Broken Fault Research Articles

Fault diagnosis of HVCB via the subtraction average based optimizer algorithm optimized multi channel CNN-SABO-SVM network

The mechanical fault diagnosis of HVCB is important to ensure the stability of electric power systems. Aiming at the problem of poor diagnostic performance of deep learning methods under limited samples, this paper proposes an HVCB operating mechanism fault diagnosis model (multi-channel CNN-SABO-SVM, MCCSS) based on multimodal data fusion features and Subtraction-Average-Based Optimizer (SABO). This model extracts and fuses features from the input two-dimensional data using a multi-channel CNN network and then uses the multimodal data fusion features to diagnose HVCB faults. Additionally, the SVM is used instead of the Softmax classifier to classify the fused features of vibration and sound, compensating for the poor diagnostic performance and generalization ability of the CNN network in small sample data scenarios. To further enhance the fault diagnosis performance of the SVM, the SABO is introduced for hyperparameter optimization of the SVM classifier. An HVCB fault test platform was established to train and test the model with limited data. The experimental results show that, compared with the multi-channel CNN-SVM and the CNN model based on unimodal signals, the proposed multi-channel CNN-SABO-SVM model improves the accuracy by 2.66% and 10.66%, respectively, and effectively addresses the challenge of circuit breaker fault diagnosis with limited samples.

Faulting by the 2023 great earthquakes of Türkiye and associated stress field and its effects on built environment

BackgroundFaulting induced by earthquakes and its analysis are of great importance for areas with high probability of earthquakes. However, there has been no particularly effective methods to evaluate the damage and applicable solutions for recovery. In this paper, the damage from Türkiye earthquakes is investigated by authors, which has important theoretical significance and practical value. On 6 February, 2023, two successive earthquakes with magnitudes (Mw) of 7.8 (called Pazarcık earthquake) and 7.6 (Ekinözü earthquake) occurred in the south-eastern part of Türkiye. The total length of the surface ruptures was more than 500 km long and resulted in striations reflecting the sinistral faulting and extensive ground deformations. In this study, the authors present the outcomes of the investigations on the surface ruptures, their characteristics, stress field associated with both earthquakes, shear strength property of fault segments and the damaging effects on various structures and made some recommendations with the purpose of how to build structures in active fault zones and decrease the negative effects of faulting on structures.ResultsBesides summarizing the main characteristics of the world-shaking Kahramanmaraş earthquake doublet in southeast Türkiye in 2023, this study described the main features of the surface ruptures, their relation to the inferred crustal stresses in Türkiye, and the damaging effects on the major engineering structures. The observations and inferences are of great significance for understanding the causes of earthquakes and future seismic risk assessment. Various laboratory experiments were performed on the samples of fault gouge gathered from the sites of surface ruptures and these experimental results provided very valuable quantitative information on the constitutive models of the fault zones. The observations clearly showed that it is almost impossible to prevent damage on structures due to surface ruptures, if certain engineering principles such as increasing higher ductility, lowering gravitational center and/or the implementations of raft foundations are followed.ConclusionsThe stress state inferences obtained from the striation of the fault surface ruptures as well as from the focal plane solutions are expected to be useful to evaluate the regional stress state of the earthquake region. Assessments indicated that the stress states in Arabian plate and Anadolu platelet are different from each other. However, in-situ direct stress measurement techniques would be quite useful to validate the stress state inferred in this study. The laboratory experiments on samples gathered from the fault outcrops of the earthquakes using direct shear tests, stick-slip tests as well as conventional tensile, compressive and triaxial tests provided the quantitative values for the parameters of constitutive laws for fault zones, which can be utilized in the numerical simulation of earthquakes. If a fault break happens to be just passing underneath the structures, it is almost impossible for mankind to prevent the damage to structures. However, the authors made some recommendations to reduce the negative effects of fault ruptures on structures. These recommendations are such that the structures should be built as ductile and redundant structures with lower center of gravity and raft foundations. Dam construction should be on active faults should be avoided. If they are to be built for whatever reason, they should be of rock-fill type. As tubular structures and tunnels would be generally subjected forced displacement field, it is recommended to utilize flexible joints, segmented and enlargement to deal such displacement fields.

Characteristics and Typical Influential Factors of Wildfire Caused by High-Voltage Transmission Line Breakage Faults

In order to investigate the characteristics and typical influential factors of wildfires caused by accidental faults in high-voltage transmission lines, a bespoke platform was constructed for the purpose of conducting simulation experiments. Discharge and ignition experiments were conducted on a variety of substrates, including kidney fern fragments, cedar needle fragments, poplar sawdust, and eucalyptus leaf fragments, to investigate the effects of different gaps on the initiation and propagation of wildfires. The results demonstrate that the discharge-inducing ignition stages can be succinctly summarized as “two phases and two points” (the discharge induction period, the gap breakdown point, the arc induction period and the fault removal point) when a suitable gap is maintained between the simulated falling lines and the vegetation surface. In the event of direct contact, the removal of the fault is not possible. The potential for ignition of the aforementioned vegetation types by the discharge is as follows: cedar needles > eucalyptus leaf fragments ≈ poplar sawdust > kidney fern fragments. As the water content increases, eucalyptus leaf fragments can still be ignited, and the breakdown voltages required for discharge-inducing ignitions gradually decrease. In the case of different forest ground vegetation types, when ignited by the discharge between the falling lines and the vegetation under conditions of proper gap and moisture content, the resulting ignition and sustained flames will promote the formation of streamer channels and further aggravate the discharges and burning processes, potentially leading to the ignition of a wildfire.

Implementation and proficiency analysis of enhanced graph algorithm for DC microgrid applications

Integrating renewable energy generation with the conventional grid supports reduces carbon emissions in the atmosphere. Despite technical advancements in protection strategies, critical issues concerning renewable integration in microgrid structures require standardized solutions. The essential aspects that need to be concentrated during securing the grids are rapid fault interruption, false tripping and blinding of protection. This study proposes an innovative approach to enhance fault isolation speed through the implementation of a grid monitoring system (GMS) coupled with a fault identification method based on Kosaraju’s algorithm. This algorithm operates on the principles of overvoltage and overcurrent detection. The study assesses the efficacy of this approach by examining its integration with a Z-source circuit breaker and conducting tests on different fault types within a 13-bus system. Real-time simulations using Opal RT software are employed to experimentally validate the proposed methodology, ensuring its efficacy in fault interruption and isolation.

Circuit Breaker Fault Diagnosis Method Based on Integrated OCSSA-VMD-CNN-BiLSTM Method

Abstract The paper proposes a novel approach to diagnose circuit breaker faults by integrating OCSSA, VMD, CNN, and BiLSTM algorithms. Firstly, OCSSA optimizes the parameters of VMD to reduce noise interference. Then, VMD decomposes the circuit breaker operating signals into characteristic signals. Secondly, CNN captures mechanical fault characteristics from the decomposed signals. Finally, BiLSTM applies CNN’s results for feature classification. Experimental results show that combining improved VMD with the CNN-BiLSTM model achieves a fault classification accuracy rate of 99.5%, surpassing other traditional models such as VMD-CNN-BiLSTM and ELMD in accuracy and robustness.

Research on Intelligent Identification Algorithm for Steel Wire Rope Damage Based on Residual Network

As a load-bearing tool, steel wire rope plays an important role in industrial production. Therefore, diagnosing the fracture and damage of steel wire ropes is of great significance for ensuring their safe operation. However, the detection and identification of wire rope breakage damage mainly focus on identifying external damage characteristics, while research on inspecting internal breakage damage is still relatively limited. To address the challenge, an intelligent detecting method is proposed in this paper for diagnosing internal wire breakage damage, and it introduces residual modules to enhance the network’s feature extraction ability. Firstly, time–frequency analysis techniques are used to convert the extracted one-dimensional magnetic flux leakage (MFL) signal into a two-dimensional time–frequency map. Secondly, the focus of this article is on constructing a residual network to identify the internal damage accurately with the features of the time–frequency map of the MFL signal being automatically extracted. Finally, the effectiveness of the proposed method in identifying broken wires is verified through comparative experiments on detecting broken wires in steel wire ropes. Three common recognition methods, the backpropagation (BP) neural network, the support vector machine (SVM), and the convolutional neural network (CNN), are used as comparisons. The experimental results show that the residual network recognition method can effectively identify internal and external wire breakage faults in steel wire ropes, which is of great significance for achieving quantitative detection of steel wire ropes.

Lightweight AC Arc Fault Detection Method by Integration of Event-Based Load Classification

As the main cause of electrical fires, arc fault detection attracts lots of attention in recent years. However, with the growing complexity of electric loads, arc fault detection becomes more difficult. This paper proposes a lightweight arc fault detection method that integrates load classification into fault detection. Firstly, according to the turn-on patterns, we divide loads into resistive, inductive and switchable loads. Load classification is developed using an event-based method. Then, arc detection method is developed for each load category, which is achieved through sequential forward floating selection. Its performance is validated by the experiment data and comparison with other reported methods. Meanwhile, the selection of classifiers, sampling rate, and sampling periods for arc fault detection are also discussed in detail. The results show that the proposed method not only achieves a high fault detection performance but also keeps a relatively low sampling rate and short sampling period. Thus, it is beneficial for practical arc fault interrupter development.

Enhanced complex wire fault diagnosis via integration of time domain reflectometry and particle swarm optimization with least square support vector machine

AbstractUrban power systems rely on intricate wire networks, known as the power grid, which form the essential electric infrastructure in cities. While these networks transmit electricity from power plants to consumers, they are vulnerable to faults caused by manufacturing errors and improper installation, posing risks to system integrity. Thus, accurate identification and assessment of these faults are crucial to prevent damage and maintain system reliability. The objective of this research is to present an innovative and efficient methodology for diagnosing complex wire networks through the application of time domain reflectometry (TDR) combined with the particle swarm optimization (PSO) and least squares support vector machine (LSSVM) algorithm. This research addresses the imperative need to accurately locate and assess breakage faults within wire networks, emphasizing their role in both power transmission and communication infrastructure. To model the TDR answer of a specific complex wire network, a forward model is established utilizing resistance, inductance, capacitance and conductance (RLCG) parameters and the finite difference time domain (FDTD) method. Subsequently, the PSO‐LSSVM approach is used to solve the inverse problem of localizing faults in complex wire networks. The experimental results validate the practicality of this approach in real‐world systems.

Open-Circuit Fault-Tolerant Control for Pentagon Winding Connected Five-Phase Current-Source Inverter Based PMSM Drives

Current-source inverter (CSI) is considered as a prospective solution for modern machine drives, for its higher reliability and superior harmonic suppression. The open-circuit fault-tolerant control for five-phase CSI-fed permanent magnet synchronous machine (PMSM) drive with pentagon winding connection is investigated. The switch open-circuit fault and winding open-circuit fault are studied. For the switch open fault, three cases are discussed including single-phase, adjacent two-phase, and nonadjacent two-phase faults. It is indicated that in pentagon circuit, despite the switch of fault phase is disabled, the corresponding phase winding is still connected in the end-to-end power circuit and contributes for torque generation. The new sets of coordinate transformation are proposed, and the mathematical modeling is presented. The fault-tolerant space vector modulation (SVM) techniques are proposed based on the reconstructed vector planes. In addition, the single winding open fault is studied, which is a specific fault case and breaks off the closed pentagon connection. An important conclusion is derived that the pentagon winding connected CSI (PWC-CSI) gains inherent fault-tolerant capability against the single winding open circuit. The control algorithm even does not need to be switched during this typical fault. Experiments are performed on verifying the proposed schemes.

Analysis of Wire Breakage Fault Caused by Galloping on 66kV Debao A and B Line in November 2023

In early November 2023, affected by extreme weather conditions such as rain, snow, and freezing, some transmission lines in northeast region experienced icing and galloping, which brought lots of tripping and disconnection faults. Taking the wire breakage faults of the 66kV Debao A and B line as examples, the causes, processes, and characteristics were all detailed analysed. It can be basically identified that after the first-order galloping of icing the wire, due to the lack of or limited effectiveness of anti-galloping measures, interphase flashover occurred. The flashover melted some of the stranded of galloping wire, resulting in dynamic tension caused by galloping concentrated towards the broken position, the remaining cross-section of the stranded. Due to repeated bending and stretching, the remaining stranded wires exceeded the limit of fracture stress, leading to fracture and overall wire breakage and landing. So the wire breakage fault in this case is strongly related to the interphase flashover caused by galloping. This article conducted fault analysis work by collecting line design, on-site meteorological conditions, and fault characteristics, providing technical support for subsequent prevention and control work.

Design considerations of series type hybrid circuit breaker (S‐HCB)

AbstractThe series‐type direct current (DC) hybrid circuit breaker (S‐HCB) concept was previously reported to offer better performance than solid‐state circuit breakers (SSCB) and hybrid circuit breakers (HCB). S‐HCB offers low conduction power loss like an HCB and ‐scale interruption time, which is even faster than an SSCB. It uses a pulse transformer to isolate the lower‐voltage high‐inductance power electronic circuit from the high‐voltage, low‐inductance main power loop. This paper provides analysis of the impact of the S‐HCB circuit components on the overall system performance and a scalable S‐HCB design guide for different DC system voltage and current ratings. In addition, system energy flow analysis is performed in the time domain to provide an understanding of how energy is delivered, dissipated, and released throughout the entire fault interruption process. The S‐HCB prototype was experimentally tested at 3 kV/30 A and 6 kV/150A with the results showing the interruption of the low fault current of 30 A and the high fault current of 150 A within 8 and maintaining the fault current at a near zero value for 300 to enable an arcless opening of a series mechanical switch. The key design challenges of S‐HCB at high voltage and high current ratings were discussed and possible solutions to mitigate those challenges were introduced.

Research on power equipment troubleshooting based on improved AlexNet neural network

Power equipment is an important component of the whole power system, so that it is obvious that it is required to develop a correct method for accurate analysis of the infrared image features of the equipment in the field of detection and recognition. This study proposes a troubleshooting strategy for the power equipment based on the improved AlexNet neural network. Multi-scale images based on the Pan model are used to determine the equipment features, and to determine the shortcomings of AlexNet neural network, such as slower recognition speed and easy overfitting. After knowing these shortcomings, it would become possible to improve the specific recognition model performance by adding a pooling layer, modifying the activation function, replacing the LRN with BN layer, and optimizing the parameters of the improved WOA algorithm, and other measures. In the simulation experiments, this paper's algorithm was compared with AlexNet, YOLO v3, and Faster R-CNN algorithms in the lightning arrester fault detection, circuit breaker fault detection, mutual transformer fault detection, and insulator fault detection improved by an average of 5.47 %, 4.69 %, and 3.42 %, which showed that the algorithm had a better recognition effect.

Federated Transfer Learning-Based Paper Breakage Fault Diagnosis

Federated Transfer Learning-Based Paper Breakage Fault Diagnosis

Rhegmatogenous retinal detachment: an analysis of 2315 eyes over a six-year period

Background: Rhegmatogenous retinal detachment (RRD) is a form of retinal detachment caused by passage of fluid from the vitreous cavity into the space between the neurosensory retina and the retinal pigment epithelium via a retinal break or full-thickness defect. At our tertiary referral center, we evaluated the clinical and epidemiological features of RRD, and we herein report the frequency of related risk factors.
 Methods: In this retrospective study, we reviewed the records of patients with a final diagnosis of RRD at an academic ophthalmological referral center in Isfahan, Iran, over a six-year period. We retrieved and reviewed data from the medical records of all eligible participants, including sex, age, laterality, lens status, macular status, type of RRD, location and number of breaks, type of surgery, rate of re-operation during the first year after initial surgery, and documented clinical risk factors for RRD. Clinical risk factors were categorized as the presence of myopic refractive error, ocular trauma, history of cataract surgery, history of other ocular surgeries, history of uveitis, or undetermined.
 Results: We included 2315 eyes of 2229 patients with a mean (standard deviation [SD]) age of 51.1 (16.9) years and a male-to-female ratio of 1.8:1. The most common quadrants containing retinal breaks were the superotemporal quadrant (34.1%), inferotemporal quadrant (23.4%), and superonasal quadrant (10.7%). Macula-involved RRD was seen in 90% of eyes (n=2083 eyes). The most frequently identified risk factors were cataract surgery (32.9%) and myopia (22.3%) in adults, and myopia (35.0%) and ocular trauma (27.4%) in the pediatric group. Most eyes underwent pars plana vitrectomy (51.3%), whereas pneumatic retinopexy (0.7%) was the least commonly selected.
 Conclusions: Our results indicate that cataract surgery and myopia are the most common risk factors for RRD in adults. Myopia and ocular trauma are the most common risk factors in pediatric patients. As observed in many studies, the characteristics of the study population, including middle age, male sex, myopia, and ocular trauma, may be associated with RRD at different rates. Further population-based longitudinal studies with larger sample sizes are required to verify these preliminary observations.

Pengendalian Kualitas Produk Menggunakan Lean Six Sigma dan Fuzzy FMEA Dalam Upaya Menekan Kecacatan Produk

Quality control is a process used to ensure the level of quality in a product or service so as to lead to customer satisfaction. UMKM Eko bubut is a UMKM that produces wooden handicrafts with product marketing that reaches the international market. In the problem of product quality control at UMKM Eko Bubut, it was found that there were five types of defects, namely wavy, rough, perforated, cracked, and broken defects. In addition, it was found that there was wastage that occurred which had an effect on disability. Solving quality control problems at UMKM Eko Bubut is by using lean six sigma to identify the sigma level and fuzzy FMEA is used to determine the main priority for repairs to the causes of production defects that occur. The results of the calculation of the defect level of bowl products, it was found that the types of defects were wavy (55.06%), rough (28.29%), perforated (7.82%), cracked (7.22%) and broken (1.61%). The identification results of the waste that occurs are waste defects (28.1%), waste overproduction (17.7%), waste waiting (16.2%), waste motion (16.0%), waste inventory (10.75%), waste transportation (6.55%), and over-processing (4.9%). The results of calculating the DPMO value obtained an average sigma of 3.27 with a DPMO value of 38890.41. This shows that for every 1,000,000 production, there is a possibility that there will be product defects in as many as 38,890 products. Factors that affect product defects are humans, raw materials, methods, machines, and the environment.

Investigation on the fault monitoring of high-voltage circuit breaker using improved deep learning.

Mechanical faults are the main causes of abnormal opening, refusal operation, or malfunction of high-voltage circuit breakers. Accurately assessing the operational condition of high-voltage circuit breakers and delivering fault evaluations is essential for the power grid's safety and reliability. This article develops a circuit breaker fault monitoring device, which diagnoses the mechanical faults of the circuit breaker by monitoring the vibration information data. At the same time, the article adopts an improved deep learning method to train vibration information of high-voltage circuit breakers, and based on this, a systematic research method is employed to identify circuit breaker faults. Firstly, vibration information data of high-voltage circuit breakers is obtained through monitoring devices, this vibration data is then trained using deep learning methods to extract features corresponding to various fault types. Secondly, using the extracted features, circuit breaker faults are classified and recognized with a systematic analysis of the progression traits across various fault categories. Finally, the circuit breaker's fault type is ascertained by comparing the test set's characteristics with those of the training set, using the vibration data. The experimental results show that for the same type of circuit breaker, the accuracy of this method is over 95%, providing a more efficient, intuitive, and practical method for online diagnosis and fault warning of high-voltage circuit breakers.

Enhancing Resilience and Reliability of Active Distribution Networks through Accurate Fault Location and Novel Pilot Protection Method

The integration of distributed generation (DG) into the decentralized access of the distribution network transforms the existing structure into an active distribution network. The alteration in fault characteristics poses significant challenges to the coordinated operation of relay protection. Fault location within the distribution network plays a vital role in facilitating fault recovery and enhancing the resilience of the power system. It proves instrumental in improving the network’s ability to withstand extreme disasters, thereby enhancing the reliability of power distribution. Therefore, this paper provides a detailed analysis of the voltage fault components occurring during various fault types within an active distribution network. Building upon the identified characteristics of voltage fault components, a novel approach for the longitudinal protection of active distribution networks is proposed. This method involves comparing the calculated values of voltage fault components with their actual values. The proposed approach is applicable to various fault scenarios, including short-circuit faults, line break faults, and recurring faults. It exhibits advantages such as insensitivity to the penetration of distributed power supplies and robustness in withstanding transition resistance. The simulation results validate the effectiveness of the proposed method, affirming its applicability to diverse protection requirements within active distribution networks.

Diagnosis method of 500kV AC substation busbar break fault based on monitoring data

Diagnosis method of 500kV AC substation busbar break fault based on monitoring data

A novel circuit breaker fault diagnosis method based on dense residual and attention mechanism

AbstractIn recent years, deep learning‐based fault diagnosis technology for high‐voltage circuit breakers (HVCB) has advanced significantly, but the working environment of HVCBs is complex, resulting in unsatisfactory fault diagnosis results of HVCBs in noisy environment and existing deep learning methods are difficult to solve this problem. This paper proposes a multi‐channel convolutional neural network combines dense residual structure and attention mechanism to achieve high‐precision and high‐robust diagnosis of HVCBs in noisy backgrounds. A dense residual network is introduced into the convolutional neural network to prevent feature loss during network propagation to preserve the difference information between the network layers as much as possible, Simultaneously, a channel attention mechanism is introduced to adaptively adjust the weights of different convolution channels. The model can extract multi‐scale features from the original signal and fully exploit the intrinsic relationship between the vibration signal and the HVCB's operating state. The experimental results show that the diagnostic method can still meet the requirements of fault diagnosis in the presence of noise, with an average diagnostic accuracy rate of 85.92% when the signal‐to‐noise ratio is −4. The model outperforms the traditional single‐channel model in terms of diagnostic accuracy and stability.

A novel hybrid physical AI-based strategy for fault severity estimation in spur gears with zero-shot learning

Fault diagnosis of gears by vibration analysis has undergone significant growth in recent years. The traditional approaches for gear diagnostics in the past were focused mainly on fault detection. Improved understanding of physics of gear interaction, together with significant progress in dynamic modelling and the new era of artificial intelligence, make it possible for researchers to take on more challenging tasks such as fault severity estimation (FSE) and gear prognosis. This study presents a novel, hybrid strategy for combining physics of failure of machine elements with AI, specifically FSE of tooth breakage faults in spur gears, by a unique fusion of dynamic modelling, experimental characterization, feature extraction, and unsupervised machine learning algorithms. The novelty of the proposed strategy is its logic flow, which starts with a fundamental and deep understanding of the physical phenomena in realistic simulations. The extracted features are selected based on physical insights. The training dataset includes healthy measured data and simulations of healthy and faulty gear to compensate the lack of faulty data in real case scenarios. Discrepancies between the simulations and reality are reduced by passing the simulated signal through transfer functions estimated from experiments. The severity of the fault is estimated according to a “traffic-light” classification strategy, in which, for the first time, the fault geometry is employed to set thresholds for health indicators (HIs). This study shows that a wide set of sensitive features should be used for an early detection, while a smaller and more basic set of features is sufficient for fault severity classification. The suggested strategy is demonstrated on data from an actual testbed; and it is shown to detect breakage faults and estimate their severity, both by zero-shot learning (that is, without training on any measured faulty instances). The strategy can be applied to other systems and likely generalized, thus showing the power of hybrid approaches that combine physical insights with data-driven models.