Voltage sag reason recognition method based on typical voltage sag waveform matching

Abstract

The invention discloses a voltage sag reason recognition method based on typical voltage sag waveform matching, and the method comprises the following steps: (A), collecting a historical monitoring waveform of a typical voltage sag historical event; (B), building a typical voltage sag event waveform library; (C), extracting a to-be-extracted voltage sag waveform; (D), carrying out the waveform matching and analysis of the wave band of the to-be-extracted voltage sag waveform and the typical voltage sag event waveform library; (E), recognizing the generation reason of a current voltage sag event waveform. The method carries out the more fundamental and accurate matching analysis of the voltage sag at the aspect of waveform, can quickly and accurately recognize the generation reason of the voltage sag event waveform, has a very high reference value for the prevention and treatment of the voltage sag, and is good in application prospect.

Description

technical field [0001] The invention relates to a voltage sag cause identification method based on typical voltage sag waveform matching, and belongs to the technical field of power quality analysis. Background technique [0002] Voltage sag, also known as voltage sag, voltage sag or voltage sag, voltage sag is an event in which the root mean square value of the power supply voltage suddenly drops in a short period of time and then quickly returns to a normal state. The typical time is generally 0.5 to 30 cycles time. [0003] With the rapid development of the economy and the wide application of sensitive power electronic equipment in industry, the losses caused by voltage sags have also increased significantly. According to the survey of a foreign power company, complaints about voltage sags account for 80% of power quality complaints, and voltage sags have become the most important power quality problem. [0004] The main causes of voltage sags are divided into three cat...

AI Technical Summary

Problems solved by technology

However, in the above method, for the extraction of voltage sag feature quantities, generally no more than 10 feature quantities are extracted, and some feature quantities that are more important in actual situations cannot be well excavated, thus affecting the accuracy of the recognition results However, using methods such as fuzzy reasoning needs to set a certain threshold. In actual situations, the short-circuit capacity of the power system, the connection mode of the transformer, and the load type will all affect the characteristics of the voltage sag to a certain extent. Therefore, it is difficult to Determining a fixed threshold makes the recognition result very accurate; moreover, the neural network requires a large number of training samples, but the actual measured data is not rich enough in practice, which affects the application of the neural network method

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