A Method Based on Floating Threshold to Solve Protection Misoperation Caused by Abnormally Large Number

Abstract

The invention provides a method based on a floating threshold to solve protection malfunction caused by abnormally large numbers, calculates the change amount of the current sampling value, calculates the floating threshold for the obtained change value, and then compares the current change value with the start-up setting with the floating threshold added. Value comparison, if it is greater than this value and meets a certain number of points, it is considered to have an abnormally large number. After the protection meets the action conditions, it will adopt a long delay time greater than the algorithm requirement to avoid false action. Otherwise, it can adopt a short delay time after the protection meets the action conditions. To quickly remove the fault. The method of the present invention can well distinguish continuous abnormal large numbers and faults, and the method has a short data window without sacrificing the action speed at the time of faults, and solves the troubles in the power system industry that continuous abnormal large numbers cause protection malfunction or cause fault actions The problem of slowing down. When the protection condition is satisfied, the variable delay time is adopted, which can not affect the normal protection action, and can operate correctly even if there are abnormally large numbers of faults.

Description

technical field [0001] The invention belongs to the technical field of electric power systems, and relates to a method based on floating thresholds to solve protection maloperation caused by abnormally large numbers. Background technique [0002] When struck by lightning or other disturbances, the electrical signal of the power system will generate abnormally large numbers, which will cause the protection device to malfunction, and it is difficult to distinguish the abnormal sampling value of the electrical quantity from the normal sampling value when the fault occurs under a short data window. At present, there are some judgments on the abnormal point data of the sampling value, but all of them have defects: [0003] 1) For example, the 3-point continuous effective discriminant method with time delay can only be applied to such Figure 4 Validity of sampled values ​​for the finite discontinuity shown in (a), but for Figure 4 (b) or Figure 4 It is not applicable to occasio...

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Problems solved by technology

[0003] 1) For example, the 3-point continuous effective discriminant method with time delay can only be applied to such Figure 4 Validity of sampled values ​​for the finite discontinuity shown in (a), but for Figure 4 (b) or Figure 4 It is not applicable to occasions with multiple discontinuities shown in (c); or the data window requires a long time and poor real-time performance, which cannot be applied to fast protection;

[0004] 2) There is also a difference algorithm that uses the comparison of before and after values, but in the case of multiple discontinuities, it is also impossible to distinguish

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