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Parameter Identification Based on QPSO Algorithm for Composite Load Model and Platform for Load Modeling

Author: LiuYang
Tutor: WangZhenShu; SongQiang
School: Shandong University
Course: Electrical Engineering
Keywords: load model parameter identification QPSO algorithm PSO algorithm global convergence platform for load modeling
CLC: TM714
Type: Master's thesis
Year: 2012
Downloads: 95
Quote: 0
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Abstract


Load modeling is one of the important basic subjects in the electric power system analysis research, and also one of the recognized problems in power system research; its research progress has also been behind the research progress of other components in power system. A lot of research for load modeling has been carried out and a powerful impetus has been achieved in load modeling research progress.This paper contains two aspects of the research work, composite load model parameters identification algorithm research and load modeling platform development, based on many references and comprehensives of the former research.In the research of parameter identification algorithms for composite load model, this paper analyzes an important influence factor restricting the PSO algorithm of the global search ability in a more in-depth way; and the influence factor is that the range of an iterationis is certain after the confirmation of iterative particle trajectory; and the range is only part of the feasible region. That limits greatly the particle swarm algorithm of the global convergence ability. For the defects existing in optimizing by PSO algorithm, A new method based on QPSO algorithm is put forward to solve composite load model parameter identification problem. The QPSO algorithm changes particles position by the uncertain track behavior characteristics of particles in quantum mechanics, to make the feasible region of particles trajectory may fill the whole area in every iterative; so that the search area of particles become expandded greatly and the algorithm has better global convergence ability. This paper identifies the parameters respectively of two composite load models, ZIP&difference equations and power function&difference equation, combining the data of composite load disturbance from dynamic model test, and verifies the effectiveness of the method that QPSO algorithm can be used in parameter identification of composite load models. This paper verifies the ability of QPSO algorithm in searching for global optimal solution by the compared text where QPSO algorithm and PSO algorithm are used to identify the parameters respectively of ZIP&difference equations; and analyzes the different effects on global convergence ability for the two algorithms, because of the Product by multiplied by particle numbers and iteration times, according to the experimental results.In the research of platform development for load modeling, this paper develops a platform development for load modeling where the functions of load information database view, compontent-based approach and measure-based approach are included. The platform is based on user/server mode,and developed by the object-oriented programming technology and MFC ODBC and ADO database programming technology. Especially for the problem that complex load model and identification algorithms are difficult to write with VC++language in measurement-based approach,this paper puts forward the method that Programming complex load model and parameter identification algorithm by mixed programming techniques based on COM compoent of VC++and Matlab, so that the platform is more rich in load models and identification algorithms, and model parameters identification ability and graphics processing power are greatly strengthened. The information platform for load modeling has many advantages,such as having relatively comprehensive functions, friendly interface, stable operation and good scalability, etc.

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CLC: > Industrial Technology > Electrotechnical > Transmission and distribution engineering, power network and power system > Theory and Analysis > Load analysis
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