Data step time identification method based on extended micro variables

Abstract

The present invention provides a data step time identification method based on extended micro variables, comprising the following steps: (1) reading step test data to obtain a set of disturbed data containing a time series; (2) calculating the extended micro variable sequence; (3) comparing the values ​​of the extended micro variables and taking the maximum value corresponding to the time t s The present invention directly identifies the step occurrence time by calculating the extended microvariable of the test data, thereby avoiding the error caused by the data fitting algorithm and having a strong identification ability for the test data containing interference. The present invention is suitable for parameter testing of various automatic control systems.

Description

Technical Field

[0001] The invention relates to the field of identification of test data containing disturbance, and in particular to a data step time identification method based on extended micro variables. Background Art

[0002] When performing parameter testing on automatic control systems such as power generation excitation systems, the step response characteristic is an important system characteristic. The acquisition of its step response indicators such as rise time and peak time requires accurate acquisition of the step response start time. However, in actual field tests, the step start time is often controlled manually, and accurate acquisition of the step response start time requires data identification. Field tests are inevitably affected by various interference factors in the environment, and the test data contains a certain amount of disturbance. If data processing is not performed, directly using the change in data volume as the criterion for the step response start time will result in large errors.

[0003] In order to eliminate the influence of disturbance, the common data preprocessing method is to filter or curve fit the data to obtain a smoother curve, and interpret the smooth curve to obtain the starting time of the step response. However, the phase delay of the filtering algorithm will cause distortion of the time domain data and produce errors; the first-order derivative of the step data is discontinuous at the step moment, and the curve fitting algorithm will lose the discontinuous characteristics, resulting in inaccurate interpretation. Summary of the invention

[0004] The present invention provides a data step time identification method based on extended micro variables. For a step curve obtained in a step test, the method can eliminate the influence of disturbance data and directly identify the step starting time by calculating the extended micro variables without data preprocessing when the data contains interference data, thereby retaining all the information of the original data and realizing accurate interpretation of the test data and improving the accuracy of the interpretation of the test data.

[0005] A data step time identification method based on extended micro variables comprises the following steps:

[0006] (1) Read the step test data to obtain a set of disturbed data containing time series:

[0007] X={t i ,x i},i=1,2,…,n;

[0008] where t i is an equally spaced time series with a time series interval of Δt, x i is a data sequence, n is a positive integer greater than or equal to 3;

[0009] (2) Calculate the extended microvariable sequence according to the following formula:

[0010] E(X) = {e i},i=1,2,…,n

[0011] where e i is time t i The extended microvariable at:

[0012] e i =(x i+m +x i+m+1 +x i+m+2 +...+x i+m+l-1 +x i-m +x i-m-1 +x i-m-2 +...+x i-m-l+1 -2lx i )×sign(x i+m+l-1 -x i-m-l+1 ), 1≤m<n, 2≤l<n

[0013] in

[0014] (3) Compare the values ​​of the extended microvariables and take the maximum value corresponding to time t s is the moment when the step occurs:

[0015] t s =t j |(e j =max{e i}).

[0016] Furthermore, when calculating the extended microvariable sequence, the value range of m is: Where m is a positive integer, δ m is the maximum disturbance value, and k is the step rise value at the sampling time interval Δt.

[0017] Beneficial effects of the present invention:

[0018] 1. Compared with the existing technology, the number of data points used is expanded to 5, which can obtain more original data information;

[0019] 2. Compared with the existing technology, the value of m can be smaller, with higher anti-interference performance;

[0020] 3. With accurate identification results of data smoothing algorithm, the expanded local mutation amount of each data point utilizes the original data information instead of the processed data information, thus avoiding the information loss caused by data smoothing algorithm. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 It is a data sequence of the data step time identification method based on extended micro variables of the present invention;

[0022] Figure 2 It is a schematic diagram of test data adopted by the present invention;

[0023] Figure 3 It is a schematic diagram of the extended microvariable calculated by the present invention. DETAILED DESCRIPTION

[0024] The technical solution of the present invention will be clearly and completely described below in conjunction with the accompanying drawings of the present invention.

[0025] Figure 1 The data sequence of the data step time identification method based on the extended microvariable of the present invention is shown, and the method comprises the following steps:

[0026] (1) Read the step test data to obtain a set of disturbed data containing time series:

[0027] X={t i ,x i}, i = 1, 2, ..., n;

[0028] where t i is an equally spaced time series with a time series interval of Δt; x i is a data sequence.

[0029] (2) Calculate the extended microvariable according to the following formula:

[0030] E(X) = {e i},i=1,2,…,n

[0031] where e i is time t i The extended microvariable at:

[0032] e i =(x i+m +x i+m+1 +x i+m+2 +...+x i+m+l-1 +x i-m +x i-m-1 +x i-m-2 +...+x i-m-l+1 -2lx i )×sign(x i+m+l-1 -x i-m-l+1 ), 1≤m<n, 2≤l<n

[0033] in

[0034] At a disturbance level no greater than δ mWhen , since the first-order derivative of the unperturbed data before and after the step is unchanged, it is easy to know that the maximum absolute value of the extended microvariable of the perturbed data before and after the step satisfies:

[0035] max{e i |}<4lδ m

[0036] At the moment of step occurrence, the first-order derivative of the original data is discontinuous. The first-order derivative of the original data before the step is 0, and the first-order derivative of the data after the step is not zero. Taking the above step as an example, assuming that the step rise value at the sampling time interval Δt is k, then the original data at the moment of step occurrence t j The expanded micro variables are:

[0037]

[0038] when Right now When the step occurs, the extended microvariable can be guaranteed to be greater than the maximum value of the extended microvariable at the non-step moment by 8lδ m The same conclusion can be drawn for the next step.

[0039] (3) Compare the values ​​of the extended microvariables and take the maximum value corresponding to time t s The moment when the up or down step occurs:

[0040] t s =t j |(e j =max{e i})

[0041] when The algorithm of the present invention can accurately identify the moment when the step occurs.

[0042] The method is further described below with reference to the accompanying drawings and a specific example:

[0043] A synchronous generator excitation system performs a step response test on the terminal voltage. After giving the excitation reference voltage a step signal, the terminal voltage step data is measured. Figure 2 Read the step test data and get a set of disturbed data containing time series as shown in Table 1 below:

[0044] Table 1

[0045]

[0046]

[0047] Calculate the extended microvariable according to the formula:

[0048] e i=(x i+m +x i+m+1 +x i+m+2 +...+x i+m+l-1 +x i-m +x i-m-1 +x i-m-2 +...+x i-m-l+1 -2lx i )×sign(x i+m+l-1 -x i-m-l+1 ) Let m be 3 and l be 3, then:

[0049] e i =(x i+3 +x i+4 +x i+5 +x i-3 +x i-4 +x i-5 -6x i )×sign(x i+5 -x i-5 )

[0050] After calculation, the expanded micro variables at each moment are shown in Table 2:

[0051] Table 2

[0052]

[0053]

[0054] The extended microvariable curve is shown in Figure 3 .

[0055] Compare the values ​​of the extended micro variables and take the maximum value:

[0056] max{e i}=632

[0057] The time t corresponding to this maximum value s If it is 0.21s, then take 0.21s as the starting time of the next step.

[0058] After obtaining the step starting time, we can further calculate indicators such as the rise time and peak time of the step response.

[0059] The above is only a specific implementation of the present invention, but the protection scope of the present invention is not limited to the step response test of the generator excitation system. The step response test of any automatic control system is included in the technical field of the present invention. The changes or replacements that can be easily thought of by technicians in this technical field within the technical scope disclosed by the present invention should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A data step time identification method based on extended micro variables, characterized in that The steps include: (1) Read the terminal voltage step data obtained by performing a step response test on the terminal voltage to obtain a set of disturbed data containing a time series: X={t i ,x i },i=1,2,…,n; where t i is an equally spaced time series with a time series interval of Δt, x i is a data sequence, n is a positive integer greater than or equal to 3; (2) Calculate the extended microvariable sequence according to the following formula: E(X)={e i },i=1,2,…,n where e i is time t i The extended microvariable at: e i =(x i+m +x i+m+1 +x i+m+2 +...+x i+m+l-1 +x i-m +x i-m-1 +x i-m-2 +...+x i-m-l+1 -2lx i )×sign(x i+m+l-1 -x i-m-l+1 ), 1≤m<n, 2≤l<n where (3) Compare the values ​​of the extended microvariables and take the maximum value corresponding to time t s is the moment when the step occurs: t s =t j |(e j =max{e i }); When calculating the extended microvariable sequence, Right now When the step occurs, the extended microvariable is guaranteed to be greater than the maximum value of the extended microvariable at the non-step moment 8lδ m , where m, l are positive integers, δ m is the maximum disturbance value, and k is the step rise value at the sampling time interval Δt.

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