Parameter Optimization Design And Its Software Development Of Amorphous Alloy Metal Dry-Type Transformer

In the context of energy conservation and emission reduction,energy and power equipment manufacturing and operation enterprises are dynamically promoting amorphous alloy transformers with energy-saving and environmental protection characteristics to reduce the no-load loss of transformers in the distribution network,thereby promoting green and low-carbon development.However,due to the high cost of raw materials,the difficulty of manufacturing,and the uncertainty and time-consuming manual calculation method of electromagnetic parameter design,the promotion and application of amorphous alloy transformers are hindered.Therefore,reducing production costs,improving product quality and shortening the design cycle are of great significance for popularizing the use of amorphous alloy transformers.In this thesis,according to the basic theory of amorphous alloy transformer design,the quantum particle swarm algorithm is introduced and improved,and the parameter optimization software of amorphous alloy dry-type transformer is designed and developed by using the Visual Basic 6.0 software platform.In the case of meeting the needs of transformer manufacturers and related technical standards,the parameter optimization design of amorphous alloy dry-type distribution transformer series products has been carried out,which has improved the product price competitiveness and shortened the product design cycle.The main research content of this thesis is as follows:(1)Combined with the basic theory and design process of amorphous alloy dry-type distribution transformer,the relevant standards to follow for design and the calculation formulas of the main technical parameters of amorphous alloy dry-type transformer are listed.The influence of some parameters on transformer performance and production cost are analyzed,which provide a theoretical basis for the establishment of a mathematical optimization model for transformer parameter calculation,the selection of objective functions and optimal variables.(2)The basic principles of traditional particle swarm optimization(PSO)and quantum particle swarm optimization(QPSO)are introduced.In response to the problem of decreasing diversity of particle population in the later stage of QPSO algorithm,which may lead to the weak local optimization ability and premature convergence,corresponding improvement strategies are proposed(the improved algorithm is named IQPSO algorithm),which adaptively adjusts the search direction and enhances the search capability to obtain higher accuracy and quality solutions.The mathematical model for parameter optimization of amorphous alloy dry-type transformer and the processing method of constraint conditions are established,the QPSO and IQPSO algorithms are applied to the mathematical model for parameter optimization of amorphous alloy dry-type transformer to provide theoretical support for its parameter optimization software design and development.(3)Based on the basic theory of amorphous alloy dry-type transformer design and the application of related algorithms,the overall structure framework of the parameter optimization design software is proposed,and the optimization design software of amorphous alloy dry-type transformer is designed and realized.The optimization software optimizes the design of the key variables of the transformer through the selection algorithm to minimize the cost of the main material of the transformer.By optimizing the reading and writing of the Excel table by the software,the optimized design scheme can be output in the form of an Excel calculation sheet.(4)SCLBH15-315/10 amorphous alloy distribution transformer is consider as the optimization object in the parameter optimization software,use the PSO algorithm,QPSO algorithm,and IQPSO algorithm to optimize the main material cost.By analyzing the main material cost optimization results,computing speed and optimization ability of the three optimization algorithms,the practicability and efficiency of the parameter optimization software and the superiority of the IQPSO algorithm are verified.