A harmonic estimation method and device suitable for large-scale electric vehicle access

Abstract

A harmonic estimation method and device adapted to large-scale electric vehicle access, the method comprising: setting a solution domain, and establishing an initial solution domain center and an initial value of the solution domain radius; using Gaussian distribution to generate a candidate solution set in the solution domain; substituting the candidate solution set into an objective function for iterative calculation, and comparing the optimal solution obtained in each iteration with the optimal solution obtained in the previous iteration, retaining the optimal solution; searching for a local optimal solution by moving the solution domain center and reducing the solution domain radius, and then using the local optimal solution to calculate the amplitude and phase of each harmonic. The harmonic estimation method proposed by the present invention has the advantages of small amount of calculation, high precision, low computational complexity, and good convergence performance, and can achieve more accurate harmonic estimation under large-scale electric vehicle access.

Description

Technical Field

[0001] The present invention relates to the technical field of signal analysis, and in particular to a harmonic estimation method and device suitable for large-scale electric vehicle access. Background Art

[0002] In recent years, due to the large-scale access of nonlinear loads such as electric vehicles to the power grid, a large number of harmonics have been generated, which seriously threatens the safe and stable operation of the power system. In order to ensure the power quality of the power grid, it is necessary to quickly and accurately estimate harmonics and interharmonics so that preventive measures can be taken to ensure the efficient and stable operation of the power system.

[0003] At present, the main harmonic estimation algorithms include Fourier transform, wavelet transform, Hilbert-Huang transform, etc. Fourier transform is the most widely used method, but it is prone to aliasing and spectrum leakage when applied to distorted signals, and cannot achieve accurate separation and estimation of harmonics. Wavelet transform has high computational complexity and may cause spectrum leakage in real-time applications. Hilbert-Huang transform can accurately analyze distorted signals, but it is only applicable to narrowband signals. When analyzing broadband signals, some frequencies may be missing, resulting in large errors. In summary, the existing harmonic estimation algorithms are difficult to achieve accurate estimation of harmonics under large-scale electric vehicle access. Summary of the invention

[0004] The purpose of the present invention is to overcome the defects and problems of low harmonic estimation accuracy in the prior art and to provide a high-precision harmonic estimation method and device suitable for large-scale electric vehicle access.

[0005] To achieve the above objectives, the technical solution of the present invention is: a harmonic estimation method suitable for large-scale electric vehicle access, comprising:

[0006] Set the solution domain and establish the initial solution domain center and initial solution domain radius;

[0007] Generate candidate solution sets in the solution domain using Gaussian distribution;

[0008] Substitute the candidate solution set into the objective function for iterative calculation, and compare the optimal solution obtained in each iteration with the optimal solution obtained in the previous iteration, and retain the optimal solution;

[0009] By moving the center of the solution domain and reducing the radius of the solution domain, a local optimal solution search is performed, and then the amplitude and phase of each harmonic are calculated using the local optimal solution.

[0010] The initial solution domain center μ0 is:

[0011] μ0=(l h +l l ) / 2

[0012] In the formula, l h and l l are the upper and lower boundaries of the solution domain, respectively.

[0013] Gaussian distribution is used to generate candidate solution sets in the solution domain, as shown in the following formula:

[0014]

[0015] Where n is the dimension of the solution space; y is the random variable; μ is the center of the solution domain; T is the solution period; ∑ is the covariance matrix.

[0016] Determine whether the candidate solution set satisfies the constraints. If not, make the candidate solution set satisfy the constraints through the following formula:

[0017]

[0018] In the formula, is the kth solution of the ith dimension; rand is the random generation function; l h and l l are the upper and lower boundaries of the solution domain, respectively.

[0019] The objective function is:

[0020]

[0021] Where N is the highest harmonic order; k is the harmonic order, which can be 1, 2, ..., N; y k is the measurement signal; kest is the function that fits the measured signal;

[0022] y kest =H[k]C[k];

[0023]

[0024] C[k]=[C1(k) C2(k) …C 2N+2 (k)] T ;

[0025] Where H[k] is the basis function representing the fundamental wave and harmonic wave; C[k] is the coefficient before each basis function; f0 is the fundamental frequency of the system; T s is the sampling period;

[0026] C=[a1cosθ1 a1sinθ1…a N cosθ N a N sinθ N B dc B dc b dc ] T;

[0027] In the formula, C is the amplitude and phase of the harmonic to be determined; a and θ are the amplitude and phase of each harmonic respectively; B dc and b dc is the attenuation term.

[0028] Search for the local optimal solution by moving the center of the solution domain and reducing the radius of the solution domain:

[0029]

[0030]

[0031] In the formula, r t is the radius of the solution domain after reduction; σ0 is the initial value of the radius of the solution domain; gammaincv is the inverse of the gamma function; x is the search vector; μ t is the center of the solution domain after moving; t is the number of iterations; MaxItr is the maximum number of iterations.

[0032] The amplitude and phase of each harmonic are calculated using the local optimal solution:

[0033]

[0034]

[0035] In the formula, a N is the amplitude of the harmonic; θ N is the harmonic phase; C 2N is the 2Nth term of vector C; C 2N-1 is the 2N-1th item of vector C.

[0036] A harmonic estimation device adapted to large-scale electric vehicle access, the device is applied to the above-mentioned method, the device comprising:

[0037] The solution domain setting module is used to set the solution domain and establish the initial solution domain center and the initial value of the solution domain radius;

[0038] A candidate solution set determination module, used for generating a candidate solution set in a solution domain using Gaussian distribution;

[0039] The optimal solution acquisition module is used to substitute the candidate solution set into the objective function for iterative calculation, and compare the optimal solution obtained in each iteration with the optimal solution obtained in the previous iteration, and retain the optimal solution;

[0040] The harmonic amplitude and phase calculation module is used to search for a local optimal solution by moving the center of the solution domain and reducing the radius of the solution domain, and then use the local optimal solution to calculate the amplitude and phase of each harmonic.

[0041] A harmonic estimation device adapted to large-scale electric vehicle access, comprising a memory and a processor;

[0042] The memory is used to store computer program code and transmit the computer program code to the processor;

[0043] The processor is used to execute the method as described above according to the instructions in the computer program code.

[0044] A computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method described above is implemented.

[0045] Compared with the prior art, the present invention has the following beneficial effects:

[0046] In a harmonic estimation method and device suitable for large-scale electric vehicle access, the eddy current search algorithm proposed in the present invention generates a candidate solution set of the amplitude and phase of each harmonic through Gaussian distribution, and uses this candidate solution set to approximate the actually measured voltage and current signals, thereby realizing accurate estimation of the amplitude and phase of each harmonic. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 The present invention is a flow chart of a harmonic estimation method suitable for large-scale electric vehicle access.

[0048] Figure 2 It is a waveform diagram of simulation results in an embodiment of the present invention.

[0049] Figure 3 2 is a graph showing the harmonic amplitude results in an embodiment of the present invention.

[0050] Figure 4 4 is a diagram of phase estimation results in an embodiment of the present invention.

[0051] Figure 5 The present invention is a structural block diagram of a harmonic estimation device suitable for large-scale electric vehicle access.

[0052] Figure 6 The present invention is a structural block diagram of a harmonic estimation device suitable for large-scale electric vehicle access.

[0053] Figure 7 It is a structural block diagram of a dual-processor embedded device in an embodiment of the present invention.

[0054] Figure 8 It is a working state diagram of a dual-processor embedded device in an embodiment of the present invention. DETAILED DESCRIPTION

[0055] The present invention is further described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0056] See also Figure 1 , a harmonic estimation method adapted to large-scale electric vehicle access, comprising:

[0057] S1. Set the solution domain and establish the initial solution domain center and the initial value of the solution domain radius.

[0058] The initial solution domain center μ0 is:

[0059] μ0=(l h +l l ) / twenty one)

[0060] In the formula, l h and l l are the upper and lower boundaries of the solution domain, respectively.

[0061] S2. Generate a candidate solution set in the solution domain using Gaussian distribution, as shown in the following formula:

[0062]

[0063] Where n is the dimension of the solution space; y is the random variable; μ is the center of the solution domain; T is the solution period; ∑ is the covariance matrix.

[0064] Determine whether the candidate solution set satisfies the constraints. If not, make the candidate solution set satisfy the constraints through the following formula:

[0065]

[0066] In the formula, is the kth solution of the ith dimension; rand is the random generation function; l h and l l are the upper and lower boundaries of the solution domain, respectively.

[0067] S3. Substitute the candidate solution set into the objective function for iterative calculation, and compare the optimal solution obtained in each iteration with the optimal solution obtained in the previous iteration, and retain the optimal solution.

[0068] The optimal solution objective function is:

[0069]

[0070] Where N is the highest harmonic order; k is the harmonic order, which can be 1, 2, ..., N; y k is the measurement signal; kest is the function that fits the measured signal;

[0071] y kest=H[k]C[k] (5)

[0072]

[0073] C[k]=[C1(k) C2(k)…C 2N+2 (k)] T (7)

[0074] Where H[k] is the basis function representing the fundamental wave and harmonic wave; C[k] is the coefficient before each basis function; f0 is the fundamental frequency of the system; T s is the sampling period;

[0075] C=[a1cosθ1 a1sinθ1...a N cosθ N a N sinθ N B dc B dc b dc ] T (8)

[0076] In the formula, C is the amplitude and phase of the harmonic to be determined; a and θ are the amplitude and phase of each harmonic respectively; B dc and b dc is the attenuation term.

[0077] S4. By moving the center of the solution domain and reducing the radius of the solution domain, a local optimal solution search is performed, and then the amplitude and phase of each harmonic are calculated using the local optimal solution.

[0078] By moving the center of the solution domain and reducing the radius of the solution domain, the scope of the solution domain is gradually reduced, and the above steps are repeated to search for the local optimal solution:

[0079]

[0080]

[0081] In the formula, r t is the radius of the solution domain after reduction; σ is the initial value of the radius of the solution domain; gammaincv is the inverse of the gamma function; x is the search vector; μ t is the center of the solution domain after moving; t is the number of iterations; MaxItr is the maximum number of iterations.

[0082] The amplitude and phase of each harmonic are calculated using the local optimal solution:

[0083]

[0084]

[0085] In the formula, a N is the amplitude of the harmonic; θ N is the harmonic phase; C 2N is the 2Nth term of vector C; C 2N-1 is the 2N-1th item of vector C.

[0086] The present invention first sets the solution domain and generates a candidate solution set using Gaussian distribution. Secondly, the optimal solution obtained in each iteration is compared with the optimal solution obtained in the previous iteration, and the optimal solution is retained. Then the center of the solution domain is moved to the point where the optimal solution was obtained in the previous iteration. As the iteration proceeds, the iteration radius is reduced and the local optimal solution search is performed. Finally, the amplitude and phase of each harmonic can be determined by iteratively calculating the harmonic amplitude and phase using the eddy current search algorithm. The harmonic estimation method proposed by the present invention decomposes the signal model into an orthogonal component and an in-phase component, for example, y=a*sin(t+θ)=a*sin(t)cos(θ)+a*cos(t)sin(θ), wherein a*sin(t)cos(θ) is an in-phase component and a*cos(t)sin(θ) is an orthogonal component; the corresponding basis function vector is H=[sin(t)cos(t)], the parameter to be estimated is C=[a*cos(θ)a*sin(θ)], and then the eddy current search algorithm is used to find the optimal model parameters under different signal-to-noise ratios, thereby determining the harmonic amplitude and phase. The present invention has the advantages of small amount of calculation, high precision, low computational complexity, good convergence performance, etc., can accurately obtain the harmonic amplitude and phase, and can more accurately realize the harmonic estimation under large-scale electric vehicle access.

[0087] Figures 2 to 4 They are respectively the simulation result waveform diagram, harmonic amplitude and phase estimation result diagram of an exemplary embodiment of the present invention. The signal expression used is shown in formula (13):

[0088]

[0089] Figure 2 The solid line in the middle represents the true harmonic function containing noise, and the dotted line represents the estimated harmonic function obtained by using the harmonic amplitude and phase estimated by the method of the present invention. It can be seen from the figure that the estimated value obtained by the method of the present invention is very close to the true value. Figure 3 It can be seen that when the number of sampling points is about 30, the amplitude estimation value obtained has basically converged. Figure 4 It can be seen that when the number of sampling points is about 20, the obtained phase estimation value has basically converged.

[0090] See also Figure 5 The present invention also provides a harmonic estimation device adapted to large-scale electric vehicle access, which is applied to the above-mentioned harmonic estimation method adapted to large-scale electric vehicle access, and the device comprises:

[0091] The solution domain setting module is used to set the solution domain and establish the initial solution domain center and the initial value of the solution domain radius;

[0092] A candidate solution set determination module, used for generating a candidate solution set in a solution domain using Gaussian distribution;

[0093] The optimal solution acquisition module is used to substitute the candidate solution set into the objective function for iterative calculation, and compare the optimal solution obtained in each iteration with the optimal solution obtained in the previous iteration, and retain the optimal solution;

[0094] The harmonic amplitude and phase calculation module is used to search for a local optimal solution by moving the center of the solution domain and reducing the radius of the solution domain, and then use the local optimal solution to calculate the amplitude and phase of each harmonic.

[0095] See also Figure 6 , the present invention also provides a harmonic estimation device adapted to large-scale electric vehicle access, including a memory and a processor;

[0096] The memory is used to store computer program code and transmit the computer program code to the processor;

[0097] The processor is used to execute the above-mentioned harmonic estimation method suitable for large-scale electric vehicle access according to the instructions in the computer program code.

[0098] A computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, a harmonic estimation method adapted to large-scale electric vehicle access is implemented.

[0099] In addition, if Figure 7 As shown, the present invention provides a dual-processor embedded device, which includes a voltage and current sensor, a signal conditioning circuit, a synchronous analog-to-digital conversion module, a programmable logic gate array and an STM32 controller. The programmable logic gate array (Field-Programmable Gate Array, FPGA) and the STM32 controller are used to fully utilize the high-speed data computing capability of the FPGA and the storage and communication functions of the STM32 controller. The signal conditioning circuit adjusts the voltage and current signals measured by the sensor into signals that can be processed by the synchronous analog-to-digital conversion module. The synchronous analog-to-digital conversion module converts the analog signal into a digital signal. The FPGA processes the signal using a harmonic estimation algorithm. The STM32 controller is responsible for the communication storage of the harmonic analysis results. As shown Figure 8As shown in the figure, after the device is powered on or reset, the system is initialized. When the terminal sends a start signal, it is transmitted to the FPGA through the CAN bus. The FPGA starts to work and calculates the information of the sensor acquisition signal using the harmonic estimation algorithm. Then, the information is sent to the STM32 through the CAN communication. After the STM32 obtains this data, it sends it to the terminal through the Ethernet bus interface.

[0100] Generally speaking, the computer instructions for implementing the method of the present invention may be carried in any combination of one or more computer-readable storage media. Non-transitory computer-readable storage media may include any computer-readable media, except for the signal itself that is temporarily propagating.

[0101] The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination thereof. More specific examples of computer-readable storage media (a non-exhaustive list) include: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EKROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In the present invention, a computer-readable storage medium may be any tangible medium containing or storing a program that may be used by or in conjunction with an instruction execution system, device, or device.

[0102] Computer program code for performing the operation of the present invention can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages, in particular, Python suitable for neural network computing and platform frameworks based on TensorFlow, PyTorch, etc. can be used. The program code can be executed entirely on the user's computer, partially on the user's computer, as an independent software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer can be connected to the user's computer or to an external computer (for example, using an Internet service provider to connect via the Internet) through any type of network, including a local area network (LAN) or a wide area network (WAN).

[0103] The above-mentioned device and non-transitory computer-readable storage medium can refer to the specific description of a harmonic estimation algorithm adapted to large-scale electric vehicle access and its beneficial effects, which will not be repeated here.

[0104] Although the embodiments of the present invention have been shown and described above, it should be understood that the above embodiments are exemplary and should not be construed as limitations on the present invention. A person skilled in the art may change, modify, replace and vary the above embodiments within the scope of the present invention.

Claims

1. A harmonic estimation method suitable for large-scale electric vehicle access, characterized in that: include: Set the solution domain and establish the initial solution domain center and initial solution domain radius; Generate candidate solution sets in the solution domain using Gaussian distribution; Substitute the candidate solution set into the objective function for iterative calculation, and compare the optimal solution obtained in each iteration with the optimal solution obtained in the previous iteration, and retain the optimal solution; The objective function is: Where N is the highest harmonic order; k is the harmonic order, which can be 1, 2, ..., N; y k is the measurement signal; kest is the function that fits the measured signal; y kest =H[k]C[k]; C[k]=[C1(k) C2(k) … C 2N+2 (k)Y T ; Where H[k] is the basis function representing the fundamental wave and harmonic wave; C[k] is the coefficient before each basis function; f0 is the fundamental frequency of the system; T s is the sampling period; C=[a1cosθ1 a1sinθ1 … a N cosθ N a N sinθ N B dc B d cb dc ] T ; In the formula, C is the amplitude and phase of the harmonic to be determined; a and θ are the amplitude and phase of each harmonic respectively; B dc and b dc is the attenuation term; Search for the local optimal solution by moving the center of the solution domain and reducing the radius of the solution domain: In the formula, r t is the radius of the solution domain after reduction; σ0 is the initial value of the radius of the solution domain; gammaincv is the inverse of the gamma function; x is the search vector; μ t is the center of the solution domain after moving; t is the number of iterations; MaxItr is the maximum number of iterations; The amplitude and phase of each harmonic are calculated using the local optimal solution: In the formula, a N is the amplitude of the harmonic; θ N is the harmonic phase; C 2N is the 2Nth term of vector C; C 2N-1 is the 2N-1th item of vector C.

2. A harmonic estimation method adapted to large-scale electric vehicle access according to claim 1, characterized in that: The initial solution domain center μ0 is: μ0=(l h +l l ) / 2; In the formula, l h and l l are the upper and lower boundaries of the solution domain, respectively.

3. The harmonic estimation method adapted to large-scale electric vehicle access according to claim 1 is characterized in that: Gaussian distribution is used to generate candidate solution sets in the solution domain, as shown in the following formula: Where n is the dimension of the solution space; y is the random variable; μ is the center of the solution domain; T is the solution period; ∑ is the covariance matrix.

4. A harmonic estimation method adapted to large-scale electric vehicle access according to claim 3, characterized in that: Determine whether the candidate solution set satisfies the constraints. If not, make the candidate solution set satisfy the constraints through the following formula: In the formula, is the kth solution of the ith dimension; rand is the random generation function; l h and l l are the upper and lower boundaries of the solution domain, respectively.

5. A harmonic estimation device adapted to large-scale electric vehicle access, characterized in that: The device is applied to the method described in any one of claims 1 to 4, and the device comprises: The solution domain setting module is used to set the solution domain and establish the initial solution domain center and the initial value of the solution domain radius; A candidate solution set determination module, used for generating a candidate solution set in a solution domain using Gaussian distribution; The optimal solution acquisition module is used to substitute the candidate solution set into the objective function for iterative calculation, and compare the optimal solution obtained in each iteration with the optimal solution obtained in the previous iteration, and retain the optimal solution; The harmonic amplitude and phase calculation module is used to search for a local optimal solution by moving the center of the solution domain and reducing the radius of the solution domain, and then use the local optimal solution to calculate the amplitude and phase of each harmonic.

6. A harmonic estimation device adapted to large-scale electric vehicle access, characterized in that: including memory and processor; The memory is used to store computer program code and transmit the computer program code to the processor; The processor is configured to execute the method according to any one of claims 1 to 4 according to instructions in the computer program code.

7. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method according to any one of claims 1 to 4 is implemented.

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