CN109635781B - Digital signal coarse data detection and correction method and system based on wavelet transformation - Google Patents
Digital signal coarse data detection and correction method and system based on wavelet transformation Download PDFInfo
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Abstract
The invention discloses a method and a system for detecting and correcting coarse data of a digital signal based on wavelet transformation, wherein the method comprises the following steps: carrying out wavelet decomposition on an acquired sinusoidal digital signal sequence of the digital metering system of the intelligent substation to be tested to obtain a high-frequency detail coefficient and a low-frequency approximate coefficient; reconstructing the sine digital signal sequence according to the high-frequency detail coefficient to obtain a reconstructed digital signal sequence; calculating the standard deviation and the average value of each absolute value of the obtained reconstructed digital signal sequence, determining the items of which the average value is less than a preset threshold value from the absolute values as coarse data items, and expanding to obtain expanded coarse data items; and removing the original sinusoidal digital signal sequence according to the expanded coarse data item, and then carrying out interpolation correction processing to obtain the sinusoidal digital signal sequence without coarse data. The invention has relatively less fitting times and relatively smaller calculated amount, and can screen two or even a plurality of continuous coarse data.
Description
Technical Field
The invention belongs to the field of detection of coarse data in a digital signal sequence of a digital metering system of an intelligent substation, and particularly relates to a digital signal coarse data detection and correction method and system based on wavelet transformation.
Background
When the digital system samples the analog signal, if there is a disturbance, such as a disturbance voltage coupled from the power supply or ground, the ADC may sample a maximum or minimum value, called coarse data.
In a digital metering system of an intelligent substation, a digital electric energy meter virtual instrument directly acquires waveform sampling data of an electronic transformer from a merging unit, and the data is digital quantity. In the process, the electronic transformer may couple some coarse data in the sampling process, and the data is a high-frequency signal; these gross data, if left unprocessed, will ultimately affect the power calculation of the digital power meter. Therefore, a proper algorithm is required to be found for detecting the coarse data and eliminating the coarse data, and the algorithm is applied for correction, which has very important significance for reducing the electric energy calculation error.
For coarse data detection, the conventional method is to take a set of numbers (usually 7 in length) of fixed length of the digital signal sequence, remove a maximum value and a minimum value, consider the two values as coarse data, and then apply least square fitting to correct the coarse data. The problems of the current method are as follows: the fitting times are more, the calculated amount is larger, and more than two continuous coarse data cannot be screened.
Disclosure of Invention
The present invention aims to provide a method and a system for detecting and correcting coarse data of a digital signal based on wavelet transformation, so as to solve the existing technical problems. The detection method has relatively fewer fitting times and relatively smaller calculated amount, and can screen two or even a plurality of continuous coarse data.
In order to achieve the purpose, the invention adopts the following technical scheme:
a digital signal coarse data detection and correction method based on wavelet transformation comprises the following steps:
step 3, solving the standard deviation and the average value of each absolute value of the reconstructed digital signal sequence obtained in the step 2, and determining the items of which the average value is less than a preset threshold value from the absolute values as coarse data items;
step 4, expanding the coarse data items obtained in the step 3 to obtain expanded coarse data items;
and 5, removing the original sinusoidal digital signal sequence in the step 1 according to the expanded coarse data items obtained in the step 4, and then carrying out interpolation correction processing to obtain a sinusoidal digital signal sequence without coarse data.
Further, in the step 1, a db2 wavelet basis function in a Daubechies wavelet system is selected to decompose the acquired sinusoidal digital signal sequence of the digital metering system of the intelligent substation to be tested to obtain a high-frequency detail coefficient and a low-frequency approximation coefficient.
Further, step 3 specifically comprises: and calculating the standard deviation and the average value of the absolute value of each item of the reconstructed digital signal sequence, and determining the items of which the absolute value minus the average value is more than twice the standard deviation as coarse data items.
Further, the extension in step 4 is to identify the previous item and the subsequent item of each coarse data item obtained in step 3 as coarse data items.
Further, the interpolation correction processing in step 5 includes: and fitting the sinusoidal digital signal sequence without the coarse data by adopting a least square method, a Lagrange interpolation method or a cubic spline interpolation method to obtain the sinusoidal digital signal sequence without the coarse data.
Further, this is achieved by MATLAB programming.
Further, in step 1, a three-layer decomposition is performed by a wavelet decomposition function [ C, L ] ═ wavedec (X, N, 'wname'),
in the formula, the matrix C is used for storing the coefficients of each layer after decomposition, and the matrix L is used for storing the lengths of the coefficients of each layer after decomposition; x is a signal needing wavelet decomposition; n is the number of decomposed layers; 'Wname' is an alternative wavelet filter.
Further, in step 2, reconstructing the signal by a wavelet reconstruction function X ' ═ wrcoef (' type ', C, L, ' wname ', N);
wherein, X' is a reconstruction signal, C and L are a coefficient matrix and a coefficient length matrix respectively;
n is the level of the selected coefficient in the reconstruction process; 'Wname' is a wavelet filter needing to be selected; type' is the choice to reconstruct the high frequency detail or low frequency approximation.
Further, in step 2, reconstructing a low frequency approximation by using a third layer of approximation coefficients; and reconstructing high-frequency details by using the detail coefficient of the first layer.
A digital signal coarse data detection and correction system based on wavelet transformation comprises:
the wavelet decomposition module is used for performing wavelet decomposition on the acquired sinusoidal digital signal sequence of the digital metering system of the intelligent substation to be detected, and decomposing to obtain a high-frequency detail coefficient and a low-frequency approximation coefficient;
the sequence reconstruction module is used for reconstructing the sinusoidal digital signal sequence according to the obtained high-frequency detail coefficient to obtain a reconstructed digital signal sequence;
the coarse data item identification module is used for solving the standard deviation and the average value of the absolute value of each item of the obtained reconstructed digital signal sequence, and identifying the item of which the average value is subtracted from the absolute value and is larger than a preset threshold value as a coarse data item;
the coarse data item expansion module is used for expanding the obtained coarse data items to obtain expanded coarse data items;
and the removing and correcting module is used for removing the original sinusoidal digital signal sequence according to the obtained expanded coarse data item and then carrying out interpolation correction processing to obtain the sinusoidal digital signal sequence without the coarse data.
Compared with the prior art, the invention has the following beneficial effects:
according to the method for detecting and correcting the coarse data of the digital signal, the local detail analysis characteristic of wavelet analysis is utilized, the coarse data position is accurately positioned and eliminated for fitting, and compared with the traditional method, the method has the characteristics of being few in fitting times and small in calculated amount; in addition, the coarse data items are expanded after being determined for the first time, the influence of continuous coarse data on wavelet analysis high-frequency detail coefficients is avoided, and compared with the traditional method for determining the maximum and minimum values in a fixed-length signal sequence as coarse data, the method can be used for discriminating two or even a plurality of continuous coarse data.
Drawings
FIG. 1 is a schematic diagram of u [ k ], z [ k ], u' [ k ] sequence comparison in an example of a digital signal coarse data detection method based on wavelet transform according to the present invention; FIG. 1(a) is a schematic diagram of a digital signal sequence u [ k ] without coarse data obtained from sinusoidal analog signal sampling; FIG. 1(b) is a schematic diagram of a constructed coarse data sequence z [ k ]; FIG. 1(c) is a schematic diagram of a sinusoidal digital signal sequence u' [ k ] containing coarse data;
FIG. 2 is a low frequency approximate reconstructed signal sequence Xa3And high frequency detail reconstruction signal sequence Xd1Comparing the schematic diagrams; FIG. 2(a) shows a low frequency approximate reconstructed signal sequence Xa3A schematic diagram; FIG. 2(b) shows a high-frequency detail reconstruction signal sequence Xd1A schematic diagram;
FIG. 3 is a schematic diagram showing a comparison between a signal sequence u' k containing coarse data and a signal sequence y k after removing the coarse data and fitting; FIG. 3(a) is a schematic view of a signal sequence u' [ k ] containing coarse data; FIG. 3(b) is a diagram showing the signal sequence y [ k ] after removing the coarse data and fitting.
Detailed Description
The invention is described in further detail below with reference to the figures and specific examples.
The invention discloses a digital signal coarse data detection method based on wavelet transformation, which comprises the following steps:
step 1: according to the transmission characteristics of the sinusoidal digital signal sequence of the intelligent substation digital metering system, selecting a db2 wavelet basis function in a Daubechies wavelet system to carry out three-layer decomposition on the sinusoidal digital signal sequence containing coarse data;
step 2: reconstructing the sinusoidal digital signal sequence by applying the first layer of high-frequency detail coefficients obtained by the decomposition in the step 1;
and step 3: calculating the standard deviation and the average value of the absolute value of each item of the reconstructed digital signal sequence, and considering the items of which the average value is more than two times of the standard deviation is subtracted from the absolute value as coarse data items;
and 4, step 4: in order to avoid two or even a plurality of continuous coarse data items from being missed, the coarse data items obtained in the step 3 are expanded, namely, the previous item and the next item of each coarse data item obtained in the step 3 are considered to be coarse data items;
and 5: and (4) removing the original sinusoidal digital signal sequence according to the coarse data items obtained in the step (4), and fitting by using a least square method after removing to obtain a sinusoidal digital signal sequence without coarse data.
Examples
The invention discloses a digital signal coarse data detection algorithm using wavelet transform, which is realized by MATLAB (or other machine languages) programming, and comprises the following steps:
1. a sinusoidal voltage waveform with an amplitude of 55Hz is constructed, u sin (2 pi ft). At a sampling frequency fsSampling it at 4kHz generates a sequence u k]And taking the previous 399 sampling points for research, namely, k is more than or equal to 1 and less than or equal to 399.
2. Constructing a sequence z [ k ], k is more than or equal to 1 and less than or equal to 399, and simulating coarse data as shown in Table 1; where the values of the entries not present in the table are 0.
TABLE 1 non-0 entries in the z [ k ] sequence
| |
50 | 51 | 52 | 100 | 101 | 102 | 130 | 131 | 137 | 138 | 164 |
| z[k] | 0.3 | -0.2 | 0.4 | -0.4 | -0.3 | 0.2 | -0.2 | 0.3 | 0.4 | 0.5 | 0.3 |
| k | 165 | 166 | 208 | 209 | 210 | 244 | 245 | 246 | 277 | 278 | 327 |
| z[k] | -0.2 | 0.4 | 0.2 | 0.3 | 0.2 | -0.3 | -0.1 | -0.4 | 0.4 | 0.3 | 0.2 |
3. Let u '[ k ] + z [ k ], 1 ≦ k ≦ 399, i.e., u' [ k ] is a sinusoidal sequence containing coarse data. The u [ k ], z [ k ], u' [ k ] sequences were plotted and compared with MATLAB, respectively, and the comparison results are shown in FIG. 1.
4. Performing 3-layer decomposition on u '[ k ] by using a wavelet decomposition function [ C, L ] ═ wavedec (X, N,' wname '), provided by MATLAB, namely [ C, L ] ═ wavedec (u' [ k ],3, 'db 2'); the matrix C is used for storing the coefficients of each layer after decomposition, and the matrix L is used for storing the lengths of the coefficients of each layer after decomposition. X is a signal needing wavelet decomposition; n is the number of layers of decomposition, and 3-level decomposition is adopted in the method; 'Wname' is an alternative wavelet filter, and 'db2' is selected in the present invention.
5. The signal is reconstructed using a wavelet reconstruction function X ' wrcoef (' type ', C, L, ' wname ', N) provided by MATLAB.
Wherein, X' is a reconstructed signal, and C and L are a coefficient matrix and a coefficient length matrix obtained in the last step.
N, selecting the level of the coefficient in the reconstruction process; 'Wname' is a wavelet filter needing to be selected, and 'db2' is selected in the invention; type' is the choice to reconstruct the low frequency approximation or the high frequency detail.
The invention reconstructs the low frequency approximation, X, using the third layer of approximation coefficientsa3Wrcoef ('a', C, L, 'db2', 3); reconstruction of high frequency details, i.e. X, using first layer detail coefficientsd1Wrcoef ('d', C, L, 'db2', 1); the low frequency approximate reconstructed signal sequence and the high frequency detail reconstructed sequence were mapped with MATLAB as shown in fig. 2.
6. From Xd1And detecting and extracting coarse data items in the sequence.
Finding Xd1Standard deviation of the sequence s, wherein Xd1The data items considered as coarse in the sequence with absolute value greater than 2s are stored in the one-dimensional array b using the find function provided by MATLAB to extract the index of the coarse data item, as shown in table 2.
TABLE 2 reconstruction of sequence X from high frequency detailsd1Extracted big data item index
| 50 | 51 | 52 | 99 | 101 | 102 | 130 | 131 | 132 | 137 | 139 | 164 |
| 165 | 166 | 207 | 209 | 243 | 244 | 245 | 246 | 277 | 279 | 327 |
Comparing tables 1 and 2, it can be seen that individual continuous gross data is missed or misjudged. To solve this problem, the one-dimensional array b is expanded as shown in table 3, and the previous and subsequent items of the coarse data in table 2 are considered to be coarse data and are denoted as b'.
TABLE 3 extended big data entry index
| 49 | 50 | 51 | 52 | 53 | 98 | 99 | 100 | 101 | 102 | 103 | 129 |
| 130 | 131 | 132 | 133 | 136 | 137 | 138 | 139 | 163 | 164 | 165 | 166 |
| 167 | 206 | 207 | 208 | 209 | 210 | 242 | 243 | 244 | 245 | 246 | 247 |
| 276 | 277 | 278 | 279 | 280 | 326 | 327 | 328 |
Comparing table 1 and table 3, the extended index of coarse data entries contains all the coarse data entries introduced in table 1.
7. Coarse data items in an array b '(Table 3) are removed from u' [ k ] (1 ≦ k ≦ 399), and new data are supplemented by applying least square fitting to form a new sequence yk ] (1 ≦ k ≦ 399). As shown in FIG. 3, a sequence u' k containing coarse data and a new sequence y k generated by removing the coarse data and re-fitting by applying wavelet transform are shown.
In summary, the invention provides a digital signal coarse data detection algorithm applying wavelet transform local analysis characteristics. The detection and correction of coarse data are realized by applying the good local analysis characteristic of wavelet transformation, which has great significance for accurate measurement of electric energy of a digital measurement system.
The invention can be applied to the front end of the merging unit of the digital metering system of the intelligent transformer substation and the front end of the digital electric energy meter to detect and correct the voltage and current digital signal sequence, solves the problem of high-frequency signals coupled by an electronic transformer in the digital metering system of the intelligent transformer substation in the sampling process, namely adverse effects of coarse data on electric energy metering, and has positive significance for realizing accurate metering of the digital metering system, promoting the construction and popularization of the digital intelligent transformer substation, ensuring good economic benefit of the electric power system and establishing trust between electric power enterprises and users.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art can make modifications and equivalents to the embodiments of the present invention without departing from the spirit and scope of the present invention, which is set forth in the claims of the present application.
Claims (8)
1. A method for detecting and correcting coarse data of a digital signal based on wavelet transformation is characterized by comprising the following steps:
step 1, performing wavelet decomposition on an acquired sinusoidal digital signal sequence of a digital metering system of an intelligent substation to be detected, and decomposing to obtain a high-frequency detail coefficient and a low-frequency approximation coefficient;
step 2, reconstructing the sinusoidal digital signal sequence according to the high-frequency detail coefficient obtained in the step 1 to obtain a reconstructed digital signal sequence;
step 3, solving the standard deviation and the average value of each absolute value of the reconstructed digital signal sequence obtained in the step 2, and determining the items of which the average value is less than a preset threshold value from the absolute values as coarse data items;
step 4, expanding the coarse data items obtained in the step 3 to obtain expanded coarse data items;
step 5, removing the original sine digital signal sequence in the step 1 according to the expanded coarse data items obtained in the step 4, and then carrying out interpolation correction processing to obtain a sine digital signal sequence without coarse data;
the extension in the step 4 is specifically to determine the previous item and the next item of each coarse data item obtained in the step 3 as coarse data items;
the interpolation correction processing in step 5 includes: and fitting the sinusoidal digital signal sequence without the coarse data by adopting a least square method, a Lagrange interpolation method or a cubic spline interpolation method to obtain the sinusoidal digital signal sequence without the coarse data.
2. The method for detecting and correcting the coarse data of the digital signals based on the wavelet transform as recited in claim 1, wherein in the step 1, a db2 wavelet basis function in a Daubechies wavelet system is selected to decompose an acquired sinusoidal digital signal sequence of the digital metering system of the intelligent substation to be measured to obtain a high-frequency detail coefficient and a low-frequency approximation coefficient.
3. The wavelet transform-based digital signal coarse data detection and correction method according to claim 1, wherein step 3 specifically comprises: and calculating the standard deviation and the average value of the absolute value of each item of the reconstructed digital signal sequence, and determining the items of which the absolute value minus the average value is more than twice the standard deviation as coarse data items.
4. The wavelet transform-based digital signal coarse data detection and correction method as recited in claim 1, characterized in that the method is implemented by MATLAB programming.
5. The wavelet transform-based digital signal coarse data detection and correction method according to claim 4, wherein in step 1, a three-layer decomposition is performed by a wavelet decomposition function [ C, L ] ═ wavedec (X, N, 'wname'),
in the formula, the matrix C is used for storing the coefficients of each layer after decomposition, and the matrix L is used for storing the lengths of the coefficients of each layer after decomposition; x is a signal needing wavelet decomposition; n is the number of decomposed layers; 'Wname' is an alternative wavelet filter.
6. The wavelet transform-based coarse data detection and correction method for digital signals according to claim 5, wherein in step 2, the signals are reconstructed by a wavelet reconstruction function X ' ═ wrcoef (' type ', C, L, ' wname ', N);
wherein, X' is a reconstruction signal, C and L are a coefficient matrix and a coefficient length matrix respectively;
n is the level of the selected coefficient in the reconstruction process; 'Wname' is a wavelet filter needing to be selected; type' is the choice to reconstruct the high frequency detail or low frequency approximation.
7. The method for detecting and correcting the coarse data of the digital signals based on the wavelet transform as recited in claim 5, wherein in the step 2, a third layer of approximation coefficients is used to reconstruct a low frequency approximation; and reconstructing high-frequency details by using the detail coefficient of the first layer.
8. A digital signal coarse data detection and correction system based on wavelet transformation, which is characterized in that, based on the method of claim 1; the system comprises:
the wavelet decomposition module is used for performing wavelet decomposition on the acquired sinusoidal digital signal sequence of the digital metering system of the intelligent substation to be detected, and decomposing to obtain a high-frequency detail coefficient and a low-frequency approximation coefficient;
the sequence reconstruction module is used for reconstructing the sinusoidal digital signal sequence according to the obtained high-frequency detail coefficient to obtain a reconstructed digital signal sequence;
the coarse data item identification module is used for solving the standard deviation and the average value of the absolute value of each item of the obtained reconstructed digital signal sequence, and identifying the item of which the average value is subtracted from the absolute value and is larger than a preset threshold value as a coarse data item;
the coarse data item expansion module is used for expanding the obtained coarse data items to obtain expanded coarse data items;
and the removing and correcting module is used for removing the original sinusoidal digital signal sequence according to the obtained expanded coarse data item and then carrying out interpolation correction processing to obtain the sinusoidal digital signal sequence without the coarse data.
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Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US6397234B1 (en) * | 1999-08-20 | 2002-05-28 | The United States Of America As Represented By The Secretary Of The Navy | System and apparatus for the detection of randomness in time series distributions made up of sparse data sets |
| CN101843480A (en) * | 2009-03-27 | 2010-09-29 | 华为技术有限公司 | Method and device for processing bioelectrical signals |
| CN105740762A (en) * | 2016-01-13 | 2016-07-06 | 陈勇 | Signal-noise separation optimization method |
| CN108009305A (en) * | 2016-10-31 | 2018-05-08 | 中南大学 | A kind of rail inspection instrument gathered data processing method based on wavelet transformation |
-
2018
- 2018-12-29 CN CN201811647429.4A patent/CN109635781B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US6397234B1 (en) * | 1999-08-20 | 2002-05-28 | The United States Of America As Represented By The Secretary Of The Navy | System and apparatus for the detection of randomness in time series distributions made up of sparse data sets |
| CN101843480A (en) * | 2009-03-27 | 2010-09-29 | 华为技术有限公司 | Method and device for processing bioelectrical signals |
| CN105740762A (en) * | 2016-01-13 | 2016-07-06 | 陈勇 | Signal-noise separation optimization method |
| CN108009305A (en) * | 2016-10-31 | 2018-05-08 | 中南大学 | A kind of rail inspection instrument gathered data processing method based on wavelet transformation |
Non-Patent Citations (1)
| Title |
|---|
| 基于小波变换和样条插值的容积脉搏波去噪方法研究;查玉华 等;《中国医学装备》;20151031;第12卷(第10期);第28-33页 * |
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