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Research And Application Of Energy-saving Optimization Control Strategy For Coal Mine Power Supply System

Posted on:2022-12-02Degree:MasterType:Thesis
Country:ChinaCandidate:X C MuFull Text:PDF
GTID:2481306770493744Subject:Security Science and Disaster Prevention
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At present,with the increase of electricity consumption of coal mine enterprises,the power loss of coal mine power supply system during operation is also rising.Therefore,it is of great practical significance to carry out research on the global energy-saving optimization control strategy and application of coal mine power supply system,to reasonably optimize and control the power loss of coal mine enterprises,and to fully exploit the power consumption and energy loss reduction potential of the power supply system.In this paper,the power supply system of coal mine is taken as the research object,the energy saving optimization control of coal mine enterprises is taken as the goal,and the chain Static Var Generator SVG compensation and energy consumption network monitoring system are adopted as the means.Based on the analysis of the existing research results of domestic and foreign scholars,the following work is done in this paper:Firstly,the current situation of coal mine power supply system is analyzed.The graphical model,logical topology diagram,node description matrix and node hierarchical model of the system are constructed.The power flow calculation based on hierarchical forward and backward generation is carried out.MATLAB programming and the analysis of the power flow calculation results are carried out.The simulation experiment is carried out on the load site of a coal mine power supply system.Secondly,the Multipopulation Genetic Algorithms with Arithmetic Crossover and Heuristic Crossover is used to solve the optimal reactive power optimization compensation scheme,so as to achieve the purpose of multi-objective,multi-angle and precise reactive power optimization compensation.The improved genetic algorithm is applied to the load site of a coal mine power supply system to solve the reactive power optimization,calculate the optimal position of reactive power compensation and the magnitude of reactive power output,and obtain the best reactive power optimization scheme.The data before and after optimization is compared to verify the effectiveness of the algorithm.Thirdly,the composition,principle and chain structure of SVG are fully researched.The excellent adaptability of SVG in the actual load scene of coal mine power supply system is analyzed.The composite control strategy of SVG is studied and verified by simulation.The active energy-saving subsystem based on SVG for fully mechanized mining face is designed,which is more accurate and instantaneous in reactive power optimization compensation,power factor reduction and power loss reduction.The application of flame-proof SVG device based on energy-saving subsystem in the coal mine power supply system is studied.The energysaving benefit test and result analysis of the device applied to the load field are carried out.Finally,in order to meet the urgent needs of coal mine enterprises to save energy and reduce consumption,a networked monitoring system for energy consumption of coal mine distribution network is designed.The monitoring system consists of the master computer monitoring system and the underground monitoring and communication substation.Through the networking of master computer,underground energy consumption monitoring and communication substations,and comprehensive energy-saving devices,the monitoring system can perform real-time monitoring,operating parameter analysis and data processing of the main equipment loads in the power supply and distribution system.The monitoring system can realize the informatization,visualization and intelligent of the power consumption process.The monitoring system provides a reliable technical platform for enterprises to achieve energy-saving optimization control.
Keywords/Search Tags:energy-saving optimization, power flow calculation, genetic algorithm, SVG, energy consumption network monitoring
PDF Full Text Request
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