CN-122026477-A - Dynamic grouping and sequential optimization-based wake flow regulation and control method and system for offshore wind farm group

CN122026477ACN 122026477 ACN122026477 ACN 122026477ACN-122026477-A

Abstract

The invention relates to the technical field of wind power control, and discloses a wake flow regulation and control method and system for an offshore wind power plant group based on dynamic grouping and sequential optimization, wherein the method is based on a Larsen wake flow model, and a wake flow loss analysis model is constructed; dynamically grouping all wind turbines in a wind farm group based on the speed loss of a target wind turbine output by an analysis wake loss model to obtain a plurality of subgroups, constructing a multi-target optimization function, sequentially optimizing each subgroup, optimizing yaw angles machine by machine according to the windward sequence, solving to obtain an optimal yaw angle combination which minimizes the multi-target optimization function, updating a dynamic grouping result and a yaw command once every preset control period, and issuing the optimal yaw angle combination of each wind turbine corresponding to the next control period obtained by solving as the control command. The real-time wake flow regulation method can ensure the optimization effect, remarkably reduce the calculation complexity and is suitable for large-scale offshore wind power plant groups.

Inventors

YAO ZHONGYUAN
ZHANG TIANCAI
YAN QIHUI
SUN JIE
HONG YIZHOU
XU CHANG
ZHANG MINGXUAN
YAN YAN
XUE FEIFEI
GU YICHENG

Assignees

盛东如东海上风力发电有限责任公司
华能国际电力江苏能源开发有限公司清洁能源分公司
河海大学

Dates

Publication Date: 20260512
Application Date: 20260127

Claims (10)

1. The wake flow regulation and control method for the offshore wind farm group based on dynamic grouping and sequential optimization is characterized by comprising the following steps of: based on the speed loss of the target wind turbine output by the analysis wake loss model, dynamically grouping all wind turbines in the wind farm group to obtain a plurality of subgroups; constructing a multi-objective optimization function considering the power generation of the wind power plant group, the fatigue loss of the yaw system and the fatigue and service life of the wind turbine; sequentially optimizing in each subgroup, optimizing yaw angles machine by machine according to the windward sequence, and solving to obtain an optimal yaw angle combination which minimizes a multi-objective optimization function; Updating a dynamic grouping result and a yaw instruction once every preset control period; And issuing the optimal yaw angle combination of each wind turbine generator set corresponding to the next control period obtained by solving as a control instruction so as to realize the regulation and control of wake flows of the offshore wind farm group.
2. The method for regulating and controlling wake flows of the offshore wind farm group based on dynamic grouping and sequence optimization as claimed in claim 1, wherein the constructing and analyzing wake flow loss model based on Larsen wake flow model comprises the following steps: the speed loss of the downstream target wind turbine is the axial distance And radial distance The expression is as follows: ; ; ; ; ; ; Wherein, the Representing the area of the wind wheel; representing wake radius; represents Prandtl0 dimension blend length; The thrust coefficient of the wind turbine generator is represented; Representing the diameter of the wind wheel; Representing an approximation; Represents the wake radius at a wake length of 9.5 rotor diameters, An empirical formula representing wake radius at a wake length of 9.5 rotor diameters; represents the atmospheric turbulence intensity at hub height; Indicating that the downstream axial distance of the wind turbine generator is The radial distance is Wind speed in wake zone at the location of (2); representing the free incoming wind speed; And the hub height of the wind turbine generator is represented.
3. The method for regulating and controlling wake flows of the offshore wind farm group based on dynamic grouping and sequence optimization as claimed in claim 1, wherein the method for dynamically grouping all wind turbines in the wind farm group to obtain a plurality of subgroups based on the speed loss of the target wind turbine group output by the analysis wake flow loss model comprises the following steps: Calculating upstream wind turbine generator For the target wind turbine generator set positioned at the downstream thereof Loss of speed caused Preset speed loss threshold Will be The target wind turbines of (2) are classified into the same subgroup.
4. The method for regulating and controlling wake flows of the offshore wind farm group based on dynamic grouping and sequence optimization as claimed in claim 3, wherein the specific steps of the dynamic grouping are as follows: Step S11, for each prediction time step of the model prediction control, cycling; Step S12, initializing the grouping result of the current model predictive control predictive time step t As shown in formula (1.1); (1.1); Wherein, the Representing the number of clusters; step S13, circulating each wind turbine generator; Wherein, the A serial number representing the target wind turbine generator set, Representing the number of target wind turbines; Step S14, under the condition of no yaw, calculating all upstream wind turbines To target wind turbine generator system Speed loss caused by the wind turbine generator set at the upstream With the target wind turbine generator system Speed loss of (2) If the speed loss threshold value K of (2) If the value is larger than or equal to kappa, the upstream wind turbine generator is considered With the target wind turbine generator system Has the value of active yaw wake flow regulation and all The set of (2) is j, as in formula (1.2); (1.2); In the formula, Is the ambient wind speed; Representing predicted time steps Upstream wind turbine generator system of (2) Is in the target wind turbine generator system Wind speed at the hub center; representing predicted time steps Is set in the air; Step S15, all upstream wind turbines meeting the formula (1.2) Is included in The group of the compounds is shown as a formula (1.3); (1.3); Wherein, the Representing predicted time steps The middle part comprises an upstream wind turbine generator set Is a group of (3); Represent the first Individual groups at predicted time steps State of (2); An index representing the group; Representing predicted time steps Is numbered in Is a group of (3); Representing a set of group numbers One of them; Step S16, combining Group by group and adding target wind turbine generator set As shown in formula (1.4); (1.4); Wherein, the Representing a set of groups; step S17, returning to the step S13 until the circulation of all the wind turbines is completed; Step S18, returning to the step S11 until the circulation of all the predicted time steps is completed; step S19, dynamic grouping is carried out, and a dynamic grouping result is initialized As shown in formula (1.5); (1.5); In the formula, The number of predicted time steps that are model predictive control; Is that The number of groups in a group; Representation of Middle (f) Each group is, Respectively represent the first A group of predicted time steps; step S110, judging whether any different groups exist, wherein the groups contain the same elements, such as formula (1.6); (1.6); Wherein, the And Representing any two different groups; step S111, if the condition of step S110 is judged to be yes, obtaining a meeting condition A collection of groups, as in formula (1.7); (1.7); Wherein, the A group indicating that the condition is satisfied; Representation number A group of yes; Respectively representing natural number sets; Step S112, will All elements of each group are combined to form 1 new group, and the old group is deleted Group (S) as in equation (1.8), returning to step S110; (1.8); Step S113, if the condition judgment result in step S110 is negative, a final grouping result G is obtained, and the process is ended.
5. The method for regulating and controlling wake flows of a wind farm group on the sea based on dynamic grouping and sequential optimization as claimed in claim 1, wherein the constructing a multi-objective optimization function considering the power generation of the wind farm group, the fatigue loss of a yaw system and the fatigue and life of the wind turbine comprises: The expression of the multi-objective optimization function is as follows: ; ; ; ; In the formula, The method is a multi-objective optimization function for active yaw wake regulation; Is that Taking into account a term of the generated power, namely a generated power boosting term; Is that Taking into account the term of fatigue loss of the yaw system, namely the yaw loss term; Is that Taking the fatigue and service life of the wind turbine into consideration, namely a power fluctuation term; 、、 the weight coefficients of the generated power lifting term, the yaw loss term and the power fluctuation term are respectively; Is the number of predicted time steps of the MPC; is a predicted time step In the matrix of yaw angles of all wind turbines; Is a zero matrix; is a target wind turbine generator system Is set to the rated power of (3); Is the angle And angle of An included angle between the two; representation target wind turbine generator system Wind speed calculation method at the center of the hub, Representation target wind turbine generator system The generated power calculation method of (a); Representing predicted time steps Is the original angle of (a); Representing predicted time steps Target wind turbine generator system Is arranged at the upper end of the frame; Is to predict the time step as Time target wind turbine generator system RMSE (a) is the standard deviation of set a; The method is a matrix of yaw angles of all wind turbines in a subgroup in all prediction time steps; is a predicted time step When in use, the target wind turbine generator system Is provided with a yaw angle of (2), The current state is indicated and the current state is indicated, Representing predicted time steps as All states of (2); Representing the predicted time step as When in use, the target wind turbine generator system Is set to a yaw angle of 0, The current state is indicated and the current state is indicated, Representing predicted time steps as All states of (2); and (5) representing the number of target wind turbines.
6. The method for regulating and controlling wake flows of an offshore wind farm group based on dynamic grouping and sequential optimization according to claim 1, wherein the sequential optimization is performed in each subgroup, yaw angles are optimized machine by machine according to a windward sequence, and an optimal yaw angle combination for minimizing a multi-objective optimization function is obtained by solving the yaw angles, which comprises the following steps: step S31, setting the number of sequentially optimized wheels and the number of yaw angles of each wheel, as shown in a formula (3.1): (3.1); Wherein, the A matrix representing the number of wheels and the number of yaw angles per wheel; A yaw angle number representing a current number of wheels; Representing the current order optimization round number; Step S32, sequentially optimizing and cycling for each round; Step S33, optimizing the optimal solution of the yaw angle in the previous round of sequential optimization and setting the number of yaw angles of the current number of rounds Forming a yaw angle test sequence The median of the yaw angle test sequence is the optimal solution of the yaw angle in the previous round of sequential optimization and is shared as shown in the formula (3.2) The number, the difference between each number is equal, and is the difference between each number of the previous round divided by the number of the round; (3.2); Wherein, the Represent the first A yaw angle; step S34, circulating according to the windward sequence of all the wind turbines in the subgroup; step S35, for each prediction time step of the model prediction framework control, cycling; Step S36, in the control and prediction time step of all model prediction frames, all wind turbine generators in the subgroup are arranged according to the windward sequence, and the wind turbine generators are distributed in the same direction as the wind turbine generators in the subgroup Step forecast time step, the first The wind turbines facing into the wind are shown as As shown in formula (3.3); (3.3); Step S37, calculating the current predicted time step, the number of sequentially optimized rounds, and the first The windward wind turbine generator set is a target wind turbine generator set The power at the time is shown as a formula (3.4), wherein the target wind turbine generator system Taking each value in the yaw angle test sequence respectively; (3.4); Wherein, the Representing current predicted time steps Number of rounds of sequential optimization Target wind turbine generator system Power at that time; representing current predicted time steps Number of rounds of sequential optimization Target wind turbine generator system The yaw angle is taken at the time; representation target wind turbine generator system A wind speed calculation method at the center of the hub; representing current predicted time steps When in use, the target wind turbine generator system Is arranged at the upper end of the frame; indicating the current order optimizing round number as A yaw angle sequence at time; representing current predicted time steps At the time of the first Yaw angles of the wind turbine generators; Representing a generated power calculation method; representing the number of target wind turbines; Step S38, based on a simplifying assumption 1, wherein the simplifying assumption 1 is that the yaw angle of the downstream wind turbine generator is irrelevant to the optimal solution of the yaw angle of the upstream wind turbine generator; step S39, returning to the step S35 until the circulation of all the predicted time steps is completed; Step S310, for each moment, only yaw angles of 1 wind turbine generator are adjusted, the yaw angles of the wind turbine generator adjusted at different moments are combined in a crossing mode, and an objective function is calculated, wherein the objective function is shown as a formula (3.5); (3.5); Wherein, the Representation of Numbering of the predicted time steps; Representing a loss value at the current yaw angle combination; Represent the first The first wind turbine generator set Predicting the power of the generated power of the time step; Representing a matrix representing yaw angle and power; representing the objective function value under the yaw angle sequence; Step S311, returning to step S34 until the circulation of all the wind turbines is completed; Step S312, finding a yaw angle combination which minimizes the multi-objective optimization function value, namely a yaw angle optimal solution under the current sequential optimization wheel number, as shown in a formula (3.6); (3.6); Wherein, the Representing an optimal solution yaw angle combination under the current sequence optimization wheel number; Step S313, if the current order optimization wheel is not the final order optimization wheel, returning to step S32 until the cycle of all order optimization wheels is completed; Step S314, if the current sequence optimizing wheel is the final sequence optimizing wheel, outputting the yaw angle optimal solution, and ending.
7. An offshore wind farm group wake regulation system based on dynamic grouping and sequential optimization, which is characterized by being used for realizing the offshore wind farm group wake regulation method based on dynamic grouping and sequential optimization as claimed in any one of claims 1-6, and comprising: the dynamic grouping module is configured to construct an analysis wake loss model based on the Larsen wake model, dynamically grouping all the wind turbines in the wind farm group based on the speed loss of the target wind turbine output by the analysis wake loss model, and obtaining a plurality of subgroups; The multi-objective optimization function construction module is configured to construct a multi-objective optimization function considering the power generation of the wind power plant group, the fatigue loss of the yaw system and the fatigue and service life of the wind turbine generator; The sequence optimizing module is configured for sequentially optimizing in each subgroup, optimizing yaw angles machine by machine according to the windward sequence, and solving to obtain an optimal yaw angle combination which minimizes a multi-objective optimizing function; the rolling execution module is configured to update the dynamic grouping result and the yaw instruction once every preset control period; the command issuing module is configured to issue the solved optimal yaw angle combination of each wind turbine generator set corresponding to the next control period as a control command so as to realize regulation and control of wake flows of the offshore wind farm group.
8. A computer readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the steps of the method for regulating wake flow of a group of offshore wind farms based on dynamic grouping and sequence optimization according to any one of claims 1-6.
9. A computer device, comprising: A memory for storing a computer program; a processor for executing the computer program to implement the steps of the method for regulating wake flow of an offshore wind farm group based on dynamic grouping and sequence optimization according to any one of claims 1-6.
10. A computer program product comprises a computer program, and is characterized in that the computer program is executed by a processor to realize the steps of the wake flow regulation method of the offshore wind farm group based on dynamic grouping and sequence optimization according to any one of claims 1-6.

Description

Dynamic grouping and sequential optimization-based wake flow regulation and control method and system for offshore wind farm group Technical Field The invention belongs to the technical field of wind power control, and relates to a wake flow regulation and control method and system for an offshore wind farm group based on dynamic grouping and sequential optimization. Background With the development of offshore wind power towards clustering and large-scale, wake interference among units in a wind power plant group is increasingly prominent. The wake flow of the upstream unit can lead to the decrease of the inflow wind speed of the downstream unit, thereby significantly reducing the power generation efficiency of the whole wind farm group. Research shows that the wake flow of the upstream unit is deflected to avoid the downstream unit through active yaw control, so that the total power generation amount of the wind power plant group can be effectively improved. The application of active yaw wake regulation to large-scale offshore wind farm groups faces three major challenges: (1) And the influence of active yaw wake flow regulation on the safety, fatigue and service life of the wind turbine generator. The active yaw wake flow regulation and control enables the wind turbine to deviate from the incoming wind direction, the safety of the wind turbine can be threatened, the fatigue of the wind turbine can be increased, and the service life of the wind turbine is reduced. (2) The wake flow calculation efficiency of active yaw wake flow regulation of the large-scale wind power plant group is low, and the dimension of the optimized object is large. In existing active yaw wake regulation, the objects of wake regulation are typically regularly arranged wind farms. For a wind farm group, its wake computation speed is slower because of its large scale compared to the wind farm. In addition, because the number of wind turbines in the wind power plant group is large, the number of objects (yaw angles of all wind turbines) to be optimized is large, namely the dimension of the optimized objects is large. (3) High-dimensional, nonlinear and strong coupling characteristics of the active yaw wake flow regulation and optimization problem. In the optimization problem of active yaw wake regulation, a common optimization objective is that the power generated by a wind farm or a wind farm group is maximum. The power generation calculation of the wind power plant or the wind power plant group has the characteristics of high dimension, nonlinearity and strong coupling, so that the optimizing difficulty is high, and the traditional optimizing algorithm is difficult to find global optimum in the priority time. The existing wind farm-level wake flow regulation method, such as global optimization based on FLORIS model, is difficult to be practically used due to calculation complexity and time limitation when being expanded to the scale of a wind farm group. Disclosure of Invention The invention aims to provide a wake flow regulation and control method and system for an offshore wind farm group based on dynamic grouping and sequential optimization, which are suitable for a real-time wake flow regulation and control method for a large-scale offshore wind farm group. In order to solve the technical problems, the invention is realized by adopting the following technical scheme. In a first aspect, the invention provides a wake flow regulation and control method for an offshore wind farm group based on dynamic grouping and sequential optimization, which comprises the following steps: based on the speed loss of the target wind turbine output by the analysis wake loss model, dynamically grouping all wind turbines in the wind farm group to obtain a plurality of subgroups; constructing a multi-objective optimization function considering the power generation of the wind power plant group, the fatigue loss of the yaw system and the fatigue and service life of the wind turbine; sequentially optimizing in each subgroup, optimizing yaw angles machine by machine according to the windward sequence, and solving to obtain an optimal yaw angle combination which minimizes a multi-objective optimization function; Updating a dynamic grouping result and a yaw instruction once every preset control period; And issuing the optimal yaw angle combination of each wind turbine generator set corresponding to the next control period obtained by solving as a control instruction so as to realize the regulation and control of wake flows of the offshore wind farm group. With reference to the first aspect, further, the constructing an analysis wake loss model based on the Larsen wake model includes: the speed loss of the downstream target wind turbine is the axial distance And radial distanceThe expression is as follows: ; ; ; ; ; ; Wherein, the Representing the area of the wind wheel; representing wake radius; represents Prandtl0 dimension blend length; The thrust coeffi