Early Warning Analysis Based On Deep Learning In Smart Grid Operation And Maintenance System

Since the 21 st century,driven by big data technology and Internet of Things technology,smart grids are constantly innovating.Smart grid is an optimized management.Through management,it can fully meet electricity demand and environmental protection requirements,promote the integration of resources,and ensure the safety,reliability and economics of grid operation.In the context of the smart grid,the traditional operation and maintenance model has been unable to meet the demand.This requires the introduction of emerging technologies in the construction of the smart grid,and the transformation to intelligent operation and maintenance with unattended and proactive warning.In the era of big data,the ideal state of intelligent operation and maintenance is to integrate the monitoring,management and fault location of operation and maintenance work through some deep learning algorithms,so this paper first studies the domestic and foreign literature on intelligent operation and maintenance and deep learning to understand The current research progress in the direction of intelligent operation and maintenance at home and abroad has laid a solid foundation for the research of this article.Secondly,an intelligent operation and maintenance platform was built and trial operation was conducted.It was found that the early warning module of the platform often had false early warnings.After reading and researching the relevant materials,it was finally decided to carry out this research from the early warning direction.Carry out innovative early warning methods.In the comparative analysis of deep learning methods,this paper chooses to use convolutional neural networks to analyze the data.Convolutional neural networks have fewer connections and parameters,and the capacity can be controlled by controlling its depth and width,which is convenient for establishing a For models with larger learning capacity,the AlexNet model is used as the basis for model selection.Improvements are made on this basis,and the training speed is increased by reducing the number of model layers,thereby improving efficiency.By comparing and verifying the experimental results of this paper with actual early warning,the effect of early warning can be achieved.For error warnings in actual stations,the accuracy of this verification can still be ensured,intelligent processing of the early warning process and the concept of unattended operation can be realized,economic and safety problems caused by errors caused by human factors can be reduced,and early warning intelligence can be more effectively realized Change.