改进微分进化算法在负荷建模中应用研究-电力系统及其自动化专业论文.docxVIP

改进微分进化算法在负荷建模中应用研究-电力系统及其自动化专业论文.docx

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改进微分进化算法在负荷建模中应用研究-电力系统及其自动化专业论文

华中科技大学硕士学位论文 华 中 科 技 大 学 硕 士 学 位 论 文 II II Abstract A load model is a mathematical representation of the relationship between a bus voltage (magnitude and frequency) and the power (active and reactive) or current flowing into the bus load. Load modeling is essential to provide secure and economic planning and operation of a power system. Much progress in the load modeling has been made over the past three decades. Various static and dynamic models based on mathematical and physical representations have been studied to describe the overall load characteristics. Load modeling is one of the most difficult problems in the power system analysis. For a long time, the research of load modeling has lagged behind the development of power industry. Based on studying the load modeling theory comprehensively, this paper introduced the basic theory of load modeling and the load model in common use. The algorithms of parameter identify are deeply researched in this paper, and the development and status of load modeling is presented in this paper. What’s more, the differences and features of these algorithms are given in the paper. This paper proposed an improved differential evolution (IDE) algorithm, which can improve the abilities of seeking the global excellent result, based on system measurements, to construct power system area load models alongside with distribution network reduction. This research is motivated to improve the flexibility and accuracy of power system load modeling and network reduction. The example of 5 nodes system from IEEE showed that the modeling was correct, and the algorithm was convergent and effective. Key Words: Area-load modeling; Improved Differential Evolution; Parameter identifying; Network reduction PAGE IV PAGE IV 目 录 摘 要 ·············································································································· I Abstract ····································································································

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