Power grid ultra-high-order harmonic detection method and device based on adaptive compressed sensing

Abstract

The invention provides a power grid ultra-high-order harmonic detection method and device based on adaptive compressed sensing. The detection method comprises the following steps: step 1, acquiring a power signal of a detected area in a power grid to obtain a discrete power signal; step 2, sampling an original signal vector; 3, carrying out DFT operation on the sampled signal matrix, and carrying out modular operation to obtain a modular value matrix and a maximum modular value; 4, constructing a module value distribution matrix according to the distribution of module values in the module value matrix; step 5, carrying out compression processing on the original signal vector by adopting a compressed sensing method, and carrying out signal reconstruction to solve a sparse coefficient vector; step 6, carrying out Fourier transform on the sparse coefficient vector, and selecting a plurality of maximum peak values in a module value sequence to carry out characteristic frequency calculation; and step 7, constructing a filter bank according to the characteristic frequency, filtering the module value sequence, and converting to obtain an ultra-high-order harmonic detection result. According to the invention, the efficiency and precision of power grid ultra-high-order harmonic detection can be improved.

Description

Technical Field

[0001] The present application relates to the field of power system detection technology, and in particular to a method and device for detecting ultra-high-order harmonics in a power grid based on adaptive compressed sensing. Background Art

[0002] With the widespread application of various power electronic devices, the nonlinear and harmonic problems in the current power system are becoming more and more serious, among which the ultra-high harmonic problem is particularly prominent. In particular, the large-scale use of electric vehicle charging piles, LED lights and various power electronic switching devices has led to the increasingly prominent problem of ultra-high harmonics in the 2-150kHz spectrum range. Due to the high frequency, small amplitude and energy concentration of ultra-high harmonics in a specific frequency band, ultra-high harmonics cause great damage to power equipment in the distribution system. Therefore, how to quickly and accurately evaluate ultra-high harmonics in the power grid has become an important issue for the stable operation of the power system.

[0003] In the prior art, frequency domain analysis methods such as DFT (discrete Fourier transform) are usually used to detect and evaluate ultra-high harmonic signals in power grid signals. However, due to the high frequency of ultra-high harmonics, a high sampling rate and many sampling points are required. The traditional discrete Fourier transform DFT method has low execution efficiency and requires a large amount of data transmission and computing resources. It is not suitable for real-time detection and evaluation tasks. In addition, since the 2-150kHz frequency band where ultra-high harmonics are located is too wide, the traditional DFT method can only achieve a rough analysis of ultra-high harmonics, but cannot perform detailed ultra-high harmonic analysis on specific signals, making it difficult to obtain detailed parameters of ultra-high harmonics, and thus unable to achieve rapid and accurate detection and evaluation of ultra-high harmonics in power grids. Summary of the invention

[0004] The purpose of this application is to provide a method and device for detecting ultra-high-order harmonics in a power grid based on adaptive compressed sensing, which can improve the efficiency and accuracy of ultra-high-order harmonic detection in the power grid, and can also improve data transmission efficiency, reduce data storage requirements and required computing resources.

[0005] To achieve the above objectives, the embodiments of the present application provide: A method for detecting ultra-high harmonics in a power grid based on adaptive compressed sensing, comprising the following steps: Step 1: Collect power signals from the inspected area in the power grid x ( t )get N Point discrete power signal x ( n ), n =1, 2,…, N , and form the original signal vector X =[ x(1), x (2), x (3), …, x ( N )]; Step 2: The original signal vector X Sampling is performed to construct ( N / V)* L dimensional sampled signal matrix , where V represents the sampling multiple, L The number of rows in the matrix corresponds to the number of intervals required to divide the ultra-high harmonic frequency range; Step 3: Sample signal matrix Perform DFT operation on each row vector in to obtain a DFT coefficient vector matrix Z, and perform modular operation on the DFT coefficient vector matrix Z to obtain a modular value matrix and a maximum modular value V m ; Step 4: According to the maximum modulus value V m Divide each modulus value in each modulus value matrix into multiple intervals, and construct a modulus value distribution matrix according to the distribution of the modulus values ​​in the modulus value matrix Q ; Step 5: According to the module value distribution matrix Q Adopting adaptive compressed sensing method to transform the original signal vector X Perform compression processing to obtain the compressed signal vector Y , and by the compressed signal vector Y Signal reconstruction is performed to solve the sparse coefficient vector S. In the adaptive compressed sensing method, the modulus distribution matrix Q Calculating sparsity K ; Step 6: Calculate the sparse coefficient vector S Perform Fourier transform and construct a complex vector from the Fourier transform coefficients H , and select the complex vector H The modulus sequence of Calculate the characteristic frequencies of the largest multiple peaks; Step 7: Construct a nonlinear filter bank based on the calculated characteristic frequencies G , using the nonlinear filter bank G to filter the module value sequence After filtering, the ultra-high harmonic detection results are obtained through logarithmic and discrete cosine transform.

[0006] In step 4, according to the maximum modulus value V m The golden section principle is used to divide each module value in each module value matrix into multiple intervals in a nonlinear partitioning manner. The calculation expression for interval partition is: in, is the division coefficient, u is the division interval number, and the value range of u is 1 to L , when u= L The corresponding interval is ; Constructing the module distribution matrix Q for: in, Represents the modulus distribution matrix Q Middle i Line j The elements of the column correspond to the sampled signal matrix Middle row vector The corresponding modulus value is distributed in The number of intervals.

[0007] As an optional implementation, step 5 uses an adaptive compressed sensing method to process the original signal vector X Compression processing includes: Step 51: Build M×N The measurement matrix Φ of dimension Φ is expressed as:

[0008] Among them, the matrix elements The mean is 0 and the variance is Gaussian distribution, M represents the dimension of the compressed signal, K Indicates sparsity; Step 52: Obtain the compressed signal vector according to the measurement matrix Φ .

[0009] As an optional implementation, in step 5, according to the module value distribution matrix Q For the original signal vector X Perform compression processing to obtain the compressed signal vector Y Expressed as Y =[ y (1), y (2), y (3), …, y ( M )],in M The value satisfies: in, Represents the module value distribution matrixQ Medium Element The corresponding sparsity conversion coefficient, Indicates rounding up.

[0010] As an optional implementation, in step 5, a compressed sampling matching pursuit algorithm is used to extract the compressed signal vector Y Perform signal reconstruction to solve the sparse coefficient vector S , the calculation expression is: Among them, Φ represents M×N dimensional measurement matrix, Ψ Gaussian sparse basis ψ The matrix formed, α and β denote the frequency and time stretching factors respectively, and Respectively represent the frequency interval width and time interval width, , Corresponding to Each element of the matrix () function, namely = , = , , F Indicates the highest frequency value that needs to be analyzed. T express N Point discrete power signal x ( n )’s corresponding time.

[0011] As an optional implementation, in step 6, the characteristic frequencies corresponding to each peak are calculated using a two-point interpolation method. : in, and Respectively represent k Characteristic frequency The positions of the spectral maximum and the second maximum near represents the correction factor, Indicates the sampling frequency.

[0012] As an optional implementation, step 7 includes: Step 71: Construct a filter bank based on each characteristic frequencyG , and use a filter bank G Modulus sequence Filter and get the filtered sequence U； Step 72: Sequence U Take the logarithm to get the sequence L =log| U | 2 , for the sequence L After performing discrete cosine transform, the ultra-high harmonic detection results are obtained V =[ v (1), v (2), …, v ( k ), …, v ( K )], v ( k ) represents the characteristic frequency The aggregate energy normalization parameter in the interval, K Represents the number of characteristic frequencies.

[0013] As an optional implementation, in step 7, the nonlinear filter bank G include K nonlinear filter, where k Filters The calculation expression is: In the formula, n =1, 2, 3, ..., N , k =1, 2, 3, ..., K , Represents a sequence of modulo values The largest peak K The peaks are arranged from left to right k The sequence number corresponding to the peak point, Indicates k Peak value and k -1 The difference in sequence number between peaks, η and μ are the filter clustering coefficients, and their expressions are: in, Indicates k The characteristic frequency corresponding to the peak point.

[0014] As an optional implementation, in step 7, the calculation expression of the ultra-high harmonic detection result is obtained as follows: In the formula, Representation sequence L The i values, K Represents a filter bank G The number of internal filters, Represents a sequence of modulo values The largest peak K The peak k The peak value and k -1 The difference in sequence number between peaks.

[0015] A power grid ultra-high harmonic detection device based on adaptive compressed sensing, comprising: processor; a memory for storing processor-executable instructions; Wherein, the processor is configured to implement the above method when executing the instructions.

[0016] Compared with the prior art, the advantages of the present application are: the present application first samples the collected original power signal and performs DFT operation to obtain the modulus matrix of the coefficient vector matrix, obtains the modulus distribution matrix according to the distribution of the modulus value, and then uses the compressed sensing method based on the modulus distribution matrix to compress the original power signal and then reconstruct the signal, uses the signal reconstruction to determine the sparse coefficient vector and calculate the modulus sequence of the Fourier transform coefficient vector, calculates the characteristic frequencies corresponding to multiple peaks, and then uses the characteristic frequencies to construct a filter group, uses the filter group to perform adaptive energy aggregation on the reconstructed signal spectrum, and realizes ultra-high-order harmonic detection. It can make full use of the adaptive compressed sensing method and adaptive energy aggregation to quickly and accurately evaluate ultra-high-order harmonics to realize the ultra-high-order harmonic detection and evaluation method of the power grid, which can not only improve the detection accuracy and reliability, but also reduce the sampling and storage costs, so that the application requirements of real-time detection of ultra-high-order harmonics can be met. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The present invention will be described in more detail below based on embodiments and with reference to the accompanying drawings, wherein: Figure 1 It is a schematic diagram of the implementation process of a power grid ultra-high harmonic detection method based on adaptive compressed sensing according to an embodiment of the present application. DETAILED DESCRIPTION

[0018] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments, but the protection scope of the present invention is not limited thereby.

[0019] Ultra-high harmonics require a high sampling rate. Therefore, at a high sampling rate, if the traditional discrete Fourier transform DFT is used, a lot of resources will be spent on calculation, and a large amount of data transmission and computing resources will be required. The Compressed Sensing (CS) algorithm is based on the sampling theory of signal sparsity. By using a small amount of linear measurement data to reconstruct sparse signals and restore the original signal, the amount of data storage and transmission can be significantly reduced. In the compressed sensing algorithm, the sparsity in the transform domain can describe the number or distribution of non-zero coefficients of the signal in the transform domain. The higher the sparsity of the signal in the sparse domain, the fewer the number of measurements required. Therefore, the sparsity will directly affect the efficiency and quality of signal compression. In traditional compressed sensing algorithms, the sparsity is usually fixed, but the sparsity of actual power grid signals may change with time and environmental changes. Fixed sparsity cannot adapt to signal changes and cannot effectively distinguish between signals and noise, resulting in large reconstruction errors and poor robustness when the sparsity changes.

[0020] The present invention is aimed at the detection of ultra-high-order harmonics in power grids. By adopting a compressed sensing method to compress the power grid signal to be tested, the amount of data in the operation process can be effectively reduced, and the efficiency of ultra-high-order harmonic detection can be improved. At the same time, an adaptive mechanism is introduced in the compression processing process. By sampling and separating the original signal, the sparsity is dynamically calculated using the modulus distribution matrix calculated by the sampled signal matrix, and the adaptation of compressed sensing is realized. The sparsity can be dynamically adjusted in real time according to the sparse characteristics of the signal, and the accuracy and robustness of real-time signal reconstruction can be effectively improved. Therefore, the accuracy and robustness of detection can be ensured under the premise of improving the efficiency and real-time performance of ultra-high-order harmonic detection.

[0021] In addition, since the bandwidth of ultra-high harmonics is extremely high, it is necessary to adopt the method of frequency band aggregation to characterize the distribution of ultra-high harmonics in different frequency bands in the signal. Frequency band aggregation is to divide the ultra-high harmonic signals into different frequency bands, and analyze and aggregate the signals in each frequency band, so as to achieve the characterization of the distribution of ultra-high harmonics in different frequency bands in the signal. Through frequency band aggregation, ultra-high harmonics in the signal can be detected more accurately. Traditional frequency band aggregation methods include discrete wavelet transform (DWT) and sliding window algorithm, but the frequency band aggregation method of discrete wavelet transform has high computational complexity and needs to be highly dependent on the selected wavelet basis function. It is actually difficult to accurately select the appropriate wavelet basis function, and the sliding window algorithm has high storage overhead and poor ability to handle burst traffic, and it is difficult to handle a large number of requests in a short period of time or drastic changes in the signal.

[0022] The present invention further calculates the characteristic frequency on the basis of compressing the power grid signal based on the adaptive compressed sensing method, and then uses the characteristic frequency to construct a nonlinear filter group centered on the characteristic frequency. It can aggregate and analyze ultra-high-order harmonics in different frequency bands, so as to more accurately reflect the ultra-high-order harmonic distribution and realize adaptive frequency band aggregation. The adaptive frequency band aggregation method can adaptively divide the ultra-high-order harmonic frequency band according to the change of the intensity of each frequency component in the measured signal, thereby effectively improving the accuracy and reliability of ultra-high-order harmonic detection.

[0023] See also Figure 1 In this embodiment, the method for detecting ultra-high harmonics in a power grid based on adaptive compressed sensing comprises the following steps: Step 1: Collect power signals from the inspected area in the power grid x ( t )get N Point discrete power signal x ( n ), n =1, 2,…, N , and form the original signal vector X =[ x (1), x (2), x (3), …, x ( N )]; Step 2: The original signal vector X Sampling, construction ( N / V)* L dimensional signal matrix ,in L represents the number of rows of the matrix, corresponding to the number of intervals required to divide the ultra-high harmonic frequency range; V represents the sampling multiple, which can be a multiple of 2; Step 3: Signal matrix Perform DFT operations on each row vector in to obtain the DFT coefficient vector matrix Z, and perform modular operations on the DFT coefficient vector matrix Z to obtain the modular value matrix and the maximum modular value V m ; Step 4: According to the maximum modulus value V m Divide each modulus value in each modulus value matrix into L intervals, construct a modulus value distribution matrix based on the distribution of modulus values ​​in the modulus value matrix Q ; Step 5: Distribute the matrix according to the modulus value Q Adopting adaptive compressed sensing method to transform the original signal vector X Perform compression processing to obtain the compressed signal vectorY , and by the compressed signal vector Y Signal reconstruction is performed to solve the sparse coefficient vector S, where the modulus distribution matrix in the adaptive compressed sensing method is Q Calculating sparsity K ; Step 6: Sparse coefficient vector S Perform Fourier transform and construct a complex vector from the Fourier transform coefficients H , and select the complex vector H The modulus sequence of Calculate the characteristic frequencies of the largest multiple peaks; Step 7: Construct a nonlinear filter bank based on the calculated characteristic frequencies G , using nonlinear filter bank G to modulus sequence After filtering, the ultra-high harmonic detection results are obtained through logarithmic and discrete cosine transform.

[0024] The above method of this embodiment obtains the modulus value matrix of the coefficient vector matrix by sampling the collected original power signal and performing DFT operation, and obtains the modulus value distribution matrix according to the distribution of the modulus value. Q , and then based on the module value distribution matrix Q Adopting adaptive compressed sensing method to compress the original power signal and then reconstruct the signal, using dynamically changing modulus distribution matrix Q Sparsity calculation can reduce the amount of data in the operation process, and effectively improve the efficiency, accuracy and robustness of ultra-high harmonic detection; signal reconstruction is used to determine the sparse coefficient vector and calculate the modulus sequence of the Fourier transform coefficient vector, and the characteristic frequencies corresponding to multiple peaks are calculated. The filter group is constructed using the characteristic frequencies, and the filter group is used to perform adaptive energy aggregation on the reconstructed signal spectrum. It can adaptively divide the ultra-high harmonic frequency band according to the changes in the intensity of each frequency component in the measured signal, and improve the accuracy and reliability of ultra-high harmonic detection, so that the adaptive compressed sensing method and adaptive energy aggregation can be fully utilized to quickly and accurately evaluate ultra-high harmonics to realize the detection and evaluation of ultra-high harmonics in the power grid, which can not only improve the accuracy and reliability of detection, but also reduce the cost of sampling and storage, so as to meet the application requirements of real-time detection of ultra-high harmonics.

[0025] In step 2 of this embodiment, the original signal vector is sampled. X Sampling can reduce the sampling frequency, thereby reducing the computational complexity and data storage volume. In a possible implementation, it is possible to construct ( N / 64)*7-dimensional signal matrix That is, V is 64. L Take 7 and construct the signal matrix As shown below: (1) in, Represents the sampled signal matrix Middle Row elements, i=1~7.

[0026] Then in step 3, the signal matrix The 7 row vectors in to By performing DFT operation, we can obtain the DFT coefficient vector matrix Z, and then obtain its modulus matrix by modulo matrix Z, and record the maximum modulus value as V m ; Then according to the maximum modulus value V m Divide each modulus value in each modulus value matrix into multiple intervals, and construct a modulus value distribution matrix based on the distribution of the modulus values ​​in the modulus value matrix Q , to dynamically calculate the sparsity using the modulus distribution matrix K , can adapt to the real-time changes of power grid signals and dynamically calculate the matching sparsity, thus improving the accuracy and robustness of signal compression and reconstruction. The distribution of modulus values ​​is the probability distribution of different modulus values ​​in different value ranges. The modulus distribution matrix Q That is the sampled signal matrix The distribution of the modulus values ​​of each element in the module, the modulus distribution matrix Q Each element in corresponds to the sampled signal matrix The number of row vectors distributed in each interval.

[0027] In one possible implementation, according to the maximum modulus value V m The golden section principle can be used to divide each module value in each module value matrix into multiple intervals, that is, the calculation expression for interval division is: (2) in, is the division coefficient, which can be taken as , u is the partition interval number, and its value range is 1 to L ; In particular, when u= L When , that is, the last partition interval, its interval range is: .

[0028] For example, L When 7 is taken, according to the above formula (2), the interval can be divided into: P1=[0, 0.382 V m ), P2=[0.382 Vm ,0.618 V m ), P3=[0.618 V m , 0.764 V m ), P4=[0.764 V m , 0.854 V m ), P5=[0.854 V m , 0.91 V m ), P6=[0.91 V m , 0.956 V m ), P7=[0.956 V m , V m ].

[0029] Then construct the module value distribution matrix Q for: (3) in, Represents the modulus distribution matrix Q Middle i Line j The elements of the column correspond to the sampled signal matrix Middle row vector The corresponding modulus value is distributed in The number of intervals, i, j =1~ L .For example, That is, row vector The number of corresponding modulus values ​​distributed in the first interval P1.

[0030] Then, after sampling according to formula (1), the modulus distribution matrix Q can be constructed as follows: (4) In this embodiment, the original signal vector is transformed by using an adaptive compressed sensing method. X Compression processing can compress high-dimensional power grid signals into low-dimensional sparse signals, improve data transmission efficiency, and effectively reduce data collection and storage requirements. In a possible implementation, step 5 uses a compressed sensing method to compress the original signal vector X Compression processing includes: Step 51: Build M×N The measurement matrix Φ of dimension Φ is expressed as: (5) Among them, the matrix elements The mean is 0 and the variance is Gaussian distribution, M represents the dimension of the compressed signal, K Represents the sparsity, which is based on the modulus distribution matrix Q Dynamically calculated; Step 52: Use the measurement matrix Φ to obtain the compressed signal vector Y : .

[0031] In the process of compression processing using the adaptive compressed sensing method, the selection of the sparse basis will directly affect the accuracy and reliability of compression and reconstruction. Q For the original signal vector X Perform compression processing to obtain the compressed signal vector Y It can be expressed as Y =[ y (1), y (2), y (3), …, y ( M )], M Represents the dimension of the compressed signal, first based on the modulus distribution matrix Q Dynamically calculate sparsity K , and then use the sparsity K Then determine the dimension of the compressed signal M The value range of can adaptively adjust the compression processing strategy according to the state of the real-time signal, ensuring that the original signal is accurately characterized by compressed sensing in the case of ultra-high harmonic changes, further improving the accuracy and robustness of signal reconstruction.

[0032] In one possible implementation, the discrete power signal x ( n )'s Gaussian transform domain sparsity K It can be calculated as follows: (6) in, Represents the modulus distribution matrix Q Middle i Line j Elements of a column The corresponding sparsity conversion coefficient, Indicates rounding up.

[0033] Dimension of compressed signal M The value must meet the following requirements: (7) In a possible implementation, in step 5, a compressed sampling matching pursuit algorithm is used to compress the compressed signal vector Y Perform signal reconstruction to solve the sparse coefficient vector S , the calculation expression is: (8) Among them, Φ represents M×N dimensional measurement matrix, the superscript “-1” indicates the pseudo-inverse matrix operation. Ψ is a sparse basis matrix, which is composed of Gaussian sparse basis ψ This embodiment adopts a dynamic sparse basis, so that the sparse basis form can be changed according to the frequency change in the measured signal, so that the original signal can be better compressed and reconstructed, and the accuracy of ultra-high harmonic analysis can be further improved.

[0034] For example, the following improved Gaussian sparse basis can be used Ψ : (9) (10) in, α and β denote the frequency and time stretching factors respectively, and Represent the frequency and time interval width respectively, , Corresponding to the sparse basis Each element of the matrix The two variables in the () function, namely = , = , For example, for a sparse basis The last element in the first row of the matrix , middle express , express .

[0035] In one possible implementation, and It can be calculated as follows: (11) in, F Indicates the highest frequency value that needs to be analyzed. T Indicates power signal N point x (n )’s corresponding time.

[0036] In this embodiment, a sparse base is constructed by adopting the above method. Ψ , sparse basis Ψ The shape will change with the frequency and time interval width of the real-time measured signal, forming a dynamic sparse basis, which can adapt to the changes of the real-time signal for accurate compression and reconstruction.

[0037] In a possible implementation, in step 6, the characteristic frequencies corresponding to each peak value may be calculated by using a two-point interpolation method. , for example, the calculation expression can be expressed as: (12) in, and Respectively represent k Characteristic frequency The positions of the spectral maximum and the second maximum near represents the correction coefficient. That is, by selecting the characteristic frequency The spectrum maximum near and the next largest value Calculate the characteristic frequency corresponding to the peak , we can get the detailed parameters of ultra-high harmonics, through this characteristic frequency And the corresponding spectral lines, the amplitude of ultra-high harmonics can be accurately calculated.

[0038] Considering that the frequency band of ultra-high harmonics is too wide, it is necessary to perform frequency band aggregation. Constructed with characteristic frequency A nonlinear filter bank centered on G , so as to aggregate and analyze ultra-high harmonics of different frequency bands. In a possible implementation, step 7 includes: Step 71: Construct a nonlinear filter bank based on each characteristic frequency G , and use a filter bank G Modulus sequence Filter and obtain the filtered K-dimensional sequence U； Step 72: Sequence U Take the logarithm to get the sequence L =log| U | 2 , for the sequence L After performing discrete cosine transform, the ultra-high harmonic detection results are obtained V =[ v (1), v (2), …, v (k ), …, v ( K )], v ( k ) represents the characteristic frequency The aggregate energy normalization parameter in the interval, K To determine the sparsity, that is, the number of characteristic frequencies.

[0039] In this embodiment, the nonlinear filter bank G Based on the peak value in the modulus sequence, the nonlinear aggregation method can efficiently display the characteristics of ultra-high harmonics in different bandwidths within the 2~150kHz frequency band. The ultra-high harmonic frequency band can be adaptively divided according to the changes in the intensity of each frequency component in the measured signal, thereby facilitating accurate analysis of the impact of ultra-high harmonics on the power system. In one possible implementation, the nonlinear filter group G Specifically by K Nonlinear filter Composition, k Filters The expression can be expressed as: (13) (14) In the formula, n =1, 2, 3, ..., N , k =1, 2, 3, ..., K , K To determine the sparsity, Represents the modulus sequence of Fourier transform coefficients The largest peak K The peaks are arranged from left to right k The sequence number corresponding to the peak point, Indicates k The peak value and k -1 The difference in sequence number between the peaks, where p (0) represents the frequency spectrum position corresponding to the frequency of 2000Hz, that is, the expression is: (15) η and μ are the filter clustering coefficients, and their expressions are: (16) in, Indicates k The characteristic frequency corresponding to the peak point.

[0040] In a possible implementation, in step 7, the calculation expression of the ultra-high harmonic detection result obtained can be expressed as: (17) In the formula, Representation sequence L The i values, K Represents the determined sparsity, corresponding to the filter group G The number of internal filters, Represents a sequence of modulo values The largest peak K The peak k The peak value and k -1 The difference in sequence number between peaks.

[0041] Since the impact of ultra-high harmonics on the power system is different in different frequency bands, and due to the influence of nonlinear frequency band division, different frequency bands are obtained by superimposing ultra-high harmonics in different bandwidths, so different frequency bands will form differences, which is not conducive to evaluating ultra-high harmonics at the same level. In this embodiment, the characteristic frequency is extracted by cosine transform Aggregate energy normalization parameter within the interval As a result of ultra-high-order harmonic detection, the difference problem formed by different frequency bands can be eliminated, so that ultra-high-order harmonics in different frequency bands can be evaluated at the same level, and the detailed parameters of ultra-high-order harmonics can be efficiently obtained, thereby quickly realizing accurate detection of ultra-high-order harmonics.

[0042] In summary, the present invention compresses and reconstructs high-dimensional power grid signals into low-dimensional sparse signals by adopting an adaptive compressed sensing method, which can effectively improve data transmission efficiency and reduce data acquisition and storage requirements. At the same time, by performing spectral analysis on the reconstructed signal, using the characteristic frequency to construct a nonlinear filter group, and using the filter group to perform adaptive energy aggregation on the reconstructed signal spectrum, accurate and efficient evaluation of ultra-high-order harmonics in the power grid can be achieved, which can effectively realize rapid and precise evaluation of ultra-high-order harmonics in the power grid.

[0043] In another embodiment of the present application, a power system frequency measurement device based on iterative optimization includes: processor; a memory for storing processor-executable instructions; The processor is configured to implement the above method when executing instructions. The processor and the memory are directly or indirectly electrically connected to realize data transmission or interaction. For example, these elements can be electrically connected through one or more communication buses or signal buses. The above control methods each include at least one software function module that can be stored in the memory in the form of software or firmware.

[0044] The processor can be an integrated circuit chip with signal processing capabilities. The processor can be a general-purpose processor, including a CPU (Central Processing Unit), an NP (Network Processor), etc.; it can also be a digital signal processor, an application-specific integrated circuit, a ready-made programmable gate array or other programmable logic device, a discrete gate or transistor logic device, a discrete hardware component. It can implement or execute the methods, steps and logic block diagrams disclosed in the embodiments of the present invention. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor, etc.

[0045] The memory can store various software programs and modules, such as the program instructions / modules corresponding to the image processing method and device provided in the embodiments of the present invention. The processor executes various functional applications and data processing by running the software programs and modules stored in the memory, that is, implementing the method in the embodiments of the present application. The memory may include but is not limited to RAM (Random Access Memory), ROM (Read Only Memory), PROM (Programmable Read-Only Memory), EPROM (ErasableProgrammable Read-Only Memory), EEPROM (Electric ErasableProgrammable Read-Only Memory), etc.

[0046] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

[0047] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0048] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 These computer program instructions can also be loaded onto a computer or other programmable data processing device, so that a series of operation steps are executed on the computer or other programmable device to produce a computer-implemented process, so that the instructions executed on the computer or other programmable device provide for implementing the process in the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0049] Although the present invention has been described with reference to preferred embodiments, various modifications may be made thereto and parts thereof may be replaced by equivalents without departing from the scope of the present invention. In particular, the various technical features mentioned in the various embodiments may be combined in any manner as long as there are no structural conflicts. The present invention is not limited to the specific embodiments disclosed herein, but includes all technical solutions falling within the scope of the claims.

Claims

1. A method for detecting ultra-high harmonics in a power grid based on adaptive compressed sensing, characterized in that the steps include: Step 1: Collect power signals from the inspected area in the power grid x ( t )get N Point discrete power signal x ( n ), n =1, 2, …, N , and form the original signal vector X =[ x (1), x (2), x (3), …, x ( N )]; Step 2: The original signal vector X Sampling is performed to construct ( N / V)* L dimensional sampled signal matrix , where V represents the sampling multiple, L The number of rows in the matrix corresponds to the number of intervals required to divide the ultra-high harmonic frequency range; Step 3: Sample signal matrix Perform DFT operation on each row vector in to obtain a DFT coefficient vector matrix Z, and perform modular operation on the DFT coefficient vector matrix Z to obtain a modular value matrix and a maximum modular value V m ; Step 4: According to the maximum modulus value V m Divide each modulus value in each modulus value matrix into multiple intervals, and construct a modulus value distribution matrix according to the distribution of the modulus values ​​in the modulus value matrix Q ; Step 5: According to the module value distribution matrix Q Adopting adaptive compressed sensing method to transform the original signal vector X Perform compression processing to obtain the compressed signal vector Y , and by the compressed signal vector Y Signal reconstruction is performed to solve the sparse coefficient vector S. In the adaptive compressed sensing method, the modulus distribution matrix Q Calculating sparsity K; Step 6: Calculate the sparse coefficient vector S Perform Fourier transform and construct a complex vector from the Fourier transform coefficients H , and select the complex vector H The modulus sequence of Calculate the characteristic frequencies of the largest multiple peaks; Step 7: Construct a nonlinear filter bank based on the calculated characteristic frequencies G , using the nonlinear filter bank G to filter the module value sequence After filtering, the ultra-high harmonic detection results are obtained through logarithmic and discrete cosine transform.

2. The method for detecting ultra-high harmonics in a power grid based on adaptive compressed sensing according to claim 1, characterized in that: In step 4, according to the maximum modulus value V m The golden section principle is used to divide each module value in each module value matrix into multiple intervals in a nonlinear partitioning manner. The calculation expression for interval partition is: in, is the division coefficient, u is the partition interval number, and u The value range is 1 to L ,when u = L The corresponding interval is ; Constructing the module distribution matrix Q for: in, Represents the modulus distribution matrix Q Middle i Line j The elements of the column correspond to the sampled signal matrix Middle row vector The corresponding modulus value is distributed in The number of intervals.

3. The method for detecting ultra-high harmonics in a power grid based on adaptive compressed sensing according to claim 1, characterized in that: Step 5: Adopt adaptive compressed sensing method to the original signal vector X Compression processing includes: Step 51: Build M×N The measurement matrix Φ of dimension Φ is expressed as: Among them, the matrix elements The mean is 0 and the variance is Gaussian distribution, M represents the dimension of the compressed signal, K Indicates sparsity; Step 52: Obtain the compressed signal vector according to the measurement matrix Φ .

4. The method for detecting ultra-high harmonics in a power grid based on adaptive compressed sensing according to claim 3 is characterized in that: In step 5, according to the module value distribution matrix Q For the original signal vector X Perform compression processing to obtain the compressed signal vector Y Expressed as Y =[ y (1), y (2), y (3), …, y ( M )],in M The value satisfies: in, Represents the module value distribution matrix Q Middle i Line j Elements of a column The corresponding sparsity conversion coefficient, Indicates rounding up.

5. The method for detecting ultra-high harmonics in a power grid based on adaptive compressed sensing according to claim 1, characterized in that: In step 5, the compressed signal vector is subjected to the compression sampling matching pursuit algorithm. Y Perform signal reconstruction to solve the sparse coefficient vector S , the calculation expression is: Among them, Φ represents M×N dimensional measurement matrix, Gaussian sparse basis ψ The matrix formed, α and β denote the frequency and time stretching factors respectively, and Respectively represent the frequency interval width and time interval width, , Corresponding to Each element of the matrix ψ () function, namely = , = , , F Indicates the highest frequency value that needs to be analyzed. T express N Point discrete power signal x ( n )’s corresponding time.

6. The method for detecting ultra-high harmonics in a power grid based on adaptive compressed sensing according to any one of claims 1 to 5, characterized in that: In step 6, the two-point interpolation method is used to calculate the characteristic frequency corresponding to each peak : in, and Respectively represent k Characteristic frequency The positions of the spectral maximum and the second maximum near represents the correction factor, Indicates the sampling frequency.

7. The method for detecting ultra-high harmonics in a power grid based on adaptive compressed sensing according to any one of claims 1 to 5, characterized in that: Step 7 includes: Step 71: Construct a nonlinear filter bank based on each characteristic frequency G , and adopts nonlinear filter bank G Modulus value sequence Filter and get the filtered sequence U； Step 72: Sequence U Take the logarithm to get the sequence L =log| U | 2 , for the sequence L After performing discrete cosine transform, the ultra-high harmonic detection results are obtained V =[ v (1), v (2), …, v ( k ), …, v ( K )], v ( k ) represents the characteristic frequency The aggregate energy normalization parameter in the interval, K Indicates the determined sparsity.

8. The method for detecting ultra-high harmonics in a power grid based on adaptive compressed sensing according to claim 7, characterized in that: In step 7, the nonlinear filter bank G include K nonlinear filter, where k Filters The calculation expression is: In the formula, n =1, 2, 3, ..., N , k =1, 2, 3, ..., K , p ( k ) represents the modulus sequence The largest peak K The peaks are arranged from left to right k The sequence number corresponding to the peak point, Indicates k Peak value and k -1 The difference in sequence number between peaks, η and μ are the filter clustering coefficients, and their expressions are: in, Indicates k The characteristic frequency corresponding to the peak point.

9. The method for detecting ultra-high harmonics in a power grid based on adaptive compressed sensing according to claim 7, characterized in that: In step 7, the calculation expression of the ultra-high harmonic detection result is obtained as follows: In the formula, Representation sequence L The i values, K Represents the determined sparsity, corresponding to the filter group G The number of internal filters, Represents a sequence of modulo values The largest peak K The peak k The peak value and k -1 The difference in sequence number between peaks.

10. A power grid ultra-high harmonic detection device based on adaptive compressed sensing, characterized in that: include: processor; a memory for storing processor-executable instructions; Wherein, the processor is configured to implement the method described in any one of claims 1 to 9 when executing the instructions.

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