hydraulic fracturing process, from fracturing to cessation, was investigated considering different fault locations, orientations, and positions along the fault. The competition between the effects of the magnitude and change rate of effective stress was notable at each fracturing stage. The effective stress magnitude is a significant controlling factor during fracturing events, with the change rate dominating when fracturing is suddenly started or stopped. Instability dominates when the magnitude of the effective stress increases (constant injection at each fracturing stage) and the change rate of effective stress decreases (the injection process is suddenly stopped). Fracturing with a high injection rate, a fault adjacent to the hydraulic fracturing location and the position of the junction between the reservoir and fault are important to reduce the Coulomb failure stress (CFS) and enhance the critical stiffness as the significant disturbance of stresses at these positions in the coupled process. Therefore, notable attention should be given to the injection rate during fracturing, fault position, and position along faults as important considerations to help reduce the potential for induced seismicity. Our model was verified and confirmed using the case of the Longmaxi Formation in the Sichuan Basin, China, in which the reported microseismic data were correlated with high critical stiffness values. This work supplies new thoughts of the seismic risk associated with HF engineering. [ABSTRACT FROM AUTHOR]

Adaptive high-rise buildings can achieve significantly reduced resource consumption by incorporating actuators, sensor systems, and a control unit into the structure to compensate for the reduced stiffness. A vital step toward the wide real-world viability of this concept is ensuring the long-term operational reliability of adaptive buildings even in case of components malfunctioning. This article presents a comprehensive mechanical model of a large-scale adaptive high-rise structure incorporating various sensor systems as well as different actuation principles. As a basis for the simulative validation under critical load scenarios, a detailed model for wind disturbances is derived, accounting for time-variant velocity and direction. A parity space-based fault diagnosis scheme is employed for the reliable diagnosis of faulty sensors and actuators. Based upon the fault diagnosis, a reconfiguration strategy for estimation and control in adaptive structures is proposed. Assembling these components yields a fault-tolerant control loop that automatically adapts to the emergence of faults in sensors and actuators. In exemplary simulation scenarios, the proposed approach is demonstrated to be effective, largely mitigating the faults' impact and recovering the nominal performance. [ABSTRACT FROM AUTHOR]

Voltage-source inverter is a significant part of power supply for various applications. In hard working conditions, the inverter is prone to failure, such as open-circuit (OC) faults, which bring great risk to the safe operation. Therefore, a simple and robust fault diagnosis method for OC faults of voltage-source inverter is proposed. First, the fault principle of OC fault is studied, and a rule of voltage sequence is found that the sequence of positive and negative abnormal voltages is related to the fault position. Then, available line voltages are used as fault diagnosis variables, the fault phase is detected by abnormal values of line voltages, and the fault switch is located according to the fault phase and the rule of voltage sequence. Different from existing OC fault diagnosis methods, the proposed method is immune to phase to phase short-circuit fault, which has not been referred to or discussed in existing literatures yet but can lead to the false diagnosis of OC fault diagnosis methods. Moreover, the proposed method is robust to load variation, switching and fundamental frequencies. The experimental tests are carried out on a dSPACE platform, and the proposed method is verified. [ABSTRACT FROM AUTHOR]

The earthquake-resistant design of lifelines, such as pipelines, tunnels and bridges, is based on the reliable representation and estimation of the seismic loading. In the case of lifeline–fault crossings, the design fault displacement is typically derived from estimates based on fault dimensions via empirical fault scaling relations for a given "design" scenario event. This approach comes with an unknown level of safety because the fault productivity and the actual distribution of earthquake events are essentially disregarded. To overcome this challenge, a simplified approach is proposed by statistically analyzing the outcome of probabilistic fault displacement hazard analyses (PFDHAs). A selection of faults from the 2020 European Fault-Source Model is used to build the logic tree and to set the range of parameters considered in the PFDHAs. The methodology allows the (mostly conservative) approximation of the fault displacement corresponding to any given return period based on readily available data, namely fault productivity, fault mechanism, fault length, and lifeline crossing location on the fault. The proposed methodology has been proposed and adopted as an informative Annex in prEN 1998-4:2022. [ABSTRACT FROM AUTHOR]

High-resistance ground faults are difficult to detect with existing ultrahigh voltage direct current (UHVDC) transmission fault detection systems because of their low sensitivity. To address this challenge, a straightforward mathematical method has been proposed for fault detection in UHVDC system based on the downsampling factor (DF) and approximation derivatives (AD). The signals at multiple sampling frequencies were analysed using the DF, and the AD approach was used to generate various levels of detail and approximation coefficients. Initially, the signals were processed with different DF values. The first, second, and third order derivatives of the generated signals were calculated by the AD method. Next, the entropy features of these signals were computed, and the Random Forest-Recursive feature elimination with cross-validation (RF-RFECV) algorithm was used to select a high-quality feature subset. Finally, an ensemble classifier consisting of Light Gradient Boosting Machine (LightGBM), K Nearest Neighbor (KNN), and Naive Bayes (NB) classifiers was utilized to identify UHVDC faults. The MATLAB/Simulink simulation software was used to develop a ±800 kV UHVDC transmission line model and perform simulation experiments with various fault locations and types. Based on the experiments, it has been established that the suggested approach is highly precise in detecting several faults on UHVDC transmission lines. The method is capable of accurately identifying low or high resistance faults, irrespective of their incidence, and is remarkably resistant to transitional resistance. Furthermore, it exhibits excellent performance in identifying faults using a small sample size and is highly reliable. [ABSTRACT FROM AUTHOR]

The research and application of bearing fault diagnosis techniques are crucial for enhancing equipment reliability, extending bearing lifespan, and reducing maintenance expenses. Nevertheless, most existing methods encounter challenges in discriminating between signals from machines operating under normal and faulty conditions, leading to unstable detection results. To tackle this issue, the present study proposes a novel approach for bearing fault detection based on graph neural networks and ensemble learning. Our key contribution is a novel stochasticity-based compositional method that transforms Euclidean-structured data into a graph format for processing by graph neural networks, with feature fusion and a newly proposed ensemble learning strategy for outlier detection specifically designed for bearing fault diagnosis. This approach marks a significant advancement in accurately identifying bearing faults, highlighting our study's pivotal role in enhancing diagnostic methodologies. [ABSTRACT FROM AUTHOR]

Once a fault occurs in the nanofiber sensor, the scientific and reliable three-dimensional (3D) human motion detection results will be compromised. It is necessary to accurately and rapidly perceive the fault signals of the nanofiber sensor and determine the type of fault, to enable it to continue operating in a sustained and stable manner. Therefore, we propose a fault signal perception method for 3D human motion detection nanofiber sensor based on multi-task deep learning. First, through obtaining the fault characteristic parameters of the nanofiber sensor, the fault of the nanofiber sensor is reconstructed to complete the fault location of the nanofiber sensor. Second, the fault signal of the nanofiber sensor is mapped by the penalty function, and the feature extraction model of the fault signal of the nanofiber sensor is constructed by combining the multi-task deep learning. Finally, the multi-task deep learning algorithm is used to calculate the sampling frequency of the fault signal, and the key variable information of the fault of the nanofiber sensor is extracted according to the amplitude of the state change of the nanofiber sensor, to realize the perception of the fault signal of the nanofiber sensor. The results show that the proposed method can accurately perceive the fault signal of a nanofiber sensor in 3D human motion detection, the maximum sensor fault location accuracy is 97%, and the maximum noise content of the fault signal is only 5 dB, which shows that the method can be widely used in fault signal perception. [ABSTRACT FROM AUTHOR]

Computer-aided weld defect recognition is transforming the field of Non-Destructive Testing by addressing the shortcomings of slow and error-prone manual inspections. This technology provides a reliable solution for detecting changes in pipeline conditions and structural damage. While conventional neural networks fall short in precise fault localization, deep learning-based object detection techniques step in to fill the gap. Addressing a real-industrial problem, particularly visually inspecting an X-ray welding database, without relying on a pre-existing benchmark presents a significant challenge in this field. Additionally, the poor quality of our welding data, which is riddled with small, sticky porosity in each image, poses several issues related to selecting the appropriate deep neural network object detector. This is yet another challenge that needs to be tackled. To direct these challenges, we introduced a novel approach based on the renowned Faster RCNN architecture to develop a model specifically designed for weld defect detection and recognition. This study dives deep into the inner workings of this newly adopted methodology. In our research, we have thoroughly parameterized, trained, tested, and validated this model. Our approach stands out through a comparative analysis with YOLO and DCNN models, highlighting the superiority of our Faster RCNN-based system. By evaluating its robustness and efficiency, our study reveals that the Faster RCNN model outperforms its counterparts in weld defect detection and localization for this specific small and sticky porosity defect type. This stands as a testament to effectively setting a new standard in this area. [ABSTRACT FROM AUTHOR]

Electrical resistivity tomography (ERT) and topographic profiles obtained by differential global positioning system were used to study depth continuity of structures showing neotectonic activity in the southwestern sector of the San Guillermo Elevated Block (SGEB), a morphostructural unit located in the Pampas region of Argentina. Data obtained from the surface and near-surface allowed us to identify the features of a fold caused by the propagation of high-angle reverse blind faults. Distribution of resistivities found in the near-surface seems to indicate westward propagation of the deformation front. One of the geoelectric levels interpreted in the ERT sections was used as a stratigraphic marker; the top of this level was compared with the depth found in wells drilled in different localities surrounding the study area, identifying lateral discontinuities that allow us to infer at least one stage of tectonic activity during the Pliocene–Early Pleistocene. The top of these deposits is shallower within the SGEB. On the other hand, the analysis of additional exploratory drilling allowed us to verify that the crystalline basement is fractured and is deeper than the western area outside the morphostructural unit. We propose that there are ancient pre-existing faults, created under an extensional regime, that were later reactivated involving multiple deformation events under a compressional regime. The normal displacement of crystalline basement blocks due to extensional tectonics would have been only partially inverted later during reactivation. Schematic block diagram showing the electrical resistivity tomography (ERT) sections and the General Roca well projected at the latitude of the ERT, representing the interpreted folding (dashed line) and the difference in level of the stratigraphic marker of the Miocene Paraná/Puelches sands. The wider and deeper block diagram (down right) shows the normally-faulted crystalline basement at depth. Note that the ERT could be a tool to map the location of the fault tips, recently reactivated with reverse displacement. SGEB: San Guillermo Elevated Block. [ABSTRACT FROM AUTHOR]

The coupling of AC and DC power will impact the protective actions on the AC side and pose a threat to the stable operation of the interconnection system. Therefore, a new longitudinal protection method is proposed based on the comprehensive distance similarity of voltage waveforms. Initially, the measured voltage and current data are extracted to calculate the reference voltage, and the voltage waveform fitting is optimized. Subsequently, the Euclidean dynamic time warp (DTW) distance and entropy weight method are utilized to process the voltage waveform, enabling the calculation of its comprehensive distance similarity. This similarity is adopted to determine fault location. A hybrid DC multi-feed AC/DC interconnection system, incorporating a line commutated converter-voltage source converter (LCC-VSC) and a line commutated converter-modular multilevel converter (LCC-MMC), was established in PSCAD, and fault data were simulated and output. The effectiveness of the protection scheme was validated using MATLAB. Simulation results demonstrate that the proposed method can accurately distinguish between faults inside and outside a region. When compared to existing protection methods, it demonstrates superior performance in resisting transition resistance and noise interference, while also mitigating the impact of data asynchronicity. The speed and reliability of the method are further enhanced. [ABSTRACT FROM AUTHOR]

This paper presents the impact of adding single-phase photovoltaic systems on the fault current of short-circuit faults in asymmetric 400 V low-voltage feeders. This study also attempts to investigate the impact of various parameters on the fault current, such as the penetration coefficient of photovoltaic systems, the impact of downstream or upstream fault on the system installation location, and distance of photovoltaic systems and fault location. Simulations were carried out in the DIgSILENT software. The results show that the fault current will be more different when using photovoltaic systems and that difference is sometimes incremental and sometimes decremental. Therefore, if the penetration coefficient of the system is high, it will sometimes have a significant impact on the protection system. [ABSTRACT FROM AUTHOR]

Accurate topology identification (TI) is essential for various applications, including fault location, load flow analysis, state estimation and system planning in the distribution networks. However, TI is vulnerable to missing measurements that may arise due to meter malfunctions and communication failure due to denial of service (DoS) attacks. Thus, a robust and novel methodology for accurate TI is proposed in this study. The proposed methodology is divided into two steps: 1) generating the recompensed measurement data by attention mechanism-based transformer model and 2) topology identification from the recompensed measurement data. For evaluating the efficacy of the proposed methodology, a comprehensive study is conducted to assess the influence of renewable energy sources (RES) on the prediction performance. This investigation aims to quantify the degree to which the integration of RES influenced the proposed approach's efficacy and robustness. The proposed approach has been tested and evaluated with varying percentages of missing data such as 10%, 30% and 50%, for the modified IEEE 37 and 69 node system. In addition, a comparative study with various reference models for the missing measurements forecasting is also conducted. The case study results indicate that the proposed approach outperforms other approaches in missing measurement forecasting and TI, while also demonstrating resilience in the context of missing measurements. The proposed approach improved the performance of the system by more than 35% for the 10%, 30%, and 50% missing percentages in the test systems. [ABSTRACT FROM AUTHOR]

When the beamforming algorithm is used to locate the fault of the internal structure, the effective fault acoustic signal will be reflected by the structure shell during its transmission process. This results in the microphone signals being polluted, and then the positioning accuracy being low. In this paper, a phase-shifted beamforming algorithm is proposed to segment the microphone signal, extract the time delay of the reflected noise by using the cross correlation property, and then subtract the reflected noise from the raw data to obtain the microphone signal with weakened reflected signal. The newly generated signals are utilized to calculate the cross-spectral matrix of beamforming, which is then used to locate vibration faults. Simulation results show that the phase-shifted beamforming algorithm can more accurately locate the vibration faults of reflective structures. [ABSTRACT FROM AUTHOR]

Diagnosis is an important technique for fault detection and location in interconnection network. The half hypercube, constructed from the hypercube, has been proven to possess several advantageous properties for interconnection networks, such as symmetry, smaller diameter, fewer edges and lower overhead. In this paper, we study the fault-tolerant embedding of Hamiltonian cycles and design an adaptive diagnosis algorithm for the half hypercube. Firstly, we prove that the half hypercube is Hamiltonian and propose an algorithm to construct a Hamiltonian cycle in the network. Furthermore, we prove that the half hypercube is Hamiltonian with no more than (⌈ n / 2 ⌉ - 1) faulty edges. Finally, we design a parallel adaptive diagnosis scheme under the PMC model, a system-level diagnosis model proposed by P, M, and C, which can identify almost all faulty vertices in five rounds. Simulation results demonstrate that the proposed algorithm is more effective, reducing running time by approximately 50% when compared to the Hamiltonian cycle embedding algorithm for the hypercube with the same dimension. [ABSTRACT FROM AUTHOR]

A shallow crustal velocity structure (above 10 km depth) is essential for understanding the crustal structures and deformation and assessing the exploration prospect of natural resources, and also provides priori information for imaging deeper crustal and mantle structure. Passive-source seismic methods are cost-effective and advantageous for regional-scale imaging of shallow crustal structures compared to active-source methods. Among these passive methods, techniques utilizing receiver function waveforms and/or body-wave amplitude ratios have recently gained prominence due to their relatively high spatial resolution. However, in basin regions, reverberations caused by near-surface unconsolidated sedimentary layers often introduce strong non-uniqueness and uncertainty, limiting the applicability of such methods. To address these challenges, we propose a two-step inversion method that uses multi-frequency P-RF waveforms and P-RF horizontal-to-vertical amplitude ratios. Synthetic tests indicate that our two-step inversion method can mitigate the non-uniqueness of the inversion and enhance the stability of the results. Applying this method to teleseismic data from a linear seismic array across the sedimentary basins in Northeast China, we obtain a high-resolution image of the shallow crustal S-wave velocity structure along the array. Our results reveal significant differences between the basins and mountains. The identification of low-velocity anomalies (≤2.8 km s−1) at depths less than 1.0 km beneath the Erlian Basin and less than 2.5 km beneath the Songliao Basin suggests the existence of sedimentary layers. Moreover, the high-velocity anomalies (∼3.4–3.8 km s−1) occurring at depths greater than 7 km in the Songliao Basin may reflect mafic intrusions emplaced during the Early Cretaceous. Velocity anomaly distribution in our imaging result is consistent with the location of the major faults, uplifts, and sedimentary depressions, as well as active-source seismic results. This application further validates the effectiveness of our method in constraining the depth-dependent characteristics of the S-wave velocity in basins with unconsolidated sedimentary cover. [ABSTRACT FROM AUTHOR]

This study aims to perform fast fault diagnosis and intelligent protection in an active distribution network (ADN) with high renewable energy penetration. Several time-domain simulations are carried out in EMTP-RV to extract time-synchronized current and voltage data. The Stockwell transform (ST) was used in MATLAB/SIMULINK to preprocess these input datasets to train the adaptive fault diagnosis deep convolutional neural network (AFDDCNN) for fault location identification, fault type identification, and fault phase-detection for different penetration levels. Based on the AFDDCNN output, the intelligent protection scheme (IDOCPS) generates the signal for isolating a faulty section of the ADN. An intelligent fault diagnosis scheme that combines ST and deep learning methods aids the artificial intelligence-based protection scheme in isolating the faulty section. This study uses the PyTorch framework to build both the AFDDCNN and IDOCPS. The proposed protection technique classifies and isolates faults and coordinates protection with minimum operating time in the IEEE 13-bus ADN. It consistently gives high accuracy for fault diagnosis and minimum operating time for the IDOCPS even when the network's topology is modified to the IEEE 34-bus ADN. The experimental results indicate that the proposed model is more accurate and provides faster fault diagnosis and isolation than state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Purpose: This paper aims to investigate the efficacy of teeth flux sensors in detecting, locating and assessing the severity of short-circuit faults in the stator windings of induction machines. Design/methodology/approach: The experimental study involves inducing short-circuit winding turn variations on the induction machine's stator and continuously measuring the RMS values across teeth flux sensors. Two crucial steps are taken for machine diagnosis: measurements under load operating conditions for fault detection and measurements under no-load conditions to determine fault location and severity. Findings: The experimental results demonstrate that the proposed approach using teeth flux sensors is reliable and effective in detecting, locating and evaluating the severity of stator winding faults. Research limitations/implications: While this study focuses on short-circuit faults, future research could explore other fault types and alternative sensor configurations to enhance the comprehensiveness of fault diagnosis. Practical implications: The methodology outlined in this paper holds the potential to significantly reduce maintenance time and costs for induction machines, leading to substantial savings for companies. Originality/value: This research contributes to the field by presenting an innovative approach that uses teeth flux sensors for a comprehensive fault diagnosis in induction machines. The originality lies in the effectiveness of this approach in providing reliable fault detection, location and severity evaluation. [ABSTRACT FROM AUTHOR]

Drive circuit is a critical part of instrumentation and control systems in nuclear reactors, and its performance directly influences the operation of nuclear reactors. However, comparing with the open circuit IGBT faults, soft faults caused by the degradation of electronic components present much slighter fluctuations to the performance of drive circuits. If the two fault modes co‐exist, traditional fault diagnosis models are prone to misclassify soft faults as the normal condition. To improve the accuracy of fault diagnosis of drive circuits, it necessitates to accurately locate the faults of drive circuits, while effectively extracting the distinguishable fault features is one of the critical factors for fault location. In this article, a fault location method combining the empirical modal decomposition (EMD) algorithm and sparse convolutional autoencoder (SCAE) is proposed. The EMD algorithm is applied to decompose the three‐phase current signals of drive circuits. An SCAE‐based feature extractor is constructed to capture high‐dimensional and sparse fault feature data with the aid of the powerful feature autonomic extraction capability of deep learning. A deep classifier is designed to locate faults in the driver circuit. A fault simulation model of the drive circuit is developed and the monitor data is collected. The effectiveness of the proposed method is validated via a real case of drive circuit in nuclear reactors. [ABSTRACT FROM AUTHOR]

Maintenance of transient stability is important for the stable operation of power systems. However, as a large amount of renewable energy sources are concerned that the transient stability deteriorates. Excitation control systems have been widely used to improve the transient stability. The defect is that only the synchronous generator (SG) near the fault location changes the excitation voltage so much immediately after the fault. In this study, we propose a novel excitation control method based on an event‐based wide‐area emergency control system. In this method, the SGs that are distant from the fault location play a role to mitigate the first swing as well as the SGs near the fault. The effectiveness of the proposed method is verified demonstrated by numerical simulation for a multi‐machine test system [ABSTRACT FROM AUTHOR]

In low-voltage AC distribution systems, when a series arc fault occurs in a branch with multiple loads operating in parallel, it will be significantly more difficult to identify. Existing arc fault detection methods make it difficult to effectively detect faults occurring in the lower-level branch. This study introduces a novel series arc fault detection approach based on the improved northern goshawk optimization adaptive base class LogitBoost (INGO-ABCLogitBoost) algorithm. Considering the zero-rest, intermittent, and random fluctuation and high-frequency features of the arc current, the zero-rest coefficient, discrete coefficient, harmonic amplitude, and wavelet entropy are proposed to establish the high-dimensional feature matrix of the arc current. The ReliefF feature selection algorithm is used to optimize feature quality and decrease feature dimensionality. Subsequently, the ABCLogitBoost fault detection model is proposed, with the INGO algorithm applied to optimize the model parameters, thus enhancing the model's diagnostic capabilities. The efficacy of the proposed diagnostic model is validated through the construction of a multi-load arc simulation system. The simulation results show that the overall fault diagnosis accuracy of the proposed method reaches 99.01% and can effectively identify the fault load types, which helps to locate the fault location. [ABSTRACT FROM AUTHOR]

Accurate fault detection in high-voltage direct current (HVDC) transmission lines plays a pivotal role in enhancing operational efficiency, reducing costs, and ensuring grid reliability. This research aims to develop a cost-effective and high-performance fault detection solution for HVDC systems. The primary objective is to accurately identify and localize faults within the power system. In pursuit of this goal, the paper presents a comparative analysis of current and voltage characteristics between the rectifier and inverter sides of the HVDC transmission system and their associated alternating current (AC) counterparts under various fault conditions. Voltage and current features are extracted and optimized using a metaheuristic approach, specifically Harris Hawk's optimization method. Leveraging machine learning (ML) and artificial neural networks (ANN), this technique demonstrates its effectiveness in generating a fault locator with exceptional accuracy. With a substantial volume of data employed for learning and training, the Harris Hawks optimization method exhibits faster convergence compared to other metaheuristic methods examined in this study. The research findings are applied to simulate diverse fault types and unknown fault locations at multiple system points. Evaluating the fault detection system's effectiveness, quantified through metrics such as specificity, accuracy, F1 score, and sensitivity, yields remarkable results, with percentages of 99.01%, 98.69%, 98.64%, and 98.67%, respectively. This research underscores the critical role of accurate fault detection in HVDC systems, offering valuable insights into optimizing grid performance and reliability. [ABSTRACT FROM AUTHOR]

Far distant wind farms are integrated nowadays with the help of Voltage Source Converter (VSC)-based HVDC grids. Rapid fault current is very common in VSC-based HVDC grids and, therefore, quick fault detection and fast executing protection methods are critical for the existence of the HVDC grids. This paper proposes a rise time-based fault detection method using local measurement data and Travelling Wave (TW) characteristics of incoming fault TW. Dominating frequency of the primary wavefront is computed by the rise time of incoming fault TW. With the help of auto-updating transmission line parameters and the dominating frequency of incoming TW, the fault location is identified. The performance of the proposed method has been observed in PSCAD Simulation with a 4-Terminal HVDC test grid in multiple scenarios and compared with the existing fault detection methods on multiple levels. The results show that the proposed method is very fast, and accurate and can complete the whole Fault Location process within a few microseconds just after the Inception of the fault. [ABSTRACT FROM AUTHOR]

The transmission line is one of the most crucial electric power system components, demanding special attention since they are subject to failures that can cause disruptions in energy supply. In this scenario, the fault location emerges as a fundamental task, providing an approximate position where the failure occurred in the line. This paper presents a method for fault location using a novel dynamic regressor selection (DRS) framework, in which we introduce embedded normalization, incorporating the data scaling process inside the framework. The DRS aims to select the most accurate models from a pool of regressors to predict the fault distance. Moreover, since there is a lack of a public dataset, this paper presents and makes a new fault dataset available to the scientific community with several features extracted from current and voltage signals of representative failure events. In our experiments, we demonstrate the importance of this embedded normalization as well as the significance in the variation of critical hyperparameters of the DRS strategy, such as the distance metric used to define the region of competence and the criterion to select the best regressors from the pool of predictors. This work also presents the definition of the oracle concept in DRS, which represents the ideal predictor selection scheme. The results demonstrate the effectiveness of the proposed method with a mean error of 0.7086 km ± 0.0068 km, representing 0.1712% ± 0.0016% of the transmission line extension (414 km). [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]