Fault Current Research Articles

Unsupervised fault diagnosis framework for underwater thruster system using estimated torques and multi-head convolutional autoencoder

The thruster system is one of the most critical parts of an autonomous underwater vehicle. Feedback, propeller, motor and driver faults are four common field faults of thruster systems. This paper investigates an unsupervised framework based on torques and multi-head convolutional autoencoders for the mentioned faults. The proposed method utilizes an extended state observer to estimate the motor load. The propeller torque is estimated using a physics-guided neural network. Meanwhile, the relationship between the patterns change among torques (motor load and propeller torque) and different faults are analyzed. Then, to avoid feature aliasing, two independent encoders of the multi-head convolutional autoencoder extract the pattern features between the torques automatically. Finally, faults are detected and classified based on the various patterns of the extracted torque features. The sea trial data verified the effectiveness of the scheme. The results show that the method can accurately detect and classify five specific faults: current feedback fault, speed feedback fault, propeller loss, propeller winding and open-circuit fault.

Centralized Protection of Networked Microgrids with Multi-Technology DERs

The structure and connections in networked microgrids consisting of two or more interconnected microgrids is influenced by the dynamic behaviors of power markets, the demand and supply interactions between market participants, and the possibility of operating in the grid-connected or islanded modes. Protection zones in the above-mentioned scenarios are dynamic and should not be determined a priori. Also, fault currents will vary depending on the operating modes, online or offline status of Distributed Energy Resources (DERs), variation of solar irradiation or wind speed, etc. This paper proposes a Centralized Intelligent Station-Level Protection (CISP) approach for the protection of various electric power equipment technologies in networked (interconnected) microgrids using adaptive protective relaying algorithms and a network theory-based zone selection algorithm. The proposed CISP approach utilizes wide area IEC 61869-9 Sampled Values (SVs) measurements and IEC 61850 Generic Object-Oriented Substation Events (GOOSE) messages, intelligently determines the protection zones, and automatically selects the protection algorithms to use in each of the protection zones based on the prevailing system topology and operating conditions. The effectiveness of the proposed CISP approach is demonstrated through real-time simulations using the RTDS®. The results obtained were promising for the various system configurations, operating conditions, and fault conditions considered.

A New Short-Circuit Current Calculation and Fault Analysis Method Suitable for Doubly Fed Wind Farm Groups.

The transient characteristics of wind farms in groups are quite different; in addition, there is a strong coupling between the wind farms and the grid, and these factors make the fault analysis of the grid with wind farm groups complicated. In order to solve this problem, a mathematical model of the converter is established based on the input-output external characteristics of the converter, and a transient model of a doubly fed wind turbine (DFIG) is presented considering the influence of the low-voltage ride-through control (LVRT) of the converter, and the effect mechanism of the LVRT strategy on the short-circuit current is analyzed. Finally, a short-circuit current calculation model of a doubly fed wind turbine with low-voltage crossing control is established. The interaction mechanism between wind farms during the fault is analyzed, and a short-circuit current calculation method of doubly fed wind farm groups is proposed. RTDS is used to verify the accuracy of the proposed short-circuit current calculation method for doubly fed field groups. On this basis, a method of power grid fault analysis after doubly fed field group access is discussed and analyzed.

Evaluation of Transfer Learning Techniques for Fault Classification in Radial Distribution Systems: A Comparative Study

Transfer learning has recently had a detectable impact on the state of the art in a wide variety of applications, and this trend is expected to continue in the near future. Both transfer learning and deep learning algorithms make use of a number of network layers, each of which may be intellectually learned and typically represents the data in a hierarchical fashion with increasing levels of abstraction. Convolutional neural networks have been proven to be exceptionally successful machine learning and deep learning techniques for a number of computer vision problems. These networks were developed by companies such as Alexa, Google, and Squeeze. Fault diagnostic strategies that are based on deep learning techniques are currently a topic of intense investigation due to their higher performance. Using transfer learning technology to carry out fault categorization in a power distribution system in a manner that is both accurate and efficient The work at hand employs a fault classification model for a radial power distribution system that is based on transfer learning and deep learning. Images of time series of three-phase fault currents are acquired via simulation with the assistance of PSCAD software as part of the proposed approach for doing so. In the next step, CNN models that are based on Alex Net, Google Net, and Squeeze Net are utilized to extract fault features from defective photos in order to categorize eleven distinct defects (using the MATLAB platform). For the categorization of defects in a radial distribution system, Alex Net, Google Net, and SqueezNet each offer accuracy of approximately 98.92%, 97.48%, and 99.82%, respectively. In this study, the classification of faults in a distribution system is accomplished with the help of AlexNet, GoogleNet, and SqueezNet. According to the findings of the simulations, the test accuracy for SqueezeNet is the highest it can be, coming in at 99.82%. Because of this, selecting it as the solution to the issue of fault classification in the test distribution system is your best option.

Current limiting and recovery characteristics of an AC fast recovery superconducting fault current-limiting breaker

With the continuous development of the power grid, the high short-circuit fault current threatens the reliability of the power system. When the short-circuit current is greater than the interrupting capacity of the circuit breaker, the fault current will not be interrupted. Resistive-type superconducting fault current limiter (R-SFCL) has little loss in normal conditions. When a short-circuit fault occurs, R-SFCL can respond within a few milliseconds and limit the fault current. But the recovery time of R-SFCL is too long to satisfy the reclosing demand in the power grid. In this paper, an AC fast recovery superconducting fault current-limiting breaker (FR-SFCB) is proposed. With the operation of a fast switch connected with the superconducting coil, one of the parallel superconducting branches is disconnected, which could provide higher current limiting resistance and decrease the fault current need to be interrupted in fault conditions. Because of the shorter current flowing time, the heat generation and temperature of the advanced disconnected branch are less which can reduce the recovery time significantly. An experimental model is established based on thyristors and superconducting tapes. The effects of prospective current (I p) and the types of superconducting tapes on the current limiting resistance and recovery time of FR-SFCB are investigated. Experimental results show that with the disconnection of the flowing branch, the current limiting resistance of the proposed FR-SFCB increases to more than 3 times of the normal R-SFCL. At the same time, when the FR-SFCB resistance after current limiting is lower than the room temperature resistance, the recovery time of the flowing branch decreases by at least 50%. With access to fast switches, the proposed FR-SFCB can suppress the short-circuit fault current effectively and improve the stability of the power system.

Crowbar Protection Scheme for Fault Ride Through in a Doubly-fed Induction Generator

This study focusses on using a crowbar protection scheme to overcome the symmetrical voltage dips in a doubly fed induction generator (DFIG). The crowbar protection scheme is integrated into the wind energy conversion system of the DFIG. The implementation of this scheme provides automated fault ride-through during sudden transients which may otherwise lead to over current faults. The increased use of renewable sources for electricity generation leads to a reduction in pollution which directly benefits the environment. Sustained usage of renewable resources will be beneficial for the environment.

Denoising graph neural network based hydraulic component fault diagnosis method

Accurately identifying faults in hydraulic components is essential for improving the reliability and safety of hydraulic systems. Since the hydraulic components of heavy-duty equipment usually work in complex and variable environments and are subject to interference from multiple sources of noise. As a result, fault diagnosis of hydraulic components is challenging, leading to the development of various fault diagnosis models. However, current hydraulic component fault diagnosis models have the following limitations: (1) only able to handle Euclidean space data and ignore the topological relationships within the signals that could provide additional useful information for fault diagnosis, (2) only diagnose faults using single-dimensional acceleration data and fail to incorporate more equipment working conditions. This study focuses on the hydraulic directional valve and proposes a fault diagnosis method based on denoising graph neural network. Firstly, computational fluid dynamics(CFD) is used to analyze the flow field of the spool and sleeve of the valve under different wear states and working conditions. It is found that different degrees of wear have similarities under different working conditions, and there is a regular correlation between the acceleration signal of the valve body and the fault mode under the same working condition. Therefore, acceleration and working condition information are selected as the raw information for fault diagnosis. Then, horizontal visibility graph(HVG) was utilized to transform acceleration signal to graph, and the graph nodes, which can incorporate multidimensional information, were set to be the equipment working conditions and acceleration values. However, the noise in the raw signal could seriously affect the quality of the HVG. To solve this problem, an adaptive threshold denoising algorithm was proposed to reduce the interference noise in the acceleration signal. The denoising threshold parameter was learned directly from the raw data. Finally, graph neural network(GNN) is applied to fault diagnosis of hydraulic components from non-Euclidean multidimensional information. Experimental results demonstrate the superior robustness and accuracy of this method in handling strong noise and multiple working conditions compared to several classical fault diagnosis methods. This research provides an effective approach for hydraulic component fault diagnosis.

A Two-Terminal Directional Protection Method for HVDC Transmission Lines of Current Fault Component Based on Improved VMD-Hilbert Transform

The traveling wave protection of high voltage direct current (HVDC) transmission lines is susceptible to the influence of transition resistance. As a backup protection, current differential protection has absolute selectivity, but usually requires an increase in delay to avoid misoperation caused by distributed capacitance on the line, resulting in a longer action time. Based on this, a two-terminal directional protection method for HVDC transmission lines is proposed based on Sparrow Search Algorithm (SSA)-Variational Mode Decomposition (VMD) and Hilbert phase difference. On the basis of analyzing the directional characteristics of the current fault component at both ends of the rectifier and inverter sides under different faults, SSA is first used to optimize the parameters of VMD. The residual components representing the direction of the current fault component at both ends are extracted through VMD, and then the Hilbert phase difference of the residual components at both ends is calculated to identify faults inside and outside the line area. In addition, fault pole selection can be achieved based on the ratio of the sum of multi-band Hilbert energy of single-terminal voltage fault components at the positive and negative poles. Simulation experiments have shown that the proposed protection scheme can quickly and effectively identify fault and has good tolerance to transition resistance and noise interference.

Photovoltaic arrays fault diagnosis based on an improved dilated convolutional neural network with feature-enhancement

Photovoltaic (PV) arrays are installed outdoors and prone to abnormalities and various faults under harsh natural conditions, reducing power conversion efficiency and the life of the PV modules, and even causing electric shock and fire. Current fault diagnosis methods are unable to accurately identify and locate faults in PV arrays in PV power systems, leading to increased operation and maintenance costs. Therefore, the feature-enhancement improved dilated convolutional neural network (CNN) is proposed for fault diagnosis of PV arrays in this paper. Firstly, aim at the problem of information loss due to data structure and spatial hierarchy within the traditional CNN, and the loss of data after down-sampling, which leads to the inability to reconstruct information, a dilated convolution is introduced to obtain a larger perceptual field while reducing the computational effort. Meanwhile, the adaptive dual domain soft threshold group convolution attention module is proposed to enhance the essential features of faults and reduce the information redundancy given the ambiguity and blindness of the feature data in PV array fault extraction. Finally, the model performance of the proposed model is validated and the operability and effectiveness of the proposed method are verified experimentally. The diagnostic results show that the average diagnostic accuracy of the proposed model is 98.95% compared with other diagnostic models, with better diagnostic accuracy and more stable diagnostic performance.

Convolutional Neural Network for Transmission Line Fault Diagnosis Based on Signal Segmentation Approach

Convolutional Neural Network (CNN) is a Deep Learning algorithm that can take in an input signal, assign importance (learnable weights and biases) to various aspects of the signals and distinguish between them. The CNN algorithm trains a sample and obtains a CNN model capable of identifying different fault types to analyse transmission line faults. Fault analysis methods typically require feature extraction to synthesise the relevant and non-redundant information from the raw signals. However, these traditional methods are time-consuming and inconsistent, and they can produce biased results due to the reliance on human expertise and experience. Hence, this research focused on developing an intelligent system for fault detection in a three-phase transmission line using CNN. This research aims to develop a CNN model for automatic fault detection in a three-phase transmission line and evaluate the performance of the CNN model for analysing transmission line faults. The three-phase transmission line model was developed using MATLAB-Simulink. CNN was implemented to detect transmission line faults. The performance of CNN based on the signal segmentation approach was evaluated through three different types of data: ideal and noise-added signal data. The simulation result shows good performance accuracy of 99.11% for the ideal case, 99.36%, and 99.39% for 20 dB and 30 dB noise-added cases, respectively. The result shows that a higher noise value in transmission line fault current could increase the performance of CNN. In conclusion, the utilisation of CNN based on a signal segmentation approach for transmission line fault analysis has showed promising performance.

The Capacity Configuration of Energy Storage System in DC Microgrid with Source-storage Integration

The occurrence of a fault in a DC microgrid can significantly jeopardize the system’s safe operation, making it a critical area of research for scholars. One key focus of this research is on developing methods to suppress the fault current, which has become a research hotspot for many scholars. Each distributed power source is connected to DC microgrid through a battery storage system to form a source-storage integration DC microgrid structure, which can have good fault current control ability and obvious improvement effect on reducing the protection technology difficulty of DC microgrid and enhancing the flexibility of distribution network. Compared with the traditional DC microgrid structure, the source-storage integration DC microgrid, whose battery storage system is changed from centralized to distributed, will smooth fluctuations in distributed power output by modifying energy storage planning principles, and also maintain bus voltage and realize fault current control with distributed power sources. Capacity planning principles for energy storage systems proposed in this paper were studied for DC microgrids with source-storage integration, and the total energy storage capacity and power under two DC microgrid structures were compared and analyzed with a scenic complementary power station as an example. Although a DC microgrid with a changed grid structure would require less than a 20% increase in the overall capacity and power of the energy storage system, according to the calculation results, the operation of the microgrid can be significantly improved, the flexibility of power dispatching of the distribution network was enhanced, and the investment cost of fault protection was reduced. Research into the capacity configuration of energy storage systems has demonstrated that DC microgrids with integrated source and storage capabilities hold significant engineering application value and practical significance.

Structural discrepancy and domain adversarial fusion network for cross-domain fault diagnosis

The current deep learning-based fault diagnosis methods have achieved excellent classification performance under supervised training, but the existence of domain discrepancy under different working conditions will greatly affect data relationship mining and fault diagnosis accuracy. Graph convolutional network (GCN) with topological structured relationships is excellent in solving these issues. In this paper, structural discrepancy and domain adversarial fusion network (SDDAFN) is proposed to learn deep high-dimensional features and eliminate distribution discrepancy across different domains. In data structure construction, the high-dimensional mapping features are first extracted accurately from the input raw signals by a feature encoder, then the output of encoder is converted into an instance graph through graph construction layer (GCL) for the convenient modeling by multi-channel kernel GCN (MCK-GCN). In network optimization, the classification loss function is introduced to optimize the discrimination boundaries of different fault types, and the integrated loss function based on structural discrepancy and domain adversarial are jointly adopted to minimize the domain distribution discrepancy. Experimental cases show the proposed method outperforms the state-of-the-art methods.

High impedance fault detection device based on edge artificial intelligence

The current high impedance fault (HIF) detection devices are mainly based on threshold method, the hardware cannot meet the computing requirements of artificial intelligence (AI), and the software structure is difficult to adapt to the upgrade requirements of AI. These AI detection algorithms are mainly based on simulation analysis and laboratory testing and have not yet been applied practically. The paper develops an edge AI-based HIF detection device, it combines the main body and independent AI module. The AI module embeds an edge identification algorithm and shares the sampling data with the main body. The detection result is achieved from intelligent classifier in edge AI module and fed back to the main device. Finally, the device is tested on the Luohe distribution network true test platform, the detection accuracy for multiple types of fault is 98.6%.

Robust Traveling Wave-Based Protection Scheme for Multiterminal DC Grids

The DC transmission line protection technology is crucial for the development of multi-terminal Voltage Source Converter (VSC)-based HVDC systems. This article proposes a robust non-unit traveling wave protection (TWP), which deals with the DC fault area identification and fault type discrimination for high impedance fault conditions. The authors applied the traveling wave (TW) reflection and refraction method for the line-mode network. The distinctive features of high-frequency components contained in the line-mode and pole-mode voltage TWs at different relay units are used for the algorithm modeling. Discrete Wavelet Transform (DWT) is selected as the time-frequency analysis tool. The performed simulations are conducted for a four-terminal VSC-HVDC system, and validate the protection feasibility and robustness. More precisely, the proposed protection scheme identifies the internal and external DC faults within 2 ms, and provides correct operation during high-impedance faults (HIF) with a 25 dB level noise interference. This protection scheme makes use of a VSC-assisted resonant current (VARC) direct current circuit breaker (DCCB), that successfully interrupts the fault currents in less than 10 ms after the fault inception. The authors also comprehensively compared the proposed scheme with the existing methods. The obtained results show that the proposed protection scheme is superior in terms of sensitivity and selectivity performance.

New pilot protection method based on the current fault component waveform similarity

AbstractIn order to improve the ability of the modular multi‐level converter to quickly clear and isolate faults in the multi‐terminal flexible DC grid, this paper analyzes the characteristics of current faults in the multi‐terminal flexible direct current grid, introduces the comprehensive distance of current fault waveform, and proposes a line protection method based on the similarity of current fault waveform. This protection method firstly uses Euclid Dynamic Time Warping (DTW) distance and entropy weight method to process electrical quantity signals, calculates the comprehensive distance of current fault waveform of two converter stations, and builds the protection criterion based on the similarity of current fault waveform. Then, the fault identification is realized by using the calculated comprehensive distance, and the fault pole identification is realized by using the ratio of comprehensive distance between positive and negative poles. Finally, PSCAD was used to build a simulation model to output the fault data under different fault conditions, and MATLAB was used to process the fault data to verify the protection algorithm. The simulation results show that the proposed method has good resistance to high resistance, and its rapidity and adaptability are further improved. Compared with other similarity methods, this method combines Euclidean DTW distance and entropy method to process the signal, realizes the fault pole identification, and can still operate correctly under the interference of 500 Ω transition resistance and 20 dB noise ratio. The research results will provide a theoretical basis for the safe and reliable operation of flexible DC power grid.

Research on Hazard Analysis and Control Strategy of Power System Dispatching Operation Based on Information Fusion Technology

Abstract In this paper, a decision model for grid fault diagnosis based on multi-source information fusion is established, and the probability of component fault is obtained by using the change of switching quantity characteristic information and electrical quantity characteristic information during grid fault, and the static fault degree obtained by switching quantity information and the voltage and current energy distortion degree and fault degree obtained by electrical quantity information is used as independent evidence bodies, which are fused by improved D-S evidence fusion technique, and the fused The results are decided by improved fuzzy C-mean decision model to finally determine the fault components. Then, the optimal control of the grid is studied, and the corresponding self-healing control model is established according to the different operating conditions of the system so as to propose a self-healing control strategy of the microgrid based on the improved particle swarm algorithm. After analysis and verification, the accuracy of the grid fault diagnosis method proposed in this paper reaches 0.9681, and the diagnosis results are consistent with the pre-defined fault elements compared with FCM. The system network loss decreases from 0.146 kW to 0.031 kW, and the maximum power supply capacity increases from 1.574 to 2.468 after using the improved particle swarm algorithm-based microgrid self-healing control strategy. Therefore, the method in this paper can improve the reliability of grid operation and resist the risk of accidents.

An Investigation of the Impact of Distributed Generation Penetration on Directional Overcurrent Relay Coordination in a Distribution Network

Distributed generation units (DGs) are rapidly becoming widespread in distribution systems due to their advantages such as power loss reduction, voltage profile improvement, and economic returns. Many researchers seek new ways to maximize their these advantages. However, their impact on the fault current is a problem for the field of power system protection. The changes in the short-circuit currents due to DGs cause the miscoordination of the directional overcurrent relays (DOCRs). In this paper, the impact of distribution generation penetration on DOCR coordination is analyzed and investigated. Besides this negative impact of DGs, their contributions to reducing power loss and improving the voltage profile are also analyzed for different DG penetration levels. The gazelle optimization algorithm is utilized to solve the DOCR coordination problem studied in this paper. The method is performed on the distribution section of the IEEE 14-bus system. It is seen that a significant number of miscoordinations occur when even the DG penetration is increased by about 10%. With the increase in DG penetration, the number of miscoordinations does not increase proportionally, but there is a proportional increase in active and reactive power loss reduction and voltage profile improvement.

Research on the identification of high-resistance ground faults in the flexible DC distribution network based on VMD–inception–CNN

With the rapid development of flexible DC distribution networks, fault detection and identification have also attracted people’s attention. High-resistance grounding fault poses a great challenge to the distribution network. The fault current is very small and random, which makes its detection and identification difficult. The traditional overcurrent protection device cannot identify and act on the fault current. Therefore, this paper proposes a fault detection method based on variational mode decomposition (VMD) combined with the convolutional neural network (CNN) of the inception module. This method first uses VMD to decompose the positive transient voltage. Second, it inputs the decomposed signal into CNN for training to obtain the optimal parameters of the model. Finally, the model performance is tested based on the PSCAD/EMTDC simulation platform. Experiments show that the detection method is accurate and effective. It can realize the accurate identification of seven different fault types.

Graph structure embedded with physical constraints-based information fusion network for interpretable fault diagnosis of aero-engine

Fault diagnosis is essential for ensuring the safety and reliability of aero-engines. Current performance-based fault diagnosis methods typically establish a mapping between measurable parameters and engine states, without taking into account the inherent physical constraints of multimodal information. In this study, a novel graph structure embedded with physical constraints is proposed to effectively fuse sensor information and physical-based model (PBM) simulation information. Since the fault information is primarily manifested in the sensor data rather than the PBM data, the probability distribution between these two types of data can serve as a constraint for constructing the edges in the graph, as it reflects the physical association among the sample points. Selected sensor parameters are used as node characteristics in the graph. A self-supervised representation learning training structure based on graph convolutional network and canonical correlation analysis can effectively utilize labeled and unlabeled data. Furthermore, a robust statistics method is embedded into the training architecture to interpret the behavior of the model by identifying the impact of data samples. The verification results indicate that the proposed model can effectively perform multimodal information fusion and achieve more efficient and high-accuracy component-level fault diagnosis.

Enhancing distribution generator impact mitigation using an adaptive protection scheme based on modified pelican optimization algorithm and active database management system

AbstractThis paper addresses the challenge of protecting electrical networks in the presence of distribution generators (DGs). The use of DGs affects fault currents, leading to miscoordination between protection relays and causing constraints on network reliability. To tackle this issue, the authors propose an adaptive protection scheme (APS) based on a modified pelican optimization algorithm (MPOA) and active database management system (ADBMS). The APS coordinates directional overcurrent relays and distance relays, while the MPOA simulates a pelican mating strategy and includes a modified internal equation. The proposed APS is further upgraded with ADBMS to save system resources by storing relay settings in the database and calling them when the state of DGs changes without running optimization algorithms. The effectiveness of the proposed APS is validated on the Institute of Electrical and Electronics Engineers (IEEE) eight‐bus test system and the IEEE 14‐bus distribution network. Results indicate that the APS can effectively protect electrical networks in the presence of DGs, while the ADBMS upgrade saves system resources.