Power System Fault Diagnosis Method Under Disaster Weather Based on Random Self‐Regulating Algorithm

The aircraft power system is the source of electrical energy for all onboard electrical equipment, and plays an important and irreplaceable role in the aircraft. Aiming at the problem that traditional aircraft power system fault diagnosis methods often require the establishment of accurate mathematical models and cannot quickly perform fault diagnosis and identification, this paper proposes an aircraft power system fault diagnosis method based on long and short-term memory networks. Taking the typical components of the aircraft power system integrally driving the generator as an example, the fault diagnosis research is carried out. The simulation model is used to collect fault data to make a fault simulation data set. After many parameter tuning experiments, an ideal fault diagnosis model is obtained. Under the existing experimental conditions and data sets, the fault diagnosis accuracy rate reaches 98.57%, which can meet the needs of actual work to a certain extent.

Targeting at diagnosing complex power system faults, a power system fault diagnosis method considering relay protection condition assessment is proposed. With relay protection condition assessment, the power system fault diagnosis model is created, based on which, the objective function considering relay protection failure is proposed, and its protection action term is modified based on the condition assessment results of protection devices. The optimal fault hypothesis is obtained using genetic algorithm with variable length chromosome, thereby fulfilling power system fault diagnosis. The availability of this method is further validated via a case of a substation.

An analytic model for fault diagnosis of power system using optimization technique is expressed as unconstrained 0-1 integer programming problem, and consequently faulty equipment identification can be solved by refined mathematical operation. Considering the configuration of automatic devices in modern power systems, such as protective relays and reclosing relays, an improved analytic model and optimization technique-based method for fault diagnosis of power system is proposed in this paper. The evaluation criteria of the presented model is improved considering the relationship of multiple main protective relays, backup protective relays, malfunctioning protective relays and reclosing relays. Improvements of analytic model for fault diagnosis of electric power system based on optimization techniques are presented firstly. A brief description about the modulars and functions of the online fault diagnosis software which is developed by the authors for Jiangsu Provincial Power Company is given. The adopted EMS data acquisition method and simulated online test results for the power system of Jiangsu Power Company are described.

In order to meet the requirements of multi-parameter and real-time fault diagnosis of China VI vehicle emission standards diesel engine air system, this paper focuses on the research of the diesel engine air system fault detection and diagnosis method based on deep learning to improve the operational safety and fault diagnosis efficiency. In this paper, the air system fault detection and diagnosis are completed based on the AE model and the CNN model, combined with the actual operation of the diesel engine. Among them, the AE model successfully detected all real-time operation faults, and the false detection rate on the health data was 0.1162%; the CNN model obtained a 90.77% fault diagnosis accuracy rate on the test set of the real-time operation data set. The results show that the model has high accuracy in the diagnosis of diesel engine faults, which is of great significance to the application of deep learning based on big data processing.

To remedy the disadvantages of conventional diesel engine fuel ejection system's fault diagnosis method, which couldn't get exact results and dispel noise effectively, a new way based on Wavelet Neural Network (WNN) which combines merits of Wavelet Transform (WT) and RBF Neural Network (RBFNN) was put forward. Moreover, after picking up fault characteristic parameters, we trained it with Genetic Algorithm (GA) and Simulated Annealing (SA). Finally we applied the improved WNN to fault diagnosis of diesel engine fuel ejection system. The results show that the algorithm is good at dispelling noise, stable, and effective in high precise fault diagnosis.

Transformer fault diagnosis is crucial for ensuring the safe and reliable operation of power systems. In this paper, we propose a novel method for transformer fault diagnosis based on the combination of Improved Grey Wolf Optimization (IGWO) and Least Squares Support Vector Machine (LSSVM). IGWO is utilized to optimize the parameters of the LSSVM model, which enhances the classification accuracy and generalization ability of the model. The proposed method first extracts relevant features from the fault data of transformers, and then applies IGWO to optimize the parameters of the LSSVM model. Experimental results on real-world transformer fault datasets demonstrate that the proposed method achieves superior performance compared to traditional fault diagnosis methods. It provides a promising approach for accurate and efficient transformer fault diagnosis in power systems.

To deal with a lot of uncertain information existing in the grid when faulted, this article uses a special Bayesian network—Noisy-or, Noisy-and nodes model to build object-oriented Bayesian networks. An error back propagation algorithm is used to train the Bayesian network so as to update the network's parameters. Finally a case simulation is presented to prove the accuracy and validity of this method for power system fault diagnosis.

This work approaches relative aspects to the alarm processing problem and fault diagnosis in system level, having as purpose filter the alarms generated during a outage and identify the equipment under fault. A methodology was developed using Artificial Neural Networks (ANN) and Genetic Algorithms (GA) in order to resolve the problem. This procedure had as initiative explore the GA capacity to deal with combinatory problems, as well as the ANN processing speed and generalization capacity. Such strategy favors a fast and robust solution.

This paper firstly establishes a mathematical model of ship power system, and then analyzes the characteristics and common faults of ship power system. D-S evidence theory method is used on research of common faults of the ship power system, to enhance the pertinence of fault diagnosis. By using multi-source information fusion diagnosis, the need for quantities of electrical data is reduced, and, it can effectively reduce the impact of protection or switch malfunction on the fault diagnosis of ship power system and thus improve the accuracy of diagnosis.

In order to increase the precision and effectiveness of power system analysis and fault diagnosis, this study aims to assess the power systems in the energy sector while utilizing artificial intelligence (AI) and environmental social governance (ESG). First, the ESG framework is presented in this study to fully account for the effects of the power system on the environment, society, and governance. Second, to coordinate the operation of various components and guarantee the balance and security of the power system, the CNN-BiLSTM power load demand forecasting model is built by merging convolutional neural network (CNN) and bidirectional long short-term memory (BiLSTM). Lastly, the particle swarm optimization (PSO) algorithm is used to introduce and optimize the deep belief network (DBN), and a power grid fault diagnostic model is implemented using the PSO technique and DBN. The model’s performance is assessed through experimentation. The outcomes demonstrate how the CNN-BiLSTM algorithm significantly increases forecasting accuracy while overcoming the drawback of just having one dimension of power load data. The values of 0.054, 0.076, and 0.102, respectively, are the root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE). Effective processing of large-scale nonlinear data is achieved in the area of power grid fault diagnosis, resulting in prediction accuracy of 96.22% and prediction time of only 129.94 s. This is clearly better than other algorithms and increases fault prediction efficiency and accuracy. Consequently, the model presented in this study not only produces impressive results in fault diagnosis and load demand forecasting, but also advances the field of power system analysis in the energy industry and offers a significant amount of support for the sustainable and intelligent growth of the energy industry.

This paper aims to build a new fault diagnosis system based on big data and artificial intelligence technology. This study first expounds the application value and prospect of big data technology and intelligent algorithm in the fault diagnosis of ship power system, and realizes the effective integration and mining of massive, multi-source and heterogeneous ship power system operation data through in-depth analysis of the characteristics of ship power system operation data. Secondly, in view of the nonlinear and time-varying characteristics of ship power system faults, this study designs and implements a fault diagnosis model that integrates intelligent algorithms such as machine learning and deep learning. The model can automatically extract key fault features, realize early warning and accurate identification of potential faults, and improve the accuracy and response speed of fault diagnosis through continuous learning and optimization. Finally, this study applies the big data intelligent system for fault diagnosis of computer-aided ship power system to a practical case to verify its effectiveness and practicability in improving fault diagnosis efficiency, reducing maintenance costs, and ensuring navigation safety. The research results have important theoretical significance and practical value for improving the level of ship operation and maintenance management and ensuring the stable operation of ship power system.

Fast and accurate fault diagnosis is the primary prerequisite for separating the faulty devices and restoring the power supply, therefore it is of great importance to develop an advanced diagnosis method to meet the power system requirements. With the perspective of information fusion, this paper proposes a novel algorithm for fault diagnosis in power system via the fusion of several different wavelet entropies. Wavelet entropy can extract the fault characteristic quickly and accurately because it combines together the advantages of Wavelet Transform and Shannon Entropy; however in some conditions it is not easy to reach a satisfying result with single wavelet because of the uncertainty and diversity of faults. Therefore, several different wavelet entropies are fused by the D-S evidence theory and the basic probability assignment is set up by a weighted average method based on norm, then a decision method based on the basic probability number is used to diagnose the faults. Simulations with EMTDC and MATLAB demonstrate that this diagnosis method can increase the supporting rate of faults and improve the accuracy and the real-time performance of fault diagnosis in power system. Results also show that the proposed algorithm is feasible and reliable for fault diagnosis.

This paper deals with information model for power grid fault diagnosis based on Common Information Model (CIM). Fault diagnosis gains information from topology information and device parameters of primary system grid based on IEC61970 standard and device information of secondary system grid based on IEC 61850. Device information contains information of protection movement wave record and protection configuration. However, the existent CIM can't fully represent power system fault diagnosis information. Thus, it is need to extend CIM. All kinds of information are based on different model, which causes different information standard. In this paper a preliminary study on CIM extensions which is based on power system fault diagnosis, has been proposed to perfect fault information model. On the basis of different standard model, this paper has discussed the theory of coordinating IEC 61850 and IEC61970 standard, and proposed using Web Ontology Language (OWL) based on Extensible Mark-up Language (XML) to the solution of this problem. A fault Information Model is then provided to the fault diagnosis system according to Component Interface Specification (CIS) in IEC61970. After receiving fault information, the fault diagnosis system starts for real-time, on-line fault diagnosis. Fault information model has solved the problem of the deficiency and non-standard of fault diagnosis information, attaining fault diagnosis information integrity and standardization, and making the fault diagnosis result fast and accurate.

This paper introduces a novel approach for power system fault diagnosis based on synchronized phasor measurements during the fault. The synchronized measurements are obtained in real time from Phasor Measurement Units (PMUs) and compared with offline thresholds determined by decision trees (DTs) to diagnose the fault. The DTs have already been trained offline using detailed power system analysis for different fault cases. While the traditional methods for fault diagnosis use the status of protective relays (PRs) and circuit breakers (CBs) to infer the fault section in the power system, the proposed method uses the available signals following the fault and thus can be trusted even in case of protection system failure. Therefore, fault diagnosis can be achieved successfully especially in blackout situations, where many CBs and and/or PRs malfunction. Simulation results on the IEEE 118-bus test system show that the proposed method is highly accurate in wide area fault diagnosis in spite of incomplete system observability.

This work proposes a software tool for fault diagnosis in power systems. The solution was developed to monitoring real-time SCADA data of the transmission and generation network of a Brazilian electric utility in order to assist operators of control centres to analyse the occurrence and to decide the best procedures to re-establish the service in order to enhance the service reliability and reduce the power restoration time. The fault diagnosis system is formulated as an optimization problem and solved through two stages: event classification at equipment level. The developed approach also identifies the malfunctioned protective devices as well the missing and false alarms. Possible fault scenarios were considered in part of a real Brazilian power system to validate the methodology.