Fault diagnosis for power system based on a special Bayesian network

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.

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.

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 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.

In this paper, intuitionistic fuzzy spiking neural P (IFSNP) systems as a variant are proposed by integrating intuitionistic fuzzy logic into original spiking neural P systems. Compared with a common fuzzy set, intuitionistic fuzzy set can more finely describe the uncertainty due to its membership and non-membership degrees. Therefore, IFSNP systems are very suitable to deal with fault diagnosis of power systems, specially with incomplete and uncertain alarm messages. The fault modeling method and fuzzy reasoning algorithm based on IFSNP systems are discussed. Two examples are used to demonstrate the availability and effectiveness of IFSNP systems for fault diagnosis of power systems. Case studies involve single fault, complex fault, and multiple faults with protection device failures and incorrect tripping signals.

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.

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.

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.

A power system fault diagnosis framework based on multi-agent system (MAS) is proposed to realize the parallelization and intelligence of grid fault diagnosis system. In the form of encapsulating multiple sub-modules of power grid fault diagnosis function, the multi-agent system model of power grid fault diagnosis with perception, reasoning and interaction is formed. Under the co-operation of agents, the fault diagnosis system is designed and the complex fault analysis of power grid is realized. Finally, taking the 3/2 wiring fault analysis as example, the MAS cooperation architecture is analyzed.

Bayesian network offers a powerful map framework that can process probabilities inference. It can be used in inference and express of uncertainty knowledge. This paper introduce a new electrical equipment fault diagnosis method based on Bayesian network (BN). For example, power transformer is very important in power system as a electrical equipment. But, it's very difficult to diagnose the fault exactly because power transformer's complexity configuration. Now, dissolved gas analysis (DGA) is the most effective and convenient method in transformer fault diagnosis. However, the codes of DGA is too absolute, so this paper advances a new transformer fault diagnosis method based on Bayesian network (BN). This method introduces BN method into transformer fault diagnosis and presents a new idea of finding out transformer faults rapidly and exactly. Then, the transformer fault diagnosis model based on Bayesian network and DGA is constructed. Finally, the application examples in the fault diagnosis of transformer are given which shows that this method is effective.

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.

Machine learning applications in power system fault diagnosis: Research advancements and perspectives

To solve the problem of fault prediction and diagnosis of household air conditioning, an improved Bayesian network (BN) fault diagnosis model is proposed. Firstly, the orthogonal defect classification (ODC) is used to make statistics and analysis of air conditioning fault data, and the structure of BN fault diagnosis model is built based on the analysis results. Then, genetic algorithm (GA) is used to optimize the conditional probability of network nodes and determine the network parameters. Finally, the cooling and heating failure data of household air conditioning were taken as an example to diagnose. Compared with the traditional BN model, the accuracy of fault diagnosis increases from 81.13% to 92.83%, which verifies the effectiveness of the model.

In order to solve the problem of fast fault diagnosis in power system, this paper introduces a method of finite state machine model of fault reasoning for distribution system under time sequence constraints. Firstly, the concept of time sequence constraint characteristics of SOE data from the SCADA alarm window is introduced, which is crucial to judge whether the alarm sequence are from an individual fault accident. Then, by fully considers the “protection-circuit breaker” action as well as its logic, the Finite State Machine model with various system fault types was constructed. Finally, to prove the effectiveness of the method, a set of alarm data in real accident which happen in 10 kV distribution systems is applied to test the model. The result shows that the model can quickly analyze SOE data within the time interval in system accident, and effectively help the operation dispatcher to handle accident and defect elimination.