Research on the construction and application of electrical fault classification system based on Bayesian algorithm

Currently, with the rapid development of the photovoltaic (PV) industry, detection and monitoring, particularly fault protection, is becoming a very important issue in maintaining the safe and stable operation of PV systems. This chapter comprises three main parts: The first part deals with the presentation of different types of PV systems, including off-grid, on-grid, hybrid PV plants Building Integrated PV, and floating PV systems. The second part presents and discusses the operating, maintenance, protection, and isolation procedures for PV plants, while the third part discusses PV monitoring systems, including an example of a modern PV monitoring system based on the Internet of Things (IoT). Various types of faults, fault detection methods, and fault diagnosis approaches are provided in the same section.

Support vector machine is a machine learning algorithm developed by Vapnik from the statistical learning theory for data classification via study from a small sample of fault data. For fault data it can isolate the fault categories accurately even though only has the small sample of data. In the present work, support vector machine's classification mechanism and its application in mechanical fault diagnosis are introduced. Therefore, give an instance the support vector machine makes fault classification for the coal mine scraper conveyor's faults. Last but not the least, put forward some of the shortcomings of the support vector machine and look forward to the direction of development of the support vector machine fault diagnosis in the future.

The timely detection and treatment of electrical equipment fault events is a key problem that needs to be solved to ensure the normal and stable operation of power systems. In order to achieve accurate diagnosis and real-time prediction of electrical equipment fault problems, the study proposes an intelligent fault diagnosis model based on PCA and optimization parameter SVM and an electrical equipment fault prediction model based on lifting limit learning machine. The simulation and fault diagnosis examples show that: After using the ISFD-POPS electrical equipment fault diagnosis model proposed in this paper for experimental testing, the fault diagnosis accuracy is greatly improved, and the established prediction model basically meets the requirements of real-time prediction, with high accuracy and strong practicality.

Various types of faults can occur in a Permanent Magnet Synchronous Machine (PMSM) system, including bearing faults, electrical short/open circuits, eccentricity faults, and demagnetization faults (DFs). A DF occurs when the magnetic strength of the PMSM's permanent magnets weakens, resulting in reduced output torque, which is undesirable in electric vehicles (EVs). This fault can be attributed to physical damage, high-temperature stress, reverse magnetic fields, and aging. Motor current signature snalysis (MCSA) is a traditional method for detecting motor faults, relying on the extraction of signal features from the stator current. In this study, a simulation model of the PMSM was developed to represent both partial and uniform DFs, allowing for the simulation of varying degrees of demagnetization. Harmonic analysis using fast Fourier transform (FFT) demonstrated that the fault diagnosis method based on harmonic wave analysis is effective only for partial DFs in PMSMs, and not applicable to uniform DFs.

The computer numerically controlled (CNC) system is the key functional component of CNC machine tool control systems, and the servo drive system is an important part of CNC systems. The complex working environment will lead to frequent failure of servo drive systems. Taking effective health management measures is the key to ensure the normal operation of CNC machine tools. In this paper, the comprehensive effect of fault prediction and fault diagnosis is considered for the first time, and a health management system for machine tool servo drive systems is proposed and applied to operation and maintenance management. According to the data collected by the system and related indicators, the technology can predict the state trend of equipment operation, identify the hidden fault characteristics in the data, and further diagnose the fault types. A health management system mainly includes fault prediction and fault diagnosis. The core of fault prediction is the gated recurrent unit (GRU). The attention mechanism is introduced into a GRU neural network, which can solve the long-term dependence problem and improve the model performance. At the same time, the Nadam optimizer is used to update the model parameters, which improves the convergence speed and generalization ability of the model and makes it suitable for solving the prediction problem of large-scale data. The core of fault diagnosis is the self-organizing mapping (SOM) neural network, which performs cluster analysis on data with different characteristics, to realize fault diagnosis. In addition, feature standardization and principal component analysis (PCA) are introduced to balance the influence of different feature scales, enhance the feature of fault data, and achieve data dimensionality reduction. Compared with the other two algorithms and their improved versions, the superiority of the health management system with high-dimensional data and the enhancement effect of fault identification are verified. The relative relationship between fault prediction and diagnosis is further revealed, and the adjustment idea of the production plan is provided for decision makers. The rationality and effectiveness of the system in practical application are verified by a series of tests of fault data sets.

This paper proposes an innovative bearing fault diagnosis method aimed at enhancing the accuracy and effectiveness of transfer learning. The innovation lies in the signal preprocessing stage, where a Noise Eliminated Intrinsic Time-Scale Decomposition (NEITD) algorithm is introduced. This algorithm adaptively decomposes unified-phase sine wave signals to effectively extract the geometric mean of the intrinsic rotational component, and selects the optimal decomposition result based on the orthogonality index, significantly improving the quality and reliability of the signals. In addition, fault diagnosis parameters are adaptively optimized using an improved adaptive deep transfer learning (ADTL) network combined with the Jellyfish Search (JS) algorithm, further enhancing diagnostic performance. By innovatively combining signal noise reduction, feature extraction, and deep learning optimization techniques, this method significantly improves fault diagnosis accuracy and robustness. Comparative simulations and experimental analyses show that the NEITD algorithm outperforms traditional methods in both signal decomposition performance and diagnostic accuracy. Furthermore, the NEITD-ADTL-JS method demonstrates stronger sensitivity and recognition capabilities across various fault types, achieving a 5.29% improvement in accuracy.

The condition monitoring and fault diagnosis of power transformers plays a significant role in the safe, stable and reliable operation of the whole power system. Dissolved gas analysis (DGA) methods are widely used for fault diagnosis, however, their accuracy is limited by the selection of DGA features and the performance of fault diagnosis models, for example, the classical support vector machine (SVM), is easily affected by unbalanced training samples. This paper presents a transformer fault diagnosis model based on chemical reaction optimization and a twin support vector machine. Twin support vector machines (TWSVMs) are used as classifiers for solving problems involving unbalanced and insufficient samples. Restricted Boltzmann machines (RBMs) are used for data preprocessing to ensure the effective identification of feature parameters and improve the efficiency and accuracy of fault diagnosis. The chemical reaction optimization (CRO) algorithm is used to optimize TWSVM parameters to select the optimal training parameters. The cross-validation (CV) method is used to ensure the reliability and generalization ability of the diagnostic model. Finally, the validity of the model is verified using real fault samples and random testing.

Under the background of the construction of “three types and two networks”, the introduction of a large number of heterogeneous intelligent terminals, ubiquitous open networks, interactive real-time control services and wide area interconnected power systems have transmitted the network security risks to all aspects of the power system. The security defense capability of power system is not only the superposition of security defense equipment, but also the excellent network security team. The continuous improvement of the level of network security is inseparable from the accumulation of network security attack and defense technology and practical experience, as well as practical tools and equipment with the characteristics of the power industry. In order to meet the increasingly severe security protection challenges, actively implement the security protection requirements of the power information system, ensure the safe and stable operation of the power system, build a network shooting range suited to the attributes of the power industry, and continuously improve the ability of network security protection, consolidate the foundation of digital transformation to ensure the safe and stable operation of power system and reliable power supply.The safe and stable operation of the information system needs to carry out comprehensive and in-depth vulnerability troubleshooting. In view of the lack of power business loophole mining and security event verification shooting range environment, this paper studies the power security shooting range construction technology.

The electrical system fault testing and diagnosis system based on I2C-bus and embedded equipment is proposed. This system is accomplished with the integration of PXI-based computer, field fault testing platform, dedicated interface adapter unit and accessories with software program of virtual instrument and database management and fault guidance system. It can obtain various state parameters of the heavy mechanized bridge by interface adapter unit and corresponding sensors. The dedicated interface adapter unit, that is the core of the hardware platform is composed of chassis, embedded microcomputer controller, I2C-bus data acquisition modules, VPC connection package and military aviation sockets. Because of its flexibility and modular design, it can support different data conversion, for example, analog, digital, and serial, while functioning as an intelligent conversion bridge between field fault testing platform or PXI computer and electrical system of heavy mechanized bridge electrical system. The fault diagnosis software is of hierarchical modular structure with integration of system management control, fault detection and circuit hardware driver modules together with fault repair and diagnosis database. Therefore it provides functions of human-machine interaction, equipment fault detection, fault diagnosis and analysis, as well as repair and maintenance guidance The fault testing and diagnosis system in this work is also featured with handy structure, lightweight, low consumption, large storage capacity, excellent expandability and intelligent inference capability. The system can be used in the online fault testing and diagnosis of similar engineering equipments. So it has important engineering and military application value to the rapid fault testing and fault diagnosis of engineering equipments.

Natural ester insulating oil is widely used in transformers due to its good fire resistance and biodegradability. It is mainly composed of triglycerides, and there are some differences in fatty acid composition and content among natural esters. Therefore, the fault diagnosis methods based on dissolved gas analysis are different. Three kinds of natural ester insulating oils are used to study the dissolved gas in oil under typical electrical and thermal faults of power transformers. Thermal faults from 200°C to 800°C and electrical faults with different discharge energies, which include partial discharge, breakdown discharge, and arc discharge, are simulated. The characteristic gases and their variation trends of natural esters under thermal and electrical faults are obtained through the experimental results. The gas generation characteristics of the three natural ester insulating oils under typical electrical and thermal faults are almost similar, but the relative percentages of characteristic gases have certain differences. Natural ester insulating oil with a higher unsaturation degree tends to produce more H 2 and less C 2 H 4 . Finally, the fault diagnosis methods are used to determine the fault gas data, and the Duval pentagon is modified and improved according to the gas generation characteristics of natural esters.

Electronics-rich analog systems are difficult to diagnose owing to their complex working mechanisms and the variability of the working environment. In recent years, deep learning has been gradually applied to the field of circuit system fault diagnosis because of its strong ability to mine the intrinsic characteristics of signals. However, the traditional deep learning method requires a lot of effort to achieve satisfactory results due to large number of parameters, complex models, slow training speed, and large datasets. The key factors for the success of traditional deep learning methods are layer-by-layer processing, feature transformation within the model, and sufficient model complexity. Deep forest (DF) is a new feature learning model that inherits the three characteristics of the traditional deep learning model but is that it is not based on neural network. It has fewer hyperparameters, a simpler model, faster training speed. In this article, an improved DF algorithm based on nonparametric predictive inference (NPI) is proposed, named NPIDF, which can better deal with small sample data. In two typical analog filter circuit fault diagnosis experiments, it is proved that DF and NPIDF achieve good diagnosis effect, and NPIDF performance is better, showing a greater advantage in small sample data.

Fault diagnosis of drainage pumps is essential for ensuring the safe and stable operation of drainage systems. However, vibration signals acquired from drainage pumps under practical operating conditions are often characterized by strong non-stationarity, unavoidable environmental interference, and fault features distributed across multiple temporal scales, which poses significant challenges to accurate multi-class fault identification. To tackle these challenges, this paper proposes a multi-scale residual attention convolutional neural network (MRANet) for drainage pump fault diagnosis. A multi-scale residual attention feature extraction (MRAFE) module is designed to jointly model fault-related information at different temporal scales by means of parallel convolutions and residual connections, while attention mechanisms are incorporated within each scale branch to enhance discriminative fault features. Furthermore, a complementary multi-level feature integration (CMFI) strategy is developed to effectively integrate features extracted at different network depths, enabling end-to-end fault identification directly from raw one-dimensional vibration signals. Extensive experiments are conducted on a drainage pump vibration dataset containing eight operating conditions. Experimental results show that MRANet achieves an accuracy of 95.43%, outperforming several benchmark models. These results verify the effectiveness and robustness of the proposed method for multi-class fault diagnosis of drainage pumps.

Transformer fault diagnosis is crucial for ensuring the safe and stable operation of power systems. However, traditional diagnostic methods suffer from issues such as insufficient feature utilization and poor model interpretability. To address these challenges, this paper proposes a hybrid fault diagnosis model. First, the model utilizes SHapley Additive exPlanations values for feature selection. Then, the improved Harris Hawks optimization is proposed to optimize the hyperparameters of eXtreme gradient boosting model (Xgboost), further improving the model’s classification performance. Experimental results demonstrate that the proposed model outperforms existing methods across multiple performance indicators, achieving an accuracy of 0.9509, which represents a 2.68% improvement compared to the Xgboost model. This model fully leverages the advantages of each method, effectively solving problems like insufficient feature utilization, poor model interpretability, and limited performance of optimization algorithms in traditional fault diagnosis methods. It provides a more reliable and efficient solution for transformer fault diagnosis.

In this paper, a fault diagnosis method of marine power system based on MANA is proposed by using the qualitative simulation analysis method of fault diagnosis. The mathematical modeling of ship power system based on modularization can make the system model clearer; The fault belongs to the transient process of the power grid. Based on the simulation analysis and power quality analysis of this transient process of the power grid, the model with built-in MANA algorithm can intuitively monitor the waveform of the input side and output side of the power grid. This paper obtains the changes of each index when the generator loses excitation fault and short circuit fault occurs in the marine power system, which can provide a theoretical reference for solving the fault in the actual marine power system and optimizing the operation of the power grid, and ensure the stable and safe operation of the marine power system.

Various types of faults occur in building energy systems throughout their life-cycles. Some faults grow gradually causing system energy penalty and performance degradation. Hence, it is crucial to implement an efficient fault diagnosis strategy and maintain optimal operations for systems. Recently, data-driven methods have got increasing interests due to the model flexibility and data availability. The fast development of data science has provided advanced data analytics to tackle data classification problems in a more convenient and efficient way. This paper attempts to investigate the potential of a promising data analysis technique, i.e., deep neural network, in classifying and diagnosing faults in a building energy system, i.e., centrifugal chiller plant. This study exploits the deep neural network based method in both supervised and unsupervised manners, and compares the fault diagnosis accuracy. Centrifugal chiller experimental data from the ASHRAE Research Project 1043(RP-1043) are used to validate the proposed method. Results show that the method can correctly diagnoses the fault data for seven typical chiller faults.