Error data identification method for secondary equipment in electric power supply system

Abstract

The invention provides an error data identification method applicable to secondary equipment of a power supply system. The method comprises the following steps: if difference between two adjacent sampling values of the secondary equipment is greater than a preset threshold, then the current data are the error data, and the error data are discarded. The theory is as follows: firstly, as primary equipment of the power supply system has large inductance and capacitance, in the case of serious fault of the power supply system, the current value or voltage value of the power supply system does not suddenly changes but changes according to an exponential function curve; secondly, a mutual inductor of the primary system and an internal mutual inductor of the secondary equipment which are used for signal transformation have a maximum range limit, so the sampling value of the second equipment has a maximum value; and thirdly, if the sampled signal has frequency, the sampled signal has the frequency characteristics. The threshold of the permissible maximum sampling value difference can be set by integrating the three steps with the specific characteristics of the secondary equipment, such as amplitude corresponding to sampling point number and sampling conversion of each cycle.

Description

technical field [0001] The invention belongs to the technical field of electric engineering, in particular to an error data identification method applicable to secondary equipment in a power supply system. Background technique [0002] The secondary equipment in the power supply system usually obtains the sampled value through sampling, and judges and controls accordingly. If there is noise or interference in the system, or if the secondary equipment has hardware failures such as abnormal sampling or software failures such as program runaway, the sampling of the secondary equipment may have wrong data, which may easily cause wrong judgment and control of the secondary equipment. [0003] Secondary equipment usually uses software filtering or hardware filtering to filter out erroneous data, but conventional software and hardware filtering methods such as Fourier transform, active or passive filtering will generate time lag, slowing down the speed of judgment and control. For...

AI Technical Summary

Problems solved by technology

If there is noise or interference in the system, or if the secondary equipment has hardware failures such as abnormal sampling or software failures such as program runaway, the sampling of the secondary equipment may have wrong data, which may easily cause wrong judgment and control of the secondary equipment.

[0003] Secondary equipment usually uses software filtering or hardware filtering to filter out erroneous data, but conventional software and hardware filtering methods such as Fourier transform, active or passive filtering will produce time lag, slowing down the speed of judgment and control

For varying sampled values, it is sometimes difficult to distinguish between real faults and bad data

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