Design And Development Of Direct Drive Drum-type Washing Machine Control System

Substation,the site to transform,collect and distribute electrical energy in the power system, plays an extremely important role in transmission and distribution system. So, the relevant operator should quickly and accurately locate the fault zone, identify and isolate the real fault for restoring normal operation of the non-fault zone as quickly as possible, when the substation occurs in fault. However, due to the complex multiple faults, incorrect action of switch or protection, the loss of information caused by channel interference and other factors, the alarm information may be uncertainty and incompleteness. Fault diagnosis becomes complicated.Based on the basis of a comprehensive analysis of the basic principles of data mining algorithms, combining the data type of smart substation, network structure and conFig.uration process level, and according to the characteristics of the data collection characteristics and intelligent substation condition monitoring information and troubleshooting information, data mining algorithms is designed in smart substation. And actual 110kV substation is used to simulate the effects of data mining method to take different condition monitoring methods to the overall and local intelligence substation.We do a simulation experiment based on a genetic algorithm for the 110kv region.We w ant to do a fault diagnosis for it. The results show that the method is fast and accurate,it is ada ptability for Smart Substation condition.It is meaningful for the realization of efficient online state monitoring of the intelligent substation. Regard the transformer as the object, we study t he application of data mining technology in the condition monitoring of the transformer.The e xperients show that the method can guarantee the calculation speed fully and the accuracy off ault judgment highly. Experiments show multicore support vector machine algorithm based on particle swarm can fully guarantee the computing speed and high precision of fault diagnosis, and the model can determine the type of transformer fault, and has a high accuracy rate.