Collaborative optimization method and device for three power systems of wind generating set and electronic equipment

Abstract

The invention relates to a wind generating set three-power system collaborative optimization method and device and electronic equipment. The method comprises the following steps: generating a node susceptance matrix and a branch-node susceptance matrix according to fan grid connection parameters; respectively obtaining a non-equilibrium node susceptance matrix and a reduced branch-node susceptance matrix through de-equilibrium node processing; generating a fan grid-connected node sensitivity matrix according to the non-equilibrium node susceptance matrix and the reduced branch-node susceptance matrix; according to the fan grid-connected node sensitivity matrix, determining the available power transmission capacity between the regions, and further determining a power transmission blockage risk judgment result; and when the blockage risk exists, determining a target node for coordinated regulation of the three-power system by using the fan grid-connected node sensitivity matrix, and generating a safety coordinated regulation control instruction of the three-power system according to the target node to control the three-power system. Therefore, collaborative optimization of the three-power system can be realized, the power transmission blockage risk is reduced, safe and stable operation of a power grid is guaranteed, and the wind curtailment problem is reduced.

Description

TECHNICAL FIELD

[0001] The present disclosure relates to the field of power generation, in particular, to a wind turbine generator system three-electricity system collaborative optimization method, device and electronic equipment. BACKGROUND

[0002] China's wind power industry is developing rapidly, however, behind the rapid expansion of the industry scale, the early construction of wind farms is gradually entering the equipment aging period, and the problems such as lagging behind in technology update and low power generation efficiency are increasingly prominent.

[0003] In terms of technology, old wind turbine generators are limited by early design standards, and there is a significant gap between their power generation capacity and that of current mainstream models. The intelligent level of their electric control systems is insufficient, and the efficiency of their conversion and variable pitch systems is low, which limits the wind energy capture and conversion capacity and makes the wind resource utilization rate much lower than the industry advanced level. In addition, due to the lack of coordination between early wind farm site selection and power grid planning, the structure of the regional power grid in some areas is weak, and the transmission channel capacity is insufficient, which easily leads to transmission congestion during the high wind power generation period. When the regional power grid cannot timely consume all the power generated by the wind farm, in order to ensure the safe and stable operation of the power grid, the dispatching department has to take power limiting measures, resulting in a large amount of wasted wind power resources, i.e., the problem of "curtailed wind power".

[0004] The curtailed wind power not only directly causes the loss of clean energy, but also exacerbates the energy supply and demand contradiction. Especially, the transmission congestion problem under high proportion of wind power grid connection has become a key bottleneck restricting wind power consumption. In areas where wind resources are rich but the power grid transmission capacity is limited, the curtailed wind power rate is high for a long time. SUMMARY

[0005] The purpose of the present disclosure is to provide a wind turbine generator system three-electricity system collaborative optimization method, device and electronic equipment to reduce the problem of curtailed wind power and improve the safety of wind turbine grid connection and the economy of wind turbine power generation.

[0006] In order to achieve the above-mentioned purpose, the first aspect of the present disclosure provides a wind turbine generator system three-electricity system collaborative optimization method, comprising: obtaining wind turbine grid connection parameters; generating a node admittance matrix and a branch-node admittance matrix according to the wind turbine grid connection parameters; performing a non-equilibrium node processing on the node admittance matrix to obtain a non-equilibrium node admittance matrix; performing a non-equilibrium node processing on the branch-node admittance matrix to obtain a reduced branch-node admittance matrix; generating a wind turbine grid connection node sensitivity matrix according to the non-equilibrium node admittance matrix and the reduced branch-node admittance matrix; determining the inter-regional available transmission capacity of a wind farm grid connection region to a target region according to the wind turbine grid connection node sensitivity matrix; determine a power transmission congestion risk decision result according to the inter-regional available power transmission capability; in a case where the power transmission congestion risk decision result represents that there is a congestion risk, determine a target node of the coordinated regulation of the three-electricity system by using the wind turbine grid-connected node sensitivity matrix; generate a coordinated regulation control instruction of the three-electricity system according to the target node; control the three-electricity system by using the coordinated regulation control instruction.

[0007] Optionally, the generating a wind turbine grid-connected node sensitivity matrix according to the non-balance node admittance matrix and the reduced branch-node admittance matrix comprises: determining a reference sensitivity matrix of the wind turbine grid-connected node according to the non-balance node admittance matrix and the reduced branch-node admittance matrix; complementing the reference sensitivity matrix by using a balance node to obtain the wind turbine grid-connected node sensitivity matrix.

[0008] Optionally, the determining the inter-regional available power transmission capability of the wind farm grid-connected region to the target region according to the wind turbine grid-connected node sensitivity matrix comprises: obtaining a power generation contribution factor vector of the wind farm grid-connected region and a load draw factor vector of the target region; determining an inter-regional power transmission sensitivity of the wind farm grid-connected region to the target region according to the power generation contribution factor vector, the load draw factor vector and the wind turbine grid-connected node sensitivity matrix; determining an available power transmission capability of each branch according to the inter-regional power transmission sensitivity; determining the inter-regional available power transmission capability according to a minimum value in the available power transmission capabilities of the branches.

[0009] Optionally, the determining a power transmission congestion risk decision result according to the inter-regional available power transmission capability comprises: adjusting power generation parameters of each branch of the wind farm grid-connected region according to the power generation contribution factor vector of the wind farm grid-connected region; if the inter-regional available power transmission capability is less than a preset available power transmission capability threshold value, determining that the power transmission congestion risk decision result represents that there is a congestion risk for the adjusted wind farm grid-connected region; if the inter-regional available power transmission capability is greater than or equal to the preset available power transmission capability threshold value, determining that the power transmission congestion risk decision result represents that there is no congestion risk for the adjusted wind farm grid-connected region.

[0010] Optionally, in the case that the power transmission blockage risk determination result represents that there is a blockage risk, the target node of the three-electricity system coordinated regulation is determined by using the wind turbine grid-connected node sensitivity matrix, including: In the case that the power transmission blockage risk determination result represents that there is a blockage risk, a constraint branch is determined, wherein the constraint branch is a branch corresponding to the inter-regional available power transmission capacity; According to the constraint branch and the wind turbine grid-connected node sensitivity matrix, a wind turbine grid-connected node sensitivity vector is determined, wherein the wind turbine grid-connected node sensitivity vector is used to represent the power flow sensitivity of all node power changes to the constraint branch; According to the maximum absolute value of the wind turbine grid-connected node sensitivity vector, the target node is determined from the constraint branch.

[0011] Optionally, the generation of the coordinated regulation control instruction of the three-electricity system according to the target node includes: In the case that the power transmission blockage risk determination result represents that there is a blockage risk, a three-electricity system response priority model is constructed according to the target node; The real-time operation parameters of the three-electricity system are input into the three-electricity system response priority model to obtain the real-time adjustment weight coefficient and the real-time response priority of the three-electricity system output by the three-electricity system response priority model, wherein the real-time operation parameters include a real-time response delay of a converter system, a real-time pitch angle of a variable pitch system and a real-time SOC value of an electric control system; The converter system adjustment instruction, the variable pitch system adjustment instruction and the electric control system adjustment instruction are adjusted according to the real-time adjustment weight coefficient; According to the real-time response priority, the coordinated regulation order of the converter system adjustment instruction, the variable pitch system adjustment instruction and the electric control system adjustment instruction is determined; According to the coordinated regulation order, the coordinated regulation control instruction including the converter system adjustment instruction, the variable pitch system adjustment instruction and the electric control system adjustment instruction is obtained.

[0012] Optionally, the method further includes: The physical limit range of the three-electricity system is obtained, wherein the physical limit range of the three-electricity system includes a converter system power change rate, a variable pitch system pitch rate range and an electric control system SOC working range; According to the three-electricity system real-time operation parameters, a power grid constraint rule is formed; According to the power grid constraint rule and the physical limit range of the three-electricity system, a branch thermal stability constraint and a node voltage constraint are generated; The real-time state monitoring value of the three-electricity system includes at least one of a real-time element temperature monitoring value, a real-time wind speed monitoring value and a real-time battery temperature detection value. The safety collaborative adjustment control instruction is obtained by adjusting the collaborative adjustment control instruction according to the modified constraint condition, so as to control the three-electricity system by using the safety collaborative adjustment control instruction.

[0013] The second aspect of the present disclosure provides a three-electricity system collaborative optimization device of a wind turbine generator set, comprising: An acquisition module is configured to acquire wind turbine grid connection parameters. A first generation module is configured to generate a node admittance matrix and a branch-node admittance matrix according to the wind turbine grid connection parameters, perform unbalanced node processing on the node admittance matrix to obtain a non-unbalanced node admittance matrix, perform unbalanced node processing on the branch-node admittance matrix to obtain a reduced branch-node admittance matrix, and generate a wind turbine grid connection node sensitivity matrix according to the non-unbalanced node admittance matrix and the reduced branch-node admittance matrix. A first determination module is configured to determine an inter-regional available power transmission capacity of a wind power plant grid connection region to a target region according to the wind turbine grid connection node sensitivity matrix. A second determination module is configured to determine a power transmission congestion risk judgment result according to the inter-regional available power transmission capacity. A third determination module is configured to determine a target node for collaborative adjustment of the three-electricity system by using the wind turbine grid connection node sensitivity matrix in a case where the power transmission congestion risk judgment result represents that there is a congestion risk. A second generation module is configured to generate a collaborative adjustment control instruction of the three-electricity system according to the target node. A control module is configured to control the three-electricity system by using the collaborative adjustment control instruction.

[0014] The third aspect of the present disclosure provides an electronic device, comprising: A memory having a computer program stored thereon; A processor configured to execute the computer program in the memory to implement the steps of the wind turbine generator set three-electricity system collaborative optimization method provided by the first aspect of the present disclosure.

[0015] The fourth aspect of the present disclosure provides a non-transitory computer readable storage medium having a computer program stored thereon, wherein the computer program is executed by a processor to implement the steps of the wind turbine generator set three-electricity system collaborative optimization method provided by the first aspect of the present disclosure.

[0016] The fifth aspect of the present disclosure provides a computer program product comprising a computer program which, when executed by a processor, implements the steps of the wind turbine three-electricity system collaborative optimization method provided by the first aspect of the present disclosure.

[0017] By the above technical solution, the wind turbine grid connection parameters are obtained; the node admittance matrix and the branch-node admittance matrix are generated according to the wind turbine grid connection parameters; the non-equilibrium node admittance matrix is obtained by performing non-equilibrium node processing on the node admittance matrix; the reduced branch-node admittance matrix is obtained by performing non-equilibrium node processing on the branch-node admittance matrix; the wind turbine grid connection node sensitivity matrix is generated according to the non-equilibrium node admittance matrix and the reduced branch-node admittance matrix; the inter-regional available transmission capability of the wind power plant grid connection area to the target area is determined according to the wind turbine grid connection node sensitivity matrix; the transmission congestion risk determination result is determined according to the inter-regional available transmission capability; in the case that the transmission congestion risk determination result represents that there is a congestion risk, the target node of the three-electricity system collaborative adjustment is determined by using the wind turbine grid connection node sensitivity matrix; the safety collaborative adjustment control instruction of the three-electricity system is generated according to the target node; and the three-electricity system is controlled by using the collaborative adjustment control instruction. In this way, the collaborative optimization of the wind turbine three-electricity system can be realized, the transmission congestion risk can be reduced, the reliability and stability of the wind power plant grid connection can be improved, the safe and stable operation of the power grid can be ensured, the wind power curtailment problem can be reduced, and the economy of wind turbine power generation can be improved.

[0018] Other features and advantages of the present disclosure will be described in detail in the following detailed description section. BRIEF DESCRIPTION OF DRAWINGS

[0019] The accompanying drawings are included to provide a further understanding of the present disclosure and constitute a part of the specification, and together with the following detailed description, serve to explain the present disclosure, but do not constitute a limitation thereof. In the drawings: Figure 1 is a flowchart of a wind turbine three-electricity system collaborative optimization method provided by an exemplary embodiment of the present disclosure.

[0020] Figure 2 is a flowchart of an inter-regional available transmission capability determination method provided by an exemplary embodiment of the present disclosure.

[0021] Figure 3 is a flowchart of a transmission congestion risk determination method provided by an exemplary embodiment of the present disclosure.

[0022] Figure 4 is a flowchart of a target node determination method provided by an exemplary embodiment of the present disclosure.

[0023] Figure 5 is a flowchart of a collaborative adjustment control instruction generation method provided by an exemplary embodiment of the present disclosure.

[0024] Figure 6 is a flow chart of a safety cooperative regulation control instruction generation method provided by an example embodiment of the present disclosure.

[0025] Figure 7 is a block diagram of a wind turbine generator unit three-electric system cooperative optimization device provided by an example embodiment of the present disclosure.

[0026] Figure 8 is a block diagram of an electronic device provided by an example embodiment of the present disclosure. DETAILED DESCRIPTION

[0027] The detailed description of the specific embodiments of the present disclosure is described in detail below with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are only used to illustrate and explain the present disclosure, and are not used to limit the present disclosure.

[0028] It should be noted that all actions of obtaining signals, information or data in the present disclosure are carried out in accordance with the corresponding data protection regulations and policies of the country where the device is located, and with the authorization of the corresponding device owner.

[0029] Figure 1 is a flow chart of a wind turbine generator unit three-electric system cooperative optimization method provided by an example embodiment of the present disclosure. As shown in Figure 1 , the method can include steps S101 to S110.

[0030] In step S101, wind turbine grid connection parameters are obtained.

[0031] For example, the wind turbine grid connection parameters can include branch reactance, branch end point voltage phase difference, node admittance, and balanced node number.

[0032] In step S102, a node admittance matrix and a branch-node admittance matrix are generated according to the wind turbine grid connection parameters.

[0033] For example, the node admittance matrix is a matrix used to describe the association relationship between nodes in the power grid, which can be a dimensional matrix. For example, the branch-node admittance matrix is used to describe the association relationship between each branch and node in the power grid, which can be a dimensional matrix.

[0034] In step S103, the node admittance matrix is processed to remove the balanced nodes, and a non-balanced node admittance matrix is obtained.

[0035] In step S104, the branch-node susceptance matrix is ​​subjected to unbalanced node processing to obtain a reduced branch-node susceptance matrix.

[0036] For example, the nodal susceptance matrix can be determined based on the susceptance node number. By removing the equilibration nodes, the susceptance matrix of the non-equilibration nodes is obtained. Similarly, the branch-node susceptance matrix can be calculated based on the balancing node number. By removing the balancing nodes, a reduced branch-node susceptance matrix is ​​obtained. .

[0037] In step S105, the wind turbine grid connection node sensitivity matrix is ​​generated based on the unbalanced node susceptance matrix and the reduced branch-node susceptance matrix.

[0038] Among them, each element in the wind turbine grid connection node sensitivity matrix is ​​used to characterize the linear impact of power changes at the wind turbine generator grid connection node on the power flow sensitivity of the grid branch.

[0039] In one embodiment, step S105 can be implemented in the following manner: The reference sensitivity matrix of the wind turbine grid-connected section is determined based on the unbalanced node susceptance matrix and the reduced branch-node susceptance matrix. The reference sensitivity matrix is ​​supplemented with balanced nodes to obtain the wind turbine grid connection node sensitivity matrix.

[0040] Thus, by supplementing and improving the matrix after the debalancing nodes are processed, the integrity of the sensitivity matrix and the consistency of its physical meaning can be guaranteed, making the analysis of the impact of node power changes on system power flow more accurate, and providing support for subsequent precise positioning of adjustment targets and formulation of control strategies.

[0041] For ease of understanding, the implementation process of steps S102 to S105 is fully explained below: In a three-electric system, the current flows through the nodes and The power of the branches between them is shown below: (1) in, For flow through nodes and The active power of the branch circuits between them For flow through nodes and The reactive power of the branch circuits between them For nodes and nodes The conductance of the intermediate branch, For nodes and nodes The susceptance value of the intermediate branch, For nodes and nodes nodes of branch roads Side-to-ground branch susceptance value; For nodes voltage amplitude, For nodes The voltage amplitude; For nodes and nodes The voltage phase angle difference.

[0042] For high-voltage transmission lines, since the reactance is much greater than the resistance, the two ends of the branch (nodes) and nodes Voltage phase angle difference The value is relatively small, so the per-unit value of the node voltage can be approximately set to 1. That is: , , , , = =1; Based on this, formula (1) is further simplified and expressed in matrix form as follows: (2) in, Let L be the column vector of active power of the L-dimensional branches. Indicates the total number of branches; yes A 3D diagonal matrix; C is The branch-node incidence matrix is ​​defined as follows: N represents the total number of nodes; θ is an N-dimensional column vector of node voltage phase angles. yes Dimensional branch-node susceptance matrix.

[0043] Branch-node susceptance matrix Balanced nodes can be removed to obtain a reduced branch-node susceptance matrix. Specifically, for any node in the system... Net injected active power The calculation method is as follows: (3) in, It is a node The self-susceptance, its value is equal to that of the node. The sum of the negative susceptance of all connected branches; express formula (3) in matrix form: (4) in, for Inject active power column vectors into dimensional nodes; for A dimensional nodal susceptance matrix.

[0044] Combine equations (2) and (4) to eliminate the N-dimensional node voltage phase angle column vector. .because Since the rows are correlated and cannot be directly reversed, we can remove the row and column containing the balancing node: (5) in, for Inject the active power column vector into the N-1 dimensional nodes after removing the row containing the slack node. for The result after removing the row and column containing the balanced node is (N-1). The (N-1)-dimensional nodal susceptance matrix, also known as the non-equilibrium nodal susceptance moment, This is the N-1 dimensional column vector of node voltage phase angles after removing the slack nodes. Solving equation (5), we obtain: (6) Since the phase angle of the slack node is 0, we can presuppose that the first node is the slack node, thus the active power column vector of the L-dimensional branch is... It can be represented as:

[0045] (7) It is to remove the matrix L is obtained after the column where the balance node is located. The branch-node susceptance matrix is ​​an (N-1) dimensional matrix, i.e., a reduced branch-node susceptance matrix. From the above equation, it can be seen that the net power injection at the balancing node has no effect on the branch power. Therefore, the intermediate variable of the wind turbine grid-connected node sensitivity matrix, i.e., the reference sensitivity matrix, can be determined. : (8) Since the net power injection at the balancing node has no effect on the power of the branch, it can be used in the reference sensitivity matrix. Add a column of all zeros to L to get L N-dimensional wind turbine grid connection node sensitivity matrix ; (9) The power flow sensitivity of the wind turbine generator's grid connection node to the grid branch is represented by the wind turbine grid connection node sensitivity matrix. The values ​​of each element in the table.

[0046] In step S106, the inter-regional available transmission capability of the wind farm grid-connected area to the target area is determined according to the wind turbine grid-connected node sensitivity matrix.

[0047] For example, the inter-regional power transmission sensitivity of the wind farm grid-connected area to the target area can be determined according to the wind turbine grid-connected node sensitivity matrix, and the inter-regional available transmission capability can be obtained according to the inter-regional power transmission sensitivity.

[0048] In step S107, the transmission congestion risk determination result is determined according to the inter-regional available transmission capability.

[0049] The available transmission capability (ATC) refers to the actual available residual transmission capability in the existing three-electric system operation condition, and is an index for measuring the further increased power transmission capability of the power grid. Deep coupling of the available transmission capability and the wind turbine three-electric system (converter system monitoring, electric control system regulation, and variable pitch system load limiting) can realize dynamic management of the transmission congestion of the wind farm as the control subject.

[0050] For example, the inter-regional available transmission capability and the preset available transmission capability threshold value can be used to determine whether there is a transmission congestion risk. The preset available transmission capability threshold value can be set in advance based on actual needs.

[0051] For example, if the inter-regional available transmission capability is less than the preset available transmission capability threshold value, it is determined that the transmission congestion risk determination result represents that there is a congestion risk; if the inter-regional available transmission capability is greater than or equal to the preset available transmission capability threshold value, it is determined that the transmission congestion risk determination result represents that there is no congestion risk.

[0052] When the inter-regional available transmission capability is less than the preset available transmission capability threshold value, it can be determined that the transmission capacity of the transmission line has approached or reached the limit, and continued increase of the transmission capacity may cause line overload, equipment damage, and even power grid failure, so it can be determined that there is a congestion risk. Conversely, if the inter-regional available transmission capability is greater than or equal to the preset available transmission capability threshold value, it can be determined that the transmission line still has sufficient transmission margin, and the transmission system runs relatively safely and stably, so it can be determined that there is no congestion risk. This determination method is simple and intuitive, and is convenient for real-time monitoring and decision-making, which can effectively prevent the transmission congestion problem and ensure the safe and stable operation of the power grid.

[0053] In step S108, when the transmission congestion risk determination result represents that there is a congestion risk, the target node of the three-electric system coordinated regulation is determined by using the wind turbine grid-connected node sensitivity matrix.

[0054] In step S109, the safety coordinated regulation control instruction of the three-electric system is generated according to the target node.

[0055] In step S110, the three-electricity system is controlled by using the coordinated regulation control instruction.

[0056] For example, the coordinated regulation control instruction can be output to the three-electricity system of the wind turbine generator set to control and regulate the three-electricity system.

[0057] Based on the above technical solution, the three-electricity system of the wind turbine generator set can be optimized, the risk of power transmission blockage can be reduced, the reliability and stability of the wind farm grid connection can be improved, the safe and stable operation of the power grid can be ensured, the problem of wind curtailment can be reduced, and the economy of wind power generation can be improved.

[0058] In a possible implementation, in step S106, the inter-regional available power transmission capacity of the wind farm grid connection area to the target area is determined according to the wind turbine grid connection node sensitivity matrix, which can be but not limited to decomposed into steps S1061 to S1064, including: In step S1061, the power generation contribution factor vector of the wind farm grid connection area and the load draw factor vector of the target area are obtained.

[0059] For example, the power generation contribution factor vector can be obtained by calculating the power generation contribution of each wind turbine to the grid connection area, and the load draw factor vector can be obtained by calculating the draw of each load point in the target area.

[0060] In step S1062, the inter-regional power transmission sensitivity of the wind farm grid connection area to the target area is determined according to the power generation contribution factor vector, the load draw factor vector, and the wind turbine grid connection node sensitivity matrix.

[0061] In step S1063, the available power transmission capacity of each branch is determined according to the inter-regional power transmission sensitivity.

[0062] For example, the inter-regional power transmission sensitivity and the available power transmission capacity of each branch can be determined by related technologies, which will not be described here.

[0063] In this way, by determining the available power transmission capacity of each branch, the power transmission potential of different branches in the power grid can be determined to find potential power transmission bottlenecks and provide support for subsequent determination of the inter-regional available power transmission capacity.

[0064] In step S1064, the inter-regional available power transmission capacity is determined according to the minimum value in the available power transmission capacity of each branch.

[0065] For example, the minimum available transmission capacity among the individual branches can be determined as the available transmission capacity between regions. Using the minimum value as the available transmission capacity between regions fully considers the weakest link in the power grid, ensuring that the power transmission of the entire region will not be affected by the overload of individual branches during the transmission process, thereby guaranteeing the safety and reliability of the power grid operation.

[0066] exist Figure 2 In the technical solution shown, by obtaining the power generation contribution and load absorption factor vectors and combining them with the sensitivity matrix, the power transmission sensitivity between regions can be accurately determined, thereby achieving accurate determination of the available power transmission capacity between regions. By comprehensively considering power generation and load factors, the power transmission potential between regions can be accurately assessed.

[0067] In one possible implementation, step S107, determining the transmission congestion risk assessment result based on the available transmission capacity between regions, can be broken down into, but is not limited to, the following steps S1071 to S1073, including: In step S1071, the power generation parameters of each branch in the wind farm grid connection area are adjusted according to the power generation contribution factor vector of the wind farm grid connection area.

[0068] For example, by adjusting the power generation parameters of each branch within the wind farm's grid-connected area using the power generation contribution factor vector, scenario correction for the wind farm's grid-connected area can be achieved. For instance, the branch with the largest power generation contribution factor vector can be identified, and the power generation parameters of other branches can be adjusted based on that branch.

[0069] By adjusting the power generation parameters of each branch in the grid-connected area of ​​the wind farm, power generation can be precisely optimized, thereby improving the overall power generation efficiency of the wind farm.

[0070] In step S1072, for the adjusted wind farm grid connection area, if the available transmission capacity between areas is less than the preset available transmission capacity threshold, then the transmission congestion risk determination result indicates that there is a congestion risk.

[0071] For example, after adjusting the power generation parameters of the wind farm's grid-connected area in step S1071, the available transmission capacity between areas can be recalculated. If the calculated result is less than a preset available transmission capacity threshold, for example, if the threshold is set at 500MW and the calculated value is 450MW, then a transmission congestion risk is identified. By promptly determining whether a transmission congestion risk exists after the adjustment, potential problems can be identified in advance, allowing time for subsequent countermeasures and ensuring the safety of power grid transmission.

[0072] In step S1073, for the adjusted wind farm grid connection area, if the available transmission capacity between areas is greater than or equal to the preset available transmission capacity threshold, then the transmission congestion risk determination result indicates that there is no congestion risk.

[0073] For example, the available transmission capacity between regions can be recalculated after adjusting the power generation parameters. If the calculation result is greater than or equal to the preset threshold, such as a threshold of 500MW and a calculated value of 520MW, then it is determined that there is no risk of transmission congestion, indicating that the adjustment of the power generation parameters is effective, can maintain the current operating state, and ensure stable power transmission from the wind farm.

[0074] exist Figure 3 The technical solution shown adjusts the power generation parameters based on the power generation contribution factor vector and judges the transmission congestion risk by combining the preset threshold. The method is simple and effective, and can detect potential congestion risks in a timely manner, providing an accurate basis for subsequent adjustment measures.

[0075] In one possible implementation, in step S108, if the transmission congestion risk assessment result indicates the existence of congestion risk, the target node for coordinated regulation of the three-electric system is determined using the wind turbine grid connection node sensitivity matrix. This can be, but is not limited to, decomposed into the following steps S1081 to S1083, including: In step S1081, if the transmission congestion risk assessment result indicates the existence of congestion risk, a constraining branch is determined. The constraining branch is the branch corresponding to the available transmission capacity between regions.

[0076] For example, if the available transmission capacity between regions is the minimum among the available transmission capacities of all branches, such as branch A having an available transmission capacity of 300MW, which is the minimum among all branches, then branch A can be identified as the constraining branch. By locating the constraining branch, we can focus on the key issues, providing a clear direction for subsequent analysis and resolution of transmission congestion problems, and improving processing efficiency.

[0077] In step S1082, the sensitivity vector of the wind turbine grid connection node is determined based on the sensitivity matrix of the constraining branch and the wind turbine grid connection node.

[0078] Among them, the wind turbine grid-connected node sensitivity vector is used to characterize the power flow sensitivity of all nodes to the constraining branch.

[0079] For example, if branch A is a constrained branch, the row vector related to branch A can be extracted from the wind turbine grid connection node sensitivity matrix. This vector is the wind turbine grid connection node sensitivity vector. In this way, the sensitivity of each node to the power flow of the constrained branch can be determined, so as to identify the target node.

[0080] In step S1083, the target node is determined from the constrained branch based on the maximum absolute value of the sensitivity vector of the wind turbine grid connection node.

[0081] For example, the nodes corresponding to each element in the wind turbine grid connection node sensitivity vector can be identified as adjustable nodes, and the absolute value of each element in the vector can be determined. If the maximum absolute value of the vector element is the value of the corresponding node 3, then node 3, this adjustable node, can be identified as the target node. In this way, the node with the greatest impact on the branch power flow can be quickly identified, facilitating subsequent targeted measures to alleviate transmission congestion.

[0082] exist Figure 4 The technical solution shown can accurately locate the key positions for coordinated regulation of the three power systems, providing a clear direction for coordinated regulation of the three power systems, thereby improving the stability of wind farm grid connection, reducing wind curtailment caused by transmission blockage, and improving the utilization rate of wind power.

[0083] In one possible implementation, in step S109, a safety coordinated adjustment and control command for the three-electric system is generated based on the target node. This can be, but is not limited to, decomposed into the following steps S1091 to S1095, including: In step S1091, if the power transmission congestion risk assessment result indicates the existence of congestion risk, a response priority model for the three-electric system is constructed based on the target node.

[0084] For example, once the risk of power transmission congestion is determined and the target node is identified, the characteristics of the target node, the functions of each part of the three-electric system and the degree of impact on power transmission can be comprehensively considered. The three-electric system adjustment weight coefficient and response priority are used as the target outputs to construct a three-electric system response priority model for the target node.

[0085] In step S1092, the real-time operating parameters of the three-electric system are input into the three-electric system response priority model to obtain the real-time adjustment weight coefficient and real-time response priority of the three-electric system output by the three-electric system response priority model.

[0086] The real-time operating parameters include the real-time response delay of the converter system, the real-time pitch angle of the pitch system, and the real-time SOC value of the electronic control system.

[0087] For example, parameters such as the converter system response delay, pitch system pitch angle, and SOC value of the electronic control system can be collected in real time by sensors, and these parameters are input into a pre-constructed response priority model for the three-electric system. This model, based on a preset algorithm, such as one based on fuzzy logic or a neural network, obtains and outputs real-time adjustment weight coefficients and real-time response priorities. The real-time output of the three-electric system response priority model can accurately respond to different operating conditions and optimize the adjustment effect.

[0088] In step S1093, the converter system adjustment command, pitch system adjustment command, and electronic control system adjustment command are adjusted according to the real-time adjustment weight coefficient.

[0089] In step S1094, the coordinated adjustment order of the converter system adjustment command, the pitch system adjustment command, and the electronic control system adjustment command is determined according to the real-time response priority.

[0090] For example, if the real-time response priority is converter system > pitch system > electronic control system, then the coordinated regulation order can be determined as follows: first execute the converter system regulation command, then the pitch system command, and finally the electronic control system command. By clearly defining the coordinated regulation order, regulation conflicts between subsystems can be avoided, ensuring the orderly progress of the regulation process.

[0091] In step S1095, according to the coordinated adjustment sequence, a coordinated adjustment control command including the converter system adjustment command, the pitch system adjustment command, and the electronic control system adjustment command is obtained.

[0092] For example, the control commands for the converter system, the pitch system, and the electronic control system can be integrated according to the coordinated control sequence determined in step S1094 to form a complete set of coordinated control commands, thereby realizing integrated control of the three systems, improving the overall system performance, and mitigating the risk of power transmission congestion.

[0093] exist Figure 5 The technical solution presented establishes a mapping between the power grid and wind turbine control through a priority model for the three-electric system (power supply, electrical system, and electronic control system), transforming the abstract transmission congestion problem into specific regulation and control commands for the wind turbine's three-electric system. Furthermore, a hierarchical optimization strategy is formed: in actual regulation, priority is given to regulating the wind turbine grid-connected nodes (fast response), while other load nodes are indirectly regulated (low cost) through energy storage / demand response, avoiding ineffective actions and ensuring regulation efficiency. This step is also the core hub for the wind turbine's three-electric system to handle transmission congestion, transforming grid state variables into controllable wind turbine variables, thus achieving coordinated optimization between the wind turbine's three-electric system and the power grid.

[0094] In one possible implementation, the coordinated optimization of the three electrical systems of the wind turbine generator provided in this disclosure further includes: In step S1096, the physical limit range of the three-electric system is obtained.

[0095] Among them, the physical limits of the three-electric system include the power change rate of the converter system, the pitch rate range of the pitch system, and the SOC operating range of the electronic control system.

[0096] For example, the physical limits of the three-electric system can be obtained through the equipment manual, historical operating data, and technical parameters provided by the manufacturer.

[0097] In step S1097, power grid constraint rules are formed based on the real-time operating parameters of the three-electric system.

[0098] For example, power grid constraint rules can be formulated based on the real-time operating parameters of the three-electric system, such as the power of the converter system, and in conjunction with the power grid safety operation standards.

[0099] In step S1098, branch thermal stability constraints and node voltage constraints are generated based on the power grid constraint rules and the physical limit range of the three-electric system.

[0100] This ensures that power grid equipment and nodes operate in a safe state, improving the safety and reliability of the power grid.

[0101] In step S1099, the branch thermal stability constraints and node voltage constraints are corrected based on the real-time status monitoring values ​​of the three-electric system to obtain the corrected constraint conditions.

[0102] Among them, the real-time status monitoring values ​​of the three-electric system may include at least one of the real-time component temperature monitoring values, real-time wind speed monitoring values, and real-time battery temperature detection values.

[0103] For example, if the real-time component temperature rises, the upper limit of the current in the branch thermal stability constraint can be adjusted (e.g., lowered) to modify the constraint conditions, making the constraints more consistent with the actual operating conditions and further ensuring operational safety.

[0104] In step S1100, the coordinated adjustment control command is adjusted according to the modified constraint conditions to obtain a safe coordinated adjustment control command.

[0105] For example, parameters such as power and pitch angle in the coordinated control command can be adjusted according to the modified constraints. By adjusting the coordinated control command, it can be ensured that the execution of the command does not violate the constraints, thus further guaranteeing the safety of the three-electric system regulation.

[0106] In step S1101, a safety coordinated adjustment control command is output to the three-electric system to control the three-electric system using the safety coordinated adjustment control command.

[0107] exist Figure 6 The technical solution shown can fully integrate the actual operating conditions of the three-electric system to ensure the reliability of control commands, thereby guaranteeing the safety of the three-electric system regulation.

[0108] It should be noted that after adjustment, congestion verification can be performed again. If congestion still exists, the available transmission capacity between regions should be recalculated, and the response priority model of the three-electric system should be updated to obtain new safety coordinated adjustment and control commands until the congestion verification is passed.

[0109] Based on the same inventive concept, this disclosure also provides a device for the coordinated optimization of the three electrical systems of a wind turbine generator set. Figure 7This is a block diagram of a wind turbine generator set's three-electric system collaborative optimization device 300 provided in an exemplary embodiment of this disclosure. (Refer to...) Figure 7 The wind turbine generator set's three-electric system collaborative optimization device 300 may include: Module 301 is used to acquire the grid connection parameters of the wind turbine; The first generation module 302 is used to generate a node susceptance matrix and a branch-node susceptance matrix based on the wind turbine grid connection parameters; perform unbalanced node processing on the node susceptance matrix to obtain an unbalanced node susceptance matrix; perform unbalanced node processing on the branch-node susceptance matrix to obtain a reduced branch-node susceptance matrix; and generate a wind turbine grid connection node sensitivity matrix based on the unbalanced node susceptance matrix and the reduced branch-node susceptance matrix. The first determining module 303 is used to determine the available inter-regional power transmission capacity of the wind farm grid-connected area relative to the target area based on the wind turbine grid-connected node sensitivity matrix. The second determining module 304 is used to determine the power transmission congestion risk assessment result based on the available power transmission capacity between the regions. The third determining module 305 is used to determine the target node for coordinated adjustment of the three-electric system by using the wind turbine grid connection node sensitivity matrix when the power transmission congestion risk determination result indicates the existence of congestion risk. The second generation module 306 is used to generate the coordinated adjustment and control command of the three-electric system according to the target node; The control module 307 is used to control the three-electric system using the coordinated adjustment control command.

[0110] Based on the above technical solutions, it is possible to achieve coordinated optimization of the three electrical systems of wind turbine generators, reduce the risk of power transmission congestion, improve the reliability and stability of wind farm grid connection, ensure the safe and stable operation of the power grid, reduce wind curtailment, and improve the economic efficiency of wind turbine power generation.

[0111] Optionally, the first generation module 302 is used to generate a wind turbine grid-connected node sensitivity matrix based on the unbalanced node susceptance matrix and the reduced branch-node susceptance matrix in the following manner: Based on the unbalanced node susceptance matrix and the reduced branch-node susceptance matrix, determine the reference sensitivity matrix of the wind turbine grid-connected section; The reference sensitivity matrix is ​​supplemented with balanced nodes to obtain the wind turbine grid connection node sensitivity matrix.

[0112] Optionally, the first determining module 303 is used to determine the available inter-regional power transmission capacity of the wind farm grid-connected area relative to the target area based on the wind turbine grid-connected node sensitivity matrix in the following manner: Obtain the power generation contribution factor vector of the grid-connected area of ​​the wind farm and the load absorption factor vector of the target area; Based on the power generation contribution factor vector, the load absorption factor vector, and the wind turbine grid connection node sensitivity matrix, the inter-regional power transmission sensitivity of the wind farm grid connection area to the target area is determined. Based on the inter-regional power transmission sensitivity, determine the available power transmission capacity of each branch; The available power transmission capacity between the regions is determined based on the minimum available power transmission capacity of each branch.

[0113] Optionally, the second determining module 304 is configured to determine the transmission congestion risk assessment result based on the available transmission capacity between the regions in the following manner: Based on the power generation contribution factor vector of the wind farm grid connection area, the power generation parameters of each branch of the wind farm grid connection area are adjusted; For the adjusted wind farm grid connection area, if the available transmission capacity between the areas is less than the preset available transmission capacity threshold, then the transmission congestion risk determination result indicates that there is a congestion risk. For the adjusted wind farm grid connection area, if the available transmission capacity between the areas is greater than or equal to the preset available transmission capacity threshold, then the transmission congestion risk determination result indicates that there is no congestion risk.

[0114] Optionally, the third determining module 305 is used to determine the target node for coordinated adjustment of the three-electric system by utilizing the wind turbine grid connection node sensitivity matrix when the transmission congestion risk assessment result indicates the existence of congestion risk, including: If the power transmission congestion risk assessment result indicates the existence of congestion risk, a restrictive branch is identified, wherein the restrictive branch is a branch corresponding to the available power transmission capacity between the regions; Based on the constrained branch and the wind turbine grid connection node sensitivity matrix, the wind turbine grid connection node sensitivity vector is determined, wherein the wind turbine grid connection node sensitivity vector is used to characterize the power flow sensitivity of all node power changes to the constrained branch; The target node is determined from the constrained branch based on the maximum absolute value of the sensitivity vector of the wind turbine grid connection node.

[0115] Optionally, the second generation module 306 is configured to generate coordinated adjustment and control commands for the three-electric system based on the target node in the following manner: If the power transmission congestion risk assessment result indicates the existence of congestion risk, a response priority model for the three-electric system is constructed based on the target node. The real-time operating parameters of the three-electric system are input into the response priority model of the three-electric system to obtain the real-time adjustment weight coefficient and real-time response priority of the three-electric system output by the response priority model of the three-electric system. The real-time operating parameters include the real-time response delay of the converter system, the real-time pitch angle of the pitch system and the real-time SOC value of the electronic control system. Adjust the converter system adjustment command, pitch system adjustment command, and electronic control system adjustment command according to the real-time adjustment weight coefficient; Based on the real-time response priority, the coordinated adjustment order of the converter system adjustment command, the pitch system adjustment command, and the electronic control system adjustment command is determined; Based on the coordinated adjustment sequence, a coordinated adjustment control command is obtained, which includes the converter system adjustment command, the pitch system adjustment command, and the electronic control system adjustment command.

[0116] Optionally, the second generation module 306 is also used for: Obtain the physical limit range of the three-electric system, wherein the physical limit range of the three-electric system includes the power change rate of the converter system, the pitch rate range of the pitch system, and the SOC operating range of the electronic control system. Based on the real-time operating parameters of the three-electric system, power grid constraint rules are formed; Based on the power grid constraint rules and the physical limit range of the three-electric system, branch thermal stability constraints and node voltage constraints are generated. The branch thermal stability constraint and node voltage constraint are corrected based on the real-time status monitoring values ​​of the three-electric system to obtain the corrected constraint conditions. The real-time status monitoring values ​​of the three-electric system include at least one of the real-time component temperature monitoring values, real-time wind speed monitoring values, and real-time battery temperature detection values. The coordinated adjustment control command is adjusted according to the modified constraint conditions to obtain a safety coordinated adjustment control command, which is then used to control the three-electric system.

[0117] Regarding the apparatus in the above embodiments, the specific manner in which each module performs its operation has been described in detail in the embodiments related to the method, and will not be elaborated upon here.

[0118] Figure 8 This is a block diagram of an electronic device 1900 provided in an exemplary embodiment of this disclosure. For example, the electronic device 1900 may be provided as a server. (Refer to...) Figure 8The electronic device 1900 includes a processor 1922, which may be one or more, and a memory 1932 for storing computer programs executable by the processor 1922. The computer program stored in the memory 1932 may include one or more modules, each corresponding to a set of instructions. Furthermore, the processor 1922 may be configured to execute the computer program to perform the aforementioned method for coordinated optimization of the three electrical systems of a wind turbine generator set.

[0119] Additionally, the electronic device 1900 may also include a power supply component 1926 and a communication component 1950. The power supply component 1926 can be configured to perform power management of the electronic device 1900, and the communication component 1950 can be configured to enable communication of the electronic device 1900, such as wired or wireless communication. Furthermore, the electronic device 1900 may also include an input / output (I / O) interface 1958. The electronic device 1900 can operate on an operating system stored in memory 1932.

[0120] In another exemplary embodiment, a computer-readable storage medium including program instructions is also provided, which, when executed by a processor, implement the steps of the above-described method for coordinated optimization of the three electrical systems of a wind turbine generator set. For example, the non-transitory computer-readable storage medium may be the memory 1932 including program instructions, which may be executed by the processor 1922 of the electronic device 1900 to complete the above-described method for coordinated optimization of the three electrical systems of a wind turbine generator set.

[0121] In another exemplary embodiment, a computer program product is also provided, which includes a computer program executable by a programmable device, the computer program having a code portion for performing the above-described wind turbine generator set three-electric system coordinated optimization method when executed by the programmable device.

[0122] The preferred embodiments of this disclosure have been described in detail above with reference to the accompanying drawings. However, this disclosure is not limited to the specific details of the above embodiments. Within the scope of the technical concept of this disclosure, various simple modifications can be made to the technical solutions of this disclosure, and these simple modifications all fall within the protection scope of this disclosure.

[0123] It should also be noted that the various specific technical features described in the above embodiments can be combined in any suitable manner without contradiction. To avoid unnecessary repetition, this disclosure will not describe the various possible combinations separately.

[0124] Furthermore, various different embodiments of this disclosure can be combined in any way, as long as they do not violate the spirit of this disclosure, they should also be regarded as the content disclosed in this disclosure.

Claims

1. A method for coordinated optimization of the three electrical systems of a wind turbine generator set, characterized in that, include: Obtain wind turbine grid connection parameters; Based on the wind turbine grid connection parameters, generate the node susceptance matrix and the branch-node susceptance matrix; The node susceptance matrix is ​​subjected to unbalanced node processing to obtain the unbalanced node susceptance matrix; The branch-node susceptance matrix is ​​subjected to unbalanced node processing to obtain a reduced branch-node susceptance matrix; Based on the unbalanced node susceptance matrix and the reduced branch-node susceptance matrix, a wind turbine grid connection node sensitivity matrix is ​​generated. Based on the wind turbine grid connection node sensitivity matrix, determine the available inter-regional power transmission capacity of the wind farm grid connection area relative to the target area; The results of the power transmission congestion risk assessment are determined based on the available power transmission capacity between the regions. When the power transmission congestion risk assessment result indicates the existence of congestion risk, the target node for the coordinated adjustment of the three-electric system is determined using the wind turbine grid connection node sensitivity matrix. Based on the target node, generate the coordinated adjustment and control command for the three-electric system; The three-electric system is controlled using the aforementioned coordinated adjustment and control commands.

2. The method according to claim 1, characterized in that, The step of generating the wind turbine grid-connected node sensitivity matrix based on the unbalanced node susceptance matrix and the reduced branch-node susceptance matrix includes: Based on the unbalanced node susceptance matrix and the reduced branch-node susceptance matrix, the reference sensitivity matrix of the wind turbine grid-connected section is determined; The reference sensitivity matrix is ​​supplemented with balancing nodes to obtain the wind turbine grid connection node sensitivity matrix.

3. The method according to claim 1, characterized in that, The step of determining the available inter-regional power transmission capacity of the wind farm grid connection area relative to the target area based on the wind turbine grid connection node sensitivity matrix includes: Obtain the power generation contribution factor vector of the grid-connected area of ​​the wind farm and the load absorption factor vector of the target area; Based on the power generation contribution factor vector, the load absorption factor vector, and the wind turbine grid connection node sensitivity matrix, the inter-regional power transmission sensitivity of the wind farm grid connection area to the target area is determined. Based on the inter-regional power transmission sensitivity, determine the available power transmission capacity of each branch; The available power transmission capacity between the regions is determined based on the minimum available power transmission capacity of each branch.

4. The method according to claim 3, characterized in that, The determination of transmission congestion risk based on the available transmission capacity between the regions includes: Based on the power generation contribution factor vector of the wind farm grid connection area, the power generation parameters of each branch of the wind farm grid connection area are adjusted; For the adjusted wind farm grid connection area, if the available transmission capacity between the areas is less than the preset available transmission capacity threshold, then the transmission congestion risk determination result indicates that there is a congestion risk. For the adjusted wind farm grid connection area, if the available transmission capacity between the areas is greater than or equal to a preset available transmission capacity threshold, then the transmission congestion risk determination result indicates that there is no congestion risk.

5. The method according to claim 1, characterized in that, When the transmission congestion risk assessment result indicates the existence of congestion risk, the target node for the coordinated regulation of the three-electric system is determined using the wind turbine grid connection node sensitivity matrix, including: If the power transmission congestion risk assessment result indicates the existence of congestion risk, a restrictive branch is identified, wherein the restrictive branch is a branch corresponding to the available power transmission capacity between the regions; Based on the constrained branch and the wind turbine grid-connected node sensitivity matrix, the wind turbine grid-connected node sensitivity vector is determined, wherein the wind turbine grid-connected node sensitivity vector is used to characterize the power flow sensitivity of all node power changes to the constrained branch; The target node is determined from the constrained branch based on the maximum absolute value of the sensitivity vector of the wind turbine grid connection node.

6. The method according to claim 1, characterized in that, The step of generating coordinated adjustment and control commands for the three-electric system based on the target node includes: If the power transmission congestion risk assessment result indicates the existence of congestion risk, a response priority model for the three-electric system is constructed based on the target node. The real-time operating parameters of the three-electric system are input into the response priority model of the three-electric system to obtain the real-time adjustment weight coefficient and real-time response priority of the three-electric system output by the response priority model of the three-electric system. The real-time operating parameters include the real-time response delay of the converter system, the real-time pitch angle of the pitch system and the real-time SOC value of the electronic control system. Adjust the converter system adjustment command, pitch system adjustment command, and electronic control system adjustment command according to the real-time adjustment weight coefficient; Based on the real-time response priority, the coordinated adjustment order of the converter system adjustment command, the pitch system adjustment command, and the electronic control system adjustment command is determined; Based on the coordinated adjustment sequence, a coordinated adjustment control command is obtained, which includes the converter system adjustment command, the pitch system adjustment command, and the electronic control system adjustment command.

7. The method according to claim 6, characterized in that, The method further includes: Obtain the physical limit range of the three-electric system, wherein the physical limit range of the three-electric system includes the power change rate of the converter system, the pitch rate range of the pitch system, and the SOC operating range of the electronic control system. Based on the real-time operating parameters of the three-electric system, power grid constraint rules are formed; Based on the power grid constraint rules and the physical limit range of the three-electric system, branch thermal stability constraints and node voltage constraints are generated. The branch thermal stability constraint and node voltage constraint are corrected based on the real-time status monitoring values ​​of the three-electric system to obtain the corrected constraint conditions. The real-time status monitoring values ​​of the three-electric system include at least one of the real-time component temperature monitoring values, real-time wind speed monitoring values, and real-time battery temperature detection values. The coordinated adjustment control command is adjusted according to the modified constraint conditions to obtain a safety coordinated adjustment control command, which is then used to control the three-electric system.

8. A device for coordinated optimization of the three electrical systems of a wind turbine generator set, characterized in that, include: The acquisition module is used to acquire the grid connection parameters of the wind turbine; The first generation module is used to generate a node susceptance matrix and a branch-node susceptance matrix based on the wind turbine grid connection parameters. The node susceptance matrix is ​​subjected to de-balancing node processing to obtain an unbalanced node susceptance matrix; the branch-node susceptance matrix is ​​subjected to de-balancing node processing to obtain a reduced branch-node susceptance matrix; and the wind turbine grid connection node sensitivity matrix is ​​generated based on the unbalanced node susceptance matrix and the reduced branch-node susceptance matrix. The first determining module is used to determine the available power transmission capacity between the wind farm grid-connected area and the target area based on the wind turbine grid-connected node sensitivity matrix. The second determining module is used to determine the power transmission congestion risk assessment result based on the available power transmission capacity between the regions; The third determining module is used to determine the target node for coordinated adjustment of the three-electric system by using the wind turbine grid connection node sensitivity matrix when the power transmission congestion risk assessment result indicates the existence of congestion risk. The second generation module is used to generate coordinated adjustment and control commands for the three-electric system based on the target node. The control module is used to control the three-electric system using the coordinated adjustment control command.

9. An electronic device, characterized in that, include: A memory on which computer programs are stored; A processor is configured to execute the computer program in the memory to implement the steps of the method for coordinated optimization of the three electrical systems of a wind turbine generator set as described in any one of claims 1-7.

10. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the method for coordinated optimization of the three electrical systems of a wind turbine generator set as described in any one of claims 1-7.

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