System Fault Analysis Research Articles

Intelligent information systems for power grid fault analysis by computer communication technology

This study aims to enhance the intelligence level of power grid fault analysis to address increasingly complex fault scenarios and ensure grid stability and security. To this end, an intelligent information system for power grid fault analysis, leveraging improved computer communication technology, is proposed and developed. The system incorporates a novel fault diagnosis model, combining advanced communication technologies such as distributed computing, real-time data transmission, cloud computing, and big data analytics, to establish a multi-layered information processing architecture for grid fault analysis. Specifically, this study introduces a fusion model integrating Transformer self-attention mechanisms with graph neural networks (GNNs) based on conventional fault diagnosis techniques. GNNs capture the complex relationships between different nodes within the grid topology, effectively identifying power transmission characteristics and fault propagation paths across grid nodes. The Transformer’s self-attention mechanism processes time-series operational data from the grid, enabling precise identification of temporal dependencies in fault characteristics. To improve system response speed, edge computing moves portions of fault data preprocessing and analysis to edge nodes near data sources, significantly reducing transmission latency and enhancing real-time diagnosis capability. Experimental results demonstrate that the proposed model achieves superior diagnostic performance across various fault types (e.g., short circuits, overloads, equipment failures) in simulation scenarios. The system achieves a fault identification and location accuracy of 99.2%, an improvement of over 10% compared to traditional methods, with an average response time of 85 milliseconds, approximately 43% faster than existing technologies. Moreover, the system exhibits strong robustness in complex scenarios, with an average fault prediction error rate of just 1.1% across multiple simulations. This study provides a novel solution for intelligent power grid fault diagnosis and management, establishing a technological foundation for smart grid operations.

Fault Monitoring and Analysis System for FACTS Systems

This study focuses on the development of a fault monitoring and analysis system specifically designed for Flexible AC Transmission Systems (FACTS). The system records analog and digital data at high resolution and frequency, enabling graphical analysis of these data. Designed for various industrial applications, this system is particularly used in distribution networks, substations, generation facilities, and factories to detect and monitor voltage and current anomalies. The system is equipped with a user-friendly interface that allows detailed examination of the recorded data.

Principle of fault direction discrimination in near area of AC grid-connected doubly-fed wind farm

Abstract With a large number of new energy sources, the current characteristics of doubly-fed fans under the circumstances of AC grid connection short-circuit faults have become the focus of research. After crowbar resistance is put in, the attributes of short-circuit current become complicated. By calculating the correlation coefficient between the voltage of the AC system and the current of the doubly-fed fan, the fault direction can be accurately judged. In this paper, the research work for new energy power system fault analysis and protection provides a new thought and method. The current characteristics of the doubly-fed fan under fault state are analyzed deeply, and the principle of fault direction discrimination based on waveform correlation coefficient is proposed. The advantage of this method is that it is independent of fault types, and can be applied to all kinds of fault situations, with high reliability and practicability. Simulation results unveil that the principle put forward can judge the fault direction accurately, reliably, and sensitively when various types of faults occur in areas near an AC grid-connected doubly-fed wind farm.

An Innovative Technique for Fault Analysis of Electric Automated Vehicles

A serious accident might occur as a direct result of a defective component or system, especially at an automated vehicle. The objective of this study is to establish a fault analysis methodology that can effectively improve the reliability of electric and automated vehicles. To achieve this, We proposed a method for identifying potential faults by combining Failure Modes and Effects Analysis (FMEA) with Fault Tree Analysis (FTA), creating a database of possible (example) faults that maps the causal relationship between causes, symptoms and faults, which enables more thorough fault analysis and serves as the foundation for further study. Using the fault database, we demonstrate a practical application involving fault injection and simulation, which can provide a more intuitive and practical representation of the effects of faults. The methodology is validated with the demonstrator vehicle from the joint project. This approach is scalable and can also be well applied to other electric automated vehicles with similar structure, providing a reliable tool to the system fault analysis for future work.

A comprehensive review of BMS fault analysis in pure electric vehicles

Pure electric vehicle technology faces numerous technical challenges during the process of independent research and development, with the safety and endurance of power batteries being particularly critical. The Battery Management System (BMS) is a core factor affecting the performance and safety of electric vehicles. The normal operation of the BMS is essential for ensuring the reliability of electric vehicles. However, in practical applications, the BMS may encounter faults that can lead to a decline in battery performance, a reduction in lifespan, and even trigger safety incidents. Therefore, the development of an efficient and accurate fault analysis and diagnostic system is particularly important. This paper systematically explores the fault issues of electric vehicle BMS, including the composition, working principles, and main functions of the BMS, as well as the classification, level definition, case analysis, and resolution measures of faults. The article also provides a detailed introduction to fault analysis methods, including qualitative and quantitative analyses. Finally, based on recent hot topics and the latest research progress, it is concluded that model-based fault diagnosis will become increasingly important. This paper aims to provide a reference for the development of the field of electric vehicle battery management fault diagnosis technology.

Distribution System Survey and Analysis: A Case Study of Kathmandu University

Load flow analysis helps to understand a power system's voltage profile and power flow distribution, enabling the identification of potential issues such as overloading and under-voltage conditions. Fault analysis ensures reliability and safety by understanding and mitigating abnormal conditions in electrical distribution systems. Together, they contribute to efficient operation, equipment protection, and overall system reliability. This paper presents the Kathmandu University distribution system's load flow and fault analysis. Starting from the careful study of the power usage of the Kathmandu University distribution system then followed by the load flow analysis employed in the Dig Silent Power factory software, critical insights into the system’s performance were unveiled. Additionally, fault analysis within the simulated distribution system provides valuable data to enhance the understanding of the different patterns of currents for the various types of faults that occur at different locations of the Kathmandu University distribution system. This study provides a comprehensive understanding of the Kathmandu University distribution system. It identifies potential issues that can help to make the Kathmandu University power system more reliable and efficient in the future.

Enhancing Solar Energy Generation: A Comprehensive Machine Learning-Based PV Prediction and Fault Analysis System for Real-Time Tracking and Forecasting

This paper addresses a notable gap in the field of photovoltaic system forecasting by introducing the Machine Learning-based PV Prediction and Fault Analysis System (ML-PVPFAS). This framework is designed to optimize the decomposition of variational systems automatically, using a multi-objective intelligent optimization method to establish its weight. The paper evaluates a range of Machine Learning (ML) and Ensemble Learning (EL) techniques for diagnosing faults in PV arrays, with a primary focus on identifying and categorizing complex faults that could affect these arrays. These faults encompass multiple anomalies and defects with similar current-voltage curves, which have not been previously examined. The analysis of prediction scores reveals that the ML-PVPFAS approach outperforms other methods, with the lowest Mean Absolute Percentage Error (MAPE) at 4.93, Root Mean Squared Error (RMSE) at 4.64, high Accuracy at 90.31%, Precision at 93.60%, a strong Pearson Correlation Coefficient of 0.90, and a fast Computation Time of 77.64 milliseconds. The results suggest that ML-PVPFAS is a dependable and practical algorithm for predicting the power output of PV solar systems, making it a valuable contribution to the field of predictive modeling.

Parameter estimation and control of a fuel cell air supply system based on an improved extended state observer

ABSTRACT Tracking control of the oxygen excess ratio (OER) and keeping the cathode air pressure relatively stable are essential for the operation of polymer electrolyte membrane fuel cell (PEMFC). In automotive fuel cell air supply systems, the cathode air pressure and OER are usually unmeasurable parameters, which makes the air supply system control and fault analysis very difficult. To solve this problem, this paper proposes an extended state observer based on the hyperbolic tangent function (HTF-ESO) to estimate the cathode air pressure of the fuel cell gas supply system, and indirectly obtains the OER according to the estimated cathode pressure, and designs the sliding mode control (SMC) and a first-order active disturbance rejection control (ADRC) for the tracking control of the OER. The simulation results show that the improved HTF-ESO effectively mitigates the initial differential peak phenomenon and reduces the initial estimation error of the cathode air pressure, and the RMSE of the cathode pressure and supply manifold pressure observation errors are reduced by 68% and 20%, respectively, compared with the present unimproved ESO. Compared with the PI, the RSME of the control error after convergence of the designed SMC and ADRC is reduced by 4.3%, and the OER can be restored to the reference value within 1 second, with good dynamic response.

MATLAB GUI application for failure analysis of electrical power system faults

This paper presents a computational algorithm in MATLAB with the Guide tool to analyse symmetrical and asymmetrical faults in electrical power systems. Fault analysis is important in the design, planning and operation of power systems, and is used to specify interrupting devices and define operating strategies that do not violate short-circuit levels. It also highlights the importance of good fault detection and coordination of protections to consider the type of fault, where it occurs, the phases involved and the evolution of the fault type.

Construction machinery hydraulic transmission system fault analysis and elimination methods

Abstract The hydraulic system plays the role of power transmission in the working process of construction machinery, but their complex structure leads to their frequent failure, which in turn leads to serious consequences. Therefore, this paper first takes the fault diagnosis and troubleshooting of the hydraulic system of construction machinery as the research theme and introduces the working principle of the hydraulic system of construction machinery and common fault mechanism and common methods of fault monitoring and diagnosis. Secondly, it proposes to analyze the key components of the hydraulic system based on the power bonding diagram theory and establish its bonding diagram model to analyze the working principle and power flow characteristics of key components. Finally, the pressure and velocity characteristic parameters are identified with the help of wavelet technology, and the energy distribution and energy entropy value of the hydraulic cylinder velocity signal are obtained by wavelet packet transform. The simulation results show that the wavelet-based analysis model proposed in this paper has good fault tolerance and robustness for fault diagnosis of the hydraulic system of construction machinery, and the correct rate of hydraulic pump fault diagnosis is up to 97%, with a correct average rate increased by 16%; the correct rate of hydraulic cylinder fault diagnosis is up to 95%, with a correct average rate increased by 13%. The wavelet analysis model can be widely applied to the fault diagnosis of hydraulic systems of various construction machinery, and it effectively improves the performance of hydraulic fault analysis systems and provides a key technology for fault identification and troubleshooting of construction machinery.

Two Terminal Instantaneous Power-Based Fault Classification and Location Techniques for Transmission Lines

Transmission lines are an important part of the power system, as they are the carriers of power from one end to another. In the event of a fault, the power transferring process is disturbed and can even damage the equipment, which is attached to the generation end as well as the user end. Most of the power systems are connected to the transmission lines, so it is very important to make the transmission lines secure. For protection purposes, relays are used, but relays only trip in the event of a fault and do not tell us about the location of the fault. The power system requires a speedy protection system. For a speedy protection system, quick and fast fault analysis and classification are required. An effective approach for the analysis of the transmission line with three sources is proposed. This method is quite effective and accurate for locating the fault and classifying its types. This technique needs power measurement from both ends simultaneously for fault diagnosis. Instantaneous power and sign power values are used for fault detection and classification. A voltage profile is used to identify the fault location. For three-phase transmission lines, voltage profiles are built up at different segment points to locate the fault. The IEEE-9 bus system is simulated for this technique. MATLAB is employed for simulation purposes. The test system is simulated with different types of faults at different locations. Relay operation has not affected the accuracy of the system. This technique has an accuracy of more than 97%. This method is quite effective for the analysis of power transmission lines. It can discriminate the fault type, identify the faulty phase of the line, and locate the point of the fault. Faults are located with errors not more than 0.45%. Moreover, the time difference between the actual fault and the calculated fault obtained from the estimated location is not more than 0.004 s. Simulations are claimed to be executed in less computational time, ensuring effective and rapid protection against faults.

Verification and Fault Analysis based on Combination of AADL and Modelica

CPS is a multidimensional complex system that can realize the interaction between computing process and physical process. Aiming at the problems of fault occurrence and uncertain behavior, this paper proposes the fault analysis stochastic hybrid automata as a formal model, the attributes of randomness and fault analysis are added through AADL behavior attachment to expand the attributes of hybrid automata, and applies the extended automata to the embedded system for system description and fault analysis. The model is used to model the fire control system, and AADL, Modelica and fault tree are combined to form a new model. The behavior is analyzed, and the conversion algorithm and conversion example are given.

An Intelligent Fault Analysis and Diagnosis System for Electromagnet Manufacturing Process Based on Fuzzy Fault Tree and Evidence Theory

Because an electromagnet has a complex structure and manufacturing process, it is difficult to analyze the overall failure of the electromagnet. In order to solve this problem, a fault intelligent analysis and diagnosis system based on fuzzy fault tree and evidence theory is proposed in this paper. First, the failure structure and fuzzy fault tree are generated according to the experience. Second, the probability of failure caused by basic events is obtained based on the data statistics of the insufficient holding force of the electromagnet in the past. Then, the probability of the basic events is given by using the synthesis rules of evidence theory. Next, the belief interval of the basic event is set as the fuzzy number, and the intelligent analysis is completed by using the calculated fuzzy importance. Finally, the validity and feasibility of the proposed method is proved by using the failure of insufficient retention force in the electromagnet manufacturing process as an example.

Optimally detecting and classifying the transmission line fault in power system using hybrid technique

In this paper, a hybrid system is proposed to predict and classifies the power system transmission line faults. The proposed technique is the consolidation of both the truncated singular value decomposition (TSVD) and Human urbanization algorithm (HUA) based Recurrent Perceptron Neural Network (RPNN), and hence it is named as TSVD-HUARPNN technique. TSVD is matrix decomposition, this technique qualify the outcome it as fast or not. In the proposed work, the qualification of the results from the TSVD is improved by a lemma theorem; it is a proven proposition which is used to obtain a larger and optimal result. For that reason, it is also known as a ”helping theorem” or an ”auxiliary theorem”. Here, it has two modules for power system fault analysis: (i) fault detection, (ii) fault classification. The first process of the proposed system is the generation of the dataset of normal and abnormal conditions of transmission line parameters of power system using TSVD. The extracted dataset is assessed by HUA-based RPNN system to classify the fault analysis that occurs in transmission system. The TSVD-HUARPNN system is used to predict and classify the fault present in the transmission line. The proposed TSVD-HUARPNN system ensures the system with less complexity for the detection and classification of the fault, therefore the accuracy of the system is increased. By then, the proposed model is activated in MATLAB/Simulink, its performance is evaluated with the existing models. The performance with noise at 20 dB of the proposed technique is 99.77%.

Smart Diagnostic Expert System for Defect in Forging Process by Using Machine Learning Process

Integrating machine learning into one of the manufacturing processes, i.e., forging, is mainly concerned with making the system more intelligent by incorporating them to exhibit global understanding. Sometimes the engineer/operator can find the defects during or after the forging operation. So, the system will need some input to identify the different types of categorized defects. And also, according to that, we will develop the intelligent fault diagnosis process. We should calculate the statistical probability theory. Now, we implement the system which is the structure of the fault analysis system for the forging process. In the structure, we demonstrate the defect of the forged part, use the given imported probability to find the possible causes, and provide some remainders to reduce the fault. For enhancement of feature needs, this work includes more integration of AI with forging.

Grounding Fault Model of Low Voltage Direct Current Supply and Utilization System for Analyzing the System Grounding Fault Characteristics

Grounding fault analysis is of vital importance for low voltage direct current (LVDC) supply and utilization systems. However, the existing DC grounding fault model is inappropriate for LVDC supply and utilization system. In order to provide an appropriate assessment model for the DC grounding fault impact on the LVDC supply and utilization system, an LVDC supply and utilization system grounding fault model is proposed in this paper. Firstly, the model is derived by utilizing capacitor current and voltage as the system state variable, which considers the impact of the converter switch state on the topology of the fault circuit. The variation of system state parameters under various fault conditions can be easily obtained by inputting system state data in normal conditions as the initial value. Then, a model solution algorithm for the proposed model is utilized to calculated the maximum fault current, the system maximum fault current with different grounding resistance is simple to acquired based on the solution algorithm. The calculation results demonstrate that grounding resistance and structure of LVDC supply and utilization system have remarkable impacts on the transient current. The effectiveness of the proposed model is verified in PSCAD/EMTDC. The simulation results indicate that the proposed method is appropriate for the system fault analysis under various fault conditions with different grounding resistance and the proposed model can offer theoretical guidance for system fault protection.

Directional Relay Based on Time-Domain Symmetrical Components With Incremental Quantities

Symmetrical components theory, presented by Fortescue in 1918, and the analysis of the time-domain presented by Lyon in 1954, have been widely used in fault analysis and power system protection algorithms. In recent times, applications in time-domain have been deeply studied given their potential for getting a quicker failure detection. There are two main techniques which apply signal processing methods in the time-domain: Travel-wave and incremental quantities. This article presents a new algorithm that allows detecting direction and gives an indication for a fault location based on the calculation of the symmetrical components and processing them as incremental quantities. The algorithm is tested in a simulation study case and on the data provided by a relay for a real-world fault.

Electrical System Design and Fault Analysis of Machine Tool Based on Automatic Control

Automatically controlled machine tools have been used extensively in the industrial field, and fault analysis methods have garnered increasing attention. This paper first describes the software and hardware design of a machine tool and then presents a fault analysis of the machine tool. The fault types of machine tools are analyzed. A signal is obtained from a vibration sensor, the characteristic value is extracted, and the fault is analyzed using a back-propagation neural network (BPNN). The experimental results show that the BPNN yields the best performance when the structure is 8-9-8, and its recognition rate is 97.22% for different types of faults. Meanwhile, the recognition rate of naive Bayes is only 76.73%, and that of a support vector machine is only 85.55%, which is significantly lower than that of the BPNN. The results show that the BPNN is effective in fault analysis and can be promoted and applied more extensively.

Research on thermal imaging fault detection system based on weibull distributed electrical system

A fault prediction analysis is performed In order to use infrared thermal imaging technology to detect electrical system faults, based on Weibull distribution technology. First, the principle is explained, the key technologies and factors of infrared thermal imaging and the electrical fault monitoring system are analyzed, and the available technologies are summarized. The detection process and analysis research are established with the help of on-site inspection and analysis results for power system fault analysis and research. The research shows that the method is effective for fault detection and trend analysis, and provides a reference for power fault detection and prediction analysis.

Multilevel Converter based System Fault Analysis

Direct Current deficiency security is the procedure to improve the Direct current system distribution, transmission structure in this review paper examinations the transitory attributes of Direct current responsibilities in a particular (MMC) staggered converter based Direct current the system connection with a numerical technique. Then, loads are reproduction like pretending by using on MATLAB/Simulink has been done by testing the exactness for the hypothetical examination. At last, the mechanical troubles of and necessities for the assurance and confinement are talked about to give the hypothetical establishment to the structure of direct current flaw insurance system.