CN111444613A - Improved EMD-based power system harmonic analysis method and device and storage medium - Google Patents
Improved EMD-based power system harmonic analysis method and device and storage medium Download PDFInfo
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Abstract
The application relates to the technical field of power harmonic measurement and detection, in particular to a method and a device for analyzing harmonic of a power system based on improved EMD and a storage medium. The method comprises the following steps: collecting a sampling signal of a harmonic wave of a power system; obtaining a local maximum point and a local minimum point of the sampling signal by using an endpoint interception method; fitting the local maximum points and the local minimum points through a fitting tool to obtain a local maximum value envelope curve and a local minimum value envelope curve; intercepting and discarding exposed end points of the intermediate original signal according to the upper local maximum value envelope and the lower local minimum value envelope to obtain an average envelope of the envelope signal without the envelope outer signal; obtaining an eigenmode function decomposed into all IMFs and residual wave components according to an EMD decomposition loop iteration method; and performing Hilbert transform on the eigenmode function to obtain the instantaneous characteristics of the signal, thereby realizing the analysis and calculation of the acquired signal.
Description
Technical Field
The application relates to the technical field of power harmonic measurement and detection, in particular to a method and a device for analyzing harmonic of a power system based on improved EMD and a storage medium.
Background
With the rapid development of the power industry and the increasing nonlinear loads, the harmonic problems caused by the arc furnace, the electric locomotive, the wind power facility and the solar energy equipment which are widely developed nowadays are increased. In practice, harmonic signals are not ideally linearly smooth, but often exhibit nonlinear non-smoothness, and most of the harmonic changes are difficult to predict, so that the healthy operation of a power grid is seriously polluted.
In recent years, various harmonic analysis methods have been proposed, including an analog filter method, fourier transform, an improvement thereof, and a wavelet transform method. Among the existing harmonic detection products, the windowed fast fourier method and the wavelet transform method are widely used.
The fourier algorithm has been developed well in practical application, but with the improvement of the requirement of harmonic analysis, the disadvantage of non-localization analysis is highlighted, and the fourier algorithm has large calculation amount, many calculation times and long time consumption, thus seriously affecting the real-time property of harmonic detection. The wavelet transform analysis algorithm may replace a fourier method to become a main harmonic analysis method in later applications, but aliasing effect occurs in wavelet transform between adjacent frequency bands, and the wavelet transform requires artificial selection of basis functions, and the difference of the basis functions causes great difference of results. Meanwhile, the two methods are very suitable for stable signals, but most harmonic signals in the power grid are nonlinear, so that the two methods are not suitable for stable signals
Disclosure of Invention
The present application provides a method, an apparatus, and a storage medium for harmonic analysis based on an improved EMD power system, in which an improved Empirical Mode Decomposition (EMD) method is used to decompose a complex nonlinear and unstable signal, and then a Hilbert Transform (HT) is performed on an eigen-mode function (IMF) obtained by decomposition to obtain an instantaneous characteristic of the signal, so that an original complex signal can be accurately analyzed and calculated.
The embodiment of the application is realized as follows:
the embodiment of the application provides a harmonic analysis method based on an improved EMD power system in a first aspect, which comprises the following steps:
collecting a sampling signal x (t) of the harmonic wave of the power system by a frequency spectrograph;
obtaining a local maximum point and a local minimum point of the sampling signal based on the sampling signal;
fitting the local maximum points and the local minimum points by a polynomial fitting tool of an MAT L AB fitting tool to obtain local maximum envelope curves and local minimum envelope curves;
intercepting and discarding an exposed end point of the middle original signal according to the local maximum value envelope at the upper part and the local minimum value envelope at the lower part to obtain an envelope signal which does not contain an envelope outer signal;
obtaining an average envelope through an average evaluation method based on the local maximum point envelope curve and the local minimum point envelope curve;
obtaining an eigenmode function decomposed into all IMFs and residual wave components according to an EMD decomposition loop iteration method based on the envelope signal not containing the envelope outer signal;
and performing Hilbert transform on the decomposed eigenmode functions to obtain instantaneous characteristics of the signals, thereby realizing the analysis and calculation of x (t).
Optionally, the exposed endpoint truncation dropping comprises the steps of: carrying out maximum and minimum value enveloping on the signals; intercepting two ends of the signal, and removing exposed end points at the two ends; the original signal is analyzed into a shorter intercepted signal.
Optionally, a polynomial Fitting tool of a MAT L AB Fitting tool is used for Fitting the local maximum points and the local minimum points to obtain a local maximum envelope Curve and a local minimum envelope Curve, and the method comprises the steps of Fitting the local maximum points and the local minimum points by using a match tool Curve Fitting tool carried by MAT L AB, visually finding the approximation degree of Fitting by selecting different polynomial times, and obtaining the square sum of errors as a standard for evaluating the Fitting degree of MAT L AB because a least square method is adopted during MAT L AB Fitting.
Optionally, based on the envelope signal not including the out-of-envelope signal, obtaining an eigenmode function decomposed into all IMFs and residual wave components according to an EMD decomposition loop iteration method, including the following steps: subtracting the signal x (t) from the mean value m (t) to obtain the residual component, i.e.: h is1(t)＝x(t)-m(t)；
Determination of h1(t) whether or not two conditions for the eigenmode function are met, and if not, h1(t) preparation ofRepeating the above steps for new signals until h1(t) satisfies the condition, expressed as: c. C1(t)＝h1(t)
c1(t) is decomposed from the original signal by subtracting c from x (t)1(t) determining the remaining r1(t), expressed as:
r1(t)＝x(t)-c1(t)
r is prepared from1(t) repeating the above steps as a new signal, and repeating the operation to obtain a residual wave r which cannot be decomposed any moren(t) end, decomposition ends, and the signal is expressed as:
alternatively, the EMD is a method of processing non-stationary signals to decompose a complex signal into a plurality of eigenmode functions and a residual signal, expressed as original wave ∑ IMFs + residual wave.
Optionally, the endpoint intercept method is used to address endpoint effects.
Optionally, the plurality of eigenmode functions, i.e. the plurality of IMFs, are waves of different single frequencies; the residual signal, i.e. the residual wave, is the trend term of the signal, i.e. the wave with a frequency below a set threshold W, which can be considered as the substrate, on which the other IMFs are built up.
A second aspect of the embodiments of the present application provides an apparatus for improving EMD power system harmonic analysis, the apparatus including at least one processor and at least one memory;
the at least one memory is for storing computer instructions;
the at least one processor is configured to execute at least a part of the computer instructions to implement the method for improving the EMD-based power system harmonic analysis according to the first aspect of the embodiments of the present application.
A third aspect of the embodiments of the present application provides a computer-readable storage medium, where the computer-readable storage medium stores computer instructions, and when at least part of the computer instructions are executed by a processor, the method is implemented as that according to the first aspect of the embodiments of the present application, the method is provided based on an improved EMD power system harmonic analysis.
The beneficial effects of the embodiment of the application include: different from the common methods for processing the end effect and calculating the maximum and minimum value envelopes, the method for calculating the maximum and minimum value envelopes by adopting the end interception and the polynomial fitting method is firstly proposed, so that the error of the result can be more favorably reduced theoretically, and the method provides important theoretical significance and practical application value for improving the electric meter, detection equipment and researchers to the complex harmonic analysis of the electric power system.
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In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are required to be used in the embodiments will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present application and therefore should not be considered as limiting the scope, and for those skilled in the art, other related drawings can be obtained from the drawings without inventive effort.
Fig. 1 shows a flow chart of a harmonic analysis method based on an improved EMD power system according to an embodiment of the present application.
Detailed Description
Certain exemplary embodiments will now be described to provide an overall understanding of the principles of the structure, function, manufacture, and use of the devices and methods disclosed herein. One or more examples of these embodiments are illustrated in the accompanying drawings. Those of ordinary skill in the art will understand that the devices and methods specifically described herein and illustrated in the accompanying drawings are non-limiting exemplary embodiments and that the scope of the various embodiments of the present application is defined solely by the claims. Features illustrated or described in connection with one exemplary embodiment may be combined with features of other embodiments. Such modifications and variations are intended to be included within the scope of the present application.
Reference throughout this specification to "embodiments," "some embodiments," "one embodiment," or "an embodiment," etc., means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment. Thus, appearances of the phrases "in various embodiments," "in some embodiments," "in at least one other embodiment," or "in an embodiment," or the like, throughout this specification are not necessarily all referring to the same embodiment. Furthermore, the particular features, structures, or characteristics may be combined in any suitable manner in one or more embodiments. Thus, the particular features, structures, or characteristics shown or described in connection with one embodiment may be combined, in whole or in part, with the features, structures, or characteristics of one or more other embodiments, without limitation. Such modifications and variations are intended to be included within the scope of the present application.
The EMD (Empirical Mode Decomposition) is a new method for processing non-stationary signals, and the time-frequency analysis method based on EMD is suitable for analyzing non-linear and non-stationary signals as well as linear and stationary signals, and better reflects the physical meaning of signals than other time-frequency analysis methods, and as a result, the complex signals are decomposed into a plurality of eigen-Mode functions and residual signals, which can be expressed as:
original wave ∑ IMFs + residual wave
The plurality of IMFs are waves of different single frequencies, and the remaining waves left after the decomposition are trend terms of signals, that is, waves with extremely low frequencies and lower than a set threshold value W (with a long period), and the threshold value may be set according to actual conditions, which is not limited in the present application. The residual wave can be viewed as the substrate upon which the other IMFs are built.
The Empirical Mode Decomposition (EMD) method described above comprises the following steps:
firstly, determining maximum value and minimum value points of a sampling sequence x (t) envelope of a current or power signal, then carrying out difference calculation on the two maximum value points by adopting a three-spline difference method to calculate the envelopes of the local maximum value and the local minimum value of the sampling sequence x (t), calculating the average envelopes m (t) of two envelope curves, and carrying out difference calculation on the signal x (t) and the average value m (t) to calculate residual components. Namely:
h1(t)＝x(t)-m(t)
secondly, determine h1(t) whether or not two conditions for the eigenmode function are met, and if not, h1(t) repeating the previous operations as a new signal up to h1(t) satisfies the condition:
c1(t)＝h1(t)
then, c1(t) is decomposed from the original signal by subtracting c from x (t)1(t) determining the remaining r1(t)。
r1(t)＝x(t)-c1(t)
Finally, r is added1(t) repeating steps (2) and (3) as a new signal to obtain r2(t) of (d). The cyclic operation gives a residual wave r which cannot be decomposed any moren(t) ends and the decomposition ends. The signals are represented as:
aiming at the end point effect and the maximum value of the classical empirical mode decomposition method, and the error of an interpolation algorithm when an envelope of the minimum value is enveloped leads to result error, the invention is an improved empirical mode decomposition method which is established on the basis of the method and is improved on the basis of the method.
The endpoint effect is that the extreme point and an interpolation algorithm determine an envelope mean value, the envelope mean value determines an IMF, when EMD decomposition is carried out, signal data of the endpoint is not completely enveloped in an envelope curve, the endpoint is not necessarily the extreme point, and therefore the signal at the endpoint generates an overshoot or undershoot phenomenon.
Fig. 1 shows a flow chart of a harmonic analysis method based on an improved EMD power system according to an embodiment of the present application.
In step S1, a signal acquisition is performed on the complex signal to obtain a sampling signal x (t).
In step S2, the local maximum point and the local minimum point are obtained for the sampled signal x (t).
The maximum and minimum points of the envelope of the sequence of samples x (t) of the current or power signal are determined.
In step S3, an envelope curve is fitted to the local maximum points and the local minimum points using a MAT L AB fitting tool using polynomial fitting, and the fitted envelope is optimized according to the evaluation criterion.
Aiming at the problem that overshoot or undershoot can occur in a cubic sample difference algorithm which is usually adopted in maximum and minimum value enveloping, a polynomial fitting method is adopted to fit the maximum and minimum value enveloping, then the average value enveloping is solved, and the method comprises the following specific steps:
in step S31, polynomial Fitting may use the match tool Curve Fitting tool of MAT L AB itself to achieve the Fitting envelope.
In step S32, the approximation degree of the fitting can be visually observed by selecting different polynomial orders, and since the fitting of MAT L AB uses the least square method, the sum of squares of errors is the standard for evaluating the fitting degree of MAT L AB.
In step S4, the exposed end points of the intermediate original signal are cut out based on the upper and lower local maximum envelopes obtained, thereby solving the end point effect problem. Thereby realizing truncation of the out-of-envelope signal.
The present application proposes an endpoint clipping method, i.e. to avoid that the envelope sought cannot contain signal endpoints,
in step S21, the signal is maximum and minimum enveloped.
In step S22, intercepting both ends of the signal, and removing exposed end points at both ends;
in step S23, the analysis original signal is changed to the analysis shorter clipped signal.
The above method can solve the problem of end-point effect.
In step S5, an average envelope m (t) of the maximum envelope and the minimum envelope is obtained.
In step S6, all IMFs and residual waves are obtained according to the method of EMD decomposition loop iteration.
In step S61, the signal x (t) is subtracted from the mean value m (t) to obtain the residual wave component. Namely:
h1(t)＝x(t)-m(t)
in step S62, h is determined1(t) whether or not two conditions for the eigenmode function are met, and if not, h1(t) repeating the previous operations as a new signal up to h1(t) satisfies the condition, expressed as:
c1(t)＝h1(t)
in step S63, c is1(t) is decomposed from the original signal by subtracting c from x (t)1(t) determining the remaining r1(t), expressed as:
r1(t)＝x(t)-c1(t)
in step S64, the above r is added1(t) repeating the above steps as a new signal, and repeating the operation to obtain a residual wave r which cannot be decomposed any moren(t) ends and the decomposition ends. The signals are represented as:
in step S7, Hilbert Transform (HT) is performed on the decomposed eigenmode functions to obtain the temporal characteristics of the signal, so that the original complex x (t) signal can be accurately analyzed and calculated.
Moreover, those skilled in the art will appreciate that aspects of the present application may be illustrated and described in terms of several patentable species or situations, including any new and useful combination of processes, machines, manufacture, or materials, or any new and useful improvement thereon. Accordingly, various aspects of the present application may be embodied entirely in hardware, entirely in software (including firmware, resident software, micro-code, etc.) or in a combination of hardware and software. The above hardware or software may be referred to as "data block," module, "" engine, "" unit, "" component, "or" system. Furthermore, aspects of the present application may be represented as a computer product, including computer readable program code, embodied in one or more computer readable media.
The computer storage medium may comprise a propagated data signal with the computer program code embodied therewith, for example, on baseband or as part of a carrier wave. The propagated signal may take any of a variety of forms, including electromagnetic, optical, etc., or any suitable combination. A computer storage medium may be any computer-readable medium that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code located on a computer storage medium may be propagated over any suitable medium, including radio, cable, fiber optic cable, RF, or the like, or any combination of the preceding.
Computer program code required for operation of various portions of the present application may be written in any one or more programming languages, including AN object oriented programming language such as Java, Scala, Smalltalk, Eiffel, JADE, Emerald, C + +, C #, VB.NET, Python, and the like, a conventional programming language such as C, Visual Basic, Fortran 2003, Perl, COBO L2002, PHP, ABAP, a dynamic programming language such as Python, Ruby, and Groovy, or other programming languages, and the like.
Additionally, the order in which elements and sequences of the processes described herein are processed, the use of alphanumeric characters, or the use of other designations, is not intended to limit the order of the processes and methods described herein, unless explicitly claimed. While various presently contemplated embodiments of the invention have been discussed in the foregoing disclosure by way of example, it is to be understood that such detail is solely for that purpose and that the appended claims are not limited to the disclosed embodiments, but, on the contrary, are intended to cover all modifications and equivalent arrangements that are within the spirit and scope of the embodiments herein. For example, although the system components described above may be implemented by hardware devices, they may also be implemented by software-only solutions, such as installing the described system on an existing server or mobile device.
Similarly, it should be noted that in the preceding description of embodiments of the application, various features are sometimes grouped together in a single embodiment, figure, or description thereof for the purpose of streamlining the disclosure aiding in the understanding of one or more of the embodiments. This method of disclosure, however, is not intended to require more features than are expressly recited in the claims. Indeed, the embodiments may be characterized as having less than all of the features of a single embodiment disclosed above.
The entire contents of each patent, patent application publication, and other material cited in this application, such as articles, books, specifications, publications, documents, and the like, are hereby incorporated by reference into this application. Except where the application is filed in a manner inconsistent or contrary to the present disclosure, and except where the claim is filed in its broadest scope (whether present or later appended to the application) as well. It is noted that the descriptions, definitions and/or use of terms in this application shall control if they are inconsistent or contrary to the statements and/or uses of the present application in the material attached to this application.
Claims (9)
1. A harmonic analysis method based on an improved EMD power system is characterized by comprising the following steps:
collecting a sampling signal x (t) of the harmonic wave of the power system by a frequency spectrograph;
obtaining a local maximum point and a local minimum point of the sampling signal based on the sampling signal;
fitting the local maximum points and the local minimum points by a polynomial fitting tool of an MAT L AB fitting tool to obtain local maximum envelope curves and local minimum envelope curves;
intercepting and discarding an exposed end point of the middle original signal according to the local maximum value envelope at the upper part and the local minimum value envelope at the lower part to obtain an envelope signal which does not contain an envelope outer signal;
obtaining an average envelope through an average evaluation method based on the local maximum point envelope curve and the local minimum point envelope curve;
obtaining an eigenmode function decomposed into all IMFs and residual wave components according to an EMD decomposition loop iteration method based on the envelope signal not containing the envelope outer signal;
and performing Hilbert transform on the decomposed eigenmode functions to obtain instantaneous characteristics of the signals, thereby realizing the analysis and calculation of x (t).
2. The improved EMD-based power system harmonic analysis method of claim 1, wherein the exposed endpoint truncation is dropped, comprising the steps of:
carrying out maximum and minimum value enveloping on the signals;
intercepting two ends of the signal, and removing exposed end points at the two ends;
the original signal is analyzed into a shorter intercepted signal.
3. The improved EMD-based power system harmonic analysis method of claim 1, wherein the local maximum and minimum points are fitted by a polynomial fitting tool of the MAT L AB fitting tool to obtain local maximum and local minimum envelope curves, comprising the steps of:
polynomial Fitting can use MAT L AB self-contained Fitting tool, Curve Fitting tool to realize Fitting envelope;
the fitting approximation degree can be visually seen by selecting different polynomial times, and because a least square method is adopted in the MAT L AB fitting process, the error sum of squares is a standard for evaluating the fitting degree of MAT L AB.
4. The improved EMD-based power system harmonic analysis method according to claim 1, wherein the eigenmode functions decomposed into all IMF and residual wave components are obtained according to an EMD decomposition loop iteration method based on the envelope signal not containing the out-of-envelope signal, and the method comprises the following steps:
subtracting the signal x (t) from the mean value m (t) to obtain the residual component, i.e.: h is1(t)＝x(t)-m(t)
Determination of h1(t) whether or not two conditions for the eigenmode function are met, and if not, h1(t) repeating the above steps as a new signal until h1(t) satisfies the condition, expressed as: c. C1(t)＝h1(t)
c1(t) is decomposed from the original signal by subtracting c from x (t)1(t) determining the remaining r1(t), expressed as:
r1(t)＝x(t)-c1(t)
r is prepared from1(t) repeating the above steps as a new signal, and repeating the operation to obtain a residual wave r which cannot be decomposed any moren(t) end, decomposition ends, and the signal is expressed as:
5. the improved EMD-based power system harmonic analysis method of claim 1, wherein the EMD is a method of processing non-stationary signals for decomposing complex signals into a plurality of eigenmode functions and residual signals, expressed as:
∑ IMFs + remainder.
6. The improved EMD based power system harmonic analysis method of claim 2, wherein the endpoint truncation method is used to account for endpoint effects.
7. The improved EMD-based power system harmonic analysis method of claim 5, wherein the plurality of eigenmode functions, i.e., IMFs, are waves of different single frequencies; the residual signal, i.e. the residual wave, is the trend term of the signal, i.e. the wave with a frequency below a set threshold W, which can be considered as the substrate, on which the other IMFs are built up.
8. An apparatus based on improved EMD power system harmonic analysis, the apparatus comprising at least one processor and at least one memory;
the at least one memory is for storing computer instructions;
the at least one processor is configured to execute at least some of the computer instructions to implement the method of any of claims 1-7.
9. A computer-readable storage medium, having stored thereon computer instructions, at least some of which, when executed by a processor, implement the method of any one of claims 1-7.
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| CN116223905A (en) * | 2022-12-14 | 2023-06-06 | 广州汇电云联互联网科技有限公司 | Harmonic frequency detection system, method, device and storage medium |
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