Research On Fault Diagnosis Model Of Complex Power Equipment Based On Machine Learning

With the continuous expansion of the scale of the power system,more and more equipment has become complicated,and the safe operating environment of the network has also been severely challenged.How to improve the reliability of equipment operation and ensure the safety and stability of power system operation has become a topic of great concern.In order to improve the operational reliability of the equipment,in addition to improving the quality and operational stability of the equipment,it can also be achieved by improving the equipment status assessment capability and improving the fault level of the diagnostic equipment.At the same time,extracting useful and valuable information from the power system is not a simple matter.Therefore,this paper combines machine learning and big data analysis and other related content,from the equipment state assessment and equipment fault diagnosis research and analysis,the specific research content is as follows.Firstly,the management status and development process of equipment production to scrapping were studied,and then research was carried out according to the status quo of power enterprise management.Finally,combined with the theory of equipment life cycle management,the whole life cycle management based on power equipment and the entire process framework diagram are proposed.Secondly,the site operation data is collected from the substation,and combined with big data technology,the application research of equipment life cycle management is analyzed.The research mainly focuses on the equipment operation intensity of power,reliability assessment and equipment health state prediction,and constructs an indirect equipment health state prediction model based on time series analysis.Then,the traditional methods of power fault diagnosis in recent years are studied,and the related content of machine learning and the application of some common neural networks in this field are summarized.Then according to the characteristics of the network model,combined with the data characteristics of the experimental analysis,a fault diagnosis model based on GRU bidirectional cyclic neural network is constructed.Finally,based on the Python environment,the GRU-based bidirectional cyclic neural network fault diagnosis model is implemented,and experiments are carried out based on transformer fault data.Through experiments,it can be found that the model constructed in this paper has good efficiency in the results of fault diagnosis and achieves high precision.