Method Study On Fault Detection Of Agricultural Smart Grid Based On Wavelet Algorithm And Bayesian Network

With the comprehensive construction of intelligent,digital and information technology grid in China in recent years,the electric power industry presents the situation of explosive data growth,and the power system in operation produces a large amount of electrical information.The frequency of faults in increasingly complex grid systems is also increasing.Over the years,large-scale power accidents in various regions have caused huge economic losses and social impacts.At the same time,building a new socialist countryside is the key development strategy of our country,among which,the development of rural agricultural power grid is the best proof.The transmission lines applied in agricultural production system are increasing continuously,and once the agricultural power grid system breaks down,Will cause enormous economic damage.Loss and serious waste of resources.Therefore,under the background of big data era,researchers put forward a higher standard,how to quickly and accurately detect,locate and ensure the reliability of agricultural transmission system is an important research topic.It is also an urgent need to speed up the development of agricultural smart grid and to build a new countryside.In the process of fault diagnosis and analysis of agricultural smart grid,its specific research contents are as follows:First of all,the short-circuit fault diagnosis,agricultural smart grid construction,power grid fault location and other aspects are analyzed.The wavelet algorithm,Bayesian network model and BP neural network application are discussed.This is the theoretical basis of this study.Secondly,the fault feature is fused based on Bayesian parameter estimation method,and the principle of Bayesian network construction learning and reasoning is analyzed,and the Bayesian network model is constructed by using Bayesian network learning method.Fault diagnosis is realized based on Bayesian network and wavelet algorithm by using joint predictive Bayesian network reasoning method with updated information.In order to further improve the effectiveness and accuracy of fault location and fault handling in agricultural smart grid,the Bayesian model is improved.On the premise of analyzing Bayesian network model and short circuit control of power network,the characteristics of short circuit are analyzed.Fault diagnosis Rate statistics.Finally,this paper analyzes the fusion of improved Bayesian model and wavelet algorithm,calculates fault attributes and fault probability verification,and puts forward a method of single terminal fault location using voltage traveling wave.BP neural network calculation and wavelet algorithm,Bayesian network fault location comparative analysis of the way to analyze,based on the Gao Jigang agricultural smart grid project test field,experimental results show that the wavelet algorithm,Bayesian network fault location and detection can effectively analyze agricultural smart grid faults.Because the voltage fault waveform is larger and easier to collect than the current fault waveform,so the final use is small.The wave algorithm finds the singularity point of voltage waveform in fault circuit,calculates the time it takes for the voltage traveling wave to commute between the fault point and the measuring point,and then calculates the distance from the fault point to the measuring point.The test results show that the fault type and distance of agricultural smart grid can be accurately determined by combining Bayesian network method and wavelet algorithm,and the expected effect is achieved.