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Harmonic Analysis Approach In Power System And Harmonic Modeling Of Electric Loads

Posted on:2010-07-01Degree:MasterType:Thesis
Country:ChinaCandidate:Z L RenFull Text:PDF
GTID:2132360278452543Subject:Power system and its automation
Abstract/Summary:PDF Full Text Request
With the development of power electromics technique, more and more the device , such as rectifier, frequency converter, electric arc furnace and so on are applied into power system, as this sort of load is nonlinear, impulsive, unbalanced, the power quality becoms worse than before, which endangers the safety, stability of power system and decreases the economy of operation. On the other hand, the device used in modern industry, commence and people's daily life, is easily influenced by power quality, therefore, the issue of improving power quality is concerned by all of us. How to minimize the damage of the harmonics is an issue of great concern to the field of power systems. The key to solution of this issue lies in the quantitative determination of the composition, magnitude and phases of harmonious waves. Nonharmonic has become the main symbol of green, the content of harmonic in power system is the important parameter in power quality, so harmonic analysis is important significance in power system.The power load is an important constituent of the electrical power system, and its modeling work has always being a difficult problem of the power system, acquiring the domestic and foreign scholars' widespread attation. Accurate power model is significant in the power system harmonic analysis and calculation, thereby further affects the power system monitoring, management and governance. Harmonic analysis has higher precision on the components of the model than the power flow. Compared with generators, transformers and transmission line modeling of the harmonic, the slow progress in harmonic modeling of electric loads, and the rough harmonic modeling of electric loads hindered the accuracy of the entire power system simulation. Therefore, the precise harmonic modeling of electric loads is of great significance.Firstly, the paper systemic and brief introduces the concept of harmonic, hazard of harmonic, harmonic source, existed problem of the traditional and new harmonic analysis algorithms which are used frequently. In the view of the several problems of different algorithms such as the low computation accuracy and the big computation quantity and so on, an artificial network based on adaptive Neural-Network based Fuzzy Inference System was presented in the paper with the harmonic model of the electric power system, and the algorithm based on the BP gradient descent rule and least squares method were researched. To validity of the algorithm in the field of power system harmonic analysis, the computer simulation and other algorithms comparison research were given in the paper. The research results showed that the harmonic analysis approach of the power system in the paper had the high computation accuracy and the fast computation speed.Secondly, the significance of load modeling, the status quo and support vector machine algorithm are reviewed, in the view of the generalized load model, a new parameter identification algorithm based on least squares support vector machine was presented in the paper, to validity of the algorithm, the computer simulation and other algorithms comparison research were given in the paper. And the research results showed that the parameter identification algorithm in the paper is feasible and effective.Thirdly, in the view of the harmonic modeling of electric loads of asymmetric load, the proposed algorithm is suitable for all types of modeling three-phase load, the simulation results indicate that the proposed algorithm has great validity and accuracy.Finally, the paper summarize the study content, moreover, it makes a prospect on which applying the harmonic analysis and harmonic modeling of electric loads in the power system.
Keywords/Search Tags:Power System, Harmonic analysis, load model, Parameter identification, ANIFS(Adaptive Neural-Network based Fuzzy Inference System), LS-SVM(Least squares support vector machines), Symmetrical component
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