Abstract/Details

Research for Power Quality Disturbance Recognition and Disturbance Data Compression

Yan, Bin Ju (严居斌) .   Sichuan University (People's Republic of China) ProQuest Dissertations Publishing,  2003. H094172.

Abstract (summary)

With the rapid development of scientific technology and national economics, power quality issues have and increasingly captured considerable attention from both utility companies and their customers. With the forming and boosting of electricity markets, the utility must meet not only the customer's requirement of increasing power quantity, but also the requirement of excellent power quality.To improve the electric power quality, we must recognize and control the disturbance sources, and make diagnose, location and classification. Power system short-term disturbance is very important in the analysis of power quality. The time and duration of voltage swell, sag and interruption are detected by using wavelet transform modulo maximum in this paper. They are recognized with wavelet transform energy distribution curve. The result of simulation indicates that it is useful to recognize voltage swell, voltage sag and voltage interruption with this method.The customer who caused power quality disturbance and decrease the level of power quality should be punished. The disturbance source must be found. The disturbance sources of voltage swell, sag and interruption are verified by the method of disturbance power. The harmonic sources are found by the method of equivalent current source. It is satisfied with the result of two networks' simulation.The frequency range of power quality disturbance is very band. The sampling frequency may be very high for the accurate analysis. It will bring a large quantity of data. The original data must be compressed in order to transfer in time and lighten the pressure of channel. The adaptive wavelet transform compressing method is adopted to compress power quality data. The compressing effect is perfect. The original signal is transformed in the wavelet field. The wavelet coefficients are gained. The high frequency wavelet coefficients those modulo are large than threshold are transferred. The signal is reconstructed base on wavelet transform reconstruct algorithm in the receiving end. The program of power quality disturbance data compression base on wavelet transform is accomplished using VC++.In the first, the power quality is summarized in this paper. In the second, the location, duration and recognition of power system short-term disturbance, such as voltage well, voltage sag and voltage interruption, is introduced. The determination of power quality disturbance source is also put forward. In the end, the disturbance data compression base on wavelet transform and its program are present.

Alternate abstract:

本文采用扰动功率法对电压骤升、电压骤降、短时停电、暂态过电压等进行扰动源的判定。对谐波采用等效电流源法判定扰动源。通过对两个网络的MATLAB仿真和分析,取得了令人满意的效果。电能质量分析中,电能质量现象由于频率范围很宽,为了对它进行准确的分析,必须高速采样,这将产生大量的数据,为了进行及时传输和减轻传输通道的压力必须对原始数据进行压缩。本文采用小波变换自适应压缩方法,对电能质量数据进行压缩,取得了良好的效果。首先对原始信号进行小波变换,得出各级小波系数,根据原始信号的波形特征和小波系数模极大值原理选取阈值,对小波变换高频系数进行阈值取样。在数据接收端,利用小波变换重构原理进行重构,得到解压缩后的数据。最后用VC++编程实现电能质量扰动数据压缩。本文首先综述了电能质量问题。然后详细介绍了电压骤升、电压骤降、短时停电等三种电力系统短期扰动定位、确定扰动持续时间和识别,以及电压骤升、电压骤降、短时停电、暂态过电压、谐波等电能质量扰动方向的判定及仿真分析,最后采用小波变换进行电能质量扰动数据压缩及程序实现。

Indexing (details)


Subject
Electrical engineering
Classification
0544: Electrical engineering
Identifier / keyword
(UMI)AAIH094172; Applied sciences; 小波变换; 扰动功率; 电能质量; 短期扰动; 自由词
Title
Research for Power Quality Disturbance Recognition and Disturbance Data Compression
Alternate title
电能质量扰动识别及扰动数据压缩的研究
Author
Yan, Bin Ju (严居斌)
Number of pages
0
Degree date
2003
School code
9072
Source
DAI-C 71/35, Dissertation Abstracts International
Place of publication
Ann Arbor
Country of publication
United States
Advisor
Yang, Hong Geng (杨洪耕)
University/institution
Sichuan University (People's Republic of China)
University location
Peoples Rep. of China
Degree
M.Eng.
Source type
Dissertation or Thesis
Language
Chinese
Document type
Dissertation/Thesis
Dissertation/thesis number
H094172
ProQuest document ID
1024711625
Copyright
Database copyright ProQuest LLC; ProQuest does not claim copyright in the individual underlying works.
Document URL
https://www.proquest.com/docview/1024711625