Network Fault Research Articles

Research on failure diagnosis analysis of plunger gas lift system using convolutional neural network with multi-scale channel attention mechanism based on wavelet transform

Plunger gas lift technology has been extensively utilized in unconventional gas fields, owing to its distinct engineering benefits. However, as development challenges intensify, fault diagnosis has grown more intricate, and conventional diagnostic techniques exhibit delays and inaccuracies. With advancements in computer science, machine learning methods have demonstrated their prowess in establishing robust correlations between data features and prediction outcomes. Consequently, this paper introduces a convolutional neural network fault diagnosis model that incorporates a multi-scale channel attention mechanism based on wavelet transform. This model dissects features across various scales using wavelet transform and leverages channel attention to adaptively select channels encompassing fault features, thereby enhancing diagnostic and recognition accuracy. Furthermore, the model integrates a hyperparameter search optimization algorithm to refine the model’s architecture and comprehensively bolster its generalization capability. A comparison with actual field data reveals that the fault diagnosis accuracy of the WT-MACNN model stands at 83.33%. Digital ablation experiments underscore the limited accuracy of the basic CNN model in fault diagnosis, but the sequential introduction of the channel attention mechanism and wavelet transform layers significantly elevates model performance. When juxtaposed with SVM, KNN, and decision tree models, the WT-MACNN model exhibits a diagnostic accuracy improvement of 73.81%, 57.13%, and 54.76%, respectively. Additionally, to assess the model’s adaptability under diverse well conditions, this study reacquired 128 sets of field data. The verification results indicate that the model’s prediction accuracy across different well conditions is 83.59%. Despite occasional misjudgments among certain operating conditions, the overall performance remains outstanding, offering dependable support for diagnosing real-world scenarios. The outcomes of numerical experiments highlight the profound advantages of the proposed deep learning model in terms of generalization ability and diagnostic accuracy, validating its superiority in plunger gas lift fault identification and carrying substantial guidance for the application of this technology.

TRACKING MAP UNTUK MONITORING GANGGUAN JTR PADA NH FUSE GARDU DISTRIBUSI

This research develops an IoT-based fault monitoring system for Low Voltage (LV) Networks using a tracking map on NH fuses, aiming to improve response time efficiency and fault detection accuracy. The system integrates sensors with a web-based platform to monitor and visualize fault locations in real-time. Test results indicate that the system is capable of detecting faults with high accuracy, with detection errors as low as 0% to 0.32%. The response time for fault detection ranges from 0.5 to 1 second, significantly faster than conventional methods. As a result, fault recovery time can be accelerated by 30-40%, potentially enhancing the reliability of the power supply. Additionally, the use of IoT technology allows more efficient monitoring and quicker response to faults, enabling faster repair processes and minimizing downtime. Although the system demonstrates promising results, the study also identifies areas for further development, particularly in integrating it with more complex distribution management systems and enhancing data analytics. Overall, the proposed system offers an innovative solution for mitigating faults in LV networks, improving reliability, and increasing operational efficiency in power distribution systems

Research on Arc Extinguishing Characteristics of Single-Phase Grounding Fault in Distribution Network

Research on Arc Extinguishing Characteristics of Single-Phase Grounding Fault in Distribution Network

System identification and fault reconstruction in solar plants via extended Kalman filter-based training of recurrent neural networks.

This article proposes using the extended Kalman filter (EKF) for recurrent neural network (RNN) training and fault estimation within a parabolic-trough solar plant. The initial step involves employing an RNN to model the system. Given the challenge of fault discernibility in the collectors, parallel EKFs are employed to reconstruct the parameters of the faults. The parameters are used independently to estimate the system output, and the type of fault is isolated based on the estimation errors using another feedforward neural network. To evaluate the effectiveness of the methodology, simulations are conducted on a loop of the ACUREX plant with irradiances from sunny and cloudy days. The results reveal a fault classification accuracy of approximately 90% and a fault reconstruction error below 3%, with even better accuracies in the cloudy dataset than in the sunny dataset.

Revolutionizing Fault Detection in Self-Healing Network via Multi-Serial Cascaded and Adaptive Network

Self-Healing Network (SHN) plays a crucial role in the realm of digital networking and the telecommunications industry. Fault detection in SHN is a critical aspect of network management and maintenance, which acts as a pivotal role in maintaining network health and minimizing disruptions. The SHN networks aim to manage faults and system failures by automating the detection process and pinpointing their origins. This proactive approach is essential to maintain network integrity and ensure a seamless user experience. In this paper, a novel multi-Serial cascaded and Adaptive Network based fault Detection in SHN (SAND-SHN) technique has been proposed for detecting faults in the SHN network. The proposed method uses the Eigen-Entropy Synthetic Minority Oversampling Technique (EE-SMOTE) to balance imbalanced data for classification and the Multi-Serial Cascaded and Adaptive Network (MSCAN), which integrates deep learning (DL) techniques to attain the final classified outcome. The proposed SAND-SHN method has been evaluated using a Python environment in terms of specific parameters such as accuracy, precision, recall, specificity, and F1-Score. The proposed technique has been evaluated using two datasets as EFCD dataset, and the SFDD dataset. The proposed SAND-SHN technique achieves a higher accuracy of 74% for RSO-MSCAN, 39.2% for DMO-MSCAN, 48.8% for BFGO-MSCAN, and 43.6% for FHO-MSCAN respectively.

Discrimination of high impedance fault in microgrid power network using semi-supervised machine learning algorithm

This work proposes a semi-supervised classification approach for discriminating high-impedance (HI) faults and other transients in a photovoltaic (PV) interconnected microgrid (MG) network. The suggested classifier combines unsupervised K-means clustering with the supervised multi-layer perceptron neural network algorithm. The K-means clustering technique is utilized in the first phase to detect and remove irrelevant instances from multiple events in the data set. To obtain the final predictions of targeted labels, clustered cases from the first phase are utilized to learn the multi-layer perceptron neural network classifier in the next phase. The suggested method outperforms stand-alone classifiers (K-means clustering and multi-layer perceptron) by providing enhanced accuracy and success rate of discriminating HI fault under standard test conditions and weather intermittency of PV. Furthermore, the results of the performance study clearly show that the suggested model is more resilient and offers superior performance than the stand-alone classifiers under the standard test condition and uncertainty of PV in MG networks.

Emerging Trends in Active Distribution Network Fault Detection

Emerging Trends in Active Distribution Network Fault Detection

Mitigating the ferroresonance resulting from HILGF clearing in a remote area 10 kV power distribution network

AbstractTo solve the problem of high impedance line‐to‐ground fault, a solution using isolation transformers for subnetwork divisions was previously implemented in the power distribution network. As a result of that, the network has been experiencing ferroresonance more often. Through modelling and simulation based on its actual parameters on the PSCAD/EMTDC, the understanding of the situation is deepened. The ferroresonance originates from the network response to the high impedance line‐to‐ground fault clearing. The subnetwork division has certainly improved the network fault damping factor, but the line capacitance reduction it caused has exposed the system to ferroresonance. To mitigate the ferroresonance, high‐frequency components are removed from the zero‐sequence current using the morphological filtering; then, through edge detection, it is used as the input signal to the control unit. The control unit then takes the ferroresonance mitigation action intelligently. The simulation results indicate that the proposed method can restore ferroresonance‐induced overvoltage and overcurrent to normal levels within 0.05 s.

Modeling of faults in the CEB electrical transmission network by approaches: KNN, Random Forests, logistic regression, SVM, ANN and gradient boosting of supervised learning

The work accumulated in this article presents the results of learning the faults that affect the CEB network. The objective is to predict failures in order to prevent these faults from creating interruptions. The network operating data from 2008 to 2015 are used as materials. The algorithms: SVM, KNN, Random Forest, Gradient Boosting, ANN and Logistic Regression were used as methods to create the models. The results are subjected to evaluation criteria namely: the confusion matrix, the area under the ROC curve and the scores (Accuracy, F1 Score, Precision and recall). A characterization of the faults is carried out. The results of the characterization reveal that there are 19 faults and the most recurrent is the short circuit, which appeared 947 times out of 2427 during the study period. The modeling results are perfect. The True Positives of the confusion matrices are greater than 450 out of 497, for the classes. Some are better than others. The unfavorable is obtained through the KNN with AUC=0.761. Its score, (Accuracy=0.955; F1 Score = 0.957; Precision=0.958; Recall=0.955), confirms this observation. The AUC=0.664, remains even more unfavorable with the SVM modeling but its score, (Accuracy=0.989; F1 Score = 0.988; Precision=0.988; Recall=0.989), exceeds that of KNN. Moreover, for the other models, their AUC exceeds 80% with the more perfect logistic regression giving: AUC=0.991; Accuracy=0.991; F1 Score = 0.991; Precision=0.991; Recall=0.991. These results confirm that, even very random and of various causes, we can predict the defects in the CEB network. However, it is necessary to use more recent data in order to apply these results in future operations.

Tool Wear State Monitoring in Titanium Alloy Milling Based on Wavelet Packet and TTAO-CNN-BiLSTM-AM

To effectively monitor the nonlinear wear variation of tools during the processing of titanium alloys, this study proposes a hybrid deep neural network fault diagnosis model that integrates the triangulation topology aggregation optimizer (TTAO), convolutional neural network (CNN), bidirectional long short-term memory network (BiLSTM), and attention mechanism (AM). Firstly, vibration signals from the machine tool spindle are acquired and subjected to the wavelet packet transform (WPT) to extract multi-frequency band energy features as model inputs. Then, the CNN and BiLSTM modules capture the features and temporal relationships of the input signals. Finally, introduction of the AM, combined with the TTAO algorithm, automatically extracts deep features, overcoming issues such as local optima and slow convergence in traditional neural networks, thereby enhancing the accuracy and efficiency of tool wear state recognition. The experimental results demonstrate that the proposed model achieves an average accuracy rate of 98.649% in predicting tool wear states, outperforming traditional backpropagation (BP) networks and standard CNN models.

Machine Learning Methods in IoT Based Embedded Systems for Classifying Physical Faults in Water Distribution Networks

Water is the most important factor for the survival of living things on Earth. Although 70% of the Earth is water, the amount of drinkable water is approximately 0.3%. Therefore, creating a sustainable water policy and carrying out studies are very important for our world and our future. Most of the potable water resources are physical losses. In the evaluations made based on metropolitan municipalities, it was seen that the water loss rate was approximately 50%. The study aims to find water pipe faults using IoT (Internet of Things) based machine learning classifiers to prevent physical losses in water distribution networks. Within the scope of this study, an experimental environment was created and an IMU (Inertial Measurement Unit) sensor was fixed on plastic pipes of different diameters and lengths. Vibration data collected in different scenarios (pressure, etc. factors) were transferred to the ThingSpeak platform over the internet. The transferred data could be monitored in real-time on a server. Physical damage in the pipes was detected using signal pre-processing, feature extraction, and feature selection algorithms on vibration data. In the study, damages were classified using machine learning-based classification (Decision Trees, k-Nearest Neighbors, Linear Discriminant, Support Vector Machines) methods to predict the type of damage (solid, hole, multi-hole). The data set revealed within the scope of the study is thought to lead to scientific studies in this field. The results obtained are close to the state-of-the-art results.

Advanced voltage relay design for distance relay coordination in power networks equipped with low‐inertia areas

AbstractIn modern power systems with high levels of distributed generation (DG), traditional protection schemes face challenges in ensuring reliable and efficient fault detection due to the complexities introduced by DG, particularly low‐inertia sources such as wind power. This paper presents an advanced protection scheme that integrates voltage relays (VRs) rather than overcurrent relays (OCRs) to improve coordination with distance relays (DRs) and enhance fault detection across multiple protection zones. By utilizing voltage measurements instead of conventional current‐based methods, the proposed scheme addresses issues such as low fault currents and mis‐coordination, which are common in DG‐integrated systems. The VR‐DR coordination improves system reliability by increasing fault detection sensitivity and selectivity, reducing the risk of mis‐coordination, and minimizing reliance on potentially inconsistent current measurements. VRs trigger faster fault isolation by operating before backup DRs, thus improving overall response times and system resilience. The VR scheme significantly outperforms traditional overcurrent relay schemes, with tripping times ranging from 0.002 to 0.956 s, compared to 0.035 to 1.184 s in the traditional scheme for three‐phase faults in a CIGRE power network. Additionally, the total tripping time is reduced from 10.5 s in the traditional scheme to 3.2 s with the VR scheme in networks with DGs under line to line to ground fault. The study demonstrates that no mis‐coordination events occurred with DRs in zone two, further emphasizing the effectiveness and reliability of the VR scheme. This innovative approach offers substantial improvements in fault management, ensuring quicker fault resolution and enhanced system stability in modern, DG‐integrated power grids.

Adaptive current protection for three‐phase short‐circuit faults in active distribution networks

AbstractTo address the impact of distributed generation (DG) access on the traditional protection configuration methods of distribution networks (DNs) and to facilitate the swift and effective protection of lines by protection devices under fault conditions, an adaptive current protection method for three‐phase short‐circuit faults in active distribution networks (ADNs), based on local information, is proposed. Initially, the applicability issues of traditional protection are examined, and the limitations of the existing protection scheme in ADNs are highlighted. Subsequently, the fault characteristics of ADNs are analysed, taking into account the output characteristics of DGs under short‐circuit faults, which form the foundation for the proposed protection method. Following this, an adaptive current protection method based on local information is introduced, utilizing a dual criterion of phase and magnitude to adjust line protection, with a specific implementation strategy for the protection scheme provided. Finally, the proposed protection scheme is validated using PSCAD/EMTDC simulation software, with results demonstrating that the scheme enables rapid and efficient protection action under fault conditions across the entire line.

Adaptive arc suppression strategy for single-phase ground faults in distribution networks using power routers

Abstract As new power systems evolve, power routers are becoming increasingly significant role in the power grid due to their capabilities in voltage transformation, fault isolation, and bidirectional power control. This paper introduces an adaptive arc suppression approach for single-phase ground faults in distribution grids based on a power router with a modular multilevel converter. The methodology adaptively engages arc suppression through voltage adjustment and current compensation, obviating the need for specific consideration of transition resistance levels. This method overcomes the limitation of existing arc suppression techniques in adapting to varying fault resistances, thereby enhancing arc suppression efficacy. Simulation outcomes substantiate the efficacy of the proposed adaptive arc suppression technique.

A High-Impedance Fault Detection Method for Active Distribution Networks Based on Time–Frequency–Space Domain Fusion Features and Hybrid Convolutional Neural Network

Traditional methods for detecting high-impedance faults (HIFs) in distribution networks primarily rely on constructing fault diagnosis models using one-dimensional zero-sequence current sequences. A single diagnostic model often limits the deep exploration of fault characteristics. To improve the accuracy of HIF detection, a new method for detecting HIFs in active distribution networks is proposed. First, by applying continuous wavelet transform (CWT) to the collected zero-sequence currents under various operating conditions, the time–frequency spectrum (TFS) is obtained. An optimized algorithm, modified empirical wavelet transform (MEWT), is then used to denoise the zero-sequence current signals, resulting in a series of intrinsic mode functions (IMFs). Secondly, the intrinsic mode functions (IMFs) are transformed into a two-dimensional spatial domain fused image using the symmetric dot pattern (SDP). Finally, the TFS and SDP images are synchronized as inputs to a hybrid convolutional neural network (Hybrid-CNN) to fully explore the system’s fault features. The Sigmoid function is utilized to achieve HIF detection, followed by simulation and experimental validation. The results indicate that the proposed method can effectively overcome the issues of traditional methods, achieving a detection accuracy of up to 98.85% across different scenarios, representing a 2–7% improvement over single models.

ResNest-SVM-based method for identifying single-phase ground faults in active distribution networks

Single-phase grounding fault is the most common fault type in the distribution network. An accurate and effective single-phase grounding fault identification method is a prerequisite for maintaining the safe and stable operation of the power grid. Most neutral points of the active distribution network are grounded through arc suppression coils. In the active distribution network, the power supply in the network changes from one to multiple, which may change the direction of the fault current. In this paper, the superposition theorem is used to analyze the difference in the boosting effect of different types of distributed generators (DG) on line mode current in the sequence network diagram when DG is connected upstream or downstream of the fault point. Secondly, the composition of the zero-mode transient current of the fault line is analyzed. A judgment method based on the superposition diagram of transient zero-sequence voltage and current is proposed. Then, this paper improves the ResNest network and modifies the classifier of the last fully connected layer to SVM. Finally, the model in PSCAD is used to simulate single-phase grounding faults to obtain the training set and validation set. These datasets are used to train and test AlexNet, ResNet50, ResNeSt, and ResNeSt-SVM. The results show that under different fault points, transition resistances, DG access upstream and downstream of the fault point, and different fault initial phase angles, the ResNest-SVM model method can accurately identify the fault line and has better anti-noise ability than the other three network structures.

Оценка влияния напряжения нулевой последовательности на показатели безопасности при однофазных замыканиях в низковольтных судовых электросетях

One of the trends in the development of marine technology is the constant increase in the degree of electrification of ships. This process is accompanied by an increase in the danger of ship electrical networks, determined primarily by the operation of electrical installations with single-phase insulation damage. The consequences of this mode largely depend on the phase capacity of the electrical network. Moreover, the influence of phase capacitance on the danger of single-phase short circuits is determined not only by its size, but also by the degree of asymmetry. It is proved that it is precisely the asymmetry of the phase capacitances that causes the formation of zero-sequence voltage in ship electrical networks. An analysis of the indicators of the main dangers of the electrical network was carried out, taking into account the influence of zero-sequence voltage on them. It is proved that in electrical networks with an isolated neutral, the response time of the protection to cut off single-phase contact currents must be selected taking into account the fact that on-board networks may have asymmetrical phase capacitances. A set of theoretical and experimental studies was carried out to assess the influence of zero-sequence voltage on quantitative indicators of the danger of a ship's electrical network. An experimental assessment of the influence of phase capacitance asymmetry on the stability of electrical discharges during single-phase faults in electrical networks with an isolated neutral was carried out. It has been demonstrated that during short circuits through an unstable electric discharge, the presence of a zero-sequence voltage leads to a significant increase in explosion and fire hazard indicators of the electrical network, such as power and energy released at the site of insulation damage. It was revealed that increased safety can be achieved by influencing the voltage on the neutral.

Wavelet Analysis of a High Impedance Fault Modeled as a Stochastic Hybrid SystemProducts

Introduction: electrical networks are sensitive to faults, and among the most common types, one of the most difficult to detect is the High Impedance Fault (HIF). This type of fault can go unnoticed due to its particular characteristics, such as the ground resistance and voltage drops in the electric arc.Objective: to propose a stochastic hybrid model for simulating high impedance faults, using the Ornstein-Uhlenbeck process to describe the random nature of these faults. The model focuses on key HIF parameters: ground resistance and voltage drops in the electric arc.Methodology: the Ornstein-Uhlenbeck process is used to model the randomness of high impedance faults. Simulations are conducted to compare the numerical results with experimental signals reported in the literature. Additionally, both continuous and discrete wavelet transforms are applied to the line current signal to analyze fault characteristics.Results: the simulations show a qualitative similarity between the numerical results obtained and the experimental signals available in the literature. Both continuous and discrete wavelet transforms reveal typical features of high impedance faults, validating the effectiveness of the proposed model.Conclusions: the proposed stochastic hybrid model for high impedance faults is effective in simulating this type of fault, and wavelet transform analyses demonstrate its ability to identify distinctive characteristics of HIFs, which can improve the detection and diagnosis of these faults in electrical networks.

Mixed Reality-Based Inspection Method for Underground Water Supply Network with Multi-Source Information Integration

Regular on-site inspection is crucial for promptly detecting faults in water supply networks (WSNs) and auxiliary facilities, significantly reducing leakage risks. However, the fragmentation of information and the separation between virtual and physical networks pose challenges, increasing the cognitive load on inspectors. Furthermore, due to the lack of real-time computation in current research, the effectiveness in detecting anomalies, such as leaks, is limited, hindering its ability to provide immediate and direct-decision support for inspectors. To address these issues, this research proposes a mixed reality (MR) inspection method that integrates multi-source information, combining building information modeling (BIM), Internet of Things (IoT), monitoring data, and numerical simulation technologies. This approach aims to achieve in situ visualization and real-time computational capabilities. The effectiveness of the proposed method is demonstrated through case studies, with user feedback confirming its feasibility. The results indicate improvements in inspection task performance, work efficiency, and standardization compared to traditional mobile terminal-based methods.

Active Distribution Network Fault Location Based on Petri Nets and Improved Particle Swarm Optimization Algorithm

Active Distribution Network Fault Location Based on Petri Nets and Improved Particle Swarm Optimization Algorithm