Study On Detection And Recognition Of Voltage Sags In Distribution System

Distribution network directly facing the majority of electricity users, due to factors such as the impact of nonlinear loads, power quality continue to show a downward trend. While at the same time, excellent power quality especially the transient are needed for sensitive equipment such as computers, communications facilities and other. Voltage sag detection and its disturbance source identification is an important prerequisite and foundation to improve the power quality. The main works and results are as follows:Firstly, the voltage sag characteristic features detection methods are thoroughly discussed and the advantages and disadvantages of each algorithm are analyzed including the RMS detection algorithm、the missing voltage technique method、the single-phase voltage transform average method、instantaneous dq transformation method、wavelet analysis method、Hilbert-Huang transform method and S transform method. Also have made the simulation to part of the algorithm.Secondly, on the basis of detailed analysis the law of detect the voltage sag starting port by using MRSVD, proposed a new method based on MRSVD to detect voltage sags, and detected voltage sags by using the proposed method. The results show that, the proposed method based on MRSVD can effectively detect the starting and ending time and drop value of the voltage sags. In addition, for the voltage sag signal with noise proposed a denoising method.based on SVD and wavelet. The simulation and experimental results show that the results for noise reduction and signal characteristics of information reserved are goodFinally, analyzed the mechanism of voltage sag caused by short-circuit fault, induction motor starting and transformer put into operation, and has made the theoretical analysis and the simulation to different types of disturbance sources. Proposed a feature extraction method of voltage sags based on S transform and bidirectional 2DPCA. KNN algorithm and BP network are used to classify different kinds of disturbance sources. The results show that, the feature extraction method of voltage sags based on S transform and bidirectional 2DPCA is effective and feasible, and in the voltage sag disturbance source identification study has good application prospects.