Network Fault Research Articles

Simulation of Faults in the Yeywa-Thaphaywa High Voltage Transmission Line using Fuzzy Logic

Faults in transmission networks frequently undermine the reliability and stability of electrical power supply, causing load shedding and posing risks to power system equipment. Effective fault classification is crucial to minimize power outages and ensure timely restoration. This study presents a fault classification method for transmission lines using fuzzy logic. The classification utilizes three-phase feeder currents, neutral current, and phase voltages as inputs to the Fuzzy Inference System (FIS). The proposed method accurately classifies the various types of faults in a high voltage transmission network, including line-to-ground fault, double line-to-ground fault, line-to-line fault, and three-phase faults. Numerous test cases demonstrate the technique's high accuracy. The simulations are carried out in MATLAB/SIMULINK using Sim Power Systems and the Fuzzy Logic Toolbox (MATLAB 2016a).

Autonomous and ubiquitous in-node learning algorithms of active directed graphs and its storage behavior

The brain’s memory system is extraordinarily complex, evidenced by the multitude of neurons involved and the intricate electrochemical activities within them, as well as the complex interactions among neurons. Memory research spans various levels, from cellular and molecular to cognitive behavioral studies, each with its own focus, making it challenging to fully describe the memory mechanism. Many details of how biological neuronal networks encode, store, and retrieve information remain unknown. In this study, we model biological neuronal networks as active directed graphs, where each node is self-adaptive and relies on local information for decision-making. To explore how these networks implement memory mechanisms, we propose a parallel distributed information access algorithm based on the node scale of the active directed graph. Here, subgraphs are seen as the physical realization of the information stored in the active directed graph. Unlike traditional algorithms with global perspectives, our algorithm emphasizes global node collaboration in resource utilization through local perspectives. While it may not achieve the global optimum like a global-view algorithm, it offers superior robustness, concurrency, decentralization, and biological feasibility. We also tested network capacity, fault tolerance, and robustness, finding that the algorithm performs better in sparser network structures.

Seismic and outcrop-based 3D characterization of fault damage zones in sandstones, Rio do Peixe Basin, Brazil

Fault damage zones, composed of sub-seismic deformation structures, are difficult to detect using seismic data. Still, they can be related to fault throw, which is widely measured in the subsurface. This research employs a multiscale approach that integrates outcrop studies with seismic reflection data to investigate the attributes of fault damage zones affecting porous sandstones. We provide an in-depth understanding of the 3D fault zone volume, investigating correlations between geometric attributes of faults (width of damage zone, frequency of subsidiaries structures, and fault termination) in the outcrop and the fault throw in the subsurface, with insights from deformation mechanisms within the fault zone. The methods encompass 1D scanlines to constrain the damage zone width on the outcrop. At the same time, the subsurface analysis uses 3D seismic data, seismic attributes, and deep learning neural network (DNN) fault volumes to interpret fault geometries and quantify fault throw at different depths. Results show that the area presents a complex fault zone with multiple fault sets, deformation bands, and fracture corridors trending mainly NE-SW, NW-SE, and E-W. The integration of surface and subsurface fault data enabled the identification of two portions in each fault set outcropping at the fault tip for E-W/ESE-WNW-striking faults and the central part of the fault for NE-SW-striking faults. Faults with the greatest length do not outcrop with the largest damage zone width since they are outcropping the tip of the fault. The parallel faults overlap their damage zones, increasing the deformation zones in the affected sandstones. Fault throw and damage zone width present a positive correlation. This relation is affected by fault segments and subsidiary faults.

Spark: A Statistical Comparison and Evaluation of Classification Algorithms for Fault Prediction in Electrical Secondary Distribution Network

Managing faults in the electrical secondary distribution network is a challenging task given the nature, size, and complexity. Predicting faults early before they occur helps in increasing the safety and reliability of the power distribution system. Various statistical and machine learning techniques are being used to predict different types of faults. This study applies classification algorithms available in the big data framework Apache Spark through its python interface PySpark to predict electrical secondary distribution network faults. The study evaluates and compares nine algorithms: Decision tree, Gradient-boosted tree, Logistic regression, Naïve Bayes, Multilayer perceptron, Random forest, Linear Support Vector Machine, One-versus-rest and Factorization machines. The research uses Friedman’s test followed by the Nemenyi post hoc test to find the significance of performance differences among the algorithms. The results show significant differences among the algorithms. Gradient-boosted tree and One-versus-rest with Gradient-boosted tree had the best performance for binary and multiclass classification, respectively, while Naïve Bayes had the worst performance.

Efficient Fault Detection and Analysis of Power System Distribution Networks by Integrating BP Data Mining

As the basic guarantee for people’s production and life, the safe operation of the power system has an important impact on the development and operation of society. To ensure the safe and stable operation of the power grid, predicting potential faults and taking reasonable preventive measures can effectively avoid the occurrence of power accidents. However, due to the difficulty in ensuring the prediction accuracy of traditional methods, there are issues of protection misoperation and rejection. Therefore, in order to achieve accurate prediction of power grid faults and avoid protection misoperation and rejection issues, a distribution network fault classification prediction model using a combination of three-layer data mining model (TLDM) and adaptive moment estimation (Adam) algorithm/random gradient descent algorithm improved backpropagation neural network (BPNN) is proposed. The implementation results showed that the classification accuracy of artificial fish school [Formula: see text] priori, [Formula: see text]-means clustering convolutional neural network model and TLDM for single-phase grounding faults was 93.2%, 91.5% and 96.6%, respectively. The classification accuracy for two-phase faults was 92.8%, 92.4% and 95.7%, respectively. The classification accuracy for two-phase grounding faults was 93.7%, 91.2% and 96.9%, respectively. The classification accuracy for three-phase faults was 93.3%, 92.1% and 97.1%, respectively. The TLDM had the highest classification accuracy. The average accuracy, average accuracy and average recall of the BPNN improved by the combination of the ADAM algorithm and random gradient descent algorithm were 94.1%, 90.9% and 88%, respectively, which were higher than the BPNN improved by the combination of ADAM algorithm and random gradient descent algorithm. The above results indicate that the proposed distribution network fault classification and prediction model has good performance and can achieve accurate prediction of distribution network faults.

Improved Graph Convolutional Neural Networks-based Cellular Network Fault Diagnosis

To solve the problem of upstream and downlink interference in cellular networks, a graph convolutional neural networks-based novel fault diagnosis method for semi-supervised cellular networks is proposed. The method designed in this study uses extremal gradient enhancement to select the optimal feature subset, and uses graph convolutional neural network to extract the fault depth feature. At the same time, the knowledge data fusion technology is raised to expand the training data of the fault diagnosis model. This technology utilizes Naive Bayes models for pre-diagnosis and enhances graph convolutional neural networks to control the influence of pre-diagnosis outcomes and training dataset size on model training accuracy. In the experiment, the fault diagnosis accuracy and efficiency of the raised method are better than those of the traditional network fault diagnosis methods. This algorithm can diagnose faults in complex cellular network environment, which has high accuracy and practicability, and can effectively improve user experience.

Development of a total quality management framework for the mobile network operations Centre

Telecommunication is one of the most fast-paced and rapidly changing sectors globally. This study develops a total quality management (TQM) framework for mobile network operation centres (NOC) to aid in network surveillance and fault management processes for a 5G transmission network. A systematic literature review was conducted using academic articles in journals and conference papers sourced from the EBSCOHost database. A fuzzy analytic hierarchical process (FAHP) was used as a multi-criteria decision-making tool to determine which criteria and factors are of the highest ranking for the development of the TQM framework. Data collection and control sheets were ranked higher, making them critical to the framework. This shows that, without good data, no problems can be identified, visualised, analysed, and solved and processes will not be improved. Process improvement is ranked last, showing it is of the lowest priority compared to the other criteria. This however, does not imply that in TQM it is of lower priority, as it is needed to keep meeting the needs of the customers. The framework includes how the TQM tools and techniques can be used in collecting, visualising, analysing, solving problems, and improving processes.

СИСТЕМА "АЛЬТРА" ДІАГНОСТУВАННЯ СТАНУ ІЗОЛЯЦІЇ ЕЛЕКТРИЧНОЇ МЕРЕЖІ ТА ЗАХИС-ТУ ЇЇ ВІД ОДНОФАЗНИХ ЗАМИКАНЬ

One of the problems in an electrical network with an isolated (compensated) neutral is the selective determina-tion of a damaged element during a single-phase earth fault. Taking into account the negative impact of single-phase earth faults on the equipment of electrical networks with an isolated or compensated neutral, as well as improving the electrical safety conditions of people and animals, it is necessary to install selective protections with the of turning off of damaged element. Selective operation of protections against such damage can be en-sured on the basis of the analysis of zero-sequence currents, phase voltages and zero-sequence voltage of the bus section from which these feeders are powered. As operational experience has shown, selective operation of pro-tection against single-phase earth faults in such networks can be ensured only with the use of digital devices. The work considers the "Altra" system, designed for diagnosing the state of insulation, protecting feeders of 6-35 kV from single-phase earth fault, recording analog and binary signals of electrical installations. Altra digital protec-tion simultaneously covers up to 28 feeders, which provides comprehensive protection against single-phase earth faults of all substation feeders. The working algorithm of the developed protection against single-phase earth faults is based on the analysis of the transient process immediately after the occurrence of damage, which provides selective identification of the damaged feeder both in networks with an isolated neutral and in networks with a compensated. The system organizes the recording of oscillograms of emergency processes and infor-mation about damage by "Altra" devices, followed by their transfer to the dispatcher's automated workplace. This makes it possible to carry out a detailed analysis of accidents and diagnose the insulation of the electrical net-work.

Fault Location Method of Distribution Network Based on VGAE-GraphSAGE

The distribution network is the main component of the power system, which undertakes the important function of power transmission and distribution. Fast and accurate distribution network fault location is one of the important means to ensure the safe and stable operation of the power system and is helpful in guiding troubleshooting, shortening the power outage time, reducing the workload of manual inspection, and reducing the social and economic losses caused by faults. Due to the development of the new distribution network, the comprehensive influence of many factors has put forward new challenges to the traditional distribution network fault location methods. In this paper, a distribution network fault location method based on the graph neural network is proposed. Firstly, the distribution network is treated as non-Euclidean graph data; secondly, variational graph auto-encoders (VGAE) are used to mine the underlying information of nodes and improve the overall anti-noise performance of the fault location method. Then the GraphSAGE model is used to aggregate the neighbor information of nodes, fully consider the influence of the surrounding lines on the target lines, and improve the output of the model to locate the distribution network line where the fault occurred. The experimental example analysis based on OpenDSS simulation software (version 9.8) proves that the proposed method has high accuracy and anti-interference, and the accuracy reached 97.81%. Moreover, the positioning result is still good in the new intelligent distribution network scenario with distributed power access, with an accuracy of 95.07% in the hybrid power generation scenario.

Effect of SF6 circuit breaker parameters on switching overvoltages evaluated based on nominal modes of converter transformers

In this work, we set out to investigate the influence of operating currents of SF6 circuit breakers on the magnitude of overvoltages during switching of converter transformers. The experience of electrical equipment operation is analyzed with a focus on the problems associated with the natural aging of insulation, overloads, and switching overvoltages. The methods of equipment diagnostics were used. According to statistical data, at present, about 37% of short circuits between phases and 42% of single-phase earth faults in electrical networks of industrial enterprises occur due to switching overvoltages. The conducted tests showed that the overvoltages emerging during switching by SF6 circuit breakers of converter transformers, which are used in aluminum smelters, may reach a three-fold rated voltage of the network. This threatens the insulation of transformer windings, cable line, and the switch itself. It was established that an increase in operating currents of an LF2 circuit breaker, not exceeding 27.8% of the rated current of the switch, leads to an increase in overvoltages during switching of converter transformers. At a further increase in the operating currents of the circuit breaker by more than 27.8% of the rated current of the circuit breaker, a sharp decrease in the value of switching overvoltages is observed. Therefore, the operating currents of SF6 circuit breakers were established to affect the level of switching overvoltages of converter transformers. The underlying mechanism of this phenomenon was determined, which should be taken into account when improving the reliability of power supply of converter transformers and, consequently, the reliability of aluminum production. The feasibly of resistive-capacitive dampers as the most effective means for limiting overvoltages during switching of converter transformers was confirmed.

Few-shot bearing fault diagnosis method based on an EEMD parallel neural network and a relation network

Bearing fault diagnosis presents challenges, such as insufficient fault samples and significant fault data distribution variation in different bearing operating conditions. These problems cause traditional deep learning models to show poor generality and accuracy during bearing fault diagnosis. To address these challenges, this paper proposed a few-shot bearing fault diagnosis method based on an Ensemble Empirical Mode Decomposition (EEMD) parallel neural network and a relation network (RN). First, the original bearing fault vibration signal was decomposed by EEMD, while the decomposed signal components were processed via Short Time Fourier Transfor (STFT) to obtain a two-dimensional time-frequency feature map. Then, a parallel neural network was used for initial fault feature extraction, after which the extracted features were fused to construct a more accurate multi-dimensional fault feature map. A more precise fault feature vector was generated via the feature embedding module of the RN, and the fault features of the support and query sets were stitched to create a fault feature vector set. Finally, the relation module of the RN was used for the nonlinear distance determination of the fault feature vector set and to generate the relation score for the few-shot variable condition bearing fault diagnosis. In this paper, EEMD module is introduced into RN to construct multi-dimensional fault characteristics of the original fault signal. Original signal decomposition, STFT transformation and splicing effectively improve the randomness and blindness of convolution operations, improve the accuracy of fault feature extraction in RN, and thus improve the overall diagnostic performance of the model. The experimental results showed that the proposed model obtained higher diagnostic accuracy than the matching network (MN) and RN meta-learning fault diagnosis (MLFD) methods. The accuracy of fault diagnosis of the model in 5Way-Nshot is >80%, and the accuracy of fault diagnosis in 5Way-10shot is the highest at 95.2%.

Fault location method for new distribution networks based on waveform matching of time–frequency travelling waves

AbstractSome existing fault location methods for distribution networks rely too much on the local wave head information of the time or frequency domain signals, making it difficult to adapt to the increasingly complex structure and operating conditions of the distribution network after the new energy access. For improvement, the travelling wave (TW) time and frequency ranges that can effectively avoid waveform distortion caused by new energy access are analysed. The one‐to‐one matching relationship between the TW waveforms in these ranges and the fault positions is revealed. A fault TW time–frequency matrix with specific time and frequency windows is constructed, and a new distribution network fault location method is proposed based on the matching technique of waveform features, which realises the accurate fault location by exploring the proportionality between the cumulative trend of the matrix energy amplitude deviation and the fault point position. Simulation test results show that the proposed method is not affected by the complex structure of distribution networks such as new energy access and overhead‐cable line mixing on fault location and flexibly transforms the fault location problem into a time–frequency TW waveform matching problem, which improves the accuracy and robustness of the fault location for new distribution networks to a degree.

A fault tolerant node placement algorithm for WSNs and IoT networks

The operation of Internet of Things (IoT) Network and Wireless Sensor Networks (WSNs) can be often disrupted by a number of factors, such as node faults, security attacks, as well as path disconnections. Despite any disruptions or any failures of network components the functionality and performance of the network should remain unaltered. An approach that can assist in resolving the above issues is the use of mobile nodes. In this work, we present a Fault Tolerant Node Placement Algorithm (FTNPA) that utilizes mobile nodes for addressing failures in the network. We initially propose a Mobile Fault Tolerant (MobileFT) Framework, that supplies the two main functionalities of the Fault Tolerant Node Placement Algorithm (FTNPA), which are the detection and the recovery. Then, we present two variations of the FTNPA algorithm, the Decentralized FTNPA and the Centralized FTNPA. The first variation uses a decentralized detection method, where the detection is performed by the neighboring nodes in the network, as well as a local recovery method, where a mobile node is placed in a certain position to assist the affected area. Whereas, the second variation employs a centralized detection method, where the sink is responsible for the detection, and a recovery method that creates alternatives paths of mobile nodes towards a destination node. Simulation results show that the proposed algorithms can significantly contribute to the detection and recovery of faults in IoT Networks and WSNs.

Lightning risk assessment of active distribution network with distributed photovoltaic system

Lightning strikes are the main reason for the fault of active distribution network. It is of great significance to study the risk assessment of lightning in active distribution network for lightning protection. Taking an active distribution network in a certain region as the research object, considering the mutual influence between the photovoltaic side and the distribution line side during lightning strikes, the method of lightning risk assessment for the active distribution network with photovoltaic system was proposed. Electrical geometric models were constructed to calculate the fault rate of the photovoltaic side and the trip rate of the distribution line side, and the risk assessment was conducted based on the calculation results. The results show that when the lightning strike is closest to the tower on the photovoltaic side, the lightning fault rate on the photovoltaic side increases from 0.957 times/year to 3.0766 times/year. When the photovoltaic side is struck by lightning, the intrusion of lightning impulse can double the lightning trip rate of the adjacent three towers on the distribution line side, indicating a higher risk level. It is recommended to focus on the protection measurement for the three towers near the photovoltaic system.

SSG-Net: A Multi-Branch Fault Diagnosis Method for Scroll Compressors Using Swin Transformer Sliding Window, Shallow ResNet, and Global Attention Mechanism (GAM).

The reliable operation of scroll compressors is crucial for the efficiency of rotating machinery and refrigeration systems. To address the need for efficient and accurate fault diagnosis in scroll compressor technology under varying operating states, diverse failure modes, and different operating conditions, a multi-branch convolutional neural network fault diagnosis method (SSG-Net) has been developed. This method is based on the Swin Transformer, the Global Attention Mechanism (GAM), and the ResNet architecture. Initially, the one-dimensional time-series signal is converted into a two-dimensional image using the Short-Time Fourier Transform, thereby enriching the feature set for deep learning analysis. Subsequently, the method integrates the window attention mechanism of the Swin Transformer, the 2D convolution of GAM attention, and the shallow ResNet's two-dimensional convolution feature extraction branch network. This integration further optimizes the feature extraction process, enhancing the accuracy of fault feature recognition and sensitivity to data variability. Consequently, by combining the global and local features extracted from these three branch networks, the model significantly improves feature representation capability and robustness. Finally, experimental results on scroll compressor datasets and the CWRU dataset demonstrate diagnostic accuracies of 97.44% and 99.78%, respectively. These results surpass existing comparative models and confirm the model's superior recognition precision and rapid convergence capabilities in complex fault environments.

Enhancing Network Fault Detection with Precision Predictive AI

Traditional methods for managing and predicting faults must be revised in today's complex network landscape. Predictive Artificial Intelligence (AI) offers a proactive solution, using advanced algorithms and machine learning to analyze vast data, detect patterns, and prevent issues before they escalate. This approach significantly enhances network reliability, reduces downtime, improves operational efficiency, and has transformative potential in network management. This white paper explores this potential, providing real-world examples and integration strategies. We also discuss its benefits and challenges, highlighting its promise for ensuring stable and resilient network operations.

Distribution network fault regionalized localization based on improved dung beetle optimization

Distribution network fault regionalized localization based on improved dung beetle optimization

Fault Diagnosis of Distributed Energy Distribution Network Based on PSO-BP

With the increasing scale of distribution network at distribution time, its complexity grows geometrically, and its fault diagnosis becomes more and more difficult. Aiming at the slow convergence and low accuracy of traditional backpropagation neural network in dealing with single-phase ground faults, the study proposes a backpropagation neural network based on improved particle swarm optimization. The model optimizes the weights and acceleration constants of the particle swarm algorithm by introducing dynamic coefficients to enhance its global and local optimization seeking ability. It is also applied in optimizing the parameters of backpropagation neural network and constructing the routing model and ranging model for fault diagnosis about distributed energy distribution network. The simulation results revealed that the maximum absolute error of the improved method is 0.08. While the maximum absolute errors of the traditional backpropagation neural network and the particle swarm optimized backpropagation neural network were 0.65 and 0.10, respectively. The fluctuation of the relative errors of the research method was small under different ranges of measurements. At 8.0 km, the minimum relative error was 0.39% and the maximum relative error was 2.81%. The results show that the improved method proposed in the study significantly improves the accuracy and stability of fault diagnosis and localization in distribution networks and is applicable to complex distribution network environments. The method has high training efficiency and fault detection capability and provides an effective tool for distribution network fault management.

Location Method of Single-Phase to Ground Fault in Distribution Network Based on Time-Frequency Matrix Analysis of Traveling Wave

Location Method of Single-Phase to Ground Fault in Distribution Network Based on Time-Frequency Matrix Analysis of Traveling Wave

Examination of AI Enhanced Distributed Systems and its Effects on Software Engineering

Distributed systems are groups of independent computers that appear to the system's users as a single coherent system. These systems involve several critical factors, including network communication, concurrency, and fault tolerance, which are vital in software engineering. For example, cloud computing platforms like Amazon Web Services (AWS) use distributed systems to offer scalable, on-demand resources to millions of users worldwide. The introduction of Artificial Intelligence (AI) into software engineering marks a transformative period that reshapes traditional development processes and breaks down the complexity of distributed systems. AI automates routine tasks and simplifies complex processes, serving as a digital collaborator that enables developers to focus on strategic thinking and creativity. One significant advantage of using AI in distributed systems within software engineering is enhanced resource optimization. AI can identify irregularities that might indicate hardware malfunctions and take preventative measures to address them, such as rerouting traffic to healthier servers. However, distributed systems also have challenges, including increased complexity in system design, difficulty ensuring data consistency, and potential security vulnerabilities. The intricate structure of these systems can result in problems with high startup costs, safety risks, and the accuracy of the data provided. Integrating AI into distributed systems offers both significant advantages and disadvantages. This study evaluates how AI impacts the efficiency and security of distributed systems in software engineering. By analyzing the advantages and disadvantages of AI-enhanced distributed systems, we can gain a comprehensive understanding of their overall effectiveness and implications in the field.