CN117972312A - Power transformation data processing method and device - Google Patents
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Abstract
The invention discloses a power transformation data processing method and device. Wherein the method comprises the following steps: acquiring a power transformation monitoring data sequence of target power transformation equipment, and performing signal decomposition on the power transformation monitoring data sequence based on empirical mode decomposition to acquire a plurality of original eigen mode functions corresponding to the power transformation monitoring data sequence, wherein the power transformation monitoring data sequence comprises power transformation data to be processed corresponding to different monitoring time points; determining correlation coefficients of a plurality of original eigenmode functions and a power transformation monitoring data sequence, wherein the original eigenmode functions with the phase relation number smaller than or equal to a preset coefficient threshold value are used as first eigenmode functions, and the rest original eigenmode functions are used as second eigenmode functions; and determining a third eigenmode function based on the function kurtosis of the first eigenmode function, and determining target power transformation monitoring data corresponding to the target power transformation equipment based on the second eigenmode function and the third eigenmode function so as to solve the technical problem of high power transformation data processing calculation complexity.
Description
Technical Field
The invention relates to the technical field of power systems, in particular to a power transformation data processing method and device.
Background
With the continuous development of digital signal processing technology, the monitoring data processing research on transformers and high-voltage circuit breakers is also in continuous depth.
In the prior art, the transformation data processing is realized mainly by adopting methods such as Fourier transform threshold filtering, wavelet analysis, self-adaptive morphological filtering and the like.
However, the electrical data processing method in the prior art has the technical problems of high computational complexity, low efficiency and the like.
Disclosure of Invention
The invention provides a power transformation data processing method and device, which are used for solving the technical problems of high complexity and low efficiency of power transformation data processing in the prior art.
According to an aspect of the present invention, there is provided a transformation data processing method, including: acquiring a power transformation monitoring data sequence of target power transformation equipment, and performing signal decomposition on the power transformation monitoring data sequence based on empirical mode decomposition to acquire a plurality of original eigen mode functions corresponding to the power transformation monitoring data sequence, wherein the power transformation monitoring data sequence comprises power transformation data to be processed corresponding to different monitoring time points; determining correlation coefficients of a plurality of original eigenmode functions and the power transformation monitoring data sequence, taking the original eigenmode functions with the correlation coefficients smaller than or equal to a preset coefficient threshold as first eigenmode functions, and taking the rest original eigenmode functions as second eigenmode functions; and determining a third eigenmode function based on the function kurtosis of the first eigenmode function, and determining target power transformation monitoring data corresponding to the target power transformation equipment based on the second eigenmode function and the third eigenmode function.
According to another aspect of the present invention, there is provided a transformation data processing device, comprising: the system comprises an original eigenmode function acquisition module, a power transformation monitoring data acquisition module and a power transformation processing module, wherein the original eigenmode function acquisition module is used for acquiring a power transformation monitoring data sequence of target power transformation equipment, performing signal decomposition on the power transformation monitoring data sequence based on empirical mode decomposition, and acquiring a plurality of original eigenmode functions corresponding to the power transformation monitoring data sequence, wherein the power transformation monitoring data sequence comprises power transformation data to be processed corresponding to different monitoring time points; the intrinsic mode function classification module is used for determining correlation coefficients of a plurality of original intrinsic mode functions and the power transformation monitoring data sequence, taking the original intrinsic mode functions with the correlation coefficients smaller than or equal to a preset coefficient threshold as first intrinsic mode functions and taking the rest original intrinsic mode functions as second intrinsic mode functions; and the target power transformation monitoring data determining module is used for determining a third eigenmode function based on the function kurtosis of the first eigenmode function and determining target power transformation monitoring data corresponding to the target power transformation equipment based on the second eigenmode function and the third eigenmode function.
According to another aspect of the present invention, there is provided an electronic apparatus including:
At least one processor; and
A memory communicatively coupled to the at least one processor; wherein,
The memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the transformation data processing method according to any one of the embodiments of the present invention.
According to another aspect of the present invention, there is provided a computer readable storage medium storing computer instructions for causing a processor to execute the transformation data processing method according to any one of the embodiments of the present invention.
According to the technical scheme, firstly, a power transformation monitoring data sequence of target power transformation equipment is obtained, signal decomposition is carried out on the power transformation monitoring data sequence based on empirical mode decomposition, and a plurality of original eigenmode functions corresponding to the power transformation monitoring data sequence are obtained, wherein the power transformation monitoring data sequence comprises power transformation data to be processed corresponding to different monitoring time points. The transformation data of the target transformation equipment is decomposed into a plurality of intrinsic mode functions, so that the constituent components of the transformation data can be displayed more intuitively, and each component can be analyzed and processed independently, thereby being beneficial to understanding the inherent structure and characteristics of the transformation data more deeply. And then determining correlation coefficients of a plurality of original eigenmode functions and the power transformation monitoring data sequence, taking the original eigenmode functions with the correlation coefficients smaller than or equal to a preset coefficient threshold as a first eigenmode function and taking the rest of original eigenmode functions as a second eigenmode function, so that preliminary classification of eigenmode functions based on correlation of the original eigenmode functions and the power transformation monitoring data sequence is realized. And finally, determining a third eigenmode function based on the function kurtosis of the first eigenmode function, and determining target power transformation monitoring data corresponding to the target power transformation equipment based on the second eigenmode function and the third eigenmode function. The method and the device realize screening of the eigen mode functions based on the function kurtosis dimension and the correlation coefficient dimension, and further jointly determine the target power transformation monitoring data corresponding to the target power transformation equipment based on the screened eigen mode functions, so that the reliability of the power transformation monitoring data can be effectively improved. The method solves the technical problem of high calculation complexity of the power transformation data processing in the prior art, and has the beneficial effects of reducing the calculation complexity of the power transformation data processing, improving the reliability of the power transformation data and improving the efficiency of the power transformation data processing.
It should be understood that the description in this section is not intended to identify key or critical features of the embodiments of the invention or to delineate the scope of the invention. Other features of the present invention will become apparent from the description that follows.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly described below, and it is apparent that the drawings in the following description are only some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a flowchart of a power transformation data processing method according to a first embodiment of the present invention;
fig. 2 is a flowchart of a power transformation data processing method according to a second embodiment of the present invention;
Fig. 3 is a schematic structural diagram of a power transformation data processing device according to a third embodiment of the present invention;
fig. 4 is a schematic diagram of the structure of an electronic device 10 that may be used to implement an embodiment of the present invention.
Detailed Description
In order that those skilled in the art will better understand the present invention, a technical solution in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in which it is apparent that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the present invention without making any inventive effort, shall fall within the scope of the present invention.
It should be noted that the terms "first," "second," and "object" in the description of the present invention and the claims and the above drawings are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that the embodiments of the invention described herein may be implemented in sequences other than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
Example 1
Fig. 1 is a flowchart of a power transformation data processing method according to an embodiment of the present invention, where the method may be performed by a power transformation data processing device, and the power transformation data processing device may be implemented in hardware and/or software, and the power transformation data processing device may be configured in an electronic device. As shown in fig. 1, the method includes:
S110, acquiring a power transformation monitoring data sequence of the target power transformation equipment, and performing signal decomposition on the power transformation monitoring data sequence based on empirical mode decomposition to acquire a plurality of original eigenmode functions corresponding to the power transformation monitoring data sequence, wherein the power transformation monitoring data sequence comprises power transformation data to be processed corresponding to different monitoring time points.
In this embodiment, the target power transformation device may be a power device such as a transformer and a circuit breaker, which performs power transformation data processing thereon. Specifically, the transformation monitoring data sequence may be a transformation data sequence formed by transformation data to be processed, which is acquired by the target transformation device in the operation process. The power transformation data to be processed can be power transformation data to be subjected to data processing. Specifically, the power transformation data to be processed may be original power transformation data of the target power transformation device collected at different monitoring time points, or data obtained by performing and processing on the original power transformation data of the target power transformation device collected at different monitoring time points. The preprocessing includes, but is not limited to, one or more of interpolation processing, normalization processing, screening processing and the like. The empirical mode decomposition may be a method for decomposing the electrical monitoring data sequence, which may be decomposed into a plurality of eigenmode functions. The original eigenmode functions may be obtained based on further processing of the plurality of eigenmode functions. The eigenmode function can reflect different frequency components in the substation monitoring data and can be further used for analyzing the frequency components of the substation monitoring data signals, extracting features, denoising and other processing. The transformation data of the target transformation equipment is decomposed into a plurality of original eigenmode functions, so that visual display can be conveniently and rapidly carried out on the constituent components of the transformation data, and each component can be independently analyzed and processed, thereby being beneficial to deeper understanding of the internal structure and characteristics of the data and subsequent data analysis and processing.
Optionally, acquiring the power transformation monitoring data sequence of the target power transformation device includes: acquiring primary monitoring data corresponding to primary equipment and secondary detection data corresponding to secondary equipment; the secondary equipment comprises equipment for assisting the primary equipment in working; and determining a power transformation monitoring data sequence based on the primary detection data and the secondary monitoring data.
In this embodiment, the primary device may be a device used on a main system for power generation, transmission and distribution, that is, a device directly used for power generation and use. Such as generators, transformers, circuit breakers, disconnectors, bus bars, power cables, transmission lines, etc. The primary monitoring data may be power transformation monitoring data collected directly from the primary device. The secondary device may be a device that assists in the operation of the primary device. The work assistance can be the work assistance of controlling, protecting, supervising and measuring the primary equipment. For example, the secondary device may be a meter for measuring electrical parameters in a circuit, such as a voltmeter, ammeter, power meter, and electric energy meter. The secondary devices may also be relays, operating switches, buttons, automatic control devices, computers, signaling devices, control cables, and power supply means for providing energy to these devices. The secondary monitoring data may be power transformation monitoring data acquired based on the secondary device. The power transformation monitoring data sequence is determined based on the primary detection data and the secondary monitoring data, the monitoring data of the power transformation equipment can be collected in an omnibearing and multi-angle mode based on the whole electric loop, the sample collection richness of the power transformation monitoring data is improved, and the data reliability of power transformation monitoring data processing is improved.
Optionally, performing signal decomposition on the substation monitoring data sequence based on empirical mode decomposition to obtain a plurality of original eigenmode functions corresponding to the substation monitoring data sequence, including: obtaining extreme points in the substation monitoring data sequence, and obtaining envelope quantities corresponding to the substation monitoring data sequence based on a cubic spline interpolation function and the extreme points; processing the power transformation monitoring data sequence based on the envelope quantity, and updating the power transformation monitoring data sequence based on the processing result; and returning to execute the operation of acquiring the extreme points in the power transformation monitoring data sequence so as to obtain a plurality of original eigen-mode functions corresponding to the power transformation monitoring data sequence.
In this embodiment, the signal decomposition is performed on the power transformation monitoring data sequence based on the empirical mode decomposition, which may be that the signal decomposition is performed on the power transformation monitoring data sequence with nonlinear and non-stationarity characteristics based on the empirical mode decomposition, so as to obtain a plurality of eigenmode functions (INTRINSIC MODE FUNCTIONS, IMFs). IMFs may represent the oscillating components of signals generated by the electrical monitoring data sequences over different time scales. The extreme points in the substation monitoring data sequence may be maximum points or minimum points corresponding to the IMFs signal functions. After the extreme points in the power transformation monitoring data sequence are obtained, adjacent extreme electricity can be connected through a cubic spline interpolation function, an envelope curve corresponding to the original IMFs is constructed, and then the envelope quantity corresponding to the power transformation monitoring data sequence is determined based on the envelope curve.
The envelope amounts corresponding to the different monitoring time points may be the same or different.
The variable power monitoring data sequence is processed based on the envelope amount, the envelope amount may be subtracted on the basis of the variable power monitoring data sequence, and then the variable power monitoring data sequence may be updated based on a processing result of removing the envelope amount. Specifically, for the to-be-processed power transformation data of each monitoring time point, subtracting the envelope quantity corresponding to the monitoring time point to obtain processed power transformation data, and updating the processed power transformation data into the to-be-processed power transformation data. In the embodiment of the invention, the processing of acquiring the envelope quantity corresponding to the substation monitoring data sequence and removing the envelope quantity of the substation monitoring data sequence can be repeatedly executed until the local maximum value and the local minimum value are no longer existed in the substation monitoring data sequence. In this case, the transformation monitoring data sequence may be decomposed into a plurality of original eigenmode functions with the same oscillation frequency. Through the steps, complex nonlinear and nonstationary power transformation monitoring data signals can be decomposed into a plurality of original eigenmode functions with the same oscillation frequency, and each original eigenmode function represents a local oscillation component of the signals on different time scales, so that the time-frequency characteristics of the power transformation monitoring data can be better described.
Optionally, the extreme points include maximum points and minimum points; obtaining the envelope quantity corresponding to the substation monitoring data sequence based on the cubic spline interpolation function and the extreme point comprises the following steps: acquiring an upper envelope curve based on the cubic spline interpolation function and the maximum value point, and acquiring a lower envelope curve based on the cubic spline interpolation function and the minimum value point; and determining the envelope quantity corresponding to the substation monitoring data sequence based on the upper envelope curve and the lower envelope curve.
In this embodiment, the maximum value points and the minimum value points may be a plurality of local maximum value points and a plurality of local minimum value points corresponding to the signal function generated by the electrical monitoring data sequence. Specifically, the upper envelope curve can be obtained by curve fitting with the maximum point as the interpolation node based on a cubic spline interpolation function. And the lower envelope curve can be obtained by curve fitting by taking the minimum value point as an interpolation node based on a cubic spline interpolation function. The envelope amount corresponding to the substation monitoring data sequence can then be determined based on combining the upper and lower envelopes. For example, at a specific time point, a larger value of the upper envelope and the lower envelope may be selected as the envelope amount corresponding to the electrical monitoring data sequence, or the envelope amount corresponding to the electrical monitoring data sequence may be determined based on superposition of the upper envelope and the lower envelope.
Optionally, determining the envelope amount corresponding to the substation monitoring data sequence based on the upper envelope line and the lower envelope line includes: and taking the average value of the upper envelope curve and the lower envelope curve as the envelope quantity corresponding to the power transformation monitoring data sequence.
In this embodiment, the values of the upper envelope and the lower envelope may be added at any time point and then averaged to obtain the envelope amount of the power transformation monitoring data sequence at that time point. The calculation method of the present embodiment has the advantage that the trend of the variation of the upper envelope and the lower envelope can be considered at the same time. Thus, the envelope characteristics of the original signal can be accurately reflected.
S120, determining correlation coefficients of a plurality of original eigenmode functions and the power transformation monitoring data sequence, wherein the original eigenmode functions with the phase relation number smaller than or equal to a preset coefficient threshold value are used as first eigenmode functions, and the rest original eigenmode functions are used as second eigenmode functions.
In this embodiment, the determining the correlation coefficients of the plurality of original eigen-mode functions and the power transformation monitoring data sequence may be calculating the correlation coefficients of the original eigen-mode functions and the power transformation monitoring data sequence based on the spearman rank correlation coefficient, the kendel rank correlation coefficient, the decision coefficient, the point two-column correlation coefficient, and the like. The preset coefficient threshold may be a threshold value of a correlation coefficient preset according to an actual situation. The original eigenmode function with the phase relation smaller than or equal to the preset coefficient threshold value can be used as the first eigenmode function, and the correlation degree between the original eigenmode function in the first eigenmode function and the power transformation monitoring data sequence can be lower. The original eigenmode function with the phase relation number larger than the preset coefficient threshold value can be used as a second eigenmode function, and the correlation degree between the original eigenmode function in the second eigenmode function and the power transformation monitoring data sequence can be higher. It can be appreciated that abnormal data such as noise data in the transformer monitoring data sequence is more likely to exist in the first eigenmode function. The original eigenmode function with the correlation coefficient smaller than or equal to the preset coefficient threshold value is used as a first eigenmode function, the rest original eigenmode functions are used as a second eigenmode function, and the original eigenmode functions can be initially classified based on correlation with the power transformation monitoring data sequence so as to perform data denoising and other data processing on abnormal data such as noise data in the original eigenmode functions.
S130, determining a third eigenmode function based on the function kurtosis of the first eigenmode function, and determining target power transformation monitoring data corresponding to the target power transformation equipment based on the second eigenmode function and the third eigenmode function.
In this embodiment, the functional kurtosis may be a scale reflecting the spike characteristics of the eigenmode function. The function kurtosis can correspond to the eigenmode function with noise under the condition of higher function kurtosis. The determination of the third eigenmode function based on the functional kurtosis of the first eigenmode function may be based on calculating the functional kurtosis of the first eigenmode function, and the first eigenmode function corresponding to the range of the specific functional kurtosis may be screened and determined as the third eigenmode function. The target power transformation monitoring data may be processed power transformation monitoring data corresponding to the target power transformation device. The determining the target transformation monitoring data corresponding to the target transformation equipment based on the second eigenmode function and the third eigenmode function may be determining the target transformation monitoring data by overlapping the second eigenmode function and the third eigenmode function, calculating an average value, and the like. The third eigenmode function which accords with the kurtosis range of the specific function and the second eigenmode function which has higher correlation degree with the transformation monitoring data sequence are overlapped, the average value is calculated and the like to be processed, the target transformation monitoring data is determined, the processes of denoising, correlation screening and the like of the transformation monitoring data can be conveniently and rapidly realized, and more reliable transformation data can be obtained.
According to the technical scheme, the primary monitoring data corresponding to the primary equipment and the secondary detection data corresponding to the secondary equipment are obtained, and the power transformation monitoring data sequence is determined based on the primary detection data and the secondary monitoring data. Then, extreme points in the substation monitoring data sequence are obtained, and envelope quantities corresponding to the substation monitoring data sequence are obtained based on a cubic spline interpolation function and the extreme points; processing the power transformation monitoring data sequence based on the envelope quantity, and updating the power transformation monitoring data sequence based on the processing result; and returning to execute the operation of acquiring the extreme points in the power transformation monitoring data sequence so as to obtain a plurality of original eigen-mode functions corresponding to the power transformation monitoring data sequence. The transformation data of the target transformation equipment is decomposed into a plurality of intrinsic mode functions, so that visual display can be conveniently and rapidly carried out on the constituent components of the transformation data, and each component can be independently analyzed and processed, thereby being beneficial to deeper understanding of the internal structure and characteristics of the data and subsequent data analysis and processing. And then determining correlation coefficients of a plurality of original eigenmode functions and the power transformation monitoring data sequence, taking the original eigenmode functions with the phase relation number smaller than or equal to a preset coefficient threshold value as a first eigenmode function, and taking the rest original eigenmode functions as a second eigenmode function, so that the preliminary classification of the eigenmode functions based on the correlation of the original eigenmode functions and the power transformation monitoring data sequence is realized. And finally, determining a third eigenmode function based on the function kurtosis of the first eigenmode function, and determining target power transformation monitoring data corresponding to the target power transformation equipment based on the second eigenmode function and the third eigenmode function. The method and the device realize screening of the eigen mode functions based on the function kurtosis dimension and the correlation coefficient dimension, further jointly determine the target power transformation monitoring data corresponding to the target power transformation equipment based on the screened eigen mode functions, and effectively improve the reliability of the power transformation monitoring data. The method solves the technical problem of high calculation complexity of the power transformation data processing in the prior art, and has the beneficial effects of reducing the calculation complexity of the power transformation data processing, improving the reliability of the power transformation data and improving the efficiency of the power transformation data processing.
Example two
Fig. 2 is a flowchart of a power transformation data processing method according to a second embodiment of the present invention, where the method for determining correlation coefficients between an original eigenmode function and a power transformation monitoring data sequence and the method for determining target power transformation monitoring data are specifically described based on the foregoing embodiments. Reference is made to the description of this example for a specific implementation. The technical features that are the same as or similar to those of the foregoing embodiments are not described herein. As shown in fig. 2, the method includes:
S210, acquiring a power transformation monitoring data sequence of the target power transformation equipment, and performing signal decomposition on the power transformation monitoring data sequence based on empirical mode decomposition to acquire a plurality of original eigenmode functions corresponding to the power transformation monitoring data sequence, wherein the power transformation monitoring data sequence comprises power transformation data to be processed corresponding to different monitoring time points.
S220, acquiring covariance between the transformation monitoring data sequence and the plurality of original eigenmode functions.
Where covariance= (monitored data minus monitored data expected) times (IMF minus IMF expected) expected. Where it is desirable to say that the simple points are averages, sums, and then divided by numbers.
In this embodiment, the covariance between the transformation monitoring data sequence X and the plurality of original eigenmode functions Y is obtained, which may be based on the following equation,
Wherein n may be the number of transformation data in the transformation monitoring data sequence and may be equal to the number of original eigenmode functions. X i may be the substation monitoring data, Y i may be the original eigenmode function,Can be the expected value of power transformation monitoring data,/>May be the expected value of the original eigenmode function and cov (X, Y) may be the covariance between the transformation monitoring data sequence X and the plurality of original eigenmode functions Y.
S230, acquiring a first variance of the substation monitoring data sequence and a second variance of the original eigenmode functions.
Where variance is the average of the square values of the differences between each sample value and the average of the population of sample values, representing the dynamic component of the signal energy (the square of the mean is the static component), reflecting the degree of dispersion between the data. In this embodiment, the first variance may be a variance of at least part of the to-be-processed transformation data in the transformation monitoring data sequence, and the second variance may be a variance of a plurality of original eigenmode functions.
S240, determining correlation coefficients of the plurality of original eigenmode functions and the power transformation monitoring data sequence based on the covariance, the first variance and the second variance.
In this embodiment, a preset correlation coefficient calculation method may be adopted, and correlation coefficients of the plurality of original eigen-mode functions and the power transformation monitoring data sequence may be determined based on the covariance, the first variance and the second variance. For example, the correlation coefficient calculation method may be pearson correlation coefficient or the like. Specifically, pearson correlation coefficients may be calculated as correlation coefficients of the plurality of raw eigen-mode functions with the power transformation monitoring data sequence based on the covariance, the first variance, and the second variance.
Optionally, determining correlation coefficients of the plurality of raw eigenmode functions with the power transformation monitoring data sequence based on the covariance, the first variance, and the second variance includes: calculating the product of the first variance and the second variance, calculating the quotient of the covariance and the product, and determining the correlation coefficients of a plurality of original eigen-mode functions and the power transformation monitoring data sequence based on the quotient; or calculating the quotient of the covariance and the first variance, then calculating the product of the quotient multiplied by the second variance, and determining the correlation coefficients of a plurality of original eigen-mode functions and the power transformation monitoring data sequence based on the product; or calculating the quotient of the covariance and the second variance, then calculating the product of the quotient multiplied by the first variance, and based on the product, the correlation coefficients of a plurality of original eigen-mode functions and the power transformation monitoring data sequence, etc.
Further, the correlation coefficients of the plurality of original eigen-mode functions and the power transformation monitoring data sequence are determined based on the quotient or the product, wherein the quotient or the product is used as the correlation coefficient of the plurality of original eigen-mode functions and the power transformation monitoring data sequence, the quotient or the product is adjusted based on a preset adjustment coefficient, and the adjusted value is used as the correlation coefficient of the plurality of original eigen-mode functions and the power transformation monitoring data sequence. Illustratively, the preset adjustment factor may be a multiplication factor, and the value may be between 0 and 1. Specifically, a product of the preset adjustment coefficient multiplied by a quotient or product may be taken as the adjusted value.
In an alternative example, correlation coefficient = covariance/first variance second variance.
In this embodiment, the product of the first variance and the second variance may be calculated first, then the quotient of the covariance and the product may be calculated, and finally the pearson correlation coefficients of the plurality of original eigen-mode functions and the power transformation monitoring data sequence may be determined based on the quotient. The value range of the pearson correlation coefficient can be between-1 and 1, wherein when the correlation coefficient is close to 1, the pearson correlation coefficient can represent that a plurality of original eigen-mode functions are positively correlated with the power transformation monitoring data sequence; when the correlation coefficient is close to-1, the correlation coefficient can represent that a plurality of original eigenmode functions are in negative correlation with the transformation monitoring data sequence; when the correlation coefficient approaches 0, the wireless relationship between the plurality of original eigenmode functions and the power transformation monitoring data sequence can be represented.
S250, taking an original eigenmode function with the phase relation number smaller than or equal to a preset coefficient threshold value as a first eigenmode function, and taking the rest original eigenmode functions as a second eigenmode function.
S260, determining the function kurtosis corresponding to the first eigenmode function, and taking the first eigenmode function with the function kurtosis being larger than or equal to a preset kurtosis threshold value as the noise eigenmode function.
In this embodiment, the function kurtosis may be calculated for the first eigenmode function, and the function kurtosis corresponding to the first eigenmode function may be determined. A larger function kurtosis generally indicates that the function curve has a sharper peak and thus may mean that noise or non-important information is contained in the eigenmode function signal. Thus, the first eigenmode function with a function kurtosis greater than or equal to the preset kurtosis threshold may be taken as the noise eigenmode function.
S270, removing the noise eigenmode function from the first eigenmode function to obtain a third eigenmode function.
In this embodiment, the noise eigenmode function may be removed from the first eigenmode function, and the remaining eigenmode function without noise is used as the third eigenmode function.
S280, superposing the second eigenmode functions and the third eigenmode functions, and determining target power transformation monitoring data corresponding to the target power transformation equipment based on a superposition result.
In this embodiment, a plurality of second eigen-mode functions with a higher degree of correlation with the transformation monitoring data sequence and a plurality of third eigen-mode functions with noise eigen-mode functions removed may be superimposed, and then the target transformation monitoring data corresponding to the target transformation device may be determined based on the superimposed result. Noise elimination and reliability improvement of the transformer monitoring data are realized.
According to the technical scheme, firstly, the power transformation monitoring data sequence of the target power transformation equipment is obtained, signal decomposition is carried out on the power transformation monitoring data sequence based on empirical mode decomposition, and a plurality of original eigenmode functions corresponding to the power transformation monitoring data sequence are obtained. Then covariance between the transformation monitoring data sequence and a plurality of original eigenmode functions is obtained; acquiring a first variance of the substation monitoring data sequence and a second variance of a plurality of original eigenmode functions; and determining correlation coefficients of the plurality of original eigenmode functions and the power transformation monitoring data sequence based on the covariance, the first variance and the second variance. Finally, taking the original eigenmode function with the correlation coefficient smaller than or equal to a preset coefficient threshold value as a first eigenmode function, and taking the rest original eigenmode functions as a second eigenmode function; determining the function kurtosis corresponding to the first eigenmode function, and taking the first eigenmode function with the function kurtosis being greater than or equal to a preset kurtosis threshold value as a noise eigenmode function; removing the noise eigenmode function from the first eigenmode function to obtain a third eigenmode function; and superposing the second eigenmode functions and the third eigenmode functions, and determining target power transformation monitoring data corresponding to the target power transformation equipment based on a superposition result. The method and the device realize noise elimination and reliability improvement of the eigen mode function based on the function kurtosis dimension and the correlation coefficient dimension, further jointly determine the target power transformation monitoring data corresponding to the target power transformation equipment based on the screened eigen mode function, effectively remove the noise data and improve the reliability of the power transformation monitoring data. The method solves the technical problem of high calculation complexity of the power transformation data processing in the prior art, and has the beneficial effects of reducing the calculation complexity of the power transformation data processing, improving the reliability of the power transformation data and improving the efficiency of the power transformation data processing.
Example III
Fig. 3 is a schematic structural diagram of a power transformation data processing device according to a third embodiment of the present invention. As shown in fig. 3, the apparatus includes: an original eigenmode function acquisition module 310, an eigenmode function classification module 320, and a target transformation monitoring data determination module 330.
The original eigenmode function obtaining module 310 is configured to obtain a transformation monitoring data sequence of the target transformation device, perform signal decomposition on the transformation monitoring data sequence based on empirical mode decomposition, and obtain a plurality of original eigenmode functions corresponding to the transformation monitoring data sequence, where the transformation monitoring data sequence includes to-be-processed transformation data corresponding to different monitoring time points; the eigenmode function classification module 320 is configured to determine correlation coefficients of the plurality of original eigenmode functions and the substation monitoring data sequence, take an original eigenmode function with a phase relation number smaller than or equal to a preset coefficient threshold as a first eigenmode function, and take the remaining original eigenmode functions as a second eigenmode function; the target transformation monitoring data determining module 330 is configured to determine a third eigenmode function based on the kurtosis of the function of the first eigenmode function, and determine target transformation monitoring data corresponding to the target transformation device based on the second eigenmode function and the third eigenmode function.
On the basis of the above technical solution, further, the target transformation monitoring data determining module 330 includes a third eigenmode function determining unit.
The third eigenmode function determining unit is used for determining the function kurtosis corresponding to the first eigenmode function, and the first eigenmode function with the function kurtosis being larger than or equal to a preset kurtosis threshold value is used as the noise eigenmode function; and removing the noise eigenmode function from the first eigenmode function to obtain a third eigenmode function.
On the basis of the above technical solution, further, the target transformation monitoring data determining module 330 includes a target transformation monitoring data determining unit.
The target transformation monitoring data determining unit is used for superposing the second eigenmode functions and the third eigenmode functions, and determining target transformation monitoring data corresponding to the target transformation equipment based on superposition results.
On the basis of the above technical solution, further, the original eigenmode function obtaining module 310 includes a transformation monitoring data sequence determining unit.
The power transformation monitoring data sequence determining unit is used for acquiring primary monitoring data corresponding to primary equipment and secondary detection data corresponding to secondary equipment; the secondary equipment comprises equipment for assisting the primary equipment in working; and determining a power transformation monitoring data sequence based on the primary detection data and the secondary monitoring data.
Based on the above technical solution, further, the original eigenmode function obtaining module 310 is specifically configured to: obtaining extreme points in the substation monitoring data sequence, and obtaining envelope quantities corresponding to the substation monitoring data sequence based on a cubic spline interpolation function and the extreme points; processing the power transformation monitoring data sequence based on the envelope quantity, and updating the power transformation monitoring data sequence based on the processing result; and returning to execute the operation of acquiring the extreme points in the power transformation monitoring data sequence so as to obtain a plurality of original eigen-mode functions corresponding to the power transformation monitoring data sequence.
On the basis of the above technical solution, further, the original eigenmode function obtaining module 310 is specifically configured to obtain an upper envelope based on a cubic spline interpolation function and a maximum point, and obtain a lower envelope based on a cubic spline interpolation function and a minimum point; and determining the envelope quantity corresponding to the substation monitoring data sequence based on the upper envelope curve and the lower envelope curve.
On the basis of the above technical solution, further, the original eigenmode function obtaining module 310 is specifically configured to take an average value of the upper envelope and the lower envelope as an envelope amount corresponding to the substation monitoring data sequence.
Based on the above technical solution, further, the eigenmode function classification module 320 includes a correlation coefficient calculation unit.
The correlation coefficient calculation unit is used for acquiring covariance between the power transformation monitoring data sequence and the plurality of original eigenmode functions; acquiring a first variance of the substation monitoring data sequence and a second variance of a plurality of original eigenmode functions; and determining correlation coefficients of the plurality of original eigenmode functions and the power transformation monitoring data sequence based on the covariance, the first variance and the second variance.
The power transformation data processing device provided by the embodiment of the invention can execute the power transformation data processing method provided by any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the execution method.
Example IV
Fig. 4 shows a schematic diagram of the structure of an electronic device 10 that may be used to implement an embodiment of the invention. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. Electronic equipment may also represent various forms of mobile devices, such as personal digital processing, cellular telephones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be exemplary only, and are not meant to limit implementations of the inventions described and/or claimed herein.
As shown in fig. 4, the electronic device 10 includes at least one processor 11, and a memory, such as a Read Only Memory (ROM) 12, a Random Access Memory (RAM) 13, etc., communicatively connected to the at least one processor 11, in which the memory stores a computer program executable by the at least one processor, and the processor 11 may perform various appropriate actions and processes according to the computer program stored in the Read Only Memory (ROM) 12 or the computer program loaded from the storage unit 18 into the Random Access Memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device 10 may also be stored. The processor 11, the ROM 12 and the RAM 13 are connected to each other via a bus 14. An input/output (I/O) interface 15 is also connected to bus 14.
Various components in the electronic device 10 are connected to the I/O interface 15, including: an input unit 16 such as a keyboard, a mouse, etc.; an output unit 17 such as various types of displays, speakers, and the like; a storage unit 18 such as a magnetic disk, an optical disk, or the like; and a communication unit 19 such as a network card, modem, wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information/data with other devices via a computer network, such as the internet, and/or various telecommunication networks.
The processor 11 may be a variety of general and/or special purpose processing components having processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various specialized Artificial Intelligence (AI) computing chips, various processors running machine learning model algorithms, digital Signal Processors (DSPs), and any suitable processor, controller, microcontroller, etc. The processor 11 performs the respective methods and processes described above, for example, the transformation data processing method.
In some embodiments, the transformation data processing method may be implemented as a computer program, which is tangibly embodied on a computer-readable storage medium, such as the storage unit 18. In some embodiments, part or all of the computer program may be loaded and/or installed onto the electronic device 10 via the ROM 12 and/or the communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the transformation data processing method described above may be performed. Alternatively, in other embodiments, the processor 11 may be configured to perform the transformation data processing method in any other suitable way (e.g., by means of firmware).
Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuit systems, field Programmable Gate Arrays (FPGAs), application Specific Integrated Circuits (ASICs), application Specific Standard Products (ASSPs), systems On Chip (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs, the one or more computer programs may be executed and/or interpreted on a programmable system including at least one programmable processor, which may be a special purpose or general-purpose programmable processor, that may receive data and instructions from, and transmit data and instructions to, a storage system, at least one input device, and at least one output device.
A computer program for carrying out methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the computer programs, when executed by the processor, cause the functions/acts specified in the flowchart and/or block diagram block or blocks to be implemented. The computer program may execute entirely on the machine, partly on the machine, as a stand-alone software package, partly on the machine and partly on a remote machine or entirely on the remote machine or server.
In the context of the present invention, a computer-readable storage medium may be a tangible medium that can contain, or store a computer program for use by or in connection with an instruction execution system, apparatus, or device. The computer readable storage medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. Alternatively, the computer readable storage medium may be a machine readable signal medium. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
To provide for interaction with a user, the systems and techniques described here can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and a pointing device (e.g., a mouse or a trackball) through which a user can provide input to the electronic device. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic input, speech input, or tactile input.
The systems and techniques described here can be implemented in a computing system that includes a background component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such background, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), wide Area Networks (WANs), blockchain networks, and the internet.
The computing system may include clients and servers. The client and server are typically remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server can be a cloud server, also called a cloud computing server or a cloud host, and is a host product in a cloud computing service system, so that the defects of high management difficulty and weak service expansibility in the traditional physical hosts and VPS service are overcome.
It should be appreciated that various forms of the flows shown above may be used to reorder, add, or delete steps. For example, the steps described in the present invention may be performed in parallel, sequentially, or in a different order, so long as the desired results of the technical solution of the present invention are achieved, and the present invention is not limited herein.
The above embodiments do not limit the scope of the present invention. It will be apparent to those skilled in the art that various modifications, combinations, sub-combinations and alternatives are possible, depending on design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the scope of the present invention.
Claims (10)
1. A power transformation data processing method, characterized by comprising:
Acquiring a power transformation monitoring data sequence of target power transformation equipment, and performing signal decomposition on the power transformation monitoring data sequence based on empirical mode decomposition to acquire a plurality of original eigen mode functions corresponding to the power transformation monitoring data sequence, wherein the power transformation monitoring data sequence comprises power transformation data to be processed corresponding to different monitoring time points;
determining correlation coefficients of a plurality of original eigenmode functions and the power transformation monitoring data sequence, taking the original eigenmode functions with the correlation coefficients smaller than or equal to a preset coefficient threshold as first eigenmode functions, and taking the rest original eigenmode functions as second eigenmode functions;
And determining a third eigenmode function based on the function kurtosis of the first eigenmode function, and determining target power transformation monitoring data corresponding to the target power transformation equipment based on the second eigenmode function and the third eigenmode function.
2. The method of claim 1, wherein the determining a third eigenmode function based on a functional kurtosis of the first eigenmode function comprises:
Determining the function kurtosis corresponding to the first eigenmode function, and taking the first eigenmode function with the function kurtosis being larger than or equal to a preset kurtosis threshold value as a noise eigenmode function;
And removing the noise eigenmode function from the first eigenmode function to obtain a third eigenmode function.
3. The method of claim 1, wherein the determining the target power transformation monitoring data corresponding to the target power transformation device based on the second and third eigenmode functions comprises:
And superposing the second eigenmode functions and the third eigenmode functions, and determining target power transformation monitoring data corresponding to the target power transformation equipment based on a superposition result.
4. The method of claim 1, wherein the obtaining the sequence of power transformation monitoring data for the target power transformation device comprises:
Acquiring primary monitoring data corresponding to primary equipment and secondary detection data corresponding to secondary equipment; the secondary equipment comprises equipment for assisting the primary equipment in working;
the power transformation monitoring data sequence is determined based on the primary detection data and the secondary monitoring data.
5. The method of claim 1, wherein the performing signal decomposition on the transformation monitoring data sequence based on empirical mode decomposition to obtain a plurality of original eigenmode functions corresponding to the transformation monitoring data sequence includes:
obtaining an extreme point in the substation monitoring data sequence, and obtaining envelope quantity corresponding to the substation monitoring data sequence based on a cubic spline interpolation function and the extreme point;
processing the power transformation monitoring data sequence based on the envelope quantity, and updating the power transformation monitoring data sequence based on a processing result;
And returning to execute the operation of acquiring the extreme points in the transformation monitoring data sequence so as to obtain a plurality of original eigen-mode functions corresponding to the transformation monitoring data sequence.
6. The method of claim 5, wherein the extreme points comprise a maximum point and a minimum point; the obtaining the envelope quantity corresponding to the substation monitoring data sequence based on the cubic spline interpolation function and the extreme point comprises the following steps:
Acquiring an upper envelope curve based on the cubic spline interpolation function and the maximum value point, and acquiring a lower envelope curve based on the cubic spline interpolation function and the minimum value point;
And determining the envelope quantity corresponding to the power transformation monitoring data sequence based on the upper envelope curve and the lower envelope curve.
7. The method of claim 6, wherein the determining an envelope amount corresponding to the substation monitoring data sequence based on the upper envelope and the lower envelope comprises:
And taking the average value of the upper envelope curve and the lower envelope curve as the envelope quantity corresponding to the power transformation monitoring data sequence.
8. The method of claim 1, wherein said determining correlation coefficients of a plurality of said raw eigenmode functions with said power transformation monitoring data sequence comprises:
acquiring covariance between the transformation monitoring data sequence and a plurality of original eigenmode functions;
acquiring a first variance of the transformation monitoring data sequence and second variances of a plurality of original eigenmode functions;
And determining correlation coefficients of a plurality of original eigenmode functions and the power transformation monitoring data sequence based on the covariance, the first variance and the second variance.
9. The method of claim 8, wherein the determining correlation coefficients of the plurality of raw eigenmode functions with the power transformation monitoring data sequence based on the covariance, the first variance, and the second variance comprises:
Calculating the product of the first variance and the second variance, calculating the quotient of the covariance and the product, and determining correlation coefficients of a plurality of original intrinsic mode functions and the power transformation monitoring data sequence based on the quotient.
10. A power transformation data outlier processing apparatus, comprising:
The system comprises an original eigenmode function acquisition module, a power transformation monitoring data acquisition module and a power transformation processing module, wherein the original eigenmode function acquisition module is used for acquiring a power transformation monitoring data sequence of target power transformation equipment, performing signal decomposition on the power transformation monitoring data sequence based on empirical mode decomposition, and acquiring a plurality of original eigenmode functions corresponding to the power transformation monitoring data sequence, wherein the power transformation monitoring data sequence comprises power transformation data to be processed corresponding to different monitoring time points;
The intrinsic mode function classification module is used for determining correlation coefficients of a plurality of original intrinsic mode functions and the power transformation monitoring data sequence, taking the original intrinsic mode functions with the correlation coefficients smaller than or equal to a preset coefficient threshold as first intrinsic mode functions and taking the rest original intrinsic mode functions as second intrinsic mode functions;
And the target power transformation monitoring data determining module is used for determining a third eigenmode function based on the function kurtosis of the first eigenmode function and determining target power transformation monitoring data corresponding to the target power transformation equipment based on the second eigenmode function and the third eigenmode function.
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