Broadband measurement method and apparatus for power system, and computer device

Abstract

A broadband measurement method and apparatus for a power system, and a computer device, which relate to the technical field of broadband measurement. The method comprises: acquiring a target electrical signal; on the basis of a local mean decomposition algorithm, decomposing in a time domain the target electrical signal into a plurality of signal components having a single frequency; determining the instantaneous frequency of each signal component; on the basis of the instantaneous frequencies, determining a corresponding time-frequency transformation algorithm and a corresponding time-frequency transformation parameter; and on the basis of the corresponding time-frequency transformation parameter, using the corresponding time-frequency transformation algorithm to perform time-frequency transformation on each signal component, so as to obtain a broadband spectrum that comprises frequency-domain information corresponding to each signal component. The method is applicable to spectrum analysis performed on a voltage / current signal that includes a plurality of signal components, the calculation is quickly and precisely performed, the broadband measurement requirements of a new power system can be met, and a strong support is provided for constructing a wide-frequency-oscillation wide-area monitoring system for the power system.

Description

Power system broadband measurement method, device and computer equipment

[0001] This application claims priority to the Chinese patent application filed with the China Patent Office on October 25, 2023, with application number 202311387144.2 and invention name “Wideband measurement method, device and computer equipment for power system”, the entire contents of which are incorporated by reference into this application. Technical Field

[0002] The present application relates to the field of broadband measurement technology, and in particular to a method, device, and computer equipment for broadband measurement of a power system. Background Art

[0003] In the context of new power systems, large-scale renewable energy generation, high-voltage direct current (HVDC) transmission, and high-power DC loads are rapidly developing. The degree of power electronics in all links of the power system, from source to grid to load, is gradually increasing, and the power system is moving toward a high proportion of renewable energy generation and power electronics. With the large-scale grid-connected renewable energy and the high proportion of power electronics connected, the interaction between renewable energy generators and the grid is causing new characteristics in the system's oscillation problem, resulting in broadband oscillations with frequencies ranging from a few Hz to several thousand Hz. In the future, with the construction and development of new power systems, it is foreseeable that broadband oscillations will become increasingly serious, posing a serious threat to the safe and reliable operation of the power grid.

[0004] Wideband oscillations are fundamentally different from traditional power angle oscillations, voltage oscillations, and frequency oscillations. From a signal processing perspective, wideband oscillations cause the system's voltage and current signals to contain large non-power frequency components. These large non-power frequency components distort the voltage and current signals, causing abnormal power system conditions. Therefore, precise system observation is essential to achieve accurate spectrum analysis of electrical signals over a wide frequency range. Traditional spectrum analysis methods suffer from insufficient accuracy and slow computational speed over wide frequency ranges.

[0005] Summary of the Invention

[0006] The purpose of this application is to solve at least one of the above technical deficiencies, especially the problems of insufficient accuracy and slow calculation speed of the spectrum analysis method in the prior art over a wide frequency range.

[0007] In a first aspect, the present application provides a method for broadband measurement of a power system, comprising:

[0008] Acquiring target electrical signals;

[0009] Based on the local mean decomposition algorithm, the target electrical signal is decomposed into multiple single-frequency signal components in the time domain;

[0010] Determine the instantaneous frequency of each signal component;

[0011] According to the instantaneous frequency, determine the corresponding time-frequency conversion algorithm and time-frequency conversion parameters;

[0012] According to the corresponding time-frequency conversion parameters and using the corresponding time-frequency conversion algorithm, the signal components are time-frequency converted to obtain a broadband spectrum including the frequency domain information corresponding to each signal component.

[0013] In one embodiment, determining a corresponding time-frequency conversion algorithm according to the instantaneous frequency includes:

[0014] If the instantaneous frequency is below the first threshold, determining that the corresponding time-frequency conversion algorithm is a discrete Fourier algorithm;

[0015] If the instantaneous frequency is greater than the first threshold, it is determined that the corresponding time-frequency conversion algorithm is a fast Fourier algorithm.

[0016] In one embodiment, the time-frequency conversion parameters include the sampling window type, and the corresponding time-frequency conversion parameters are determined according to the instantaneous frequency, including:

[0017] If the instantaneous frequency is below the first threshold, the corresponding sampling window type is determined to be a rectangular window;

[0018] If the instantaneous frequency is greater than the first threshold and below the second threshold, determining that the corresponding sampling window type is a Hanning window;

[0019] If the instantaneous frequency is greater than the second threshold and less than the third threshold, the corresponding sampling window type is determined to be a Blackman window.

[0020] In one embodiment, the time-frequency conversion parameter includes a sampling window length, and the corresponding time-frequency conversion parameter is determined according to the instantaneous frequency, including:

[0021] Determine the target frequency based on the mapping relationship between instantaneous frequency and frequency;

[0022] Determine the cycle length based on the instantaneous frequency;

[0023] The sampling window length is obtained according to the period length and the target frequency.

[0024] In one embodiment, determining the target frequency based on the mapping relationship between the instantaneous frequency and the frequency includes:

[0025] If the instantaneous frequency is below the first threshold, determining the target frequency to be the first number;

[0026] If the instantaneous frequency is greater than the first threshold and less than the second threshold, the target frequency is determined to be a second number; the second threshold is greater than the first threshold, and the second number is greater than the first number;

[0027] If the instantaneous frequency is greater than the second threshold and less than the third threshold, the target frequency is determined to be a third number; the third threshold is greater than the second threshold, and the third number is greater than the first number and less than the second number.

[0028] In one embodiment, the first threshold is 100 Hz, the second threshold is 650 Hz, the third threshold is 2500 Hz, the first number is 2, the second number is 8, and the third number is 3.

[0029] In one embodiment, based on the local mean decomposition algorithm, the target electrical signal is decomposed into multiple single-frequency signal components in the time domain, including:

[0030] Using the current target electrical signal as the intermediate electrical signal;

[0031] Find all local maximum and local minimum points of the intermediate electrical signal;

[0032] Perform cubic spline interpolation on the local maximum point to obtain the upper envelope function, and perform cubic spline interpolation on the local minimum point to obtain the lower envelope function;

[0033] According to the upper envelope function and the lower envelope function, the local mean function and the local envelope function of this round are obtained;

[0034] Determine whether the local envelope function of this round is equal to 1;

[0035] If not, subtract the local mean function from the intermediate electrical signal and divide the result by the local envelope function, and return to the step of finding all local maximum points and local minimum points of the intermediate electrical signal to continue the execution;

[0036] If yes, then multiply the local envelope functions of this round and all previous ones to obtain the envelope signal, and take the local mean function of this round as the pure FM signal, and obtain the signal component according to the envelope signal and the pure FM signal;

[0037] After obtaining the new signal component, determine whether the iteration end condition is met;

[0038] If not, subtract the newly obtained signal component from the target electrical signal, and return to the step of using the current target electrical signal as the intermediate electrical signal to continue execution;

[0039] If so, the target electrical signal is decomposed into multiple single-frequency signal components in the time domain.

[0040] In one embodiment, determining whether an iteration termination condition is satisfied includes:

[0041] If the number of local maximum points and local minimum points in the newly obtained signal component is less than the first threshold, it is determined that the iteration end condition is met; otherwise, it is determined that the iteration end condition is not met.

[0042] In a second aspect, the present application provides a power system broadband measurement device, comprising:

[0043] A data acquisition module, used to acquire target electrical signals;

[0044] A signal decomposition module is used to decompose the target electrical signal into multiple single-frequency signal components in the time domain based on the local mean decomposition algorithm;

[0045] A frequency determination module, for determining the instantaneous frequency of each signal component;

[0046] A selection module is used to determine the corresponding time-frequency conversion algorithm and time-frequency conversion parameters according to the instantaneous frequency;

[0047] The time-frequency conversion module is used to perform time-frequency conversion on the signal components according to the corresponding time-frequency conversion parameters and using the corresponding time-frequency conversion algorithm to obtain a broadband spectrum including the frequency domain information corresponding to each signal component.

[0048] In a third aspect, the present application provides a computer device comprising one or more processors and a memory, wherein the memory stores computer-readable instructions. When the computer-readable instructions are executed by the one or more processors, the steps of the power system broadband measurement method in any of the above embodiments are executed.

[0049] It can be seen from the above technical solutions that the embodiments of the present application have the following advantages:

[0050] Based on any of the above embodiments, the local mean decomposition method is first used to decompose the target electrical signal into signal components with the same instantaneous frequency. Since each signal component fluctuates within its own frequency band and has a specific instantaneous frequency, the power system analysis has different analysis requirements for signals with different instantaneous frequencies. Therefore, according to the instantaneous frequency of each signal component, the corresponding time-frequency conversion parameters are adaptively selected and the time-frequency conversion is performed, and the entire broadband spectrum is finally obtained under the premise of meeting different analysis requirements. This method can meet the analysis requirements of different frequencies, achieve appropriate calculation speed and calculation accuracy for the signal of each frequency point in the broadband spectrum, effectively meet the broadband measurement requirements of the new power system, and provide strong support for the construction of a broadband oscillation wide-area monitoring system for the power system. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0052] FIG1 is a flow chart of a method for broadband measurement of a power system according to an embodiment of the present application;

[0053] FIG2 is a schematic diagram of the decomposition of a simulation signal by the local mean decomposition algorithm;

[0054] FIG3 is a schematic diagram of a spectrum of a simulated signal analyzed using the power system broadband measurement method of the present application;

[0055] FIG4 is a schematic diagram of a module of a broadband measurement device for a power system according to an embodiment of the present application;

[0056] FIG5 is a diagram showing the internal structure of a computer device according to an embodiment of the present application. DETAILED DESCRIPTION

[0057] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0058] Referring to Figure 1 , this application provides a method for broadband measurement of power systems. Broadband refers to a wider frequency band than typical spectrum analysis. Broadband measurement here primarily involves decomposing complex time-domain signals into frequency-domain signals of varying frequencies and determining the frequency-domain information of each frequency-domain signal. Frequency-domain information generally includes amplitude, phase, frequency, and the like. The method for broadband measurement of power systems provided in this embodiment includes steps S102 to S110.

[0059] S102: Acquire a target electrical signal.

[0060] It can be understood that the target electrical signal is an electrical signal collected from the power system and needs to be subjected to spectrum analysis, which can be a voltage signal or a current signal.

[0061] S104 , based on a local mean decomposition algorithm, decompose the target electrical signal into multiple single-frequency signal components in the time domain.

[0062] As can be understood, the target electrical signal is a non-stationary time-domain signal composed of multiple components. Traditional techniques directly use time-frequency conversion algorithms to convert the target electrical signal into a frequency-domain signal, thereby obtaining a spectrum. However, since the target electrical signal includes components with multiple frequencies, directly performing time-frequency conversion on the entire target electrical signal without tailoring the frequency characteristics results in low accuracy. The local mean decomposition algorithm, on the other hand, can separate a non-stationary, multi-component time-domain signal into the sum of several signal components, each with a different instantaneous frequency. Each signal component decomposed by the local mean decomposition algorithm can be represented by a product function consisting of an envelope signal representing the instantaneous amplitude of the product function and a pure frequency-modulated signal representing the instantaneous frequency of the product function. Since the signal components obtained by the local mean decomposition algorithm belong to different instantaneous frequencies and have different analysis requirements, adaptively selecting the appropriate time-frequency conversion algorithm and parameters for the signal components achieves high frequency resolution while ensuring computational speed.

[0063] S106: Determine the instantaneous frequency of each signal component.

[0064] Since the frequency of each signal component changes over time in the time domain, it can reflect the frequency of the signal component at the time point when the method of this embodiment is used to measure. In some embodiments, if an expression for each signal component is obtained using the local mean decomposition algorithm, the arc cosine of the signal component is calculated, and then the time derivative is divided by 2π to obtain an expression for the instantaneous frequency. The measurement time point is substituted into this expression to obtain the instantaneous frequency.

[0065] S108: Determine a corresponding time-frequency conversion algorithm and time-frequency conversion parameters according to the instantaneous frequency.

[0066] It can be understood that before analyzing signal components of different frequencies, this embodiment needs to select the corresponding time-frequency conversion algorithm according to its instantaneous frequency, and needs to set the time-frequency conversion parameters used under the time-frequency conversion algorithm, such as sampling window length, sampling window type, overlap coefficient, etc. The time-frequency conversion parameters will affect the amount of calculation, the frequency resolution, etc. In this embodiment, since the instantaneous frequencies of each signal component are different, the requirements for spectrum analysis of signals of different frequencies during power system spectrum analysis are also different. For example, some frequencies require high timeliness priority, while some frequencies require high accuracy priority. Therefore, after determining the instantaneous frequency of each signal component, the corresponding time-frequency conversion parameters can be set according to the requirements of the frequency.

[0067] S110 , performing time-frequency conversion on the signal components according to corresponding time-frequency conversion parameters and using a corresponding time-frequency conversion algorithm to obtain a broadband spectrum including frequency domain information corresponding to each signal component.

[0068] It can be understood that for each decomposed signal component, after determining the time-frequency conversion parameters and video conversion algorithm, the time-domain signal component can be converted into a frequency-domain signal. The result of the conversion includes an expression for the frequency-domain signal. Based on this expression, the frequency-domain signal of each signal component, such as amplitude, phase, frequency, etc., can be obtained. Generally speaking, since the amplitude represents the energy level at that frequency point, the broadband spectrum can be expressed as an image, such as an image formed by adding the frequency domain information of each signal component to the coordinate system with frequency as the horizontal axis and amplitude as the vertical axis. In addition, the time-frequency conversion algorithm here can be the same for all different signal components, or different time-frequency conversion algorithms can be selected for different signal components, or some can be the same and some can be different.

[0069] Based on the broadband measurement method of the power system in this embodiment, the local mean decomposition method is first used to decompose the target electrical signal into signal components with the same instantaneous frequency. Since each signal component fluctuates within its own frequency band and has a specific instantaneous frequency, the power system analysis has different analysis requirements for signals with different instantaneous frequencies. Therefore, according to the instantaneous frequency of each signal component, the corresponding time-frequency conversion parameters are adaptively selected and time-frequency conversion is performed, and the entire broadband spectrum is finally obtained under the premise of meeting different analysis requirements. This method can meet the analysis requirements of different frequencies, achieve appropriate calculation speed and calculation accuracy for the signal of each frequency point in the broadband spectrum, can effectively meet the broadband measurement requirements of the new power system, and provide strong support for the construction of a broadband oscillation wide-area monitoring system for the power system.

[0070] In one embodiment, determining a corresponding time-frequency conversion algorithm according to the instantaneous frequency includes:

[0071] (1) If the instantaneous frequency is below the first threshold, the corresponding time-frequency conversion algorithm is determined to be the discrete Fourier algorithm.

[0072] (2) If the instantaneous frequency is greater than the first threshold, the corresponding time-frequency conversion algorithm is determined to be the fast Fourier algorithm.

[0073] It will be appreciated that this embodiment selects different time-frequency conversion algorithms within the target frequency band of broadband measurement, with the first threshold as the boundary. For lower-frequency signal components, i.e., signal components with instantaneous frequencies below the first threshold, the Discrete Fourier Transform (DFT) algorithm is selected. For higher-frequency signal components, i.e., signal components with instantaneous frequencies greater than the first threshold, the Fast Fourier Transform (FFT) algorithm is selected to improve processing efficiency. The Discrete Fourier Transform (DFT) and Fast Fourier Transform (FFT) algorithms herein include both their original algorithms and their improved algorithms.

[0074] In one embodiment, the time-frequency conversion parameter includes a sampling window type, and determining the corresponding time-frequency conversion parameter according to the instantaneous frequency includes:

[0075] (1) If the instantaneous frequency is below the first threshold, the corresponding sampling window type is determined to be a rectangular window.

[0076] (2) If the instantaneous frequency is greater than the first threshold and less than the second threshold, the corresponding sampling window type is determined to be a Hanning window.

[0077] (3) If the instantaneous frequency is greater than the second threshold and less than the third threshold, the corresponding sampling window type is determined to be a Blackman window.

[0078] It can be understood that this embodiment selects different time-frequency conversion algorithms within the target frequency band of broadband measurement, with the first threshold and the second threshold as the boundary. For signal components with lower frequencies, i.e., signal components with instantaneous frequencies below the first threshold, a rectangular window suitable for the low frequency band is selected. For signal components with medium frequencies, i.e., signal components with instantaneous frequencies greater than the first threshold and below the second threshold, a Hanning window suitable for the medium frequency band is selected. For signal components with high frequencies, i.e., signal components with instantaneous frequencies greater than the second threshold and below the third threshold, a Blackman window suitable for the high frequency band is selected.

[0079] In the time-frequency conversion algorithm, it is necessary to use a window function to intercept a certain length of signal on the signal component in the time domain for sampling and calculation. This intercepted length is the sampling window length. The longer the sampling window length, the higher the frequency resolution, but the greater the amount of calculation. Frequency analysis at different frequency points has different requirements for accuracy and speed. Therefore, in one embodiment, the time-frequency conversion parameters that need to be set specifically according to the instantaneous frequency also include the sampling window length. Specifically, the following steps are included:

[0080] (1) Determine the target frequency based on the mapping relationship between instantaneous frequency and frequency.

[0081] (2) Determine the cycle length based on the instantaneous frequency.

[0082] (3) According to the cycle length and the target frequency, the sampling window length is obtained.

[0083] It will be appreciated that this embodiment determines the sampling window length in cycles. The target cycle represents the number of cycles to be included in the sampling window at the instantaneous frequency. Target cycles corresponding to different instantaneous frequencies can be preset, i.e., a mapping relationship between instantaneous frequency and cycle can be preset. After determining the instantaneous frequency of any signal component, the cycle length can be determined using this mapping relationship. Therefore, the sampling window length can be obtained by multiplying the target cycle by the cycle length, as the cycle length and instantaneous frequency are inversely proportional to each other. Based on this, after determining the target cycle and cycle length based on the instantaneous frequency, the sampling window length corresponding to the signal component can be determined. Furthermore, even if two signal components have the same target cycle, the different cycle lengths of the two signal components will result in different sampling window lengths. This results in the frequency resolution of the two signal components also varying adaptively based on the instantaneous frequency. Compared to conventional techniques that use the same time window length to perform discrete Fourier transform on the undecomposed target electrical signal, this method maintains a consistent frequency resolution across the entire frequency band, hindering adaptive analysis of different frequencies.

[0084] In one embodiment, determining the target frequency based on the mapping relationship between the instantaneous frequency and the frequency includes:

[0085] (1) If the instantaneous frequency is below a first threshold, the target frequency is determined to be a first number.

[0086] (2) If the instantaneous frequency is greater than the first threshold and less than the second threshold, the target frequency is determined to be a second number. The second number is greater than the first number.

[0087] (3) If the instantaneous frequency is greater than the second threshold and less than the third threshold, the target frequency is determined to be a third number. The third number is greater than the first number and less than the second number.

[0088] It can be understood that the third threshold is the upper limit of the target frequency band for broadband measurement. The first and second thresholds are used to divide the target frequency band into three segments. Signals with frequencies below the first threshold have a significant impact on grid stability and security and need to be identified as quickly as possible, prioritizing computational efficiency. Therefore, their corresponding target frequency is the smallest first number. Signals with frequencies greater than the first threshold and below the second threshold are relevant to power quality assessment. Power system managers need to trace the source of harmonics based on the detection results, and the detection results used as the basis for accountability require high accuracy. Therefore, computational accuracy should be prioritized, so their corresponding target frequency is the largest second number. Signals with frequencies greater than the first threshold and below the second threshold are less complex, also primarily for computational efficiency. However, due to their shorter cycle length, a third number of target frequencies, larger than the first number and smaller than the second number, can be selected. In one specific embodiment, the first threshold is 100Hz, the second threshold is 650Hz, and the third threshold is 2500Hz. The first number is 2, the second number is 8, and the third number is 3.

[0089] In one embodiment, based on the local mean decomposition algorithm, the target electrical signal is decomposed into multiple single-frequency signal components in the time domain, including:

[0090] (1) The current target electrical signal is used as the intermediate electrical signal.

[0091] (2) Find all local maximum and local minimum points of the intermediate electrical signal.

[0092] That is, traverse the target electrical signal and find the local extreme value point of the amplitude. Let x(t) represent the intermediate electrical signal, and the sequence formed by sampling it is x(n). The discriminant of the local maximum value point can be: (x(n-1)<x(n))&&(x(n)> x(n+1));

[0093] The discriminant of the local minimum point can be: (x(n-1)>x(n))&&(x(n) <x(n+1))。

[0094] (3) Perform cubic spline interpolation on the local maximum points to obtain the upper envelope function, and perform cubic spline interpolation on the local minimum points to obtain the lower envelope function.

[0095] Cubic spline interpolation is a numerical computational method that approximates a given set of discrete data points using a set of cubic polynomial functions, ensuring that each function satisfies the interpolation conditions on its corresponding subinterval and that continuity of the function value, first-order derivative, and second-order derivative is maintained at the junction of all subintervals. In other words, using cubic spline interpolation, local maximum and minimum points can be fitted to the envelope function and envelope function of a continuous function in the time domain, respectively.

[0096] (4) According to the upper envelope function and the lower envelope function, the local mean function and the local envelope function of this round are obtained.

[0097] Specifically, the local mean function can be and the local inclusion function can be respectively:

[0098] Among them, m i (t) is the local mean function at the i-th iteration, a i (t) is the local envelope function at the i-th iteration, Env max (t) is the upper envelope function, Env min (t) is the lower envelope function.

[0099] (5) Determine whether the local envelope function of this round is equal to 1.

[0100] It can be understood that the local mean decomposition algorithm is a double loop. The outer loop is to subtract the newly obtained signal component from the target electrical signal each time to become the current target electrical signal, and then use the current target electrical signal as the intermediate electrical signal to perform the inner loop. The inner loop is used to decompose a signal component from the intermediate electrical signal. When separating one of the signal components, the basis for judging whether the separation is completed is that the local envelope function is equal to 1. When the function is equal to 1, it means that the local mean function of this round is a pure frequency modulation function. Multiplying all the local envelope functions in this inner loop, the envelope signal (i.e., amplitude signal) of the pure frequency modulation function is obtained. Multiplying the envelope signal and the pure frequency modulation signal can obtain the signal component of this inner loop. It can be seen that in the inner loop, it is judged whether the local envelope function of this round is equal to 1, and then based on the judgment result, step (6) is entered to continue this inner loop or step (7) is entered to end this inner loop.

[0101] (6) If not, the intermediate electrical signal is subtracted from the local mean function and then divided by the local envelope function, and the process returns to the step of finding all the local maximum and local minimum points of the intermediate electrical signal to continue.

[0102] (7) If yes, then multiply the local envelope functions of this round and all previous ones to obtain the envelope signal, and take the local mean function of this round as the pure FM signal, and obtain the signal component according to the envelope signal and the pure FM signal.

[0103] (8) After obtaining the new signal component, determine whether the iteration end condition is met.

[0104] It can be understood that the iteration end condition here refers to the condition for the end of the outer loop, that is, all the required signal components have been decomposed from the target electrical signal. In some embodiments, step (8) is specifically if the number of local maximum points and local minimum points in the newly obtained signal component is less than the first threshold, then it is determined that the iteration end condition is met; otherwise, it is determined that the iteration end condition is not met. It can be understood that the principle of this judgment is that when the number of local extreme points is less than the first threshold, it means that the frequency of the signal component is low, and the frequency components contained in the remaining amount after subtracting multiple signal components from the target electrical signal are not many, and it is meaningless to continue decomposing, so the decomposition can be stopped. The first threshold can be 20. It can be seen that after an inner loop ends, it is determined whether the iteration end condition is met, and then based on the judgment result, step (9) is entered to continue a new inner loop to continue decomposing the signal components or step (10) is entered to end the entire outer loop.

[0105] (9) If not, the newly obtained signal component is subtracted from the target electrical signal, and the process returns to the step of finding all local maximum points and local minimum points of the target electrical signal to continue.

[0106] (10) If so, the target electrical signal is decomposed into multiple single-frequency signal components in the time domain.

[0107] The method provided in this application is used for example analysis and verification, and the following simulation signal is used for analysis: x(t)=(1+0.25cos(2π*110t))(0.25cos(2π*1100t))+5cos(2π*50t);

[0108] The simulation signal is composed of a high-frequency modulation signal with a frequency of 1100Hz and an amplitude of 0.25 (1+0.25cos(2π*110t) represents the amplitude modulation component) and an industrial frequency signal with an amplitude of 5. First, the simulation signal is decomposed based on the local mean decomposition algorithm to obtain the envelope signal and the corresponding pure frequency modulation signal. By multiplying the two, all single-frequency signal components and residual components are obtained. According to the principle of frequency from high to low, the multiple PF components obtained by decomposition are sorted. The decomposition diagram of the simulation signal is shown in Figure 2. Based on the data of the PF1 component and the PF2 component, the PF1 component contains the time-frequency characteristics of the high-frequency modulation signal in the simulation signal, and the PF2 component contains the time-frequency characteristics of the industrial frequency signal. The mean square error of the PF1 component is 0.0049, and the mean square error of the PF2 component is 2.8532*10 -5. It can be concluded that the reconstruction accuracy of the local mean decomposition algorithm used in this application can effectively meet the actual requirements and can effectively extract the features of the broadband resonant signal sequence. On the basis of completing the feature extraction of the broadband resonant signal sequence, for the PF1 component and the PF2 component, the corresponding time window is taken for DFT calculation, and the amplitude spectrum of the PF1 component and the PF2 component is finally obtained as shown in Figure 3. The frequency of the high-frequency modulated signal is 1100Hz and the amplitude is 0.250032; the frequency of the power frequency signal is 50Hz and the amplitude is 4.99985. The experimental results show that the amplitude frequency obtained by feature extraction and single frequency PF component analysis is consistent with the simulation signal. It can be seen from the above experimental results that the method proposed in this application can accurately extract the features in the broadband resonant signal sequence, verifying the feasibility of the method proposed in this application. It can be used as a processing and analysis method for broadband signals of power systems, which can effectively meet the broadband measurement requirements of new power systems and provide strong support for the construction of broadband oscillation wide-area monitoring systems for power systems.

[0109] The present application provides a power system broadband measurement device, as shown in FIG4 , which includes a data acquisition module 410 , a signal decomposition module 420 , a frequency determination module 430 , a selection module 440 and a time-frequency conversion module 450 .

[0110] The data acquisition module 410 is used to acquire target electrical signals.

[0111] The signal decomposition module 420 is configured to decompose the target electrical signal into multiple single-frequency signal components in the time domain based on a local mean decomposition algorithm.

[0112] The frequency determination module 430 is used to determine the instantaneous frequency of each signal component.

[0113] The selection module 440 is used to determine the corresponding time-frequency conversion algorithm and time-frequency conversion parameters according to the instantaneous frequency.

[0114] The time-frequency conversion module 450 is configured to perform time-frequency conversion on the signal components corresponding to the time-frequency conversion parameters using the corresponding time-frequency conversion algorithm according to the corresponding time-frequency conversion parameters to obtain a broadband spectrum including frequency domain information corresponding to each signal component.

[0115] In one embodiment, the selection module 440 is configured to determine that the corresponding time-frequency conversion algorithm is a discrete Fourier algorithm if the instantaneous frequency is below a first threshold; and to determine that the corresponding time-frequency conversion algorithm is a fast Fourier algorithm if the instantaneous frequency is greater than the first threshold.

[0116] In one embodiment, the time-frequency conversion parameters include a sampling window type, and the selection module 440 is configured to determine that the corresponding sampling window type is a rectangular window if the instantaneous frequency is below a first threshold; determine that the corresponding sampling window type is a Hanning window if the instantaneous frequency is greater than the first threshold and below a second threshold; and determine that the corresponding sampling window type is a Blackman window if the instantaneous frequency is greater than the second threshold and below a third threshold.

[0117] In one embodiment, the selection module 440 is configured to determine the target frequency based on a mapping relationship between the instantaneous frequency and the frequency; determine the cycle length based on the instantaneous frequency; and obtain the sampling window length based on the cycle length and the target frequency.

[0118] In one embodiment, the selection module 440 is configured to determine the target frequency as a first number if the instantaneous frequency is below a first threshold; determine the target frequency as a second number if the instantaneous frequency is greater than the first threshold and below a second threshold; the second number is greater than the first number; and determine the target frequency as a third number if the instantaneous frequency is greater than the second threshold and below a third threshold; the third number is greater than the first number and less than the second number.

[0119] In one embodiment, the signal decomposition module 420 is used to take the current target electrical signal as the intermediate electrical signal; find all local maximum points and local minimum points of the intermediate electrical signal; perform cubic spline interpolation on the local maximum points to obtain an upper envelope function, and perform cubic spline interpolation on the local minimum points to obtain a lower envelope function; obtain the local mean function and the local envelope function of this round based on the upper envelope function and the lower envelope function; determine whether the local envelope function of this round is equal to 1; if not, subtract the intermediate electrical signal from the local mean function and then divide it by the local envelope function, and return the intermediate electrical signal. The steps of all local maximum points and local minimum points of the electrical signal are continued; if so, all local envelope functions of this round and the previous round are multiplied to obtain the envelope signal, and the local mean function of this round is used as the pure frequency modulation signal, and the signal components are obtained according to the envelope signal and the pure frequency modulation signal; after obtaining the new signal components, it is determined whether the iteration end condition is met; if not, the new signal components are subtracted from the target electrical signal, and the step of taking the current target electrical signal as the intermediate electrical signal is returned to continue; if so, the target electrical signal is decomposed into multiple single-frequency signal components in the time domain.

[0120] For the specific limitations of the power system broadband measurement device, please refer to the limitations of the power system broadband measurement method above, which will not be repeated here. The various modules in the above-mentioned power system broadband measurement device can be implemented in whole or in part by software, hardware and a combination thereof. The above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory in the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules. It should be noted that the division of modules in the embodiment of the present application is schematic and is only a logical function division. There may be other division methods in actual implementation.

[0121] The present application provides a computer device comprising one or more processors and a memory, wherein the memory stores computer-readable instructions. When the computer-readable instructions are executed by the one or more processors, the steps of the power system broadband measurement method in any of the above embodiments are executed.

[0122] Schematically, as shown in FIG5 , FIG5 is a schematic diagram of the internal structure of a computer device provided in an embodiment of the present application. Referring to FIG5 , computer device 500 includes a processing component 502, which further includes one or more processors, and memory resources represented by memory 501 for storing instructions executable by processing component 502, such as applications. The application stored in memory 501 may include one or more modules, each corresponding to a set of instructions. Furthermore, processing component 502 is configured to execute instructions to perform the steps of the power system broadband measurement method according to any of the above-described embodiments.

[0123] The computer device 500 may further include a power supply component 503 configured to perform power management of the computer device 500 , a wired or wireless network interface 504 configured to connect the computer device 500 to a network, and an input / output (I / O) interface 505 .

[0124] Those skilled in the art will understand that the structure shown in FIG5 is merely a block diagram of a portion of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different arrangement of components.

[0125] The present application provides a storage medium storing computer-readable instructions. When the computer-readable instructions are executed by one or more processors, the one or more processors execute the steps of the power system broadband measurement method in any of the above embodiments.

[0126] The various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The various embodiments can be combined as needed, and the same or similar parts can be referenced to each other.

[0127] The above description of the disclosed embodiments is intended to enable one skilled in the art to implement or use the present application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application is not limited to the embodiments shown herein, but is intended to encompass the broadest scope consistent with the principles and novel features disclosed herein.

Claims

1. A broadband measurement method for a power system, characterized in that: include: Acquiring target electrical signals; Based on the local mean decomposition algorithm, the target electrical signal is decomposed into multiple single frequency signal components in the time domain; determining the instantaneous frequency of each of said signal components; According to the instantaneous frequency, determining a corresponding time-frequency conversion algorithm and time-frequency conversion parameters; According to the corresponding time-frequency conversion parameters and using the corresponding time-frequency conversion algorithm, the signal components are time-frequency converted to obtain a broadband spectrum including frequency domain information corresponding to each of the signal components.

2. The method for broadband measurement of electric power system according to claim 1, characterized in that: Determining a corresponding time-frequency conversion algorithm according to the instantaneous frequency includes: If the instantaneous frequency is below a first threshold, determining that the corresponding time-frequency conversion algorithm is a discrete Fourier algorithm; If the instantaneous frequency is greater than the first threshold, it is determined that the corresponding time-frequency conversion algorithm is a fast Fourier algorithm.

3. The method for broadband measurement of electric power system according to claim 2, characterized in that: The time-frequency conversion parameters include a sampling window type, and the corresponding time-frequency conversion parameters are determined according to the instantaneous frequency, including: If the instantaneous frequency is below the first threshold, determining that the corresponding sampling window type is a rectangular window; If the instantaneous frequency is greater than the first threshold and below the second threshold, determining that the corresponding sampling window type is a Hanning window; If the instantaneous frequency is greater than the second threshold and below the third threshold, it is determined that the corresponding sampling window type is a Blackman window.

4. The method for broadband measurement of electric power system according to claim 3, characterized in that: The time-frequency conversion parameter includes a sampling window length, and the corresponding time-frequency conversion parameter is determined according to the instantaneous frequency, including: Determining a target frequency according to a mapping relationship between the instantaneous frequency and the frequency; Determining a cycle length based on the instantaneous frequency; The sampling window length is obtained according to the cycle length and the target frequency.

5. The method for broadband measurement of electric power system according to claim 4, characterized in that: Determining a target frequency according to the mapping relationship between the instantaneous frequency and the frequency includes: If the instantaneous frequency is below the first threshold, determining the target frequency to be a first number; If the instantaneous frequency is greater than the first threshold and less than the second threshold, the target frequency is determined to be a second number; the second number is greater than the first number; If the instantaneous frequency is greater than the second threshold and less than the third threshold, the target frequency is determined to be a third number; the third number is greater than the first number and less than the second number.

6. The method for broadband measurement of electric power system according to claim 5, characterized in that: The first threshold is 100 Hz, the second threshold is 650 Hz, the third threshold is 2500 Hz, the first number is 2, the second number is 8, and the third number is 3.

7. The method for broadband measurement of electric power system according to claim 1, characterized in that: The method of decomposing the target electrical signal into a plurality of single-frequency signal components in the time domain based on the local mean decomposition algorithm includes: Using the current target electrical signal as an intermediate electrical signal; Find all local maximum points and local minimum points of the intermediate electrical signal; Performing cubic spline interpolation on the local maximum point to obtain an upper envelope function, and performing cubic spline interpolation on the local minimum point to obtain a lower envelope function; According to the upper envelope function and the lower envelope function, a local mean function and a local envelope function of this round are obtained; Determine whether the local envelope function of this round is equal to 1; If not, subtract the local mean function from the intermediate electrical signal and divide the resultant result by the local envelope function, and return to the step of finding all local maximum points and local minimum points of the intermediate electrical signal to continue the execution; If yes, then multiply the local envelope functions of this round and all previous ones to obtain an envelope signal, and take the local mean function of this round as a pure frequency modulation signal, and obtain the signal component according to the envelope signal and the pure frequency modulation signal; After obtaining the new signal component, determining whether an iteration end condition is met; If not, subtract the newly obtained signal component from the target electrical signal, and return to the step of taking the current target electrical signal as the intermediate electrical signal to continue the execution; If so, decomposing the target electrical signal into a plurality of signal components of a single frequency is completed.

8. The method for broadband measurement of electric power system according to claim 7, characterized in that: The determining whether the iteration end condition is met includes: If the number of the local maximum points and the local minimum points in the newly obtained signal component is less than a first threshold, it is determined that the iteration end condition is met; otherwise, it is determined that the iteration end condition is not met.

9. A broadband measurement device for an electric power system, characterized in that: include: A data acquisition module, used for acquiring target electrical signals; A signal decomposition module, used for decomposing the target electrical signal into a plurality of single-frequency signal components in the time domain based on a local mean decomposition algorithm; A frequency determination module, used to determine the instantaneous frequency of each of the signal components; A selection module, used to determine a corresponding time-frequency conversion algorithm and time-frequency conversion parameters according to the instantaneous frequency; The time-frequency conversion module is used to perform time-frequency conversion on the signal components according to the corresponding time-frequency conversion parameters and using the corresponding time-frequency conversion algorithm to obtain a broadband spectrum including frequency domain information corresponding to each of the signal components.

10. A computer device, characterized in that: The method comprises one or more processors and a memory, wherein the memory stores computer-readable instructions, and when the computer-readable instructions are executed by the one or more processors, the steps of the power system broadband measurement method according to any one of claims 1 to 8 are executed.

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