Research On Key Technology Of Switched Reluctance Motor Power Converter Circuit

Switched Reluctance Motor(SRM)is a new type of the advantages of both dc and ac Motor system control Motor,the latest generation infinitely adjustable-speed system.SRM’s excellent fault-tolerant operation capability has also broadened its application field,making it a promising application prospect in aerospace,electric vehicles,ships,distributed power supply systems,precision machining,textile machinery,flywheel energy storage,semiconductor processing and many other fields.SRM system is mainly composed of switched reluctance motor body,power converter,controller and position detector.The main circuit of SRM power converter has its unique flexibility and diversity in the choice of hardware topology.Therefore,a comprehensive and reliable power topology analysis system has practical guiding significance and reference value,which can facilitate further and more comprehensive optimization of SRM power converter performance,so as to improve the efficiency and performance of equipment in actual production and life.In recent years,the rise of machine learning,artificial intelligence and other fields has brought about great changes in people’s lives.At present,machine learning has been applied to intelligent control,image processing,language recognition and many other fields,but its application in switched reluctance motor research is rare.This article to apply knowledge in the field of machine learning research field of power module,switch reluctance motor based on machine learning is proposed in the collaborative filtering algorithm will switch magnetic resistance motor power circuit module parameterized,summed up for the first time at the same time sorting out and analyzing all kinds of switch reluctance motor power topology data,based on the collaborative filtering algorithms for machine learning technical guidance,and improve on traditional Euclidean distance method,using the improved type of Europe type algorithm and switched reluctance motor power key circuit topology and devices of selection as the theoretical basis,Build a relatively complete switched reluctance motor power topology recommendation system with the help of Python language.The specific implementation steps are as follows:(1)Read a large number of papers,and analyzed the key topological features of various switched reluctance motor power converters.(2)The key parameters in the circuit topology of switched reluctance motor power converter are analyzed one by one,and the relatively important parameters are extracted as the theoretical basis for establishing the recommendation system.(3)Introduce relevant algorithms of coordinated filtering in the field of machine learning,establish mathematical models and build recommendation engines.(4)Qt resource system is applied to build the user interface of recommendation system.(5)Actual verification and result analysis.