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Energy Efficiency Research On Overhead Transmission Lines Considering The Impact Of Power Quality Factors

Posted on:2021-07-12Degree:MasterType:Thesis
Country:ChinaCandidate:H LiFull Text:PDF
GTID:2492306470960409Subject:Electrical engineering
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With the progress of the times and the rapid development of science and technology,the power sector and power users are paying more and more attention to the problem of power quality caused by the new situation of modern power grids and loads.The line loss of overhead transmission lines is a natural physical phenomenon and an inevitable part of the line loss.However,the additional loss due to power quality factors is controllable loss and should be minimized.Such as harmonics,three-phase unbalance,voltage fluctuations and flicker,most of the impact on the energy efficiency of overhead transmission lines is concentrated on the study and analysis of the impact of single power quality factors.Lack of comprehensive energy efficiency analysis under common interference to various power quality problems of overhead lines.Because of the limitation of the experimental site,most of the research on power quality is limited to theoretical research,and there is a lack of support and comparative correction of the theoretical research results from the field experimental data.First of all,this paper studies the impact of individual power quality factors such as harmonics,three-phase unbalance,voltage fluctuations and flicker on the energy efficiency of overhead lines.Based on the influence of single factors,combined with the disturbance characteristics of each power quality factor,the energy efficiency changes of overhead lines under the joint action of each power quality factor were studied;secondly,based on the above theoretical analysis,this paper uses Matlab simulation tools to obtain the relationship between energy efficiency and harmonics,three-phase unbalance,voltage fluctuations and flicker of overhead transmission lines.Finally,this paper formulates a set of energy efficiency analysis plan for overhead lines under power quality disturbance.A 10 k V and 380 V experimental platform was built at He Nan provincial power experimental base to test the overhead lines in terms of harmonics,three-phase unbalance,voltage fluctuation and flicker.Perform field experiments on the power utilization efficiency of variable power quality problems,and extract,process and analyze the field experimental data through Matlab software.Use the experimental results to compare and analyze the theoretical data of overhead lines under the influence of power quality problems to verify the accuracy of the established theoretical model.After analyzing the influence of power quality on the energy efficiency of overhead lines,it is necessary to evaluate the comprehensive power quality level,aiming at the uncertainties and high-dimensional and highly nonlinear characteristics of the comprehensive evaluation of power quality.This paper applies BP neural network and Elman neural network to the comprehensive evaluation of power quality of overhead lines,skip the influence weight assignment and get the evaluation result.In this paper,power quality is divided into two main grades,qualified and unqualified.Among them,qualified is divided into 5 evaluation levels,and unqualified is divided into 3 evaluation levels.In this paper,Matlab software is used for neural network analysis,and the four power quality factors of harmonic distortion,three-phase unbalance,voltage fluctuation and flicker are used as inputs into two neural networks for power quality rating,output results classify power quality to a certain level.Through the evaluation accuracy and various error indexes,the advantages and disadvantages of the two evaluation methods are compared.The experimental results show that: the comprehensive evaluation effect of power quality based on Elman neural network is better than that based on BP neural network.
Keywords/Search Tags:Three-phase unbalance, Harmonics, Voltage fluctuation and flicker, Overhead lines, BP neural network, Elman neural network, Power quality evaluation
PDF Full Text Request
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