Global collaborative governance method, system and equipment for wide-area power quality disturbance problems

Abstract

The present invention relates to the technical field of power quality management research, and discloses a global collaborative management method, system and equipment for wide-area power quality disturbance problems, including: based on all nodes in the power grid, constructing a node weight coefficient matrix that considers power quality transfer characteristics, responsibility contribution, power quality sensitive load and management cost indicators; calculating the weighted voltage distortion rate of each node based on the node weight coefficient matrix; constructing a management scheme optimization model with the selection of management nodes and the adopted management scheme as the optimization object and the minimum sum of the weighted voltage distortion rates of all nodes in the power grid as the objective function; using the golden jackal optimization algorithm to solve the management scheme optimization model to obtain the best management node and the management scheme corresponding to the best management node. The present invention comprehensively considers various management schemes to construct an optimization model, and the proposed technology can combine the advantages of various management schemes to obtain a collaborative management method for various management schemes.

Description

Technical Field

[0001] The present invention relates to the technical field of power quality management research, and in particular to a global collaborative management method, system and equipment for wide-area power quality disturbance problems. Background Art

[0002] The "double-high" new power system is a typical wide-area large power grid with high voltage levels, wide distribution areas and various types of harmonic emission sources. Although the harmonics of single nonlinear users, new energy sites and converter stations entering the network are suppressed to meet the standards, due to the strong background harmonic fluctuations and the harmonic transmission amplification effect of cables, it is obviously no longer suitable for the harmonic problems of the "double-high" new power system to only suppress harmonics in a single nonlinear user, new energy site or converter station without considering wide-area coordination. Because the harmonics emitted by the harmonic source are coupled with the background harmonics, when it is detected that the harmonics of a certain site exceed the standard, it is considered that the nonlinear users, new energy sites or converter stations connected to the site are the dominant harmonic sources, and it may be a serious mistake to manage them accordingly.

[0003] Since harmonic sources are widely distributed in large power grids and resonance amplification may occur during the transmission process, node harmonic exceeding the standard may be the result of the combined effect of nonlinear users / new energy sites and background harmonics. Governance at only a single node cannot effectively control the wide-area harmonic problem of the power grid. At the same time, when the harmonics in the large power grid exceed the standard in a wide area and the background harmonics are the dominant harmonic source, there is no effective analysis method to select which nodes to govern. In addition, there are various types of node harmonic governance schemes. Only by comprehensively considering various governance schemes and building a wide-area harmonic governance node optimization model can the harmonic problem of the large power grid be fundamentally governed. Summary of the invention

[0004] The present invention provides a global collaborative management method, system and equipment for wide-area power quality disturbance problems to solve the above problems.

[0005] The present invention is achieved through the following technical solutions:

[0006] A global collaborative governance method for wide-area power quality disturbance problems, comprising:

[0007] Based on all nodes in the power grid, a node weight coefficient matrix is ​​constructed that takes into account power quality transfer characteristics, responsibility contribution, power quality sensitive loads and governance cost indicators;

[0008] Calculating the weighted voltage distortion rate of each node based on the node weight coefficient matrix;

[0009] Construct a governance scheme optimization model with the governance node selection and the adopted governance scheme as the optimization object and the minimum sum of weighted voltage distortion rates of all nodes in the power grid as the objective function;

[0010] The golden jackal optimization algorithm is used to solve the governance scheme optimization model to obtain the optimal governance node and the governance scheme corresponding to the optimal governance node.

[0011] As an optimization, based on all nodes in the power grid, the specific process of constructing a node weight coefficient matrix that considers power quality transfer characteristics, responsibility contribution, power quality sensitive loads and governance costs is as follows:

[0012] A1. Calculate the sub-responsibility contribution of all harmonic source nodes in the power grid to each harmonic exceeding node in the power grid:

[0013]

[0014] In the formula, HI m,x V represents the sub-responsibility contribution of harmonic source node m to harmonic exceeding node x in the power grid, x Indicates the harmonic voltage at the node x where the harmonic exceeds the standard, V m represents the harmonic voltage of the harmonic source node m on the bus where the harmonic exceeding node x is located, and τ represents V m and V x The angle difference between M represents the number of harmonic sources that have been monitored, m∈[1,M], and M≥2, I m represents the measured harmonic injection current of harmonic source node m, Z m,x It represents the harmonic transfer impedance between the bus where the harmonic exceeding node x is located and the harmonic source node m, and V0 represents the background harmonic voltage of the bus where the harmonic exceeding node x is located;

[0015] A2. Sum up the sub-responsibility contributions of the same harmonic exceeding node m to obtain the responsibility contribution index HI of the harmonic exceeding node m m , Where X is the total number of nodes exceeding the standard;

[0016] A3. The responsibility contribution index HI of the non-harmonic source node j′ j′ Recorded as 0, the node contribution index matrix HI of all nodes in the power grid is finally constructed;

[0017] A4. Construct a transfer characteristic matrix of all nodes in the power grid regarding the power quality transfer characteristics, thereby obtaining the participation factors of all nodes, wherein the transfer characteristic matrix is ​​composed of the harmonic current of each node divided by the admittance matrix corresponding to each node, and the transfer characteristic matrix is ​​expressed as:

[0018]

[0019] Where V represents the voltage vector of each node, V jrepresents the voltage of node j, LΛT=Y, Y is the admittance matrix composed of each node, Λ=diag(λ1,λ2,···,λ s ), represents the eigenvalue diagonal matrix, L and T are the left and right eigenvector matrices of the admittance matrix Y, respectively. is the current matrix composed of the harmonic currents of each node, n is the total number of nodes in the power grid, and the diagonal element L in the admittance matrix is ​​defined as js T sj is the participation factor PF of node j j , thus obtaining a participation factor matrix PF, j∈[1,n] consisting of the participation factors of all nodes in the power grid, wherein the participation factor represents a combination of the excitability and observability of the mode s resonance on the same bus;

[0020] A5. Consider the power quality sensitive load to set the sensitivity factor α of node j j , let the node governance cost index be the proportion of node j’s governance cost to the total governance cost of the entire network, denoted as Cost j , thus obtaining the sensitivity factor matrix α and the governance cost index matrix Cost composed of the sensitivity factors and governance cost indexes of all nodes in the power grid;

[0021] A6. Construct a node weight coefficient matrix Q for all nodes in the power grid based on the node contribution index matrix HI, the participation factor matrix PF, the sensitivity factor matrix α and the governance cost index matrix Cost, where Q is expressed as:

[0022]

[0023] Among them, r ji Represents the i-th node weight coefficient of node j, i∈[1,4].

[0024] As an optimization, the specific process of calculating the weighted voltage distortion rate of each node based on the node weight coefficient matrix is:

[0025] B1. Calculate the weight of each node weight coefficient in the node weight coefficient matrix based on the entropy weight method, where the weight of the i-th node weight coefficient is expressed as:

[0026]

[0027] Among them, H i represents the entropy of the weight coefficient of the i-th node,

[0028] B2. Calculate the weight of each node based on the weight of each node weight coefficient, and the weight of the node is expressed as:

[0029] wj =δ1HI j +δ2PF j +δ3α j +δ4Cost j ;

[0030] Among them, w j represents the weight of node j;

[0031] B3. Calculate the harmonic voltage of each node:

[0032]

[0033] Where h represents the harmonic order, V h Represents the effective value of the harmonic voltage of the hth harmonic at the node, Y h represents the admittance matrix of the hth harmonic, I sh It represents the effective value of the harmonic injection current of the hth harmonic passing through the node;

[0034] B4. Calculate the voltage distortion rate of each node:

[0035]

[0036] Among them, THD j represents the voltage distortion rate at node j, V 1,j Represents the effective value of the fundamental voltage at node j, V h,j is the effective value of the h-th harmonic voltage at node j;

[0037] B5. Based on the weight of each node and the voltage distortion rate, a weighted voltage distortion rate of each node is constructed. The weighted voltage distortion rate is expressed as:

[0038] THD′ j =w j THD j ;

[0039] Among them, THD′ j Represents the weighted voltage distortion rate of node j.

[0040] As an optimization, the governance scheme optimization model is expressed as:

[0041]

[0042] Among them, C cost represents the preset total governance investment, ε j Indicates whether the governance scheme is adopted at node j, ε j =1 means that the governance scheme is adopted at node j, ε j = 0 means that the governance scheme is not adopted at node j, C jThe investment required for node j’s governance scheme.

[0043] As an optimization, the golden jackal optimization algorithm is used to solve the governance scheme optimization model, and the specific process of obtaining the optimal governance node and the corresponding governance scheme is as follows:

[0044] C1. Initialize the population position and the maximum number of iterations, wherein the population includes a plurality of prey, each prey consisting of whether a node in the power grid adopts a governance scheme and a corresponding governance scheme;

[0045] C2. Calculate the fitness value of each individual's location, select the prey with the best fitness value as the male golden jackal, and the prey with the second best fitness value as the female golden jackal;

[0046] C3. In the exploration phase, the positions of the male and female golden jackals are updated. The specific formula is:

[0047] R1(t)=R M (t)-E*|R M (t)-γl*Prey(t)|;

[0048] R2(t)=R FM (t)-E*|R FM (t)-γl*Prey(t)|;

[0049] R M (t), R FM (t) represents the positions of the male and female golden jackals at the t-th iteration, R1(t) and R2(t) represent the updated positions of the male and female golden jackals at the t-th iteration, E is the escape energy of the prey, γ is a random number between 0 and 1, γl represents a random number based on Levy distribution, Prey(t) represents the prey matrix Prey at the t-th iteration;

[0050] C4. When the escape energy E of the prey is less than 1, the development phase is entered, and the positions of the male and female golden jackals are updated. The specific formula is:

[0051] R1(t)=R M (t)-E*|γl*R M (t)-Prey(t)|;

[0052] R2(t)=R FM (t)-E*|γl*R FM (t)-Prey(t)|;

[0053] C5. When the maximum number of iterations t is reached max When , the position of the golden jackal is updated and the updated position of the golden jackal is used as the output, where the update formula of the position of the golden jackal is:

[0054]

[0055] Among them, R1(t max -1) is (t max -1) iterations, R2(t max -1) is (t max -1) The position of the female golden jackal in iteration .

[0056] As an optimization, the prey matrix Prey is expressed as:

[0057]

[0058] Among them, R k represents the kth prey, R k =[R k1 R k2 ···R kd ], R ke represents the solution of the e-th dimensional optimization problem for the k-th prey, k∈[1,p], e∈[1,d], d is the dimension of the optimization problem to be solved, and p is the number of prey in the population.

[0059] As an optimization, the escape energy E of the prey is expressed as:

[0060]

[0061] Among them, E1 represents the decreasing process of prey energy, E0 represents the initial state of prey energy, r is a random number ranging from 0 to 1, c1 is a constant, and t is the current number of iterations.

[0062] As an optimization, the specific expression of the random number γl based on Levy distribution is:

[0063] γl=0.05*LF(y);

[0064] Where LF(y) is the Levy flight function, LF(y) = 0.01×(μ×σ) / (v (1 / β) ), σ is the intermediate parameter, μ and v are random numbers ranging from 0 to 1, β is a constant, Γ(·) represents the Gamma function, Γ(y) = (y-1)! .

[0065] The present invention also discloses a global collaborative governance system for wide-area power quality disturbance problems, which is used to execute the aforementioned global collaborative governance method for wide-area power quality disturbance problems, including:

[0066] The node weight coefficient matrix construction module is used to construct a node weight coefficient matrix based on all nodes in the power grid, taking into account power quality transfer characteristics, responsibility contribution, power quality sensitive loads and governance cost indicators;

[0067] A weighted voltage distortion rate calculation module, used to calculate the weighted voltage distortion rate of each node based on the node weight coefficient matrix;

[0068] A governance scheme optimization model building module is used to build a governance scheme optimization model with the governance node selection and the adopted governance scheme as the optimization objects and the minimum sum of weighted voltage distortion rates of all nodes in the power grid as the objective function;

[0069] The solution module is used to solve the governance scheme optimization model by using the golden jackal optimization algorithm to obtain the best governance node and the governance scheme corresponding to the best governance node.

[0070] The present invention also discloses a data processing device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the aforementioned global collaborative governance method for wide-area power quality disturbance problems when executing the computer program.

[0071] Compared with the prior art, the present invention has the following advantages and beneficial effects:

[0072] The present invention comprehensively considers various governance schemes to construct an optimization model. Compared with the traditional optimization model that only targets a single governance device, the proposed technology can integrate the advantages of various governance schemes. In addition, the existing method does not consider the impact of the access of the governance equipment on the network admittance. The present invention will continuously update the harmonic injection current and admittance matrix after governance during the optimization process. In order to achieve the comprehensive optimal harmonic voltage distortion of the power grid, the present invention comprehensively considers harmonic source governance and harmonic network transmission blocking, and constructs a node weight coefficient that considers harmonic transmission characteristics, responsibility contribution, power quality sensitive load and governance cost. The sum of the weighted harmonic voltage distortion rates of each node in the network is taken as the optimization target, and the total investment in harmonic governance is taken as the constraint condition. The optimal harmonic suppression strategy of the power grid is solved, and finally a collaborative governance method that comprehensively considers various governance schemes is obtained. BRIEF DESCRIPTION OF THE DRAWINGS

[0073] The drawings described herein are used to provide a further understanding of the embodiments of the present invention, constitute a part of this application, and do not constitute a limitation of the embodiments of the present invention. In the drawings:

[0074] Figure 1 It is a schematic diagram of the process of the present invention;

[0075] Figure 2 To evaluate the schematic diagram of the power grid topology;

[0076] Figure 3 The harmonic source current diagram for the setting;

[0077] Figure 4 Control the harmonic distortion rate before and after each node;

[0078] Figure 5 Optimize the model optimization process for harmonic mitigation solutions. DETAILED DESCRIPTION

[0079] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with embodiments and drawings. The exemplary embodiments of the present invention and their description are only used to explain the present invention and are not intended to limit the present invention.

[0080] This embodiment 1 provides a global collaborative governance method for wide-area power quality disturbance problems, such as Figure 1 As shown, including:

[0081] S1. Based on all nodes in the power grid, a node weight coefficient matrix is ​​constructed that takes into account power quality transfer characteristics, responsibility contribution, power quality sensitive loads and governance cost indicators;

[0082] In some embodiments, the specific process of step S1 is:

[0083] A1. Calculate the sub-responsibility contribution of all harmonic source nodes in the power grid to each harmonic exceeding node in the power grid:

[0084]

[0085] In the formula, HI m,x V represents the sub-responsibility contribution of the harmonic source node m to the harmonic exceeding node x in the power grid, that is, the projection percentage of the harmonic voltage generated by the harmonic source node m at the focus node x to the harmonic voltage of the focus node, V x Indicates the harmonic voltage at the node x where the harmonic exceeds the standard, V m represents the harmonic voltage of the harmonic source node m on the bus where the harmonic exceeding node x is located, and τ represents V m and V x The angle difference between M represents the number of harmonic sources that have been monitored, m∈[1,M], and M≥2, I m represents the measured harmonic injection current of harmonic source node m, Z m,x represents the harmonic transfer impedance (element of the harmonic transfer impedance matrix) between the bus where the harmonic exceeding node x is located and the harmonic source node m, and V0 represents the background harmonic voltage of the bus where the harmonic exceeding node x is located;

[0086] It should be noted that voltage can be written as a phasor of amplitude plus angle, and angle difference refers to the subtraction of two angles.

[0087] A2. Sum up the sub-responsibility contributions of the same harmonic exceeding node m to obtain the responsibility contribution index HI of the harmonic exceeding node m m , Where X is the total number of nodes exceeding the standard;

[0088] A3. The responsibility contribution index HI of the non-harmonic source node j′ j′ Recorded as 0, the node contribution index matrix HI of all nodes in the power grid is finally constructed;

[0089] Here, the non-harmonic source node j′ and the harmonic source node m together constitute all the nodes in the power grid.

[0090] A4. Construct a transfer characteristic matrix of all nodes in the power grid regarding the power quality transfer characteristics, thereby obtaining the participation factors of all nodes, wherein the transfer characteristic matrix is ​​composed of the harmonic current of each node divided by the admittance matrix corresponding to each node, and the transfer characteristic matrix is ​​expressed as:

[0091]

[0092] Where V represents the voltage vector of each node, V j represents the voltage of node j, LΛT=Y, Y is the admittance matrix composed of each node, Λ=diag(λ1,λ2,···,λ s ), represents the eigenvalue diagonal matrix, L and T are the left and right eigenvector matrices of the admittance matrix Y, respectively. is the current matrix composed of the harmonic currents of each node, n is the total number of nodes in the power grid, and the diagonal element L in the admittance matrix is ​​defined as js T sj is the participation factor PF of node j j , thus obtaining a participation factor matrix PF, j∈[1,n] consisting of the participation factors of all nodes in the power grid, wherein the participation factor represents a combination of the excitability and observability of the mode s resonance on the same bus;

[0093] A5. Consider the power quality sensitive load to set the sensitivity factor α of node j j , let the node governance cost index be the proportion of node j’s governance cost to the total governance cost of the entire network, denoted as Cost j , thus obtaining the sensitivity factor matrix α and the governance cost index matrix Cost composed of the sensitivity factors and governance cost indexes of all nodes in the power grid;

[0094] A6. Construct a node weight coefficient matrix Q for all nodes in the power grid based on the node contribution index matrix HI, the participation factor matrix PF, the sensitivity factor matrix α and the governance cost index matrix Cost, where Q is expressed as:

[0095]

[0096] Among them, r ji Represents the i-th node weight coefficient of node j, i∈[1,4].

[0097] S2. Calculating the weighted voltage distortion rate of each node based on the node weight coefficient matrix;

[0098] It should be noted that when solving the optimization model of the governance scheme in the subsequent Golden Jackal algorithm, the weighted voltage distortion rate must be solved. Therefore, the harmonic current I injected by the disturbance source after governance is updated according to the governance scheme. sh With the admittance matrix Y h , including the filter impedance and the harmonic current source equivalent to the active filter. Calculate the harmonic flow after treatment and calculate the weighted voltage distortion rate of each node.

[0099] The specific process of step S2 is:

[0100] B1. Calculate the weight of each node weight coefficient in the node weight coefficient matrix based on the entropy weight method. A node has a total of 4 node weight coefficients, namely node contribution index, participation factor, sensitivity factor and governance cost index. This step is to calculate the weights of node contribution index, participation factor, sensitivity factor and governance cost index respectively.

[0101] Among them, the weight of the i-th node weight coefficient is expressed as:

[0102]

[0103] Among them, H i represents the entropy of the weight coefficient of the i-th node, i=1 means that the node weight coefficient is the node contribution index, i=2 means that the node weight coefficient is the participation factor, i=3 means that the node weight coefficient is the sensitivity factor, and i=4 means that the node weight coefficient is the governance cost index.

[0104] B2. Calculate the weight of each node based on the weight of each node weight coefficient, and the weight of the node is expressed as:

[0105] w j =δ1HI j +δ2PF j +δ3α j +δ4Costj ;

[0106] Among them, w j represents the weight of node j;

[0107] B3. Calculate the harmonic voltage of each node:

[0108]

[0109] Where h represents the harmonic order, V h Represents the effective value of the harmonic voltage of the hth harmonic at the node, Y h represents the admittance matrix of the hth harmonic, I sh Represents the effective value of the harmonic current of the hth harmonic passing through the node;

[0110] B4. Calculate the voltage distortion rate of each node:

[0111]

[0112] Among them, THD j represents the voltage distortion rate at node j, V 1,j Represents the effective value of the fundamental voltage at node j, V h,j is the effective value of the h-th harmonic voltage at node j;

[0113] It should be noted that, generally speaking, when performing calculations, h only needs to be considered up to 20, that is, the harmonic frequency order is 19.

[0114] B5. Based on the weight of each node and the voltage distortion rate, a weighted voltage distortion rate of each node is constructed. The weighted voltage distortion rate is expressed as:

[0115] THD j ′=w j THD j ;

[0116] Among them, THD′ j Represents the weighted voltage distortion rate of node j.

[0117] S3. Construct a governance scheme optimization model with the governance node selection and the adopted governance scheme as the optimization object and the minimum sum of weighted voltage distortion rates of all nodes in the power grid as the objective function;

[0118] The governance scheme optimization model is expressed as:

[0119]

[0120] Among them, C cost represents the preset total governance investment, ε j Indicates whether the governance scheme is adopted at node j, ε j=1 means that the governance scheme is adopted at node j, ε j = 0 means that the governance scheme is not adopted at node j, C j The investment required for node j’s governance scheme.

[0121] S4. Use the golden jackal optimization algorithm to solve the governance scheme optimization model to obtain the best governance node and the governance scheme corresponding to the best governance node.

[0122] The specific process of step S4 is:

[0123] C1. Initialize the population position and the maximum number of iterations, wherein the population (golden jackal population) includes multiple prey, each prey consisting of whether a node in the power grid adopts a governance scheme and the corresponding governance scheme;

[0124] More specifically, the governance solution is to govern the capacity of the device.

[0125] It should be noted that the specific formula for initializing the population is:

[0126] R0=R min +rand(R max -R min );

[0127] Among them, R0 is the initial position of the golden jackal population, R max , R min They are the upper and lower boundaries of the problem to be solved, rand(R max -R min ) means taking R min and R max A random number between .

[0128] Here, the problem to be solved is the problem that needs to be optimized, that is, the type of governance solution adopted by the node (which can be understood as whether the node adopts the governance solution) and the capacity of the governance device.

[0129] C2. Calculate the fitness value of each individual's location, select the prey with the best fitness value as the male golden jackal, and the prey with the second best fitness value as the female golden jackal;

[0130] Here, the fitness function may be an objective function. The optimal fitness value refers to the minimum fitness value, and the suboptimal fitness value refers to the second smallest fitness value.

[0131] C3. In the exploration phase, i.e. searching for prey, the male golden jackal R is updated. M (t) and female golden jackal R FM (t) position, the specific formula is:

[0132] R1(t)=R M (t)-E*|RM (t)-γl*Prey(t)|;

[0133] R2(t)=R FM (t)-E*|R FM (t)-γl*Prey(t)|;

[0134] R M (t), R FM (t) represents the positions of the male and female golden jackals at the t-th iteration, R1(t) and R2(t) represent the updated positions of the male and female golden jackals at the t-th iteration, E is the escape energy of the prey, γ is a random number between 0 and 1, γl represents a random number based on Levy distribution, Prey(t) represents the prey matrix Prey at the t-th iteration;

[0135] C4. When the escape energy E of the prey is less than 1, the development phase is entered, that is, the phase of surrounding and attacking the prey, and the positions of the male and female golden jackals are updated. The specific formula is:

[0136] R1(t)=R M (t)-E*|γl*R M (t)-Prey(t)|;

[0137] R2(t)=R FM (t)-E*|γl*R FM (t)-Prey(t)|;

[0138] C5. When the maximum number of iterations tmax is reached, the position of the golden jackal is updated and the updated position of the golden jackal is used as output, wherein the update formula of the position of the golden jackal is:

[0139]

[0140] Among them, R1(t max -1) is (t max -1) iterations, R2(t max -1) is (t max -1) The position of the female golden jackal in iteration .

[0141] In some embodiments, the prey matrix Prey is represented as:

[0142]

[0143] Among them, R k represents the kth prey, R k =[R k1 R k2 ···R kd ], Rke represents the solution of the e-th dimensional optimization problem of the k-th prey, k∈[1,p], e∈[1,d], d is the dimension of the optimization problem to be solved, and p is the number of prey in the population, that is, the size of the prey population.

[0144] Here, the dimension refers to the number of nodes multiplied by the harmonic frequency multiplied by 2, d = n*(h-1)*2. That is, the type of governance plan and the capacity of the governance device must be considered for each node of each harmonic.

[0145] In some embodiments, the escape energy E of the prey is expressed as:

[0146]

[0147] Among them, E1 represents the decreasing process of prey energy, E0 represents the initial state of prey energy, r is a random number ranging from 0 to 1, c1 is a constant, c1=1.5, and t is the current number of iterations.

[0148] In some embodiments, the specific expression of the random number γl based on the Levy distribution is:

[0149] γl=0.05*LF(y);

[0150] Where LF(y) is the Levy flight function, LF(y) = 0.01×(μ×σ) / (v (1 / β) ),

[0151] μ and v are random numbers ranging from 0 to 1, β is a constant, Γ(·) represents the Gamma function, Γ(y) = (y-1)! .

[0152] Next, the present invention will be described using specific examples.

[0153] The present invention uses IEEE33 nodes for simulation calculation, and the power grid topology is as follows: Figure 2 As shown, 10 harmonic sources are set up, and the harmonic current injected by each harmonic source is randomly 1% to 10% of the fundamental current. The node harmonic current is set as follows Figure 3 As shown, the treatment method adopts single tuned filter and active filter, and the cost of treatment device is shown in Table 1-Table 2.

[0154] Table 1 Cost of single tuned filter

[0155] Single tuned filter Single tuned filter R 396.5 yuan / MW L 55.25 yuan / MVar C 32500 yuan / MVar

[0156] Table 2 Cost of active filter

[0157] Active filter 3000 yuan / A

[0158] The total investment in network governance is set at 50,000 yuan. With the total investment as the constraint condition and the sum of weighted voltage distortion rate as the objective function, a harmonic governance optimization model is established and solved using the golden jackal optimization algorithm. The harmonic distortion rate of each node before and after governance is shown in the figure below: Figure 4 As shown in the figure, it can be seen that after the treatment, the distortion rate of each node is reduced to below 0.2%. The optimization process of the harmonic control solution optimization model solved by the golden jackal optimization algorithm is as follows: Figure 5 shown.

[0159] Embodiment 2 further discloses a global collaborative governance system for wide-area power quality disturbance problems, including:

[0160] The node weight coefficient matrix construction module is used to construct a node weight coefficient matrix based on all nodes in the power grid, taking into account power quality transfer characteristics, responsibility contribution, power quality sensitive loads and governance cost indicators;

[0161] A weighted voltage distortion rate calculation module, used to calculate the weighted voltage distortion rate of each node based on the node weight coefficient matrix;

[0162] A governance scheme optimization model building module is used to build a governance scheme optimization model with the governance node selection and the adopted governance scheme as the optimization objects and the minimum sum of weighted voltage distortion rates of all nodes in the power grid as the objective function;

[0163] The solution module is used to solve the governance scheme optimization model by using the golden jackal optimization algorithm to obtain the best governance node and the governance scheme corresponding to the best governance node.

[0164] Embodiment 3 also discloses a data processing device, characterized in that it comprises: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, it implements the global collaborative governance method for wide-area power quality disturbance problems described in Embodiment 1.

[0165] The specific implementation methods described above further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific implementation method of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A global collaborative governance method for wide-area power quality disturbance problems, characterized in that: include: Based on all nodes in the power grid, a node weight coefficient matrix is ​​constructed that takes into account power quality transfer characteristics, responsibility contribution, power quality sensitive loads and governance cost indicators; The specific process is: A1. Calculate the sub-responsibility contribution of all harmonic source nodes in the power grid to each harmonic exceeding node in the power grid: In the formula, HI m,x V represents the sub-responsibility contribution of harmonic source node m to harmonic exceeding node x in the power grid, x Indicates the harmonic voltage at the node x where the harmonic exceeds the standard, V m represents the harmonic voltage of the harmonic source node m on the bus where the harmonic exceeding node x is located, and τ represents V m and V x The angle difference between M represents the number of harmonic sources that have been monitored, m∈[1,M], and M≥2, I m represents the measured harmonic injection current of harmonic source node m, Z m,x It represents the harmonic transfer impedance between the bus where the harmonic exceeding node x is located and the harmonic source node m, and V0 represents the background harmonic voltage of the bus where the harmonic exceeding node x is located; A2. Sum up the sub-responsibility contributions of the same harmonic exceeding node m to obtain the responsibility contribution index HI of the harmonic exceeding node m m , Where X is the total number of nodes exceeding the standard; A3. The responsibility contribution index HI of the non-harmonic source node j′ j′ Recorded as 0, the node contribution index matrix HI of all nodes in the power grid is finally constructed; A4. Construct a transfer characteristic matrix of all nodes in the power grid regarding the power quality transfer characteristics, thereby obtaining the participation factors of all nodes, wherein the transfer characteristic matrix is ​​composed of the harmonic current of each node divided by the admittance matrix corresponding to each node, and the transfer characteristic matrix is ​​expressed as: Where V represents the voltage vector of each node, V j represents the voltage of node j, LΛT=Y, Y is the admittance matrix composed of each node, Λ=diag(λ1,λ2,···,λ s ), represents the eigenvalue diagonal matrix, L and T are the left and right eigenvector matrices of the admittance matrix Y, respectively. is the current matrix composed of the harmonic currents of each node, n is the total number of nodes in the power grid, and the diagonal element L in the admittance matrix is ​​defined as js T sj is the participation factor PF of node j j , thus obtaining a participation factor matrix PF, j∈[1,n] consisting of the participation factors of all nodes in the power grid, wherein the participation factor represents a combination of the excitability and observability of the mode s resonance on the same bus; A5. Consider the power quality sensitive load to set the sensitivity factor α of node j j , let the node governance cost index be the proportion of node j’s governance cost to the total governance cost of the entire network, denoted as Cost j , thus obtaining the sensitivity factor matrix α and the governance cost index matrix Cost composed of the sensitivity factors and governance cost indexes of all nodes in the power grid; A6. Construct a node weight coefficient matrix Q for all nodes in the power grid based on the node contribution index matrix HI, the participation factor matrix PF, the sensitivity factor matrix α and the governance cost index matrix Cost, where Q is expressed as: Among them, r ji represents the i-th node weight coefficient of node j, i∈[1,4]; Calculating the weighted voltage distortion rate of each node based on the node weight coefficient matrix; Construct a governance scheme optimization model with the governance node selection and the adopted governance scheme as the optimization object and the minimum sum of weighted voltage distortion rates of all nodes in the power grid as the objective function; The golden jackal optimization algorithm is used to solve the governance scheme optimization model to obtain the optimal governance node and the governance scheme corresponding to the optimal governance node.

2. The global collaborative governance method for wide-area power quality disturbance problems according to claim 1 is characterized in that: The specific process of calculating the weighted voltage distortion rate of each node based on the node weight coefficient matrix is ​​as follows: B1. Calculate the weight of each node weight coefficient in the node weight coefficient matrix based on the entropy weight method, where the weight of the i-th node weight coefficient is expressed as: Among them, H i represents the entropy of the weight coefficient of the i-th node, B2. Calculate the weight of each node based on the weight of each node weight coefficient, and the weight of the node is expressed as: w j =δ1HI j +δ2PF j +δ3α j +δ4Cost j ; Among them, w j represents the weight of node j; B3. Calculate the harmonic voltage of each node: Where h represents the harmonic order, V h Represents the effective value of the harmonic voltage of the hth harmonic at the node, Y h represents the admittance matrix of the hth harmonic, I sh Represents the effective value of the harmonic current of the hth harmonic passing through the node; B4. Calculate the voltage distortion rate of each node: Among them, THD j represents the voltage distortion rate at node j, V 1,j Represents the effective value of the fundamental voltage at node j, V h,j is the effective value of the h-th harmonic voltage at node j; B5. Based on the weight of each node and the voltage distortion rate, a weighted voltage distortion rate of each node is constructed. The weighted voltage distortion rate is expressed as: THD' j =w j THD j ; Among them, THD′ j Represents the weighted voltage distortion rate of node j.

3. The global collaborative governance method for wide-area power quality disturbance problems according to claim 2 is characterized in that: The governance scheme optimization model is expressed as: Among them, C cost represents the preset total governance investment, ε j Indicates whether the governance scheme is adopted at node j, ε j =1 means that the governance scheme is adopted at node j, ε j = 0 means that the governance scheme is not adopted at node j, C j The investment required for node j’s governance scheme.

4. The global collaborative governance method for wide-area power quality disturbance problems according to claim 1 is characterized in that: The specific process of using the Golden Jackal optimization algorithm to solve the governance scheme optimization model and obtain the optimal governance node and the corresponding governance scheme is as follows: C1. Initialize the population position and the maximum number of iterations, wherein the population includes a plurality of prey, each prey consisting of whether a node in the power grid adopts a governance scheme and a corresponding governance scheme; C2. Calculate the fitness value of each individual's location, select the prey with the best fitness value as the male golden jackal, and the prey with the second best fitness value as the female golden jackal; C3. In the exploration phase, the positions of the male and female golden jackals are updated. The specific formula is: R1(t)=R M (t)-E*|R M (t)-γl*Prey(t)|; R2(t)=R FM (t)-E*|R FM (t)-γl*Prey(t)|; R M (t), R FM (t) represents the positions of the male and female golden jackals at the t-th iteration, R1(t) and R2(t) represent the updated positions of the male and female golden jackals at the t-th iteration, E is the escape energy of the prey, γ is a random number between 0 and 1, γl represents a random number based on Levy distribution, Prey(t) represents the prey matrix Prey at the t-th iteration; C4. When the escape energy E of the prey is less than 1, the development phase is entered, and the positions of the male and female golden jackals are updated. The specific formula is: R1(t)=R M (t)-E*|γl*R M (t)-Prey(t)|; R2(t)=R FM (t)-E*|γl*R FM (t)-Prey(t)|; C5. When the maximum number of iterations t is reached max When , the position of the golden jackal is updated and the updated position of the golden jackal is used as the output, where the update formula of the position of the golden jackal is: Among them, R1(t max -1) is (t max -1) iterations, R2(t max -1) is (t max -1) The position of the female golden jackal in iteration .

5. The global collaborative governance method for wide-area power quality disturbance problems according to claim 4 is characterized in that: The prey matrix Prey is expressed as: Among them, R k represents the kth prey, R k =[R k1 R k2 ··· R kd ], R ke represents the solution of the e-th dimensional optimization problem for the k-th prey, k∈[1,p], e∈[1,d], d is the dimension of the optimization problem to be solved, and p is the number of prey in the population.

6. A global collaborative governance method for wide-area power quality disturbance problems according to claim 4, characterized in that: The escape energy E of the prey is expressed as: Among them, E1 represents the decreasing process of prey energy, E0 represents the initial state of prey energy, r is a random number ranging from 0 to 1, c1 is a constant, and t is the current number of iterations.

7. A global collaborative governance method for wide-area power quality disturbance problems according to claim 4, characterized in that: The specific expression of the random number γl based on Levy distribution is: γl=0.05*LF(y); Where LF(y) is the Levy flight function, LF(y) = 0.01×(μ×σ) / (v (1 / β) ), σ is an intermediate parameter, μ and v are random numbers ranging from 0 to 1, and β is a constant. Γ(·) represents the Gamma function, Γ(y)=(y-1)! .

8. A global collaborative governance system for wide-area power quality disturbance problems, used to execute a global collaborative governance method for wide-area power quality disturbance problems as described in any one of claims 1 to 7, characterized in that: include: The node weight coefficient matrix construction module is used to construct a node weight coefficient matrix based on all nodes in the power grid, taking into account power quality transfer characteristics, responsibility contribution, power quality sensitive loads and governance cost indicators; The specific process is: A1. Calculate the sub-responsibility contribution of all harmonic source nodes in the power grid to each harmonic exceeding node in the power grid: In the formula, HI m,x V represents the sub-responsibility contribution of harmonic source node m to harmonic exceeding node x in the power grid, x Indicates the harmonic voltage at the node x where the harmonic exceeds the standard, V m represents the harmonic voltage of the harmonic source node m on the bus where the harmonic exceeding node x is located, and τ represents V m and V x The angle difference between M represents the number of harmonic sources that have been monitored, m∈[1,M], and M≥2, I m represents the measured harmonic injection current of harmonic source node m, Z m,x It represents the harmonic transfer impedance between the bus where the harmonic exceeding node x is located and the harmonic source node m, and V0 represents the background harmonic voltage of the bus where the harmonic exceeding node x is located; A2. Sum up the sub-responsibility contributions of the same harmonic exceeding node m to obtain the responsibility contribution index HI of the harmonic exceeding node m m , Where X is the total number of nodes exceeding the standard; A3. The responsibility contribution index HI of the non-harmonic source node j′ j′ Recorded as 0, the node contribution index matrix HI of all nodes in the power grid is finally constructed; A4. Construct a transfer characteristic matrix of all nodes in the power grid regarding the power quality transfer characteristics, thereby obtaining the participation factors of all nodes, wherein the transfer characteristic matrix is ​​composed of the harmonic current of each node divided by the admittance matrix corresponding to each node, and the transfer characteristic matrix is ​​expressed as: Where V represents the voltage vector of each node, V j represents the voltage of node j, LΛT=Y, Y is the admittance matrix composed of each node, Λ=diag(λ1,λ2,···,λ s ), represents the eigenvalue diagonal matrix, L and T are the left and right eigenvector matrices of the admittance matrix Y, respectively. is the current matrix composed of the harmonic currents of each node, n is the total number of nodes in the power grid, and the diagonal element L in the admittance matrix is ​​defined as js T sj is the participation factor PF of node j j , thus obtaining a participation factor matrix PF, j∈[1,n] consisting of the participation factors of all nodes in the power grid, wherein the participation factor represents a combination of the excitability and observability of the mode s resonance on the same bus; A5. Consider the power quality sensitive load to set the sensitivity factor α of node j j , let the node governance cost index be the proportion of node j’s governance cost to the total governance cost of the entire network, denoted as Cost j , thus obtaining the sensitivity factor matrix α and the governance cost index matrix Cost composed of the sensitivity factors and governance cost indexes of all nodes in the power grid; A6. Construct a node weight coefficient matrix Q for all nodes in the power grid based on the node contribution index matrix HI, the participation factor matrix PF, the sensitivity factor matrix α and the governance cost index matrix Cost, where Q is expressed as: Among them, r ji represents the i-th node weight coefficient of node j, i∈[1,4]; A weighted voltage distortion rate calculation module, used to calculate the weighted voltage distortion rate of each node based on the node weight coefficient matrix; A governance scheme optimization model building module is used to build a governance scheme optimization model with the governance node selection and the adopted governance scheme as the optimization objects and the minimum sum of weighted voltage distortion rates of all nodes in the power grid as the objective function; The solution module is used to solve the governance scheme optimization model by using the golden jackal optimization algorithm to obtain the best governance node and the governance scheme corresponding to the best governance node.

9. A data processing device, characterized in that: include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, a global collaborative management method for wide-area power quality disturbance problems as described in any one of claims 1 to 7 is implemented.

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