Method for identifying voltage sag reason

Abstract

The invention provides a method for identifying a voltage sag reason. The method for identifying the voltage sag reason comprises the following steps of (1) using the discrete Fourier transform to calculate a spectral density function, converting the voltage in a time domain to the voltage in a frequency domain, (2) calculating the characteristic quantity related to identification of the voltage sag reason, (3) defining a sag phase number N1, judging whether swell and a sag combination N3 exist or not, and (4) identifying the voltage sag reason. The method for identifying the voltage sag reason is comprehensive and higher in adaptability to changes of voltage sags, and can be easily improved, verification is carried out on the basis of really-measured data of a power grid, and the calculation result has better engineering practicability.

Description

technical field [0001] The invention relates to an identification method, in particular to an identification method for the cause of a voltage sag. Background technique [0002] Voltage sag refers to an event in which the root mean square value of the supply voltage suddenly drops to 90% to 1% of the rated value in a short period of time, and its typical duration is 0.5 to 30 cycles. At the same time, the voltage sag is often accompanied by a sudden change in the voltage phase, that is, a phase jump. The amplitude, duration and phase jump of the voltage sag are the three most important characteristic quantities of the nominal voltage sag. [0003] The main causes of voltage sag are short-circuit faults (which can be divided into symmetrical faults and asymmetrical faults), transformer input and induction motor start-up. Due to different reasons, the voltage sag phenomenon is also different. The typical waveform of the voltage effective value is shown in figure 2 . How to...

AI Technical Summary

Problems solved by technology

Fuzzy reasoning, expert systems and other methods used in existing research all need to determine a single threshold or a certain threshold range for the characteristic quantity concerned. There is no clear boundary between the voltage waveform parameters due to different voltage sags. During the voltage sag process, the voltage waveform is closely related to other factors such as grid parameters, such as system operation mode, load type, wiring mode, fault point and relative position of the monitoring point, etc., which will greatly affect the voltage sag feature

Therefore, the introduction of a fixed threshold in the judgment method will often become inaccurate due to changes in conditions, resulting in misjudgment of the cause of the voltage sag

The neural network method requires a large number of training samples to obtain more accurate test results. Since most existing monitoring systems do not identify the cause of voltage sags and cannot obtain a large amount of measured data, the application of neural network algorithms is subject to certain restrictions. limit

At the same time, most of the existing research methods use the voltage sag calculated by simulation, which is still different from the measured data of the power grid, and the accuracy of the identification method needs to be further verified.

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