Method, device and system for evaluating power grid harmonic pollution degree

Abstract

This invention discloses a method, apparatus, and system for evaluating the degree of harmonic pollution in a power grid. The method includes acquiring harmonic sample data from various power grids and calculating the central moments of each order of the harmonic sample data; constructing a maximum entropy model for the harmonics based on the central moments of the power grid harmonic sample data; converting the maximum entropy model into an optimization model; optimizing the optimization model using a modified hole-punching function to obtain the global optimum value of the optimization model; generating a harmonic probability distribution function based on the global optimum value and the maximum entropy model; and using the harmonic probability distribution function to evaluate the degree of harmonic pollution in the power grid. This invention enables accurate solving of the harmonic probability distribution function and allows for the evaluation of the degree of harmonic pollution in the power grid using the harmonic probability distribution function.

Description

Technical Field

[0001] This invention belongs to the field of power system power quality algorithm analysis, specifically involving a method, device and system for evaluating the degree of harmonic pollution in power grids. Background Technology

[0002] With economic and social development, a large number of nonlinear loads exist in the current power system. Simultaneously, due to the country's vigorous promotion of new energy technologies in recent years, the penetration rate of distributed generation in the power grid is increasing. Distributed generation, connected to the grid via grid-connected inverters, contributes to the increasingly serious pollution of grid harmonic voltage and current. To trace harmonic sources and facilitate control of grid harmonic levels, it is necessary to understand the characteristics and distribution of grid harmonics. Therefore, harmonic characteristic analysis of the power grid has become one of the hot topics in current power system research. Harmonic power flow is one of the important methods for harmonic analysis; it calculates the distribution of each harmonic and can reflect the harmonic characteristics to a certain extent.

[0003] However, deterministic harmonic power flow fails to reflect the uncertainty of harmonic distribution caused by nonlinear load fluctuations in the power grid. Furthermore, distributed power sources such as photovoltaic and wind power inherently possess intermittency and uncertainty, which significantly impacts the uncertainty of harmonic power flow. Probabilistic harmonic power flow, by introducing probability distribution characteristics to characterize the distribution features of harmonic sources, can effectively describe the influence of random factors on harmonic power flow distribution, thus better reflecting the actual situation of harmonic power flow and providing richer information for operational decision-makers.

[0004] Probabilistic harmonic power flow can be analyzed using the maximum entropy principle to determine the harmonic probability distribution characteristics. The maximum entropy principle states that information entropy reaches its maximum value under known constraints (information). Using this method to calculate the distribution characteristics of harmonic sources, a probability distribution solution with minimal bias can be obtained under the constraints of known harmonic data. However, when there are numerous constraints, the parameters of the constructed maximum entropy model become excessive. Using conventional parameter-solving methods to obtain these parameters leads to a large computational load and often fails to yield an exact solution. Therefore, determining the parameters of the harmonic probability distribution function based on the harmonic source data characteristics and the maximum entropy principle, fitting the harmonic probability distribution function, and using the harmonic probability distribution function to evaluate the degree of harmonic pollution in the power grid has become a crucial research topic. Summary of the Invention

[0005] To address the aforementioned problems, this invention proposes a method, apparatus, and system for evaluating the degree of harmonic pollution in power grids. This method can accurately solve the harmonic probability distribution function and use the harmonic probability distribution function to evaluate the degree of harmonic pollution in power grids.

[0006] To achieve the above-mentioned technical objectives and effects, the present invention is implemented through the following technical solution:

[0007] In a first aspect, the present invention provides a method for evaluating the degree of harmonic pollution in a power grid, comprising:

[0008] Obtain harmonic sample data for each power grid and calculate the central moments of each order of harmonic sample data for each power grid.

[0009] Based on the central moments of each order of the power grid harmonic sample data, a maximum entropy model of the harmonics is constructed.

[0010] The maximum entropy model is then converted into an optimization model.

[0011] The optimization model is optimized using a modified hole-punching function to obtain the global optimum of the optimization model;

[0012] Based on the global optimum and maximum entropy model, a harmonic probability distribution function is generated;

[0013] The degree of harmonic pollution in the power grid is evaluated using the harmonic probability distribution function.

[0014] Optionally, the step of acquiring harmonic sample data for each power grid and calculating the central moments of each order of harmonic sample data for each power grid includes:

[0015] Obtain harmonic sample data for each power grid;

[0016] Calculate the expected value of the power grid harmonic sample data;

[0017] Based on the expected value, the central moments of each order of the harmonic sample data of each power grid are calculated.

[0018] Optionally, the central moments of each order of the power grid harmonic sample data are calculated using the following formula:

[0019]

[0020]

[0021] In the formula, Let X be the i-th order central moment of the power grid harmonic sample data, m be the order of the central moment, T be the total number of samples in the power grid harmonic sample data, and X be the total number of samples in the power grid harmonic sample data. j For the j-th power grid harmonic sample data, This represents the expected value of the power grid harmonic sample data.

[0022] Optionally, the maximum entropy model includes an objective function and constraints;

[0023] The expression for the objective function is:

[0024]

[0025] In the formula, x is a random harmonic variable, H(x) is the information entropy of the random harmonic variable x, f(x) is the probability distribution function of the random harmonic variable x to be determined, and Ω is the integration interval;

[0026] The expression for the constraint condition is:

[0027]

[0028]

[0029] In the formula, u i (x) represents the numerical characteristic of the i-th central moment of the random variable x. Let be the i-th order central moment of the power grid harmonic sample data, and m be the order of the central moment.

[0030] Optionally, the objective function of the optimization model is expressed as:

[0031]

[0032] In the formula, Let R(i) be the estimated solution of the Lagrange coefficients, and let u be the estimation error of the i-th order central moment. i (x) represents the numerical characteristic of the i-th central moment of the random variable x. Let be the i-th order central moment of the power grid harmonic sample data, and m be the order of the central moment.

[0033] Optionally, the expression for the modified hole-punching function is:

[0034]

[0035] In the formula, G(y,y) * ) represents the correction function for hole punching; g(y) represents the objective function of the optimization model; y * Let y be the local minimum point to be found, and let y be the global minimum point to be found. q>0, p>0, p, q, y0 are given constants; Ω is the interval of integration; ||·|| is the Euclidean norm.

[0036] Optionally, optimizing the optimization model using a modified hole-punching function to obtain the global optimum of the optimization model includes the following steps:

[0037] Repeat the following steps within the preset number of loops:

[0038] The first local minimum point y of the objective function of the optimization model is obtained using a classical nonlinear programming algorithm.* ;

[0039] The modified hole-punching function G(y,y) is constructed at the local minimum point. * );

[0040] The modified hole-punching function G(y,y) is constructed using a classical nonlinear programming algorithm. * Find the second local minimum value y;

[0041] If G(y,y) * If )≤0, the second local minimum y is a better local minimum, let y * =y, as the new local minimum, construct the corrected hole-punching function again at the new local minimum, and solve G(y,y) again. * );

[0042] If G(y,y) * If )>0, take p:=dp, where d is the proportionality constant and p min Let p be the minimum value.

[0043] If p>p min Substitute p into G(y,y) * ), and resolve G(y,y) * );

[0044] If p≤p min Let p:=p / d, q:=q / d, if q max Substitute p, q into G(y, y * ), and resolve G(y,y) * Otherwise, consider y = (λ1, λ2, ... λ) m ) is the global minimum point, which is the global optimum of the optimization model.

[0045] Optionally, generating the harmonic probability distribution function based on the global optimum and the maximum entropy model includes the following steps:

[0046] Based on the global optimal value y=(λ1,λ2,…λ m ), calculate

[0047] Based on the objective function and constraints of the maximum entropy model, the Lagrange equation is constructed.

[0048] Taking the partial derivative of the Lagrange equation generates a probability distribution function that conforms to the maximum entropy distribution.

[0049]

[0050] Let λ0, λ i u i ​(x) Substitute the probability distribution function that conforms to the maximum entropy distribution into (x) to generate the harmonic probability distribution function.

[0051] Secondly, the present invention provides an evaluation device for the degree of harmonic pollution in a power grid, comprising:

[0052] The calculation module is used to acquire harmonic sample data of each power grid and calculate the central moments of each order of harmonic sample data of each power grid.

[0053] The model building module is used to construct the maximum entropy model of the harmonics based on the central moments of each order of the power grid harmonic sample data.

[0054] The model conversion module is used to convert the maximum entropy model into an optimization model.

[0055] The model optimization module is used to optimize the optimization model using the modified hole punching function to obtain the global optimum value of the optimization model;

[0056] The harmonic probability distribution function generation module is used to generate a harmonic probability distribution function based on the global optimum and the maximum entropy model.

[0057] The evaluation module is used to evaluate the degree of power grid harmonic pollution using the harmonic probability distribution function.

[0058] Thirdly, the present invention provides a system for evaluating the degree of harmonic pollution in a power grid, including a storage medium and a processor;

[0059] The storage medium is used to store instructions;

[0060] The processor is configured to operate according to the instructions to perform the method according to any one of the first aspects.

[0061] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0062] This invention transforms the maximum entropy model for obtaining the harmonic source probability distribution function into an objective function model for obtaining the optimization model. It then uses a modified hole-punching function to find the global optimal solution for the optimization model parameters, fits the harmonic probability distribution function, and finally uses the harmonic probability distribution function to evaluate the degree of harmonic pollution in the power grid. This invention avoids the complex calculations of analytical methods for maximum entropy nonlinear equations and overcomes the limitation of conventional optimization algorithms easily getting trapped in local optima of the objective function. It has a faster solution speed and is beneficial for the accurate solution of the harmonic probability distribution function. Attached Figure Description

[0063] To make the content of this invention easier to understand, the invention will be further described in detail below with reference to specific embodiments and accompanying drawings, wherein:

[0064] Figure 1 This is a flowchart of a method for evaluating the degree of harmonic pollution in a power grid according to an embodiment of the present invention;

[0065] Figure 2 The curves are generated by the power grid harmonic pollution level evaluation method in one embodiment of the present invention, showing the probability distribution functions of each harmonic. Detailed Implementation

[0066] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the scope of protection of the invention.

[0067] The application principle of the present invention will be described in detail below with reference to the accompanying drawings.

[0068] Example 1

[0069] This invention provides a method for evaluating the degree of harmonic pollution in a power grid, such as... Figure 1 As shown, it includes the following steps:

[0070] (1) Obtain harmonic sample data for each power grid and calculate the central moments of each order of harmonic sample data for each power grid;

[0071] (2) Based on the central moments of each order of the power grid harmonic sample data, construct the maximum entropy model of the harmonics;

[0072] (3) Convert the maximum entropy model into an optimization model;

[0073] (4) Optimize the optimization model using the modified hole-punching function to obtain the global optimum of the optimization model;

[0074] (5) Based on the global optimal value and the maximum entropy model, generate the harmonic probability distribution function;

[0075] (6) The degree of power grid harmonic pollution is evaluated using the harmonic probability distribution function.

[0076] In one specific embodiment of the present invention, the step of acquiring harmonic sample data of each power grid and calculating the central moments of each order of harmonic sample data of each power grid includes:

[0077] Obtain harmonic sample data for each power grid, denoted as {X1, X2, ..., X...} T In specific implementation, the power grid harmonic sample data can be harmonic voltage data or harmonic current data.

[0078] Calculate the expected value of the power grid harmonic sample data; the formula for calculating the expected value is:

[0079]

[0080] T is the total number of samples in the power grid harmonic sample data, X j For the j-th power grid harmonic sample data, The expected value of the power grid harmonic sample data;

[0081] Based on the expected value, the central moments of each order of the power grid harmonic sample data are calculated; the central moments of each order of the power grid harmonic sample data are calculated using the following formula:

[0082]

[0083] In the formula, Let be the i-th order central moment of the power grid harmonic sample data, and m be the order of the central moment.

[0084] To solve for the harmonic probability distribution in a power grid, the maximum entropy principle is introduced. This principle contains minimal subjective factors and can solve for a probability distribution with maximum information entropy using known conditions. In one specific embodiment of this invention, the maximum entropy model includes an objective function and constraints.

[0085] The expression for the objective function is:

[0086]

[0087] In the formula, x is a random harmonic variable, H(x) is the information entropy of the random harmonic variable x, f(x) is the probability distribution function of the random harmonic variable x to be determined, and Ω is the integration interval;

[0088] The expression for the constraint condition is:

[0089]

[0090]

[0091] In the formula, u i (x) represents the numerical characteristic of the i-th central moment of the random variable x. Let be the i-th order central moment of the power grid harmonic sample data, and m be the order of the central moment.

[0092] In one specific embodiment of the present invention, converting the maximum entropy model into an optimization model includes the following steps:

[0093] Lagrange equations (3) and (4) are constructed using the Lagrange algorithm.

[0094]

[0095] In the formula, f(x) is the probability distribution function to be determined for the random harmonic variable x; λ0,λ i ,i=1,2,…,m are the Lagrange coefficients; u i (x) represents the numerical characteristic of the i-th central moment of the random variable x; Ω represents the integration interval.

[0096] Taking the partial derivative of equation (5) with respect to f(x), we obtain the probability distribution function that conforms to the maximum entropy distribution:

[0097]

[0098] To obtain equation (6), we need to find the Lagrange coefficients λ0 and λ1. i Substituting equation (4) into equation (6), we get: (i = 1, 2, ..., m)

[0099]

[0100] Simplify to get

[0101]

[0102] Take the partial derivative of equation (7) get

[0103]

[0104] Take the partial derivative of equation (8) get

[0105]

[0106] From equations (9) and (10), we get

[0107]

[0108] In equation (11), m takes different values ​​to obtain information about the parameter λ. i The nonlinear equation system. Solving the above equation will yield an estimated solution that approximates the exact solution. However, there is an error, let the error be:

[0109]

[0110] In the formula, R(i) represents the error between the actual value and the estimated value. This is an estimated solution for the Lagrange coefficients.

[0111] To ensure that the obtained estimate is as close as possible to the exact value and to minimize the sum of the absolute values ​​of the errors, the objective function minR of the optimization model is constructed as follows:

[0112]

[0113] To better adapt to the characteristics of the objective function and improve the optimization solution speed, this invention proposes a new two-parameter corrected hole-punching function. Specifically, the expression of the corrected hole-punching function is as follows:

[0114]

[0115]

[0116] In the formula, G(y,y) * ) represents the correction function for hole punching; g(y) represents the objective function of the optimization model; y * Let y be the local minimum point to be found, and let y be the global minimum point to be found. q>0, p>0, p, q, y0 are given constants; Ω is the interval of integration; ||·|| is the Euclidean norm.

[0117] The optimization of the optimization model using the modified hole-punching function to obtain the global optimum of the optimization model includes the following steps:

[0118] 1. Initialization:

[0119] Choose the initialization parameters for the hole-punching function: p0∈(0,1), q0∈(100,1000), and the scaling factor d∈(0,1);

[0120] Select the parameter variation limit value p min and q max The loop stops when the parameter exceeds the change limit.

[0121] Choose the objective function: g(y) = minR.

[0122] 2. Use the classical nonlinear programming algorithm to find the initial local minimum point y of the objective function g(y). * = (λ1,λ2,…λ) m ) * ;

[0123] 3. Initialize the parameters p = p0, q = q0.

[0124] 4. Construct a modified hole-punching function at the local minimum:

[0125]

[0126] 5. Use the classical nonlinear programming algorithm to find the new local minimum y = (λ1, λ2, ... λ) of the constructed modified hole-punching function. m If G(y,y) * If ≤ 0, it means y is a better local minimum. Let y* =y, go to 4. If G(y,y) * If )>0, take p:=dp, if p>p min If p:=p / d, go to 4. Otherwise, go to 6.

[0127] 6. Take q:=q / d, if q max Proceed to step 4. Otherwise, assume y = (λ1, λ2, ... λ) m ) is the global minimum point, which is the global optimum of the optimization model.

[0128] The process of generating the harmonic probability distribution function based on the global optimum and the maximum entropy model includes the following steps:

[0129] Based on the global optimal value y=(λ1,λ2,…λ m ), calculate

[0130] Based on the objective function and constraints of the maximum entropy model, the Lagrange equation is constructed.

[0131] Taking the partial derivative of the Lagrange equation generates a probability distribution function that conforms to the maximum entropy distribution.

[0132]

[0133] Let λ0, λ i u i (x) Substitute the probability distribution function that conforms to the maximum entropy distribution into (x) to generate the harmonic probability distribution function.

[0134] In one specific embodiment of the present invention, a sample of 110kV wind power harmonic current data is selected. Taking the 5th, 7th, 11th, and 13th harmonics as examples, the method proposed in this embodiment is used to generate curves of the probability distribution function of each harmonic. See details [link to specific implementation]. Figure 2 .

[0135] The national standard "Power Quality - Harmonics in Public Power Grids" (GB / T14549-1993) specifies the permissible values ​​and testing methods for harmonics in public power grids. The 95% probability value in harmonic measurements is used as the standard for judging whether harmonic voltage and current in the power grid exceed limits. After removing the largest 5% of data on the function curve, the maximum value of the remaining data is taken as the 95% probability value of the harmonic current. The 95% probability values ​​of each harmonic current are obtained and compared with the permissible values ​​of each harmonic current in the power grid, as shown in Table 1 below.

[0136] Table 1

[0137] Harmonic number 5 7 11 13 95% probability value (A) 2.9100 2.6777 1.8762 1.0820 Permissible current (A) 9.6 6.8 4.3 3.7

[0138] ​As can be seen from the data in Table 1, the 5th, 7th, 11th and 13th harmonic currents in the selected harmonic current sample data do not exceed the specified allowable harmonic current values, and the harmonic pollution is not serious.

[0139] Example 2

[0140] This invention provides a device for evaluating the degree of harmonic pollution in a power grid, comprising:

[0141] The calculation module is used to acquire harmonic sample data of each power grid and calculate the central moments of each order of harmonic sample data of each power grid.

[0142] The model building module is used to construct the maximum entropy model of the harmonics based on the central moments of each order of the power grid harmonic sample data.

[0143] The model conversion module is used to convert the maximum entropy model into an optimization model.

[0144] The model optimization module is used to optimize the optimization model using the modified hole punching function to obtain the global optimum value of the optimization model;

[0145] The harmonic probability distribution function generation module is used to generate a harmonic probability distribution function based on the global optimum and the maximum entropy model.

[0146] The evaluation module is used to evaluate the degree of power grid harmonic pollution using the harmonic probability distribution function.

[0147] The rest are the same as in Example 1.

[0148] Example 3

[0149] This invention provides a system for evaluating the degree of harmonic pollution in a power grid, comprising a storage medium and a processor;

[0150] The storage medium is used to store instructions;

[0151] The processor is configured to operate according to the instructions to execute the method according to any one of Embodiment 1.

[0152] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0153] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0154] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0155] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0156] The embodiments of the present invention have been described above with reference to the accompanying drawings. However, the present invention is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms under the guidance of the present invention without departing from the spirit and scope of the claims. All of these forms are within the protection scope of the present invention.

[0157] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of this invention is defined by the appended claims and their equivalents.

Claims

1. A method for evaluating the degree of harmonic pollution in a power grid, characterized in that, include: Obtain harmonic sample data for each power grid and calculate the central moments of each order of harmonic sample data for each power grid. Based on the central moments of each order of the power grid harmonic sample data, a maximum entropy model of the harmonics is constructed. The maximum entropy model is then converted into an optimization model. The optimization model is optimized using a modified hole-punching function to obtain the global optimum of the optimization model; Based on the global optimum and maximum entropy model, a harmonic probability distribution function is generated; The degree of harmonic pollution in the power grid is evaluated using the aforementioned harmonic probability distribution function; The expression for the modified hole-punching function is: In the formula, G(y,y) * ) represents the correction function for hole punching; g(y) represents the objective function of the optimization model; y * Let y be the local minimum point to be found, and let y be the global minimum point to be found. q>0, p>0, p, q, y0 are given constants; Ω is the interval of integration; ||·|| is the Euclidean norm; The optimization of the optimization model using the modified hole-punching function to obtain the global optimum of the optimization model includes the following steps: Repeat the following steps within the preset number of loops: The first local minimum point y of the objective function of the optimization model is obtained using a classical nonlinear programming algorithm. * ; Construct a modified hole-punching function G(y,y) at the first local minimum point. * ); The modified hole-punching function G(y,y) is constructed using a classical nonlinear programming algorithm. * Find the second local minimum value y; If G(y,y) * If )≤0, the second local minimum y is a better local minimum, let y * =y, as the new local minimum, construct the corrected hole-punching function again at the new local minimum, and solve G(y,y) again. * ); If G(y,y) * If ) > 0, take p: = dp, where d is the proportionality constant and p min Let p be the minimum value. If p > p min Substitute p into G(y,y) * ), and resolve G(y,y) * ); If p≤p min Let p:=p / d, q:=q / d, if Substitute p, q into G(y, y * ), and resolve G(y,y) * Otherwise, consider y = (λ1, λ2, ... λ) m ) is the global minimum point, which is the global optimum of the optimization model.

2. The method for evaluating the degree of harmonic pollution in a power grid according to claim 1, characterized in that: The acquisition of harmonic sample data for each power grid and the calculation of the central moments of each order of harmonic sample data for each power grid include: Obtain harmonic sample data for each power grid; Calculate the expected value of the power grid harmonic sample data based on the harmonic sample data of each power grid. Based on the expected value, the harmonic sample data of each power grid, and the total number of samples of the power grid harmonic sample data, the central moments of each order of the harmonic sample data of each power grid are calculated.

3. The method for evaluating the degree of harmonic pollution in a power grid according to claim 2, characterized in that: The central moments of each order of the power grid harmonic sample data are calculated using the following formula: In the formula, Let X be the i-th order central moment of the power grid harmonic sample data, m be the order of the central moment, T be the total number of samples in the power grid harmonic sample data, and X be the total number of samples in the power grid harmonic sample data. j For the j-th power grid harmonic sample data, This represents the expected value of the power grid harmonic sample data.

4. The method for evaluating the degree of harmonic pollution in a power grid according to claim 1, characterized in that: The maximum entropy model includes an objective function and constraints; The expression for the objective function is: In the formula, x is a random harmonic variable, H(x) is the information entropy of the random harmonic variable x, f(x) is the probability distribution function of the random harmonic variable x to be determined, and Ω is the integration interval; The expression for the constraint condition is: Where u i (x) represents the numerical characteristic of the i-th central moment of the random variable x. Let be the i-th order central moment of the power grid harmonic sample data, and m be the order of the central moment.

5. The method for evaluating the degree of harmonic pollution in a power grid according to claim 4, characterized in that: The objective function of the optimization model is expressed as follows: In the formula, Let R(i) be the estimated solution of the Lagrange coefficients, and let u be the estimation error of the i-th order central moment. i (x) represents the numerical characteristic of the i-th central moment of the random variable x. Let be the i-th order central moment of the power grid harmonic sample data, and m be the order of the central moment.

6. The method for evaluating the degree of harmonic pollution in a power grid according to claim 4, characterized in that: The process of generating the harmonic probability distribution function based on the global optimum and the maximum entropy model includes the following steps: Based on the global optimal value y=(λ1,λ2,…λ m ), calculate Based on the objective function and constraints of the maximum entropy model, the Lagrange equation is constructed. Taking the partial derivative of the Lagrange equation generates a probability distribution function that conforms to the maximum entropy distribution. Let λ0, λ i u i (x) Substitute the probability distribution function that conforms to the maximum entropy distribution into (x) to generate the harmonic probability distribution function.

7. A device for evaluating the degree of harmonic pollution in a power grid, characterized in that, include: The calculation module is used to acquire harmonic sample data of each power grid and calculate the central moments of each order of harmonic sample data of each power grid. The model building module is used to construct the maximum entropy model of the harmonics based on the central moments of each order of the power grid harmonic sample data. The model conversion module is used to convert the maximum entropy model into an optimization model. The model optimization module is used to optimize the optimization model using the modified hole punching function to obtain the global optimum value of the optimization model; The harmonic probability distribution function generation module is used to generate a harmonic probability distribution function based on the global optimum and the maximum entropy model. An evaluation module is used to evaluate the degree of power grid harmonic pollution using the harmonic probability distribution function; The expression for the modified hole-punching function is: In the formula, G(y,y) * ) represents the correction function for hole punching; g(y) represents the objective function of the optimization model; y * Let y be the local minimum point to be found, and let y be the global minimum point to be found. q>0, p>0, p, q, y0 are given constants; Ω is the interval of integration; ||·|| is the Euclidean norm; The optimization of the optimization model using the modified hole-punching function to obtain the global optimum of the optimization model includes the following steps: Repeat the following steps within the preset number of loops: The first local minimum point y of the objective function of the optimization model is obtained using a classical nonlinear programming algorithm. * ; Construct a modified hole-punching function G(y,y) at the first local minimum point. * ); The modified hole-punching function G(y,y) is constructed using a classical nonlinear programming algorithm. * Find the second local minimum value y; If G(y,y) * If )≤0, the second local minimum y is a better local minimum, let y * =y, as the new local minimum, construct the corrected hole-punching function again at the new local minimum, and solve G(y,y) again. * ); If G(y,y) * If ) > 0, take p: = dp, where d is the proportionality constant and p min Let p be the minimum value. If p > p min Substitute p into G(y,y) * ), and resolve G(y,y) * ); If p≤p min Let p:=p / d, q:=q / d, if Substitute p, q into G(y, y * ), and resolve G(y,y) * Otherwise, consider y = (λ1, λ2, ... λ) m ) is the global minimum point, which is the global optimum of the optimization model.

8. A system for evaluating the degree of harmonic pollution in a power grid, characterized in that: Including storage media and processor; The storage medium is used to store instructions; The processor is configured to operate according to the instructions to perform the method according to any one of claims 1-6.

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