Study And Application Of Reactive Power Optimization In Rural Distribution Network

The smooth operation of power transformers is of great significance to the production and life of the whole country and the people.Therefore,in order to ensure the normal operation of the transformers,the workers will periodically perform power outages,but this maintenance and repair method gives people a normal life.It is very inconvenient.In order to solve this problem,a transformer fault online monitoring system came into being.However,the diagnostic results of such monitoring systems are usually vague and generally not expected.The operation of the transformer is not simple and s imple,and there is a complex nonlinear relationship between the type of fault and the results presented by the monitoring system,making this problem even more difficult.In this paper,we discuss the use of BP cross-network to establish an online monitoring system.At the same time,we also explore many improved algorithms,and choose the appropriate algor ithm by field investigation to compensate for the shortcomings of traditional gas monitoring.And put forward a method of integrating big data into one system to solve the problem of large difference in output of each monitoring subsystem.A crossover network line monitoring system is used to solve the problem of inaccurate fault monitoring.That is to say,a large number of sub-monitoring systems that can detect different objects are used to enumerate the types of faults that can be diagnosed by each subsystem,thereby deducing the intersection of the fault types monitored by different subsystems,according to the overlap between the overlapping regions and the subsystems.Correspondingly,the corresponding relationship between the transformer fault and the subsystem can be obtained,and when there is an abnormality in the subsystems of different arrays,it can be known what fault has occurred.A network monitoring system is established by a single linear relationship to realize a multi-line comprehensive diagnosis fault monitor ing system.Finally,according to the cross-coincidence part of the monitoring objects of each monitoring system,the elastic BP neural network is used as a fitting tool to establish an intelligent transformer online monitoring system with more accurate diagnosis and quicker locking,which reduces the labor cost of maintenance and improves the level of electricity consumption..Finally,the offline ver ification proves that the system can determine the location of the fault efficiently and accurately,and can effectively predict the fault and put it into use in large quantities.