Wind farm network construction method and device

Abstract

This application discloses a wind farm grid construction method and apparatus for intelligently controlling the grid-following and grid-building characteristics of wind turbines. The method comprises: determining the active power output ratio of renewable energy sources in the grid; when the active power output ratio is greater than a preset value, determining the number N of wind turbines required to participate in grid construction based on the active power output ratio; and scoring the wind turbines in the wind farm, and selecting the N wind turbines with the highest scores from the wind farm to participate in grid construction. In this solution, the grid-following and grid-building characteristics of the wind turbines and wind farm are automatically and intelligently controlled based on the active power output ratio of renewable energy sources, thereby enhancing the flexibility of the grid.

Description

Technical Field

[0001] The present application relates to the technical field of wind power grid construction, and in particular to a method and device for wind farm grid construction. Background Art

[0002] A hybrid grid-connected wind farm refers to a wind farm equipped with both grid-connected and grid-connected wind turbines. Grid-connected wind turbines are capable of islanding, independently contributing to the grid, and can flexibly switch between grid-connected and standalone operation. They are suitable for weak, low-inertia, and high-proportion renewable energy power systems. Grid-connected wind turbines rely on the grid and must be connected to it for operation. They cannot provide voltage and frequency support on their own and cannot achieve autonomous networking. They are suitable for strong grids dominated by synchronous generators and cannot operate in isolated islands. Therefore, when a wind farm is grid-connected, the number of grid-connected wind turbines in the wind farm must be appropriately determined. In areas with weak grids, the proportion of grid-connected wind turbines can be increased, while in areas with strong grids, the proportion can be reduced. Existing technologies cannot flexibly and dynamically adjust the grid-connected mode based on the actual situation of renewable energy. Excessive grid-connected wind turbines can affect the stable operation of the public grid; too few grid-connected wind turbines can weaken the grid's active support capabilities.

[0003] Therefore, how to provide a wind farm grid construction method to intelligently control the grid-following and grid-construction characteristics of wind turbines and wind farms and improve the flexibility of the power grid has become a technical problem that needs to be solved urgently. Summary of the Invention

[0004] The present application provides a wind farm grid construction method and device for intelligently controlling the grid following and grid construction characteristics of wind turbines to improve the flexibility of the grid.

[0005] This application provides a wind farm network construction method, comprising:

[0006] Determine the proportion of active power output of renewable energy in the power grid;

[0007] When the active power output ratio is greater than a preset value, the number N of wind turbines required to participate in the network formation is determined according to the active power output ratio;

[0008] Wind turbines in the wind farm are scored, and N wind turbines with the highest scores are selected from the wind farm to participate in the grid construction.

[0009] The beneficial effects of this application include: determining the active output ratio of renewable energy in the power grid; when the active output ratio is greater than a preset value, determining the number N of wind turbines required to participate in grid construction based on the active output ratio; scoring the wind turbines in the wind farm, and selecting the N wind turbines with the highest scores from the wind farm to participate in grid construction. Because the wind turbines and wind farms can be automatically and intelligently controlled based on the active output ratio of renewable energy, the flexibility of the power grid is improved.

[0010] In one embodiment, determining the active output ratio of renewable energy in the power grid includes:

[0011] Obtain the active output value of renewable energy in the power grid and the active output value of all power sources in the power grid;

[0012] Substitute the active output value of the new energy in the power grid and the active output values ​​of all power sources in the power grid into the following first preset formula to determine the active output proportion of the new energy in the power grid:

[0013] z=P re / P total ×100%;

[0014] Among them, z is the active power output ratio of new energy, P re is the active power output value of renewable energy in the power grid, P total It is the active output value of all power sources in the grid.

[0015] In one embodiment, determining the number N of wind turbine groups required to participate in network construction according to the active output ratio includes:

[0016] Determining a proportion coefficient according to the active output proportion;

[0017] Substitute the proportion coefficient into the following second preset formula to determine the number N of wind turbine groups required to participate in the network construction:

[0018] N=Ceil(P / P n ×100%×N total ×K);

[0019] Among them, Ceil function is the upward rounding function; P is the real-time active power output of the wind farm; Pn is the rated active power output of the wind farm; N total is the number of grid-type wind turbines in the wind farm; K is the proportion coefficient.

[0020] In one embodiment, determining the proportion coefficient according to the active output proportion includes:

[0021] When the active output ratio is less than or equal to the first preset ratio, determining the ratio coefficient to be the first coefficient;

[0022] When the active output ratio is greater than the first preset ratio and less than or equal to the second preset ratio, determining the ratio coefficient to be the second coefficient;

[0023] When the active output ratio is greater than the second preset ratio, determining the ratio coefficient to be the third coefficient;

[0024] Among them, the first preset proportion is smaller than the second preset proportion, the first coefficient is smaller than the second coefficient, and the second coefficient is smaller than the third coefficient.

[0025] In one embodiment, scoring the wind turbines in the wind farm includes:

[0026] Obtain the historical mean time between failures, real-time active power output capability of the unit, and fatigue load of the fan;

[0027] Adjust the weights corresponding to the historical mean time between failures, the real-time active power output capability of the unit, and the fatigue load of the unit according to the actual needs of the wind farm;

[0028] The wind turbines in the wind farm are scored according to the historical mean time between failures of the turbines, the real-time active power output capability of the turbines, the fatigue load conditions of the turbines and the adjusted weights.

[0029] In one embodiment, adjusting the weights corresponding to the historical mean time between failures of the units, the real-time active output capacity of the units, and the fatigue load of the wind turbines according to the actual needs of the wind farm includes:

[0030] When the actual demand of the wind farm is to increase the stability of the network, the weight corresponding to the historical mean time between failures of the units is increased;

[0031] When the actual demand of the wind farm is to increase the grid revenue, the weight corresponding to the real-time active power output capacity of the unit is increased;

[0032] When the actual demand of the wind farm is to increase the life of the unit, the weight corresponding to the fatigue load condition of the unit is increased.

[0033] In one embodiment, the wind turbines in the wind farm are scored based on the historical mean time between failures, the real-time active output capacity of the turbines, the fatigue load of the turbines, and the adjusted weights, including:

[0034] Substitute the historical mean time between failures, the real-time active output capacity of the unit, the fatigue load of the unit, and the adjusted weight into the following third preset formula to determine the score of each wind turbine in the wind farm:

[0035] S i =A1×T i / T total +A2×Pi / Ptotal +A3×DEL total / DEL i ;

[0036] Among them, S i is the score of the i-th unit; A1, A2 and A3 are weight coefficients; T i is the historical mean time between failures of the i-th unit, T total is the sum of the historical mean time between failures of all wind turbines in the network; P i is the real-time active power output of unit i, P total The sum of the real-time active power output of all wind turbines in the network; DEL i is the operating fatigue of unit i, DEL total It is the sum of fatigue life of all grid wind turbine units.

[0037] In one embodiment, the method further comprises:

[0038] Obtain the real-time active power output and reserve coefficient of the wind turbines participating in the network construction;

[0039] Substitute the real-time active power output value and the reserved coefficient into the following fourth preset formula to determine the reserved active power output value of each wind turbine group participating in the wind farm grid:

[0040] P ref,i =min(L×P 实时,i , P n,i ×K×B i );

[0041] Among them, P ref,i is the reserved active power output value of the i-th wind turbine group; P 实时,i is the real-time active power output value of the i-th wind turbine group; P n,i is the rated active output value of the i-th wind turbine; K is the proportion coefficient; B i is the reservation coefficient of the i-th wind turbine group; L is the maximum reservation ratio.

[0042] In one embodiment, the reservation coefficient is determined as follows:

[0043] Obtaining the maximum power disturbance value of each grid wind turbine group and the rated active power output value of each grid wind turbine group within a first preset time period;

[0044] Substitute the maximum power disturbance value of each grid-forming wind turbine group and the rated active output value of each grid-forming wind turbine group into the following fifth preset formula to determine the reserved coefficient of each wind turbine group participating in the wind farm grid formation:

[0045]

[0046] Among them, B i is the reserved coefficient of the i-th wind turbine group; ΔP max is the maximum power disturbance value, P n,i is the rated active output value of the i-th wind turbine group, D is the load regulation effect coefficient, H is the system equivalent inertia constant, and f0 is the rated frequency.

[0047] The present application also provides a wind farm network construction device, comprising:

[0048] The first determination module is used to determine the active output ratio of renewable energy in the power grid;

[0049] A second determining module is configured to determine the number N of wind turbines required to participate in network construction according to the active output ratio when the active output ratio is greater than a preset value;

[0050] The selection module is used to score the wind turbines in the wind farm and select N wind turbines with the highest scores from the wind farm to participate in the network construction.

[0051] In one embodiment, the first determining module includes:

[0052] The first acquisition submodule is used to obtain the active output value of the new energy in the power grid and the active output value of all power sources in the power grid;

[0053] The first determination submodule is configured to substitute the active output value of the new energy in the power grid and the active output values ​​of all power sources in the power grid into the following first preset formula to determine the active output proportion of the new energy in the power grid:

[0054] z=P re / P total ×100%;

[0055] Among them, z is the active power output ratio of new energy, P re is the active power output value of renewable energy in the power grid, P total It is the active output value of all power sources in the grid.

[0056] In one embodiment, the second determining module includes:

[0057] A second determining submodule is configured to determine a proportion coefficient according to the active power output proportion;

[0058] The third determination submodule is configured to substitute the proportion coefficient into the following second preset formula to determine the number N of wind turbine groups required to participate in the network construction:

[0059] N=Ceil(P / P n ×100%×N total ×K);

[0060] Among them, Ceil function is the upward rounding function; P is the real-time active power output of the wind farm; Pn is the rated active power output of the wind farm; N total is the number of grid-type wind turbines in the wind farm; K is the proportion coefficient.

[0061] In one embodiment, the second determining submodule is further configured to:

[0062] When the active output ratio is less than or equal to the first preset ratio, determining the ratio coefficient to be the first coefficient;

[0063] When the active output ratio is greater than the first preset ratio and less than or equal to the second preset ratio, determining the ratio coefficient to be the second coefficient;

[0064] When the active output ratio is greater than the second preset ratio, determining the ratio coefficient to be the third coefficient;

[0065] Among them, the first preset proportion is smaller than the second preset proportion, the first coefficient is smaller than the second coefficient, and the second coefficient is smaller than the third coefficient.

[0066] In one embodiment, the selection module includes:

[0067] The second acquisition submodule is used to obtain the historical mean time between failures of the unit, the real-time active power output capacity of the unit and the fatigue load of the fan;

[0068] The adjustment submodule is used to adjust the weights corresponding to the historical mean time between failures of the unit, the real-time active power output capacity of the unit, and the fatigue load of the unit according to the actual needs of the wind farm;

[0069] The scoring submodule is used to score the wind turbines in the wind farm according to the historical mean time between failures of the turbines, the real-time active output capacity of the turbines, the fatigue load conditions of the turbines and the adjusted weights.

[0070] In one embodiment, the adjustment submodule is further configured to:

[0071] When the actual demand of the wind farm is to increase the stability of the network, the weight corresponding to the historical mean time between failures of the units is increased;

[0072] When the actual demand of the wind farm is to increase the grid revenue, the weight corresponding to the real-time active power output capacity of the unit is increased;

[0073] When the actual demand of the wind farm is to increase the life of the unit, the weight corresponding to the fatigue load condition of the unit is increased.

[0074] In one embodiment, the scoring submodule is further configured to:

[0075] Substitute the historical mean time between failures, the real-time active output capacity of the unit, the fatigue load of the unit, and the adjusted weight into the following third preset formula to determine the score of each wind turbine in the wind farm:

[0076] S i =A1×T i / T total +A2×P i / Ptotal +A3×DEL total / DEL i ;

[0077] Among them, S i is the score of the i-th unit; A1, A2 and A3 are weight coefficients; T i is the historical mean time between failures of the i-th unit, T total is the sum of the historical mean time between failures of all wind turbines in the network; P i is the real-time active power output of unit i, P total The sum of the real-time active power output of all wind turbines in the network; DEL i is the operating fatigue of unit i, DEL total It is the sum of fatigue life of all grid wind turbine units.

[0078] In one embodiment, the apparatus further comprises:

[0079] The acquisition module is used to obtain the real-time active power output value and reserve coefficient of the wind turbine groups participating in the network construction;

[0080] The third determination module is configured to substitute the real-time active power output value and the reserved coefficient into the following fourth preset formula to determine the reserved active power output value of each wind turbine group participating in the wind farm network:

[0081] P ref,i =min(L×P 实时,i , P n,i ×K×B i );

[0082] Among them, P ref,i is the reserved active power output value of the i-th wind turbine group; P 实时,i is the real-time active power output value of the i-th wind turbine group; P n,i is the rated active output value of the i-th wind turbine; K is the proportion coefficient; B i is the reservation coefficient of the i-th wind turbine group; L is the maximum reservation ratio.

[0083] In one embodiment, the reservation coefficient is determined as follows:

[0084] Obtaining the maximum power disturbance value of each grid wind turbine group and the rated active power output value of each grid wind turbine group within a first preset time period;

[0085] Substitute the maximum power disturbance value of each grid-forming wind turbine group and the rated active output value of each grid-forming wind turbine group into the following fifth preset formula to determine the reserved coefficient of each wind turbine group participating in the wind farm grid formation:

[0086]

[0087] Among them, B i is the reserved coefficient of the i-th wind turbine group; ΔP max is the maximum power disturbance value, P n,i is the rated active output value of the i-th wind turbine group, D is the load regulation effect coefficient, H is the system equivalent inertia constant, and f0 is the rated frequency.

[0088] The present application also provides a wind farm network construction system, comprising:

[0089] at least one processor; and,

[0090] a memory communicatively connected to the at least one processor; wherein,

[0091] The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to implement the wind farm grid construction method recorded in any of the above embodiments.

[0092] The present application also provides a computer-readable storage medium. When the instructions in the storage medium are executed by a processor corresponding to the wind farm networking system, the wind farm networking system can implement the wind farm networking method described in any of the above embodiments.

[0093] Other features and advantages of the present application will be described in the following description, and in part will become apparent from the description, or will be understood by practicing the present application. The purposes and other advantages of the present application can be realized and obtained by the structures particularly pointed out in the written description, claims, and drawings.

[0094] The technical solution of the present application is further described in detail below through the accompanying drawings and examples. BRIEF DESCRIPTION OF THE DRAWINGS

[0095] The accompanying drawings are used to provide a further understanding of the present application and constitute a part of the specification. Together with the embodiments of the present application, they are used to explain the present application and do not constitute a limitation of the present application. In the accompanying drawings:

[0096] Figure 1 This is a flow chart of a wind farm network construction method in one embodiment of the present application;

[0097] Figure 2 This is a structural diagram of a wind farm network construction device in one embodiment of the present application;

[0098] Figure 3 Schematic diagram of the hardware structure of a wind farm networking system in one embodiment of the present application. DETAILED DESCRIPTION

[0099] The preferred embodiments of the present application are described below in conjunction with the accompanying drawings. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present application and are not used to limit the present application.

[0100] Figure 1 This is a flow chart of a wind farm network construction method in one embodiment of the present application. Figure 1 As shown, the method can be implemented as the following steps S101-S103:

[0101] In step S101, the active output ratio of renewable energy in the power grid is determined;

[0102] In step S102, when the active power output ratio is greater than a preset value, the number N of wind turbine groups required to participate in the grid formation is determined according to the active power output ratio;

[0103] In step S103 , the wind turbines in the wind farm are scored, and N wind turbines with the highest scores are selected from the wind farm to participate in the grid construction.

[0104] In this application, when the wind farm is connected to the grid, in order to reasonably determine the wind turbines participating in the grid construction, first of all, the active output ratio of renewable energy in the grid is determined. In the power system, the change of the renewable energy output ratio has an important impact on the stable operation of the grid and the operation mode of the wind farm. In order to accurately grasp the contribution of renewable energy in the grid, the active output value P of renewable energy in the grid is obtained. re And the active output value P of all power sources in the power grid total Substitute the active output value of the new energy in the power grid and the active output value of all power sources in the power grid into the following first preset formula to determine the active output proportion of the new energy in the power grid:

[0105] z=P re / P total ×100%;

[0106] Among them, z is the active output ratio; P re is the active power output value of renewable energy in the power grid, P total It is the active output value of all power sources in the grid.

[0107] When the active power output does not exceed the preset value, the wind farm is controlled to be in a grid-following mode.

[0108] When the active output ratio is greater than a preset value, the number of wind turbines N that need to participate in the grid is determined based on the active output ratio. When the active output ratio of renewable energy in the grid reaches a certain level, the wind farm needs to adjust its operating mode and increase the participation of grid-forming wind turbines to enhance the active support capacity of the grid. Specifically, first, the ratio coefficient K is determined based on the active output ratio z;

[0109] When the active output proportion is less than or equal to the first preset proportion, the proportion coefficient is determined to be the first coefficient; when the active output proportion is greater than the first preset proportion and less than or equal to the second preset proportion, the proportion coefficient is determined to be the second coefficient; when the active output proportion is greater than the second preset proportion, the proportion coefficient is determined to be the third coefficient, wherein the first preset proportion is less than the second preset proportion, the first coefficient is less than the second coefficient, and the second coefficient is less than the third coefficient.

[0110] In one embodiment of the present application, considering that when the active output of renewable energy accounts for less than 30%, the power grid is mainly dominated by synchronous generator power supplies, and the new energy power station can meet the power grid demand with traditional field-level power control technology, if wind farm networking and power grid networking are adopted, different problems may arise, which may affect the stability of the power grid and have a negative impact on the power grid. When the active output of renewable energy accounts for 30-50%, it is appropriate to invest in a small proportion of grid-forming wind turbines to cope with the overall fluctuation of the power grid affected by the fluctuation of the active output of renewable energy. When the active output of renewable energy accounts for more than 50%, the active output of renewable energy accounts for a very high proportion. In this scenario, renewable energy should assume more responsibility for stabilizing the power grid and need to provide more grid-forming capabilities and grid-forming wind turbines to maintain the stable operation of the new power system dominated by renewable energy. Therefore, the first preset ratio is 30%, and the second preset ratio is 50%. The first coefficient, the second coefficient, and the third coefficient are 0, 1, and 2, respectively. That is, when z≤30%, K is 0, indicating that the output of renewable energy accounts for a low proportion and no network support is required; when 30%<z≤50%, K is 1, indicating that the output of renewable energy accounts for a large proportion and the station needs to provide general network support; when z>50%, K is 2, indicating that the output of renewable energy accounts for a particularly large proportion and strong network support is required.

[0111] Then, the proportion coefficient is substituted into the following second preset formula to determine the number N of wind turbine groups that need to participate in the network construction:

[0112] N=Ceil(P / P n ×100%×N total ×K);

[0113] Among them, Ceil function is the upward rounding function; P is the real-time active power output of the wind farm; P nis the rated active power output of the wind farm; N total is the number of grid-type wind turbines in the wind farm; K is the proportion coefficient.

[0114] The wind turbines in the wind farm are scored, and the N wind turbines with the highest scores are selected from the wind farm to participate in the grid construction. To ensure that the wind turbines participating in the grid construction have good operating performance and reliability, the wind turbines in the wind farm need to be scored. First, the historical mean time between failures, the real-time active output capacity of the turbines, and the fatigue load of the wind turbines are obtained. Then, the weights corresponding to the historical mean time between failures, the real-time active output capacity of the turbines, and the fatigue load of the turbines are adjusted according to the actual needs of the wind farm.

[0115] For example, because the probability of failure of units with long historical mean time between failures during operation is relatively low, the stability of the network can be improved. When the actual demand of the wind farm is to increase the stability of the network, the weight corresponding to the historical mean time between failures of the units is increased. Since units with strong real-time active output capacity can generate more electricity in the same period of time, thereby improving the economic benefits of the network, when the actual demand of the wind farm is to increase the revenue of the network, the weight corresponding to the real-time active output capacity of the units is increased. When the actual demand of the wind farm is to increase the life of the units, the weight corresponding to the fatigue load condition of the units is increased. The wind turbines in the wind farm are scored based on the historical mean time between failures of the units, the real-time active output capacity of the units, the fatigue load condition of the units and the adjusted weights. The historical mean time between failures of the units, the real-time active output capacity of the units, the fatigue load condition of the units and the adjusted weights are substituted into the following third preset formula to determine the score of each wind turbine in the wind farm:

[0116] S i =A1×T i / T total +A2×P i / P total +A3×DEL total / DEL i ;

[0117] Among them, S i is the score of the i-th unit; A1, A2 and A3 are weight coefficients; T i is the historical mean time between failures of the i-th unit, T total is the sum of the historical mean time between failures of all wind turbines in the network; P i is the real-time active power output of unit i, P total The sum of the real-time active power output of all wind turbines in the network; DEL i is the operating fatigue of unit i, DEL total It is the sum of fatigue life of all grid wind turbine units.

[0118] In the above scoring formula, the fatigue life of a grid-connected wind turbine refers to the time it takes from the start of operation to the generation of fatigue cracks or failure due to periodic loads during its participation in the frequency regulation of the power grid. The total fatigue life is a decreasing value. When the unit is in a brand new state, it corresponds to the initial value of the fatigue life, which is also the maximum value of the fatigue life. As the unit runs, the operating fatigue of the unit will gradually increase, and the fatigue life will gradually decrease. The operating fatigue of the unit reflects the degree of fatigue damage accumulated by the structural components of the wind turbine due to long-term operation. The larger the value, the more serious the accumulated fatigue damage of the unit and the shorter the fatigue life. There are many specific calculation methods for operational fatigue, such as real-time collection of operating parameters such as wind speed, rotation speed, and power, and calculation through fatigue load models; the digital twin model of the wind turbine can also be used to simulate the load spectrum under different working conditions, and the load-fatigue mapping relationship can be established in combination with long-term operation data to obtain the corresponding operational fatigue. DEL total / DEL i The crew with lower fatigue level will get higher score.

[0119] It can be understood that A1+A2+A3=1, and its ratio depends on the wind farm network benefits and unit life.

[0120] To increase grid stability, increase the weight of A1 and decrease A2 and A3. This means the most important factor in selecting wind turbines for grid deployment is the mean time between failures (MTBF). This approach is suitable for scenarios where the active power output of renewable energy sources (K) is between 30% and 50%.

[0121] If the goal of increasing grid-connected revenue is to be achieved, the A2 coefficient needs to be increased and A1 and A3 reduced. That is, when selecting a wind turbine for grid connection, the turbine's real-time active and reactive power output capacity should be the most important consideration. This choice is suitable for situations where the proportion of new energy active power output K is above 50%.

[0122] To maximize turbine lifespan, A3 should be increased while A1 and A2 should be decreased. This means that when selecting grid-connected wind turbines, the operational fatigue load of the turbine should be the most important consideration. This approach is suitable for scenarios where the active output of renewable energy sources (K) is between 30% and 50%. Of course, the values ​​of A1, A2, and A3 can also be used within other K ranges based on actual operating conditions, highlighting the flexibility of the multi-objective algorithm and the customization needs of wind farms.

[0123] Finally, the wind turbines are ranked from highest to lowest based on their calculated scores, and the N wind turbines with the highest scores are selected to participate in the grid construction. The wind farm is then switched to grid construction mode with load. This ensures that the wind turbines participating in the grid construction have good operating performance and reliability, and improves the wind farm's ability to actively support the power grid.

[0124] In addition, due to the randomness and instantaneous changes in the actual load of the power system, in order to quickly respond to such load fluctuations, timely fill the power gap, avoid frequency over-limit, and ensure the stable operation of the power system. The wind turbine groups participating in the network need to reserve active power when constructing the network. Specifically, the real-time active output value and the reserved coefficient of the wind turbine groups participating in the network are obtained; the real-time active output value and the reserved coefficient are substituted into the following fourth preset formula to determine the reserved active output value of each wind turbine group participating in the network construction of the wind farm:

[0125] P ref,i =min(L×P 实时,i , P n,i ×K×B i );

[0126] Among them, P ref,i is the reserved active power output value of the i-th wind turbine group; P 实时,i is the real-time active power output value of the i-th wind turbine group; P n,i is the rated active output value of the i-th wind turbine; K is the proportion coefficient; B i is the reservation coefficient of the i-th wind turbine group; L is the maximum reservation ratio.

[0127] L can be preset and is set to 50% by default. In small-scale wind farms or areas with stable load fluctuations, the reserved coefficient B i A fixed preset value, such as 10%, can be selected to improve response speed. In large-scale wind farm clusters or areas with a high proportion of new energy access, the configuration can also be optimized according to actual conditions to accurately match the unit characteristics and optimize the spare capacity.

[0128] In one embodiment, the maximum power disturbance value of each grid-connected wind turbine group and the rated active output value of each grid-connected wind turbine group within a first preset time period are obtained; the maximum power disturbance value of each grid-connected wind turbine group and the rated active output value of each grid-connected wind turbine group are substituted into the following fifth preset formula to determine the reservation coefficient of each wind turbine group participating in the wind farm grid:

[0129]

[0130] Among them, B i is the reserved coefficient of the i-th wind turbine group; ΔP max is the maximum power disturbance value, P n,i is the rated active output value of the i-th wind turbine group, D is the load regulation effect coefficient, H is the system equivalent inertia constant, and f0 is the rated frequency.

[0131] The load regulation coefficient, D, typically ranges from 1 to 3 and can be pre-set. It measures the load power's ability to change with frequency. Its value indicates the load's sensitivity to system frequency. For every 1% change in load power, the frequency changes by approximately 1% to 3%. This formula ensures that the system frequency remains within its limits even under maximum power disturbances.

[0132] In another embodiment of the present application, the active output standard deviation and average active output value of each grid-connected wind turbine in the past second preset time period, the number of failures of each grid-connected wind turbine in the past third preset time period, and the average number of failures of wind turbines in the wind farm in the past third preset time period are obtained; the active output standard deviation and average active output value of each grid-connected wind turbine in the past second preset time period, the number of failures of each grid-connected wind turbine in the past third preset time period, and the average number of failures of wind turbines in the wind farm in the past third preset time period are substituted into the following sixth preset formula to determine the reservation coefficient of each wind turbine participating in the grid of the wind farm:

[0133] ;

[0134] Among them, B i B is the reserved coefficient of the i-th wind turbine group; base is the basic reserved ratio (such as 5%). i is the standard deviation of the active power output of the i-th wind turbine in the second preset period in the past; is the average active power output value of the i-th wind turbine group in the past second preset period; T fail,i The number of failures of the i-th wind turbine group in the past third preset period; T avg is the average number of faults in the wind farm in the past third preset period; α and β are adjustment coefficients, which can be pre-determined by fitting (such as α=0.3, β=0.2).

[0135] In addition, in this application, during steady-state operation, the reactive output of the wind turbines is strived to approach 0, and the voltage at the machine end is maintained near the per-unit value. Then, when the grid is needed to provide support, the units can output reactive power at maximum capacity. Reserving reactive power is extremely critical for the power system. It not only stabilizes the voltage within the normal range and ensures the reliable operation of electrical equipment, but also compensates for the phase difference caused by inductive and capacitive loads to maintain system stability. At the same time, it can reduce the voltage drop and loss of the transmission line, improve transmission efficiency, and increase the power factor, enhance the efficiency of electric energy utilization, and ensure the efficient and stable operation of the power system.

[0136] The wind farm grid construction system provided by the present application includes functions such as high-precision monitoring of power quality, intelligent control module following grid construction characteristics, AGC control module, AVC control module, primary frequency regulation and virtual inertia control, and rapid voltage regulation. According to the classification of active power control and reactive power control, active power control is divided into AGC control, primary frequency regulation and virtual inertia; reactive power control is divided into AVC control and rapid voltage regulation. Among them, the wind farm power quality monitoring module collects power quality data of the high / low voltage side of the wind farm grid connection point, each collection line and the high and low voltage side of each box-type transformer in real time and at high speed and millisecond level by installing smart meters at different voltage levels in the wind farm. The power quality data mainly includes key data such as voltage, current, frequency, harmonics, active power, reactive power, etc.; and then provides key inputs to active support functions such as primary frequency regulation, virtual inertia, and rapid voltage regulation.

[0137] In terms of communication, the wind farm networking system exchanges data with the grid dispatching system and the online grid strength assessment system. It also utilizes smart meters to collect real-time, high-speed power quality data from multiple wind farm nodes, including key information such as voltage and current. It also communicates with the wind turbine control system via the energy management system's high-speed communication protocol, acquiring a wide range of data, including wind turbine status and power. Specifically, the grid dispatching system's AGC (Automatic Control) system (AGC) dynamically issues target values ​​for total active power to the wind farm based on various factors, enabling minute-by-minute grid frequency stability control. The AVC system dynamically issues voltage and other target values ​​based on grid node voltage conditions for voltage regulation. The primary frequency regulation system monitors relevant actions in real time to suppress undesirable grid frequency fluctuations, proactively supporting grid frequency stability at the millisecond and second level. Power quality monitoring monitors the power quality of various power sources in real time, and any anomalies are addressed through other system control measures. The online grid strength assessment system comprehensively assesses grid security from multiple dimensions, including carrying capacity, stability, reliability, resilience, flexibility, and adaptability, based on the varying active power output ratios of asynchronous generators in the power system.

[0138] In this application, the active power control of the grid-type wind turbine is mainly divided into two parts. The first is to realize the rapid adjustment of the primary frequency modulation and virtual inertia according to the grid rules. According to the current requirements, the active power adjustment range is ±10%Pn (rated power of the wind turbine), and this part of the active power control is no longer controlled by the wind farm grid system. However, considering that the wind turbine end is in extremely weak grid conditions, it is recommended that the actual grid frequency adopt the grid frequency of the wind farm grid connection point, which is more accurate. The second is that the active power control outside ±10%Pn is still controlled according to the energy management system. When the primary frequency modulation and virtual inertia occur, the primary frequency modulation and virtual inertia are given priority, and then enter the AGC control mode after they are completed, that is, the wind farm grid system controls its active power mode. For the active power control of the grid-following wind turbine, it has always been controlled by the wind farm grid system, which will not be repeated here.

[0139] Active power control at a wind farm in grid-forming mode requires continuous execution of grid AGC commands. First, target active power values ​​for the grid-following and grid-forming wind turbine clusters are issued. Next, a determination is made as to whether the wind farm requires primary frequency regulation or virtual inertia. If so, the grid-forming wind turbines complete control in a very short time. Simultaneously, the wind farm's active power shortfall is quickly calculated. If there is no shortfall, no commands are issued to the grid-forming wind turbines. If there is a shortfall, ∆P, target values ​​are issued to the grid-forming wind turbines according to the power allocation algorithm, thereby achieving primary frequency regulation or virtual inertia requirements. After primary frequency regulation and virtual inertia are completed, continuous AGC commands must be executed.

[0140] Reactive power control for a wind farm in grid-connected mode requires the implementation of grid AVC instructions. First, a reactive power target value is issued for the grid-connected wind turbine cluster. Then, a determination is made as to whether the voltage at the wind farm's grid connection point requires rapid adjustment. If a reactive power shortfall persists from the grid-connected wind turbines, the reactive power shortfall (∆Q) for the entire farm is rapidly calculated. Then, based on the wind turbine terminal voltage, the reactive power target value for the grid-connected wind turbines is issued according to a distribution algorithm to meet the wind farm's rapid voltage regulation requirements. If rapid voltage regulation is not required, the reactive power of the grid-connected wind turbine cluster can be dynamically adjusted.

[0141] To control active power in grid-following mode, the wind farm must execute grid AGC commands. First, the active power target for the grid-following wind turbines is issued. The system then determines whether the grid frequency at the wind farm's connection point triggers a primary frequency modulation or virtual inertia. If so, it quickly calculates the active power difference, ∆P, and issues the active power control target to the grid-following wind turbines. After the primary frequency modulation and virtual inertia are complete, grid AGC commands resume.

[0142] To control reactive power in grid-following mode, the wind farm must implement AVC control. First, the grid-following cluster reactive power target is issued. Then, the grid-connected voltage is determined to trigger rapid voltage regulation. If so, the reactive power difference is quickly calculated, and the reactive power control target is issued to the grid-following wind turbines. After rapid voltage regulation is complete, the grid AVC command continues to be supported to maintain stable grid voltage operation.

[0143] The beneficial effects of this application include: determining the active output ratio of renewable energy in the power grid; when the active output ratio is greater than a preset value, determining the number N of wind turbines required to participate in grid construction based on the active output ratio; scoring the wind turbines in the wind farm, and selecting the N wind turbines with the highest scores from the wind farm to participate in grid construction. Because the wind turbines and wind farms can be automatically and intelligently controlled based on the active output ratio of renewable energy, the flexibility of the power grid is improved.

[0144] In one embodiment, the above step S101 may be implemented as the following steps A1-A2:

[0145] In step A1, the active output value of the new energy in the power grid and the active output value of all power sources in the power grid are obtained;

[0146] In step A2, the active output value of the new energy in the power grid and the active output values ​​of all power sources in the power grid are substituted into the following first preset formula to determine the active output proportion of the new energy in the power grid:

[0147] z=P re / P total ×100%;

[0148] Among them, z is the active power output ratio of new energy, P re is the active power output value of renewable energy in the power grid, P total It is the active output value of all power sources in the grid.

[0149] In one embodiment, the above step S102 may be implemented as the following steps B1-B2:

[0150] In step B1, a proportion coefficient is determined according to the active power output proportion;

[0151] In step B2, the proportion coefficient is substituted into the following second preset formula to determine the number N of wind turbine groups required to participate in the network construction:

[0152] N=Ceil(P / P n ×100%×N total ×K);

[0153] Among them, Ceil function is the upward rounding function; P is the real-time active power output of the wind farm; P n is the rated active power output of the wind farm; N total is the number of grid-type wind turbines in the wind farm; K is the proportion coefficient.

[0154] In one embodiment, the above step B1 may be implemented as the following steps B11-B13:

[0155] In step B11, when the active power output ratio is less than or equal to the first preset ratio, the ratio coefficient is determined to be the first coefficient;

[0156] In step B12, when the active power output ratio is greater than the first preset ratio and less than or equal to the second preset ratio, the ratio coefficient is determined to be the second coefficient;

[0157] In step B13, when the active power output ratio is greater than the second preset ratio, the ratio coefficient is determined to be a third coefficient;

[0158] The first preset proportion is smaller than the second preset proportion, the first coefficient is smaller than the second coefficient, and the second coefficient is smaller than the third coefficient.

[0159] In one embodiment, the scoring of wind turbines in the wind farm in step S103 may be implemented as the following steps C1-C3:

[0160] In step C1, the historical mean time between failures of the unit, the real-time active power output capacity of the unit, and the fatigue load of the fan are obtained;

[0161] In step C2, the weights corresponding to the historical mean time between failures of the unit, the real-time active power output capacity of the unit, and the fatigue load of the unit are adjusted according to the actual needs of the wind farm;

[0162] In step C3, the wind turbines in the wind farm are scored according to the historical mean time between failures of the turbines, the real-time active output capacity of the turbines, the fatigue load conditions of the turbines and the adjusted weights.

[0163] In one embodiment, the above step C2 may be implemented as the following steps C21-C23:

[0164] In step C21, when the actual demand of the wind farm is to increase the grid stability, the weight corresponding to the historical mean time between failures of the unit is increased;

[0165] In step C22, when the actual demand of the wind farm is to increase the grid connection benefit, the weight corresponding to the real-time active power output capability of the unit is increased;

[0166] In step C23, when the actual demand of the wind farm is to increase the life of the generator set, the weight corresponding to the fatigue load condition of the generator set is increased.

[0167] In one embodiment, the above step C3 may be implemented as follows:

[0168] Substitute the historical mean time between failures, the real-time active output capacity of the unit, the fatigue load of the unit, and the adjusted weight into the following third preset formula to determine the score of each wind turbine in the wind farm:

[0169] S i =A1×T i / T total +A2×P i / Ptotal +A3×DEL total / DEL i ;

[0170] Among them, S i is the score of the i-th unit; A1, A2 and A3 are weight coefficients; T iis the historical mean time between failures of the i-th unit, T total is the sum of the historical mean time between failures of all wind turbines in the network; P i is the real-time active power output of unit i, P total The sum of the real-time active power output of all wind turbines in the network; DEL i is the operating fatigue of unit i, DEL total It is the sum of fatigue life of all grid wind turbine units.

[0171] In one embodiment, the method can also be implemented as the following steps D1-D2:

[0172] In step D1, the real-time active power output value and reserve coefficient of the wind turbine groups participating in the network are obtained;

[0173] In step D2, the real-time active power output value and the reserved coefficient are substituted into the following fourth preset formula to determine the reserved active power output value of each wind turbine group participating in the wind farm grid:

[0174] P ref,i =min(L×P 实时,i , P n,i ×K×B i );

[0175] Among them, P ref,i is the reserved active power output value of the i-th wind turbine group; P 实时,i is the real-time active power output value of the i-th wind turbine group; P n,i is the rated active output value of the i-th wind turbine; K is the proportion coefficient; B i is the reservation coefficient of the i-th wind turbine group; L is the maximum reservation ratio.

[0176] In one embodiment, the reservation coefficient in step D1 is determined by the following steps E1-E2:

[0177] In step E1, the maximum power disturbance value of each grid wind turbine group and the rated active power output value of each grid wind turbine group within a first preset time period are obtained;

[0178] In step E2, the maximum power disturbance value of each grid-forming wind turbine group and the rated active output value of each grid-forming wind turbine group are substituted into the following fifth preset formula to determine the reserved coefficient of each wind turbine group participating in the wind farm grid formation:

[0179]

[0180] Among them, B i is the reserved coefficient of the i-th wind turbine group; ΔP max is the maximum power disturbance value, P n,iis the rated active output value of the i-th wind turbine group, D is the load regulation effect coefficient, H is the system equivalent inertia constant, and f0 is the rated frequency.

[0181] Figure 2 FIG. 1 is a structural diagram of a wind farm network construction device in one embodiment of the present application. Figure 2 As shown, the device includes:

[0182] The first determining module 201 is used to determine the active output ratio of new energy sources in the power grid;

[0183] The second determining module 202 is configured to determine the number N of wind turbines required to participate in the grid construction according to the active output ratio when the active output ratio is greater than a preset value;

[0184] The selection module 203 is used to score the wind turbines in the wind farm and select N wind turbines with the highest scores from the wind farm to participate in the grid construction.

[0185] In one embodiment, the first determining module includes:

[0186] The first acquisition submodule is used to obtain the active output value of the new energy in the power grid and the active output value of all power sources in the power grid;

[0187] The first determination submodule is configured to substitute the active output value of the new energy in the power grid and the active output values ​​of all power sources in the power grid into the following first preset formula to determine the active output proportion of the new energy in the power grid:

[0188] z=P re / P total ×100%;

[0189] Among them, z is the active power output ratio of new energy, P re is the active power output value of renewable energy in the power grid, P total It is the active output value of all power sources in the grid.

[0190] In one embodiment, the second determining module includes:

[0191] A second determining submodule is configured to determine a proportion coefficient according to the active power output proportion;

[0192] The third determination submodule is configured to substitute the proportion coefficient into the following second preset formula to determine the number N of wind turbine groups required to participate in the network construction:

[0193] N=Ceil(P / P n ×100%×N total ×K);

[0194] Among them, Ceil function is the upward rounding function; P is the real-time active power output of the wind farm; Pn is the rated active power output of the wind farm; N total is the number of grid-type wind turbines in the wind farm; K is the proportion coefficient.

[0195] In one embodiment, the second determining submodule is further configured to:

[0196] When the active output ratio is less than or equal to the first preset ratio, determining the ratio coefficient to be the first coefficient;

[0197] When the active output ratio is greater than the first preset ratio and less than or equal to the second preset ratio, determining the ratio coefficient to be the second coefficient;

[0198] When the active output ratio is greater than the second preset ratio, determining the ratio coefficient to be the third coefficient;

[0199] Among them, the first preset proportion is smaller than the second preset proportion, the first coefficient is smaller than the second coefficient, and the second coefficient is smaller than the third coefficient.

[0200] In one embodiment, the selection module includes:

[0201] The second acquisition submodule is used to obtain the historical mean time between failures of the unit, the real-time active power output capacity of the unit and the fatigue load of the fan;

[0202] The adjustment submodule is used to adjust the weights corresponding to the historical mean time between failures of the unit, the real-time active power output capacity of the unit, and the fatigue load of the unit according to the actual needs of the wind farm;

[0203] The scoring submodule is used to score the wind turbines in the wind farm according to the historical mean time between failures of the turbines, the real-time active output capacity of the turbines, the fatigue load conditions of the turbines and the adjusted weights.

[0204] In one embodiment, the adjustment submodule is further configured to:

[0205] When the actual demand of the wind farm is to increase the stability of the network, the weight corresponding to the historical mean time between failures of the units is increased;

[0206] When the actual demand of the wind farm is to increase the grid revenue, the weight corresponding to the real-time active power output capacity of the unit is increased;

[0207] When the actual demand of the wind farm is to increase the life of the unit, the weight corresponding to the fatigue load condition of the unit is increased.

[0208] In one embodiment, the scoring submodule is further configured to:

[0209] Substitute the historical mean time between failures, the real-time active output capacity of the unit, the fatigue load of the unit, and the adjusted weight into the following third preset formula to determine the score of each wind turbine in the wind farm:

[0210] S i =A1×T i / T total +A2×P i / Ptotal +A3×DEL total / DEL i ;

[0211] Among them, S i is the score of the i-th unit; A1, A2 and A3 are weight coefficients; T i is the historical mean time between failures of the i-th unit, T total is the sum of the historical mean time between failures of all wind turbines in the network; P i is the real-time active power output of unit i, P total The sum of the real-time active power output of all wind turbines in the network; DEL i is the operating fatigue of unit i, DEL total It is the sum of fatigue life of all grid wind turbine units.

[0212] In one embodiment, the apparatus further comprises:

[0213] The acquisition module is used to obtain the real-time active power output value and reserve coefficient of the wind turbine groups participating in the network construction;

[0214] The third determination module is configured to substitute the real-time active power output value and the reserved coefficient into the following fourth preset formula to determine the reserved active power output value of each wind turbine group participating in the wind farm network:

[0215] P ref,i =min(L×P 实时,i , P n,i ×K×B i );

[0216] Among them, P ref,i is the reserved active power output value of the i-th wind turbine group; P 实时,i is the real-time active power output value of the i-th wind turbine group; P n,i is the rated active output value of the i-th wind turbine; K is the proportion coefficient; B i is the reservation coefficient of the i-th wind turbine group; L is the maximum reservation ratio.

[0217] In one embodiment, the reservation coefficient is determined as follows:

[0218] Obtaining the maximum power disturbance value of each grid wind turbine group and the rated active power output value of each grid wind turbine group within a first preset time period;

[0219] Substitute the maximum power disturbance value of each grid-forming wind turbine group and the rated active output value of each grid-forming wind turbine group into the following fifth preset formula to determine the reserved coefficient of each wind turbine group participating in the wind farm grid formation:

[0220]

[0221] Among them, B i is the reserved coefficient of the i-th wind turbine group; ΔP max is the maximum power disturbance value, P n,i is the rated active output value of the i-th wind turbine group, D is the load regulation effect coefficient, H is the system equivalent inertia constant, and f0 is the rated frequency.

[0222] The present application also provides a wind farm network construction system, comprising:

[0223] at least one processor; and,

[0224] a memory communicatively connected to the at least one processor; wherein,

[0225] The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to implement the wind farm grid construction method recorded in any of the above embodiments.

[0226] Figure 3 FIG. 1 is a schematic diagram of the hardware structure of a wind farm network system in one embodiment of the present application. Figure 3 As shown, the wind farm grid system includes:

[0227] at least one processor 320; and,

[0228] A memory 304 in communication with the at least one processor 320; wherein,

[0229] The memory 304 stores instructions that can be executed by the at least one processor 320 , and the instructions are executed by the at least one processor 320 to implement the wind farm grid construction method described in any of the above embodiments.

[0230] Reference Figure 3 The wind farm grid system 300 may include one or more of the following components: a processing component 302 , a memory 304 , a power supply component 306 , a multimedia component 308 , an audio component 310 , an input / output (I / O) interface 312 , a sensor component 314 , and a communication component 316 .

[0231] Processing component 302 generally controls the overall operation of wind farm grid system 300. Processing component 302 may include one or more processors 320 to execute instructions to perform all or part of the steps of the aforementioned method. Furthermore, processing component 302 may include one or more modules to facilitate interaction between processing component 302 and other components. For example, processing component 302 may include a multimedia module to facilitate interaction between multimedia component 308 and processing component 302.

[0232] The memory 304 is configured to store various types of data to support the operation of the wind farm grid system 300. Examples of such data include instructions for any application or method operating on the wind farm grid system 300, such as text, images, videos, etc. The memory 304 can be implemented by any type of volatile or non-volatile storage device, or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk, or optical disk.

[0233] The power supply component 306 provides power to various components of the wind farm grid system 300. The power supply component 306 may include a power management system, one or more power supplies, and other components associated with generating, managing, and distributing power to the wind farm grid system 300.

[0234] The multimedia component 308 includes a screen that provides an output interface between the wind farm networking system 300 and the user. In some embodiments, the screen may include a liquid crystal display (LCD) and a touch panel (TP). If the screen includes a touch panel, it may be implemented as a touch screen to receive input signals from the user. The touch panel includes one or more touch sensors to sense touches, slides, and gestures on the touch panel. The touch sensors can detect not only the boundaries of a touch or slide action, but also the duration and pressure associated with the touch or slide action. In some embodiments, the multimedia component 308 may also include a front-facing camera and / or a rear-facing camera. When the wind farm networking system 300 is in an operating mode, such as a capture mode or a video mode, the front-facing camera and / or the rear-facing camera can receive external multimedia data. Each front-facing camera and the rear-facing camera may have a fixed optical lens system or have variable focal length and optical zoom capabilities.

[0235] The audio component 310 is configured to output and / or input audio signals. For example, the audio component 310 includes a microphone (MIC) configured to receive external audio signals when the wind farm networking system 300 is in an operating mode, such as alarm mode, recording mode, voice recognition mode, or voice output mode. The received audio signals may be further stored in the memory 304 or transmitted via the communication component 316. In some embodiments, the audio component 310 also includes a speaker for outputting audio signals.

[0236] I / O interface 312 provides an interface between processing component 302 and peripheral interface modules, such as a keyboard, click wheel, buttons, etc. These buttons may include but are not limited to: a home button, volume buttons, a start button, and a lock button.

[0237] The sensor assembly 314 includes one or more sensors for providing status assessments of various aspects of the wind farm grid system 300. For example, the sensor assembly 314 may include an acoustic sensor. In addition, the sensor assembly 314 may detect the on / off status of the wind farm grid system 300, the relative positioning of components, such as the display and keypad of the wind farm grid system 300, and the operating status of the wind farm grid system 300 or a component of the wind farm grid system 300. The sensor assembly 314 may include a proximity sensor configured to detect the presence of nearby objects without any physical contact. The sensor assembly 314 may also include an optical sensor, such as a CMOS or CCD image sensor, for use in imaging applications. In some embodiments, the sensor assembly 314 may also include an accelerometer, a gyroscope, a magnetic sensor, a pressure sensor, or a temperature sensor.

[0238] The communication component 316 is configured to enable the wind farm networking system 300 to provide wired or wireless communication capabilities with other devices and the cloud platform. The wind farm networking system 300 can access a wireless network based on communication standards, such as WiFi, 2G or 3G, or a combination thereof. In an exemplary embodiment, the communication component 316 receives broadcast signals or broadcast-related information from an external broadcast management system via a broadcast channel. In an exemplary embodiment, the communication component 316 also includes a near-field communication (NFC) module to facilitate short-range communication. For example, the NFC module can be implemented based on radio frequency identification (RFID) technology, infrared data association (IrDA) technology, ultra-wideband (UWB) technology, Bluetooth (BT) technology, and other technologies.

[0239] In an exemplary embodiment, the wind farm networking system 300 can be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to execute the wind farm networking method described in any of the above embodiments.

[0240] The present application also provides a computer-readable storage medium. When the instructions in the storage medium are executed by a processor corresponding to the wind farm networking system, the wind farm networking system can implement the wind farm networking method described in any of the above embodiments.

[0241] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware. Furthermore, the present application may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage and optical storage) containing computer-usable program code.

[0242] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0243] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0244] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0245] Obviously, those skilled in the art may make various changes and modifications to this application without departing from the spirit and scope of this application. Thus, if these modifications and variations of this application fall within the scope of the claims of this application and their equivalents, this application is intended to include these modifications and variations.

Claims

1. A wind farm network construction method, characterized in that: include: Determine the proportion of active power output of renewable energy in the power grid; When the active power output ratio is greater than a preset value, the number N of wind turbines required to participate in the network formation is determined according to the active power output ratio; Scoring the wind turbines in the wind farm, and selecting N wind turbines with the highest scores from the wind farm to participate in the grid construction; After selecting N wind turbines with the highest scores from the wind farm to participate in grid construction, the method further includes: Obtain the real-time active power output and reserve coefficient of the wind turbines participating in the network construction; Substitute the real-time active power output value and the reserved coefficient into the following fourth preset formula to determine the reserved active power output value of each wind turbine group participating in the wind farm grid: P ref,i =min(L×P 实时,i ,P n,i ×K×B i ); Among them, P ref,i is the reserved active power output value of the i-th wind turbine group; P 实时,i is the real-time active power output value of the i-th wind turbine group; P n,i is the rated active output value of the i-th wind turbine; K is the proportion coefficient; B i is the reservation coefficient of the i-th wind turbine group; L is the maximum reservation ratio.

2. The method according to claim 1, wherein Determining the active output ratio of renewable energy in the power grid includes: Obtain the active output value of renewable energy in the power grid and the active output value of all power sources in the power grid; Substitute the active output value of the new energy in the power grid and the active output values ​​of all power sources in the power grid into the following first preset formula to determine the active output proportion of the new energy in the power grid: z=P re / P total ×100%; Among them, z is the active power output ratio of new energy, P re is the active power output value of renewable energy in the power grid, P total It is the active output value of all power sources in the grid.

3. The method according to claim 1, wherein The determining the number N of wind turbines required to participate in the grid construction according to the active output ratio includes: Determining a proportion coefficient according to the active output proportion; Substitute the proportion coefficient into the following second preset formula to determine the number N of wind turbine groups required to participate in the network construction: N=Ceil(P / P) n ×100%×N total ×K); Among them, Ceil function is the upward rounding function; P is the real-time active power output of the wind farm; Pn is the rated active power output of the wind farm; N total is the number of grid-type wind turbines in the wind farm; K is the proportion coefficient.

4. The method according to claim 3, wherein Determining the proportion coefficient according to the active output proportion includes: When the active output ratio is less than or equal to the first preset ratio, determining the ratio coefficient to be the first coefficient; When the active output ratio is greater than the first preset ratio and less than or equal to the second preset ratio, determining the ratio coefficient to be the second coefficient; When the active output ratio is greater than the second preset ratio, determining the ratio coefficient to be the third coefficient; Among them, the first preset proportion is smaller than the second preset proportion, the first coefficient is smaller than the second coefficient, and the second coefficient is smaller than the third coefficient.

5. The method according to claim 1, wherein The scoring of wind turbines in a wind farm includes: Obtain the historical mean time between failures, real-time active power output capability of the unit, and fatigue load of the fan; Adjust the weights corresponding to the historical mean time between failures, the real-time active power output capability of the unit, and the fatigue load of the unit according to the actual needs of the wind farm; The wind turbines in the wind farm are scored according to the historical mean time between failures of the turbines, the real-time active power output capability of the turbines, the fatigue load conditions of the turbines and the adjusted weights.

6. The method according to claim 5, wherein The weights corresponding to the historical mean time between failures of the units, the real-time active output capacity of the units, and the fatigue load of the wind turbines are adjusted according to the actual needs of the wind farm, including: When the actual demand of the wind farm is to increase the stability of the network, the weight corresponding to the historical mean time between failures of the units is increased; When the actual demand of the wind farm is to increase the grid revenue, the weight corresponding to the real-time active power output capacity of the unit is increased; When the actual demand of the wind farm is to increase the life of the unit, the weight corresponding to the fatigue load condition of the unit is increased.

7. The method according to claim 5, wherein The wind turbines in the wind farm are scored based on the historical mean time between failures, the real-time active power output capability of the turbines, the fatigue load of the turbines and the adjusted weights, including: Substitute the historical mean time between failures, the real-time active output capacity of the unit, the fatigue load of the unit, and the adjusted weight into the following third preset formula to determine the score of each wind turbine in the wind farm: S i =A1×T i / T total +A2×P i / Ptotal +A3×DEL total / OF THE i ; Among them, S i is the score of the i-th unit; A1, A2 and A3 are weight coefficients; T i is the historical mean time between failures of the i-th unit, T total is the sum of the historical mean time between failures of all wind turbines in the network; P i is the real-time active power output of unit i, P total The sum of the real-time active power output of all wind turbines in the network; DEL i is the operating fatigue of unit i, DEL total It is the sum of fatigue life of all grid wind turbine units.

8. The method according to claim 1, wherein The reservation coefficient is determined as follows: Obtaining the maximum power disturbance value of each grid wind turbine group and the rated active power output value of each grid wind turbine group within a first preset time period; Substitute the maximum power disturbance value of each grid-forming wind turbine group and the rated active output value of each grid-forming wind turbine group into the following fifth preset formula to determine the reserved coefficient of each wind turbine group participating in the wind farm grid formation: Among them, B i is the reserved coefficient of the i-th wind turbine group; ΔP max is the maximum power disturbance value, P n,i is the rated active output value of the i-th wind turbine group, D is the load regulation effect coefficient, H is the system equivalent inertia constant, and f0 is the rated frequency.

9. A wind farm network construction device, used in the wind farm network construction method according to any one of claims 1 to 8, characterized in that: include: The first determination module is used to determine the active output ratio of renewable energy in the power grid; A second determining module is configured to determine the number N of wind turbines required to participate in network construction according to the active output ratio when the active output ratio is greater than a preset value; The selection module is used to score the wind turbines in the wind farm and select N wind turbines with the highest scores from the wind farm to participate in the network construction.

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