Local Fault Detection Research Articles

Improving Transmission Line Fault Diagnosis Based on EEMD and Power Spectral Entropy.

The fault diagnosis on a transmission line based on the characteristics of the power spectral entropy is proposed in this article. The data preprocessing for the experimental measurement is also introduced using the EEMD. The EEMD is used to preprocess experimental measurements, which are nonlinear and non-stationary fault signals, to overcome the mode mixing. This study focuses on the fault location detection of transmission lines during faults. The proposed method is adopted for different fault types through simulation under the fault point by collecting current and voltage signals at a distance from the fault point. An analysis and comprehensive evaluation of three-phase measured current and voltage signals at distinct fault locations is conducted. The form and position of the fault are distinguished directly and effectively, thereby significantly improving the transmission line efficiency and accuracy of fault diagnosis.

Fault Monitoring and Analysis of Distributed New Energy Grids Using Simulation Data Models

Data analysis is essential for fault identification and detection in smart grids in order to maintain grid monitoring. Many DL algorithms have been developed recently for data analysis applications related to smart grids. To resolve these challenges, the study suggests a Deep Neural Network (DNN) for data-driven fault location detection and type of fault classification by exploiting the Modified Sand Cat Swarm Optimization (MSCSO) optimization. Here, the DNN is used to diagnose the faults and determine the position of the sites. Numerous synthetic field data sets derived from simulated models of different transmission line types are used for training and testing. The position and type of faults are predicted by the DNN classifier based on the fault signal features, in which the DNN weights are ideally tuned using a novel MSCSO optimization method that is the improved concept of Sand Cat Swarm Optimization (SCSO). Lastly, MATLAB/Simulink is used to implement the suggested DNN-based method for fault classification and its localization in transmission line. An intellectual IEEE 6-node network model is utilized to confirm the efficacy and reliability of these methods. The outcomes demonstrated its effectiveness in giving the system operator precise and detailed analytics for fault identification.

Fault diagnosis method for distribution network based on improved deep learning

Abstract Due to the complexity of the grid structure, limited fault samples, and difficulty in obtaining them, there is a need to improve the accuracy of fault diagnosis in distribution networks. To address the issue of insufficient training samples in the target domain leading to poor training results, a fault diagnosis method for distribution networks based on improved deep learning is proposed. Firstly, finite element simulation is employed to generate fault data under different operating conditions, obtaining a sufficient number of samples for feature extraction and enhancement. Secondly, a deep convolutional neural network (DCNN) model is constructed, and the model is trained using the sample data. Finally, the model is used for fault diagnosis in distribution networks under different operating conditions. Experimental results demonstrate that the fault diagnosis model achieves high testing accuracy and is capable of detecting fault locations and types with high precision.

Fault Type and Fault Location Detection in Transmission Lines with 6-Convolutional Layered CNN

In this article, we propose a data-driven method for short-circuit fault detection in transmission lines that exploits the capabilities of convolutional neural networks (CNNs). CNNs, a class of deep feedforward neural networks, can autonomously detect different features from data, eliminating the need for manual intervention. To mitigate the effects of noise and increase network robustness, we present a CNN architecture with six convolutional layers. The study uses a single busbar power system model developed with the PSCAD simulation program to evaluate the performance of the proposed method. The proposed CNN method is also compared with machine learning methods such as LSTM, SVM and ELM. Our results show a high success rate of 98.4% across all fault impedances, confirming the effectiveness of the proposed CNN methods in accurately detecting short-circuit faults based on current and voltage measurements.

An Innovative Approach for Enhancing Relay Coordination in Distribution Systems Through Online Adaptive Strategies Utilizing DNN Machine Learning and a Hybrid GA-SQP Framework

The present study addresses the issue of varying fault locations within a distribution system, which leads to fluctuations in short-circuit currents and requires the implementation of adaptive protection strategies for network reliability. This paper presents a novel adaptive protection scheme that specifically considers these fault location variations using directional overcurrent relays (DOCRs). Unlike previous research on adaptive protection, which does not adequately account for fault location variations, this method employs deep neural networks (DNNs) for online fault location detection. In the verification process, the effectiveness of the proposed methodologies was assessed by analyzing the time derivative of a trained machine learning model for fault identification. This approach enables the immediate detection of fault locations within the distribution system and facilitates the transmission of the setting group index to activate preset optimal coordination parameter values for the system relays. Crucially, the proposed method ensures that the coordination constraints remain intact across various adaptive settings, relying on precise fault identification through machine learning. The practical significance of this approach lies in its applicability to real-world systems because the proposed solutions and protective settings can be easily implemented using commercially available relays. To evaluate its effectiveness, the adaptive protection scheme was tested on three distribution networks: IEEE 14-Bus, 15-Bus and 30-Bus. The comparative test results highlight that the proposed method significantly improves the speed of the protection system for distribution networks when compared to existing studies, making it a valuable contribution to enhancing network reliability and performance.

Evaluation of a Local Fault Detection Algorithm for HVDC Systems

A great increase in the amount of energy generated from clean and renewable sources integrated in the electric power system is expected worldwide in the coming years. High Voltage Direct Current (HVDC) systems are seen as a promising alternative to the traditional Alternating Current (AC) systems for the expansion of the electric power system. However, to achieve this vision, there are some remaining challenges regarding HVDC systems which need to be solved. One of the main challenges is related to fault detection and location in HVDC grids. This paper reviews the main protection algorithms available and presents the evaluation of a local fault detection algorithm for DC faults in a multi-terminal Voltage Source Conversion (VSC) based HVDC grid. The paper analyses the influence of the DC voltage sampling frequency and the cable length in the performance of the algorithm.

Wavelet-ANN Based Detection of Fault Location of Hybrid Renewable Energy Sources Connected Power Transmission System

Wavelet-ANN Based Detection of Fault Location of Hybrid Renewable Energy Sources Connected Power Transmission System

Internal Short-Circuit Fault Location of Rail Transit Auxiliary Inverter Based on Voltage Deviation

In order to improve the ability of locating and detecting the internal short-circuit fault of rail transit auxiliary inverter, a method of locating the internal short-circuit fault of rail transit auxiliary inverter based on voltage deviation is proposed. The fault sensing information acquisition module of rail transit auxiliary inverter is constructed by using the detection method of fundamental frequency characteristics of differential mode current of excitation winding, and the voltage deviation analysis of rail transit auxiliary inverter under internal short-circuit fault state is realized by using the methods of MVCR excitation winding control and output reactive power adjustment. Combined with the dynamic distribution characteristics of output harmonics and initial phase of rail transit auxiliary inverter, the equivalent compensation and capacity expansion methods of excitation winding circuit are adopted. The signal test model of fault location is established, and the spectral characteristics of the internal short-circuit fault signal of rail transit auxiliary inverter are extracted. A fault joint feature location and monitoring model is established by using the data clustering analysis method of fusion difference, and the internal short-circuit fault location detection of inverter is realized according to the voltage deviation distribution characteristics of inverter in short-circuit state. The test results show that the accuracy of short-circuit fault location in rail transit auxiliary inverter is high, and the dynamic estimation ability of inverter output voltage deviation is strong.

Fault identification and classification algorithm for high voltage transmission lines based on a fuzzy-neuro-fuzzy approach

ABSTRACT Traditional techniques are used for fault location detection in high-voltage transmission lines that mostly depend on traveling waves and impedance-based techniques suffer from large errors owing to the intricacy of fault modeling for various types of faults. Although single-line to ground faults are dominant in high-voltage transmission lines, fault resistance as well as fault inception angle might distort the current fault detection techniques. In addition, other types of faults exist and that raises the need to develop an accurate fault detection technique to minimize the recovery time. The current paper introduces a fuzzy and neuro-fuzzy algorithm to detect, analyze, and locate different faults taking place in high-voltage transmission lines. A MATLAB Simulink Model is used for analyzing different fault cases; fault detection and classification are done by the Fuzzy Interface System (FIS), while fault location detection is done using the Adaptive Neuro-Fuzzy Interface System (ANFIS). The introduced algorithm is evaluated via the Mean Square Error (MSE) technique. The results showed full success in detecting and identifying different fault types, with a 0.0042 validity performance factor for fault location detection using ANFIS.

Precision local anomaly positioning technology for large complex electromechanical systems

In recent years, Prognostics Health Management (PHM) technology has become an important reference technology in fields such as avionics and electromechanical systems due to its ability to reduce costs and achieve state based maintenance and autonomous support. However, with the operation of large and complex electromechanical systems (ES), the data generated gradually ages the status of components, and traditional PHM technology is difficult to solve the problem of electromechanical system components becoming more complex. Based on this, this study takes the hydraulic actuator cylinder as an example to construct a local component fault detection model. Firstly, fault data features are extracted using wavelet packet energy spectrum, and then a fault detection model is constructed based on support vector machine (SVM). In response to the shortcomings of SVM, a smooth support vector machine (SSVM) is proposed to replace SVM, and an improved crow search algorithm (ICSA) is used to improve SVM. Finally, an intelligent detection model for hydraulic actuator cylinder faults based on ICSA-SSVM was constructed based on the above algorithms. The experimental results show that the ICSA-SSVM model has the fastest Rate of convergence, among which, the positioning accuracy is 0.96, the fitting degree is 0.984, the fault detection accuracy is 99.16 %, the recall value is 94.52 %, and the AUC value is 0.986, all of which are better than the existing fault detection models. From this, it can be seen that the precise local anomaly localization technology for large-scale complex electromechanical systems based on the ICSA-SSVM algorithm proposed in this study can improve the efficiency and accuracy of fault detection, achieve accurate and intelligent detection of ES local anomalies, and have certain positive significance for the development of China’s industry.

Mechanistic block‐based attention mechanism stacked autoencoder for describing typical unit connection industrial processes and their monitoring

AbstractThe overall information of a process can be obtained through global modelling, and the local information is easily ignored in the research of the industrial process monitoring of unit connection. Thus, finding the global type of faults is easy, but occurs at the expense of drowning out the local faults. The use of block modelling can highlight local information, thereby improving local fault detection capability. However, the connection information between blocks is usually ignored in block modelling, which makes finding fault classes that only affect the connection relationship between blocks difficult. A mechanistic block‐based attention mechanism stacked autoencoder (MB‐AMSAE) monitoring method is proposed in this paper. The industrial process is divided into several parts in accordance with its mechanistic relationships, and each part represents an independent block. Self‐attention is used to focus on the information of each block itself. Cross‐attention is adopted to focus on the information between blocks, and this information is fused to form new blocks. The new block is used as the feature of the original block, and the original block is reconstructed by using a stacked autoencoder. The corresponding control limit is obtained in accordance with the reconstruction ability of normal samples, and whether the working conditions are normal is judged according to the control limit. The proposed algorithm is used in numerical simulation, Tennessee‐Eastman processes, and is compared with other advanced algorithms based on its fault detection capability. Results show the effectiveness of the MB‐AMSAE algorithm in process monitoring.

Towards Progressively Detecting Faults in a Large Power System: A Visual Analytics Approach

Electricity serves as the foundation of everyday life. Grid operators are obliged to provide stable power supply to end users. However, given the scale and complexity of modern nationwide grids, it has become increasingly challenging to locate faults quickly and accurately during power failure. Previous locating techniques mainly focus on data analyzing methods, while little work is done using visual analytics approach. This paper presents a progressive visual analytics procedure (i.e., overview, detecting faulted bus candidates, locating buses, and looking into bus details) to detect fault location and figure out possible faulted buses. In particular, an efficient clustering algorithm associated with a novel visualization for time-varying series as well as multiple and coordinated views is developed to locate faulted buses intuitively and quickly. Case studies and expert feedback have indicated that our approach is contributing to real-world applications.

Speed monitoring and fault detection in bearings using an embedded piezoelectric transducer under speed-varying condition

For more than three decades, vibration monitoring by accelerometers has been a common technique in the health monitoring of bearings and rotating machines. These sensors are typically mounted on the housing of the system to collect the vibration data. However, the susceptibility of accelerometers to surrounding noise and vibration has attracted more attention toward embedded sensors in bearings. Also, the current trend toward intelligent manufacturing and the Internet of Things (IoT) requires mechanical components with integrated sensors to monitor their health status. In this research, a previously developed piezoelectric transducer embedded in a bearing housing is further investigated for condition monitoring of bearings. By using this transducer, the rotational speed of the bearing in the variable speed condition is measured. The results show a great correlation between the estimated speed compared with an encoder. Moreover, the performance of the transducer in local fault detection in the speed-varying condition is investigated. According to the results, it can be concluded that this low-cost and self-sensing transducer can be successfully used for condition monitoring and speed measurement in bearings.

Fault detection and classification in DC microgrid clusters

With the rising popularity of DC microgrids, clusters of such grids are beginning to emerge as a practical and economical option. Short circuit problems in a DC microgrid clusters can cause overcurrent damage to power electronic devices. Protecting DC lines from large fault currents is essential. This paper presents a novel localized fault detection and classification technique for the protection of DC microgrid clusters. In this paper, a variational mode decomposition (VMD) and artificial neural network (ANN) based technique is proposed for accurate and effective fault detection and classification. This research aims to train an ANN that can detect and classify faults in DC microgrid clusters with multiple sources and loads by applying VMD to extract features of current signals. Different types of short circuit faults such as Pole to Pole and Pole to ground faults are considered under various grid operating conditions. The proposed method is capable of real-time fault detection and diagnosis, which can help prevent system failures and minimize downtime. The results indicate that the proposed approach is efficient and effective in detecting/classifying faults in DC microgrid clusters improving the reliability and system safety. The performance evaluation is carried out through rigorous case studies in MATLAB/Simulink environment to prove the efficacy of the proposed method. The VMD-ANN approach is shown to outperform other traditional signal processing techniques in terms of accuracy and robustness. Moreover, the proposed method is applicable to a wide range of DC microgrid clusters, making it a versatile and valuable tool for future research and development.

Enhanced two-terminal impedance-based fault location using sequence values

<span lang="EN-US">Fault at transmission line system may lead to major impacts such as power quality problems and cascading failure in the grid system. Thus, it is very important to locate it fast so that suitable solution can be taken to ensure power system stability can be retained. The complexity of the transmission line however makes the fault point identification a challenging task. This paper proposes an enhanced fault detection and location method using positive and negative-sequence values of current and voltage, taken at both local and remote terminals. The fault detection is based on comparison between the total fault current with currents combination during the pre-fault time. While the fault location algorithm was developed using an impedance-based method and the estimated fault location was taken at two cycles after fault detection. Various fault types, fault resistances and fault locations have been tested in order to verify the performance of the proposed method. The developed algorithms have successfully detected all faults within high accuracy. Based on the obtained results, the estimated fault locations are not affected by fault resistance and line charging current. Furthermore, the proposed method able to detect fault location without the needs to know the fault type.</span>

Localized fault detection and classification technique for low voltage DC microgrid

Integration of Distributed Energy Resources (DERs) into conventional power system, results in variable amplitude and orientation of fault current posing new challenges to the traditional protection schemes for fault identification and classification in DC microgrid. In this paper, a new protection technique based on the change in reactor power determined at one end of the line during transient operation is proposed. Thus, the proposed technique employs localized measurement to detect and classify a fault, once the change in reactor power crosses a pre-defined threshold limit. The effectiveness of the proposed technique is determined for various faults and operational transients such as DER outage, load variation, AC-side faults etc. Finally, the simulation results are compared with schemes available in the literature, which further confirms the reliability and resilience of the proposed technique capable of identifying and classifying internal faults.

ANN-Based Statistical Computation for Remote End Fault Monitoring of the IEEE 14 Bus Microgrid Network

This paper focuses on an artificial neural network-based fog computing approach for remote end fault super vision in microgrid systems. The work presented here considers an Artificial Neural Network-based adaptive Line-to-Ground (L-G) and Line-to-Line (L-L) fault monitoring system for a standard IEEE 14 bus microgrid. Discrete Wavelet Transformation (DWT)-based study of statistical parameters of the currents going out from the various generator buses is carried out both in healthy and faulty situations. Faults (L-G and L-L) are created at different load buses and also an algorithm is proposed for detecting fault location. The rule set proposed here is unaffected by variations in fault resistance, making it very much suitable for ground fault monitoring. It provided satisfactory results when examined with various unknown cases. A fog computing layer helps in fast execution and fewer data storage requirements in the cloud. This assessment may be expanded for other kinds of faults in a microgrid system.

Detection of fault location and paleoseismic evidence in the cultural heritage site around Gyeongju, SE Korea

Detection of fault location and paleoseismic evidence in the cultural heritage site around Gyeongju, SE Korea

Active detection fault diagnosis and fault location technology for LVDC distribution networks

Under the rapid development of direct current (DC) transmission technology, the interconnection between converter stations in the DC network has been further improved, and a looped network topology has been gradually formed. Practical applications have shown that loop troubleshooting takes a long time to perform and requires substantial field experience, which has considerably reduced the efficiency of troubleshooting and the safety factor of low-voltage direct current (LVDC) distribution networks. The complex, transient characteristics and short duration of power electronic system faults limit the application of passive detection and protection technology, raising difficulties in DC line fault detection and location. To adapt to the development of power systems, this paper presents an active detection fault diagnosis and fault location technology for LVDC distribution networks based on a converter. After a line fault occurs, the fault type is identified by injecting detection signals into the fault line through the converter, and the fault location is determined by calculating harmonic impedance. The method can detect the fault of the system in time to reduce loss when the system on the converter side is faulty but can still run. It effectively utilizes the converter equipment in power systems and does not require the installation of additional detection devices, which is largely unaffected by fault resistance in fault location, and has accurate detection results and fast speed.

DPMSLM Demagnetization Fault Detection Based on Texture Feature Analysis of Grayscale Fusion Image

This study investigates a novel image morphology texture feature extraction method to realize the demagnetization fault location and severity detection of double-sided permanent magnet synchronous linear motor (DPMSLM). Initially, according to the constraints of DPMSLMs topology structure, the three lines magnetic density signal in motor air gap is extracted by finite element analysis as effective fault signal. Then, the grayscale fusion image (GFI) method is introduced to transform 1D data signal to 2D fused grayscale image which can better describe the demagnetization fault information. The unique features are visualized using the image enhancement techniques, and the image morphology texture features such as the area, Euler number, perimeter operator, correlation of binary image and so on can be extracted to constitute the demagnetization fault indexes. In addition, fisher score (FS) is used for feature optimization which can reduce the feature dimension. Furthermore, the two-level multiverse optimization support vector machine (MVO-SVM) algorithm is established to conduct demagnetization fault classification. Comparison experiments with other classifiers show that the MVO-SVM has a high fault identification accuracy of more than 98.3% and low running time less than 2.57s. Finally, the motor prototype experiment results show that the proposed method can accurately identify the location and severity of DPMSLM demagnetization faults, and it is an effective and feasible method which can be applied in DPMLM batch demagnetization inspection before delivery.