CN117929840B - Harmonic estimation method and device suitable for large-scale electric automobile access - Google Patents
Harmonic estimation method and device suitable for large-scale electric automobile access Download PDFInfo
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Abstract
A harmonic estimation method and device suitable for large-scale electric vehicle access comprises the steps of setting a solving domain, establishing an initial solving domain center and a solving domain radius initial value, generating a candidate solution set in the solving domain by means of Gaussian distribution, substituting the candidate solution set into an objective function to conduct iterative calculation, comparing an optimal solution obtained in each iteration with an optimal solution obtained in the previous iteration, reserving the optimal solution, searching a local optimal solution by moving the solving domain center and reducing the solving domain radius, and obtaining amplitude and phase of each harmonic by means of the local optimal solution calculation. The harmonic estimation method provided by the invention has the advantages of small operand, high precision, low calculation complexity and good convergence performance, and can realize more accurate harmonic estimation under large-scale electric automobile access.
Description
Technical Field
The invention relates to the technical field of signal analysis, in particular to a harmonic estimation method and device suitable for large-scale electric automobile access.
Background
In recent years, because nonlinear loads such as electric vehicles are connected into a power grid in a large scale, a large number of harmonic waves are generated, and the safe and stable operation of a power system is seriously threatened. In order to ensure the electric energy quality of the power grid, the harmonic wave and the inter-harmonic wave must be estimated rapidly and accurately so as to take preventive measures to ensure the efficient and stable operation of the power system.
The existing harmonic estimation algorithm mainly comprises Fourier transform, wavelet transform, hilbert-Huang transform and the like. Fourier transform is the most widely used method, but when applied to distorted signals, aliasing and spectrum leakage easily occur, and accurate harmonic separation estimation cannot be achieved. The wavelet transformation has high computational complexity, and spectrum leakage can occur in real-time application. The hilbert-yellow transform can analyze the distorted signal more accurately, but is only applicable to narrowband signals, and may miss part of the frequencies when analyzing wideband signals, resulting in larger errors. In summary, the existing harmonic estimation algorithm is difficult to realize accurate estimation of the harmonic under the access of the large-scale electric automobile.
Disclosure of Invention
The invention aims to overcome the defect and problem of low harmonic estimation precision in the prior art and provides a harmonic estimation method and device with high precision, which are suitable for large-scale electric automobile access.
In order to achieve the purpose, the technical scheme of the invention is that the harmonic estimation method suitable for large-scale electric automobile access comprises the following steps:
Setting a solving domain, and establishing an initial solving domain center and a solving domain radius initial value;
Generating a candidate solution set in a solution domain by using Gaussian distribution;
Substituting the candidate solution set into the objective function for iterative computation, comparing the optimal solution obtained in each iteration with the optimal solution obtained in the previous iteration, and reserving the optimal solution;
And (3) searching a local optimal solution by moving the center of the solution domain and reducing the radius of the solution domain, and then calculating the amplitude and the phase of each subharmonic by using the local optimal solution.
The initial solution domain center μ 0 is:
μ0＝(lh+ll)/2
where l h and l l are the upper and lower boundaries of the solution domain, respectively.
A candidate solution set is generated in the solution domain using gaussian distribution as shown in the following equation:
In the formula, n is the dimension of a solution space, y is a random variable, mu is the center of a solution domain, T is the solution period, and sigma is a covariance matrix.
Judging whether the candidate solution set meets the constraint, if not, enabling the candidate solution set to meet the constraint through the following formula:
In the formula, The kth solution of the ith dimension, rand is a random generation function, and l h and l l are the upper and lower boundaries of the solution domain, respectively.
The objective function is:
Wherein N is the highest harmonic frequency, k is the harmonic frequency, the values are 1,2, N, y k is the measurement signal, y kest is the function of fitting the measurement signal;
ykest=H[k]C[k];
C[k]=[C1(k) C2(k) …C2N+2(k)]T;
Wherein, H k is the basic function representing fundamental wave and harmonic wave, C k is the coefficient before each basic function, f 0 is the system fundamental frequency, T s is the sampling period;
C＝[a1cosθ1 a1sinθ1…aNcosθNaNsinθNBdc Bdc bdc]T;
Wherein, C is the amplitude and phase of the harmonic to be solved, a and theta are the amplitude and phase of each subharmonic respectively, and B dc and B dc are attenuation terms.
And carrying out local optimal solution search by moving the center of the solution domain and reducing the radius of the solution domain:
Where r t is the reduced solution domain radius, σ 0 is the initial value of the solution domain radius, gammaincv is the inverse of the gamma function, x is the search vector, μ t is the center of the solution domain after movement, t is the number of iterations, and MaxItr is the maximum number of iterations.
And calculating by using a local optimal solution to obtain the amplitude and the phase of each subharmonic:
Where a N is the amplitude of the harmonic, θ N is the harmonic phase, C 2N is the 2N-th term of vector C, and C 2N-1 is the 2N-1-th term of vector C.
A harmonic estimation device adapted to large-scale electric vehicle access, the device being applied to the method described above, the device comprising:
The solving domain setting module is used for setting a solving domain and establishing an initial solving domain center and a solving domain radius initial value;
A candidate solution set determination module for generating a candidate solution set in a solution domain using a gaussian distribution;
the optimal solution acquisition module is used for substituting the candidate solution set into the objective function to carry out iterative computation, comparing the optimal solution obtained in each iteration with the optimal solution obtained in the previous iteration, and reserving the optimal solution;
And the harmonic amplitude and phase calculation module is used for searching a local optimal solution by moving the center of the solving domain and reducing the radius of the solving domain, and then calculating the amplitude and phase of each subharmonic by using the local optimal solution.
A harmonic estimation device suitable for large-scale electric automobile access comprises a memory and a processor;
The memory is used for storing computer program codes and transmitting the computer program codes to the processor;
The processor is configured to perform the method as described above according to instructions in the computer program code.
A computer readable storage medium having stored thereon a computer program which when executed by a processor implements a method as described above.
Compared with the prior art, the invention has the beneficial effects that:
According to the harmonic estimation method and device suitable for large-scale electric vehicle access, the proposed eddy current search algorithm generates the candidate solution set of the amplitude and the phase of each subharmonic through Gaussian distribution, and the candidate solution set is used for approximating the voltage and current signals actually measured, so that accurate estimation of the amplitude and the phase of each subharmonic is realized.
Drawings
Fig. 1 is a flowchart of a harmonic estimation method suitable for large-scale electric vehicle access.
FIG. 2 is a waveform diagram of simulation results in an embodiment of the present invention.
FIG. 3 is a graph of harmonic amplitude results in an embodiment of the invention.
Fig. 4 is a diagram of a phase estimation result in an embodiment of the present invention.
Fig. 5 is a block diagram of a harmonic estimation device adapted to large-scale electric vehicle access according to the present invention.
Fig. 6 is a block diagram of a harmonic estimation device adapted to large-scale electric vehicle access according to the present invention.
FIG. 7 is a block diagram of a dual processor embedded device in accordance with an embodiment of the present invention.
FIG. 8 is a diagram illustrating an operation of a dual processor embedded device according to an embodiment of the present invention.
Detailed Description
The invention is described in further detail below with reference to the accompanying drawings and detailed description.
Referring to fig. 1, a harmonic estimation method suitable for large-scale electric vehicle access includes:
s1, setting a solving domain, and establishing an initial solving domain center and a solving domain radius initial value.
The initial solution domain center μ 0 is:
μ0＝(lh+ll)/2 (1)
where l h and l l are the upper and lower boundaries of the solution domain, respectively.
S2, generating a candidate solution set in a solution domain by using Gaussian distribution, wherein the candidate solution set is shown in the following formula:
In the formula, n is the dimension of a solution space, y is a random variable, mu is the center of a solution domain, T is the solution period, and sigma is a covariance matrix.
Judging whether the candidate solution set meets the constraint, if not, enabling the candidate solution set to meet the constraint through the following formula:
In the formula, The kth solution of the ith dimension, rand is a random generation function, and l h and l l are the upper and lower boundaries of the solution domain, respectively.
S3, substituting the candidate solution set into the objective function for iterative computation, comparing the optimal solution obtained in each iteration with the optimal solution obtained in the previous iteration, and reserving the optimal solution.
The optimal solution objective function is:
Wherein N is the highest harmonic frequency, k is the harmonic frequency, the values are 1,2, N, y k is the measurement signal, y kest is the function of fitting the measurement signal;
ykest=H[k]C[k] (5)
C[k]=[C1(k) C2(k)…C2N+2(k)]T (7)
Wherein, H k is the basic function representing fundamental wave and harmonic wave, C k is the coefficient before each basic function, f 0 is the system fundamental frequency, T s is the sampling period;
C＝[a1cosθ1 a1sinθ1...aNcosθN aNsinθN Bdc Bdcbdc]T (8)
Wherein, C is the amplitude and phase of the harmonic to be solved, a and theta are the amplitude and phase of each subharmonic respectively, and B dc and B dc are attenuation terms.
S4, searching a local optimal solution by moving the center of the solving domain and reducing the radius of the solving domain, and then calculating the amplitude and the phase of each subharmonic by using the local optimal solution.
The method comprises the steps of gradually narrowing the range of a solution domain by moving the center of the solution domain and reducing the radius of the solution domain, and repeating the steps to search the local optimal solution:
Where r t is the reduced solution domain radius, σ is the initial value of the solution domain radius, gammaincv is the inverse of the gamma function, x is the search vector, μ t is the center of the solution domain after movement, t is the number of iterations, and MaxItr is the maximum number of iterations.
And calculating by using a local optimal solution to obtain the amplitude and the phase of each subharmonic:
Where a N is the amplitude of the harmonic, θ N is the harmonic phase, C 2N is the 2N-th term of vector C, and C 2N-1 is the 2N-1-th term of vector C.
The invention firstly sets a solving domain and utilizes Gaussian distribution to generate a candidate solution set. And comparing the optimal solution obtained in each iteration with the optimal solution obtained in the previous iteration, and reserving the optimal solution. And then the center of the solving domain is moved to a point in the last iteration to obtain the optimal solution. And reducing the iteration radius along with the iteration, and searching the local optimal solution. Finally, the amplitude and the phase of each subharmonic can be determined after the amplitude and the phase of the harmonic are subjected to iterative computation by using an eddy current searching algorithm. The harmonic estimation method provided by the invention is characterized in that a signal model is decomposed into a quadrature 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 the in-phase component, a×cos (t) sin (θ) is the quadrature component, a corresponding basis function vector is H= [ sin (t) cos (t) ], a parameter to be estimated is C= [ a×cos (θ) a×sin (θ) ], and then an eddy current search algorithm is utilized to find optimal model parameters under different signal to noise ratios, so as to determine harmonic amplitude and phase. The method has the advantages of small operand, high precision, low calculation complexity, good convergence performance and the like, can accurately obtain the harmonic amplitude and the phase, and can accurately realize the harmonic estimation under the access of large-scale electric vehicles.
Fig. 2 to 4 are simulation result waveform diagrams, harmonic amplitude and phase estimation result diagrams, respectively, according to an exemplary embodiment of the present invention. The signal expression used is as shown in formula (13):
in fig. 2, the solid line represents the real harmonic function containing noise, the dotted line represents the estimated harmonic function obtained after the amplitude and phase of the harmonic estimated by the method of the present invention, and it can be seen that the estimated value obtained by the method of the present invention is very close to the real value. As can be seen from fig. 3, the obtained amplitude estimation value has substantially converged when the number of sampling points is about 30. As can be seen from fig. 4, when the number of sampling points is about 20, the obtained phase estimation value has basically converged.
Referring to fig. 5, the invention further provides a harmonic estimation device suitable for large-scale electric vehicle access, which is applied to the above harmonic estimation method suitable for large-scale electric vehicle access, and the device comprises:
The solving domain setting module is used for setting a solving domain and establishing an initial solving domain center and a solving domain radius initial value;
A candidate solution set determination module for generating a candidate solution set in a solution domain using a gaussian distribution;
the optimal solution acquisition module is used for substituting the candidate solution set into the objective function to carry out iterative computation, comparing the optimal solution obtained in each iteration with the optimal solution obtained in the previous iteration, and reserving the optimal solution;
And the harmonic amplitude and phase calculation module is used for searching a local optimal solution by moving the center of the solving domain and reducing the radius of the solving domain, and then calculating the amplitude and phase of each subharmonic by using the local optimal solution.
Referring to fig. 6, the invention further provides a harmonic estimation device suitable for large-scale electric automobile access, which comprises a memory and a processor;
The memory is used for storing computer program codes and transmitting the computer program codes to the processor;
The processor is used for executing the harmonic estimation method suitable for large-scale electric automobile access according to the instructions in the computer program codes.
A computer readable storage medium having stored thereon a computer program which when executed by a processor implements a harmonic estimation method adapted to large-scale electric vehicle access.
In addition, as shown in fig. 7, the present invention provides a dual processor embedded device, which includes a voltage-current sensor, a signal conditioning circuit, a synchronous analog-to-digital conversion module, a Programmable logic gate array (FPGA) and an STM32 controller, and uses a Programmable logic gate array (fieldprogrammable GATE ARRAY, FPGA) and the STM32 controller, and fully uses the high-speed data computing capability of the FPGA and the memory communication function of the STM32 controller. The signal conditioning circuit adjusts the voltage and current signals measured by the sensor into signals which 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 signals with a harmonic estimation algorithm. The STM32 controller is responsible for the communication storage of the harmonic analysis results. As shown in fig. 8, after the device is powered up or reset, the system is initialized. When the terminal sends out a starting signal, the starting signal is transmitted to the FPGA through the CAN bus, the FPGA starts to enter a working state, information of signals collected by the sensor is calculated by using a harmonic estimation algorithm, and then the information is transmitted to the STM32 through CAN communication. After obtaining these data, STM32 sends them to the terminal through the ethernet bus interface.
In general, the computer instructions to implement the methods of the present invention may be carried in any combination of one or more computer-readable storage media. The non-transitory computer-readable storage medium may include any computer-readable medium, except the signal itself in temporary propagation.
The computer readable storage medium can be, for example, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples (a non-exhaustive list) of the computer-readable storage medium include an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EKROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of this document, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.
Computer program code for carrying out operations of the present invention may be written in one or more programming languages, or combinations thereof, including an object oriented programming language such as Java, smalltalk, C ++ and conventional procedural programming languages, such as the "C" language or similar programming languages, particularly Python languages suitable for neural network computing and TensorFlow, pyTorch-based platform frameworks may be used. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any number of types of networks, including a Local Area Network (LAN) or a Wide Area Network (WAN), or be connected to an external computer (for example, through the Internet using an Internet service provider).
The foregoing devices and non-transitory computer readable storage medium may refer to a specific description of a harmonic estimation algorithm and beneficial effects adapted for large-scale electric vehicle access, and will not be described herein.
While embodiments of the present invention have been shown and described above, it should be understood that the above embodiments are illustrative and not to be construed as limiting the invention, and that variations, modifications, alternatives, and variations may be made to the above embodiments by one of ordinary skill in the art within the scope of the invention.
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104484727A (en) * | 2015-01-12 | 2015-04-01 | 江南大学 | Short-term load prediction method based on interconnected fuzzy neural network and vortex search |
| CN114236233A (en) * | 2021-04-13 | 2022-03-25 | 内蒙古电力(集团)有限责任公司内蒙古电力科学研究院分公司 | Application of electrical characteristic and harmonic source characteristic of nonlinear load |
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| GB201309823D0 (en) * | 2013-06-01 | 2013-07-17 | Metroic Ltd | Current measurement |
| CN109444505B (en) * | 2018-05-03 | 2021-12-10 | 国网江苏省电力公司盐城供电公司 | Harmonic current detection algorithm for electric vehicle charging station |
| CN109946518B (en) * | 2019-03-27 | 2021-03-26 | 河南天通电力有限公司 | Power harmonic signal analysis method and analysis equipment based on Bayes method |
| CN110161311B (en) * | 2019-05-17 | 2020-09-18 | 华中科技大学 | Detection method for harmonic waves and inter-harmonic waves |
| CN113365282B (en) * | 2021-06-22 | 2023-04-07 | 成都信息工程大学 | WSN obstacle region covering deployment method |
| CN116187968A (en) * | 2022-10-12 | 2023-05-30 | 国网重庆市电力公司电力科学研究院 | Substation Equipment Condition Evaluation Method and System, Related Equipment and Medium |
| CN116203432B (en) * | 2023-03-23 | 2023-10-20 | 广东工业大学 | Method for predicting battery state of charge based on CSO optimized unscented Kalman filter |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN104484727A (en) * | 2015-01-12 | 2015-04-01 | 江南大学 | Short-term load prediction method based on interconnected fuzzy neural network and vortex search |
| CN114236233A (en) * | 2021-04-13 | 2022-03-25 | 内蒙古电力(集团)有限责任公司内蒙古电力科学研究院分公司 | Application of electrical characteristic and harmonic source characteristic of nonlinear load |
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