Research On Meteorological Disaster Fault Prediction For Power Grid Based On Equipment Vulnerability

Stable power supply is an important energy guarantee to meet the needs of the country's economic and social development,and it is the key to ensuring social order and maintaining long-term law and order.When uncontrollable factors such as natural disasters and various man-made events occur,power grid equipment is susceptible to external factors and malfunctions during operation,which directly affects the safety and stability of society and the daily life of the general public.The occurrence of power grid equipment failure is related to equipment manufacturing process level,equipment operation and maintenance level,external natural condition factors and human uncontrollable factors.At present,the power grid is becoming stronger on the equipment side with the improvement of equipment manufacturing levels and the operation and maintenance capabilities of power grid enterprises.However,uncontrollable human factors are not easy to predict,so external meteorological disasters have become an important incentive for power grid equipment to malfunction.Based on comprehensive equipment conditions,this paper proposes a method for fault prediction of power grid equipment based on stacked auto-encoders.We get parameters that quantify equipment vulnerability levels based on grid equipment state parameters and operation and maintenance data.Based on these parameters,the meteorological factors at different fault history periods are corrected.Using the feature extraction capabilities of the stacked auto-encoders,we explore an association mapping relationship from meteorological information to grid faults,and use this mapping relationship to predict the probability of grid equipment failure under the influence of natural disasters.First of all,this paper quantitatively analyzes the environmental factors,manufacturing parameters,and equipment operation and maintenance information of the equipment.Based on the internal and external conditions,we propose a quantitative method for the vulnerability index of power grid equipment suitable for fault prediction.By grading and assigning various types of data,we obtain the comprehensive vulnerability index of the equipment.Using these as correction amounts,normalize the weather data at the time of the previous failure.It also serves as the basis for the correction of the input data of the following model.Secondly,based on the stacked auto-encoders,we proposed a correlation between meteorological factors and grid equipment faults,and built a fault prediction model for meteorological information.Based on the analysis of the impact of meteorological conditions on power grid failures,we select appropriate refined meteorological data to comprehensively represent the meteorological conditions.Based on the analysis of the stack auto-encoder network structure and calculation process,we use the network's superior feature extraction capabilities to predict the incidence of failures.Using deep learning networks to establish a correlation between meteorological factors and fault information.Stacked auto-encoders can extract data features by learning the original input data layer by layer.Each layer is based on the expression of the bottom layer,but it is often more abstract and more suitable for complex classification and other tasks.Finally,using the equipment vulnerability index,we correct for meteorological factors at the time of the previous failures.These are normalized to the same type of equipment at the current stage,and after correction,a mapping relationship between meteorological factors and grid faults is established.Then we propose a new method of deep self-learning,which utilizes the features of the stacked auto-encoders to learn the original data layer by layer,and realizes the prediction research of power grid equipment failure.Based on the historical weather data and historical fault data of power grid equipment,this paper uses MATLAB platform for simulation.The simulation results are used to verify the fault prediction method proposed in this paper.The fault prediction method proposed in this paper has also been proved by example analysis.This prediction method can accurately establish the corresponding mapping relationship between meteorological information and equipment faults.It can comprehensively improve the ability to predict and judge the faults of power grid equipment,so as to judge and formulate measures for faults of power grid equipment in advance.At the same time,it also provides important data support for improving the early warning capability of the power grid and the level of disaster prevention and mitigation.