CN119070374A - A virtual-real combined wind farm maximum power output control method and system - Google Patents
A virtual-real combined wind farm maximum power output control method and system Download PDFInfo
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Abstract
本发明公开了一种虚实结合的风电场最大功率输出控制方法及系统，本发明虚实结合的风电场最大功率输出控制方法包括采集并存储真实风电场各真实风电机组的风速测量值和推力系数，以及真实风电场对应的虚拟风电场中各虚拟风电机组的状态信息；根据各真实风电机组的风速测量值修正工程尾流模型中的参数；求解风电场动态最大发电量控制问题并根据求解结果向真实风电场下发控制指令使其按照最大发电量发电，根据修正参数后的工程尾流模型计算风速预测值并生成控制指令下发给虚拟风电场。本发明旨在通过基于虚拟风电场和真实风电场信息交互，动态提升风电场整体发电量。
The present invention discloses a virtual-real wind farm maximum power output control method and system. The virtual-real wind farm maximum power output control method of the present invention includes collecting and storing wind speed measurement values and thrust coefficients of each real wind turbine in a real wind farm, and state information of each virtual wind turbine in a virtual wind farm corresponding to the real wind farm; correcting parameters in an engineering wake model according to wind speed measurement values of each real wind turbine; solving the wind farm dynamic maximum power generation control problem and issuing control instructions to the real wind farm according to the solution results so that it generates power according to the maximum power generation, and calculating the wind speed prediction value according to the engineering wake model after the correction parameters and generating control instructions to send to the virtual wind farm. The present invention aims to dynamically improve the overall power generation of the wind farm by interacting information between the virtual wind farm and the real wind farm.
Description
Technical Field
The invention belongs to the technical field of wind power generation, and particularly relates to a virtual-actual combined wind farm maximum power output control method and system.
Background
The rapid development of wind power generation technology has been attracting attention worldwide. As the scale of wind farms continues to increase, industry is faced with numerous challenges in both scientific research and industrial applications. One of the most serious problems is wake effects, which lead to loss of power production and load imbalance in wind farms. The existence of wake flow effect enables complex pneumatic coupling influence to exist between the upstream wind turbine and the downstream wind turbine, and the mechanical state change of the upstream wind turbine can directly influence the inflow wind speed of the downstream wind turbine. In order to improve the overall power generation capacity of the wind power plant, a plurality of wind turbines need to be cooperatively controlled.
At present, a great deal of researches treat wind speed as a constant value, and the power output of a wind turbine generator is expressed by a mathematical formula, so that the maximum power generation amount of a wind power plant is controlled and converted into a high-dimensional nonlinear non-convex problem, and a heuristic algorithm or a distributed iterative algorithm is adopted to solve the problem. The processing mode not only ignores the uncertainty and the fluctuation of the wind speed, but also can not always realize the optimal under the condition of the change of the wind speed, and ignores the dynamic characteristics of the wind turbine, so that the fatigue load of the wind turbine can be suddenly increased. In addition, few researches consider the time delay characteristic of wake effects, which causes that the optimal control quantity of the downstream wind turbine generator is not matched with the current inflow wind speed, and the effect of improving the overall power generation capacity cannot be achieved. The characteristic is that wake flow influence caused by mechanical state change of an upstream wind turbine cannot influence a downstream wind turbine immediately, and downstream unit state information required by optimal control cannot be measured in real time. In summary, in the field of operation optimization control of the current wind farm, a method for controlling maximum power output of the wind farm, which is suitable for dynamically inflow wind speed and considers dynamic characteristics of wind turbines, is needed to solve the problem that downstream turbine state information required by optimization control cannot be measured in real time due to wake delay characteristics, so that a more suitable method is provided for dynamically improving overall power generation of the wind farm.
Disclosure of Invention
Aiming at the problems in the prior art, the invention provides a virtual-real combined wind power plant maximum power output control method and a system, and aims to dynamically improve the overall power generation capacity of a wind power plant based on information interaction between a virtual wind power plant and a real wind power plant.
In order to solve the technical problems, the invention adopts the following technical scheme:
a virtual-real combined wind farm maximum power output control method comprises the following steps:
S1, acquiring and storing wind speed measured values and thrust coefficients of all real wind turbines of a real wind power plant and state information of all virtual wind turbines in a virtual wind power plant corresponding to the real wind power plant;
s2, correcting parameters in the engineering wake flow model according to wind speed measured values of all real wind turbines;
And S3, solving the control problem of the dynamic maximum power generation of the wind power plant, issuing a control instruction to the real wind power plant according to the solving result to generate power according to the maximum power generation, calculating a wind speed predicted value according to the engineering wake model after the parameters are corrected, and issuing the control instruction to the virtual wind power plant.
Optionally, the virtual wind farm in the step S1 comprises virtual wind turbines formed by a digital model corresponding to the real wind turbines, the number and the spatial arrangement of the virtual wind turbines are consistent with those of the real wind turbines, a simulated virtual wake effect exists in the virtual wind farm, the virtual wake effect is simulated to be a wind speed predicted value calculated according to the engineering wake model after the parameters are corrected and is used as a virtual wind speed without considering wake delay time, and the state information of the downstream virtual wind turbines at the current moment can reflect the future state information of the real wind turbines corresponding to the virtual wind turbines based on the following formula:
,
Wherein, In the virtual wind power plant, the ith virtual wind power unit is arranged in the virtual wind power plantStatus information of the time of day,For the ith real wind turbine generator in the real wind power plantStatus information of the time of day,Is wake delay time, and has:
,
Wherein, For the geographic distance between the 1 st real wind turbine generator and the i th real wind turbine generator along the wind direction in the real wind power plant,Is the average inflow wind speed.
Optionally, when the parameters in the engineering wake model are corrected in step S2, the functional expression of the wake model correction optimization problem is:
,
In the above-mentioned method, the step of, For the number of real wind turbines in a real wind farm,For the ith real wind turbine generator calculated according to the thrust coefficient of each real wind turbine generator and the Jensen wake modelA predicted value of the wind speed at the moment in time,In order to acquire the ith real wind turbine generator systemA measurement of the wind speed at a moment in time, whereinAnd correcting parameters in the engineering wake model according to wind speed measured values of all the real wind turbines refers to correcting and optimizing problems aiming at the wake model, and correcting the optimal wake expansion coefficient of a Jensen wake model in the engineering wake model.
Optionally, a function expression for performing postponement processing on the wind speed measured value of each real wind turbine generator is as follows:
,
,
Wherein, For the ith real wind turbine generator in the real wind power plantThe thrust coefficient of the moment of time,In order to correct parameters in an engineering wake model, an ith real wind turbine generator set in a real wind power plantThe thrust coefficient of the moment of time,Obtaining the ith real wind turbine generator in the real wind power plant after delay treatmentThe wind speed measurement at the moment in time,In order to correct parameters in an engineering wake model, an ith real wind turbine generator set in a real wind power plantThe wind speed measurement at the moment in time,For wake delay time, the superscript R represents measurement data of a real wind farm, and the superscript C represents data used when modifying parameters in an engineering wake model.
Optionally, solving the control problem of the wind farm dynamic maximum power generation amount in step S3 and issuing a control command to the real wind farm according to the solving result to generate power according to the maximum power generation amount includes:
S3.1, establishing a state space model of the virtual wind turbine shown in the following formula:
,
Wherein, For the state matrix of the ith virtual wind turbineThe derivative of (i) th virtual wind turbine generator system state matrixIs thatControl matrix of ith virtual wind turbine generator system,The generator rotating speed variation quantity of the ith virtual wind turbine generator system,The electromagnetic torque variation quantity of the ith virtual wind turbine generator system,For the pitch angle variation of the ith virtual wind turbine,In order to issue a control command of the electromagnetic torque variation quantity of the ith virtual wind turbine generator,In order to issue a control instruction of the pitch angle variation of the ith virtual wind turbine generator,The inflow wind speed variation of the ith virtual wind turbine generator system,、、AndThe coefficient matrix of the ith virtual wind turbine generator system is:
,,
,,
Wherein, In order to achieve the gear box ratio change,Is the equivalent mass of the wind wheel and the generator,The mechanical torque of the impeller of the ith virtual wind turbine generator set,For the generator rotating speed of the ith virtual wind turbine,Is the time constant of the generator and,For the time constant of the pitch mechanism,For the pitch angle of the ith virtual wind turbine,The mechanical torque of the ith virtual wind turbine generator system,For the ith virtual wind power electromagnetic torque of the unit;
,
Wherein, State matrix for virtual wind farmIs the derivative of the virtual wind farm state matrixControl matrix of virtual wind power plant for matrix formed by state matrix of each virtual wind motorFor the matrix formed by the control matrix of each virtual wind motor, the wind speed variation of the virtual wind power plantA matrix formed by the inflow wind speed variation of each virtual wind turbine,、、AndThe matrix is formed by coefficient matrixes of the virtual wind motors respectively;
s3.3, using thrust coefficient of the ith virtual wind turbine generator set And inflow wind speedThe delta form of constructing the Jensen wake model for the variables is shown as follows:
,
Wherein, The variation of wake wind speed caused by the ith virtual wind generator set to the nth virtual wind generator set,The wake wind speed caused by the ith virtual wind generator set to the nth virtual wind generator set,The thrust coefficient variation of the ith virtual wind turbine generator system,Inflow wind speed for ith virtual wind turbineAnd has:
,
,
Wherein, To correct the optimal wake expansion coefficient of the Jensen wake model in the engineering wake model,For the distance between the ith virtual wind turbine generator and the nth virtual wind turbine generator along the wind direction,For the radius of the impeller wheel,In order to cover the area for the wake,The swept area of the impeller;
s3.4, using the inflow wind speed of the ith virtual wind turbine generator set Wake wind speed caused by the ith virtual wind turbine generator to the nth virtual wind turbine generatorWriting an increment form of an energy conservation wake superposition model for the variable, and erecting the increment form of the Jensen wake model in parallel to obtain an inflow wind speed predicted value of the nth virtual wind turbine generator setIn increments of:
,
Wherein, The virtual wind turbine generator set is a virtual wind turbine generator set which causes wake flow influence on the virtual wind turbine generator set n;
S3.5, according to the inflow wind speed predicted value of the nth virtual wind turbine generator set Determining the incremental form of the inflow wind speed predicted value of the ith virtual wind turbine unit to the nth virtual wind turbine unit:
,
Wherein, AndIs a coefficient matrix, and has:
,,
Wherein the coefficient matrix AndThe expression of any element in (a) is:
,;
Based on coefficient matrix For the lower triangular matrix, and all diagonal line elements are 0, simplifying the increment form of the inflow wind speed predicted value from the ith virtual wind turbine generator to the nth virtual wind turbine generator into:
;
S3.6, substituting the increment form of the inflow wind speed predicted value of the ith virtual wind turbine generator set to the nth virtual wind turbine generator set into the state space model of the virtual wind power plant to obtain a control equation of the virtual wind power plant:
;
s3.7, constructing a maximum power generation amount control problem based on a control equation of the virtual wind power plant:
,
Wherein, For the rated power of the wind farm,For the number of real wind turbines in a real wind farm,Is the electromagnetic power initial value of the virtual wind turbine generator set i,Is the initial value change rate of the electromagnetic power of the virtual wind turbine generator set i,AndDetermining constraints of a maximum power generation amount control problem, wherein the constraints comprise constraints for preventing the overaction of the wind turbine and constraints for avoiding overload or shutdown risks, and the functional expressions of the constraints for preventing the overaction of the wind turbine are as follows:
,
Wherein, AndRespectively the electromagnetic torque increment and the pitch angle increment of the ith real wind turbine generator,AndRespectively the initial values of the electromagnetic torque and the pitch angle of the ith real wind turbine generator,AndThe upper limit of each control change of the electromagnetic torque and the pitch angle is respectively,AndThe function expression of the constraint for avoiding overload or shutdown risk is as follows:
Wherein, In the form of a matrix of lower power limits,For the power matrix to be rated for,,WhereinAs a lower power limit of the power supply,For the rated power of the electric motor,A matrix of all 1 elements in dimension n x 1; In the form of a power sensitivity matrix, ,;
S3.8, converting the increment form and the maximum power generation control problem of the virtual wind power plant into a standard quadratic programming problem for quick solving, and solving the obtained optimal control quantityAndDirectly distributed to the virtual wind power plant, and distributed to the real wind power plant after being processed by combining wake delay time.
Optionally, when the wake delay time delay combined processing in step S3.7 is allocated to the real wind farm, the function expression of the wake delay time delay combined processing is as follows:
,
,
Wherein, For the ith virtual wind turbine generator systemThe amount of change in the electromagnetic torque at the moment,For the ith virtual wind turbine generator systemThe amount of change in the electromagnetic torque at the moment,For the ith virtual wind turbine generator systemThe amount of change in pitch angle at the moment in time,For the ith virtual wind turbine generator systemThe amount of change in pitch angle at time.
Optionally, in step S3, calculating a wind speed predicted value according to the engineering wake model after the parameters are corrected and issuing the wind speed predicted value to the virtual wind farm, which means that the ith real wind turbine calculated according to the thrust coefficient of each real wind turbine and the Jensen wake model is located in the virtual wind farmPredicted value of wind speed at timeAnd issuing the virtual wind power plant.
In addition, the invention also provides a virtual-real combined wind farm maximum power output control system, which comprises a microprocessor and a memory which are connected with each other, wherein the microprocessor is programmed or configured to execute the virtual-real combined wind farm maximum power output control method.
Furthermore, the invention also provides a computer readable storage medium, wherein a computer program or instructions is stored in the computer readable storage medium, and the computer program or instructions are programmed or configured to execute the virtual-real combined wind farm maximum power output control method through a processor.
Furthermore, the invention provides a computer program product comprising a computer program or instructions programmed or configured to execute the virtual-real combined wind farm maximum power output control method by a processor.
Compared with the prior art, the method has the advantages that the method comprises the steps of collecting and storing wind speed measured values and thrust coefficients of all real wind turbines of a real wind power plant and state information of all virtual wind turbines in a virtual wind power plant corresponding to the real wind power plant, correcting parameters in an engineering wake model according to the wind speed measured values of all the real wind turbines, solving a wind power plant dynamic maximum generating capacity control problem, sending a control command to the real wind power plant according to a solving result to enable the real wind power plant to generate power according to the maximum generating capacity, calculating a wind speed predicted value according to the engineering wake model after correcting the parameters, and generating the control command to send the wind speed predicted value to the virtual wind power plant.
Drawings
FIG. 1 is a schematic diagram of a basic flow of a method according to an embodiment of the present invention.
Fig. 2 is a diagram of a simulation system for a simulation case in an embodiment of the present invention.
Fig. 3 is a schematic diagram of a simulation system for simulating a case in an embodiment of the present invention.
Fig. 4 is a simulation verification diagram of the overall power generation capacity improvement of the wind farm.
FIG. 5 is a graph showing the comparison of the power output of the real wind turbines 1-6 under different control strategies.
Fig. 6 is a comparison diagram of fatigue loads of the real wind turbine 1 under different control strategies of the invention.
Fig. 7 is a comparison diagram of fatigue loads of the real wind turbine 6 under different control strategies according to the present invention.
FIG. 8 is a comparison of mechanical states of the same wind turbines between a virtual wind farm and a real wind farm according to the present invention.
Detailed Description
As shown in fig. 1, the method for controlling the maximum power output of the wind farm by combining virtual and real power in the embodiment comprises the following steps:
S1, acquiring and storing wind speed measured values and thrust coefficients of all real wind turbines of a real wind power plant and state information of all virtual wind turbines in a virtual wind power plant corresponding to the real wind power plant;
s2, correcting parameters in the engineering wake flow model according to wind speed measured values of all real wind turbines;
And S3, solving the control problem of the dynamic maximum power generation of the wind power plant, issuing a control instruction to the real wind power plant according to the solving result to generate power according to the maximum power generation, calculating a wind speed predicted value according to the engineering wake model after the parameters are corrected, and issuing the control instruction to the virtual wind power plant.
As shown in fig. 2 and 3, the real wind power plant comprises real wind power units, a state information measuring system and a state information measuring system, wherein the real wind power plant is used for providing wind speed measured values and thrust coefficients of all the real wind power units for a data acquisition and storage module and receiving control instructions of an active wake flow control module, and has the characteristics that the real wind power plant comprises the real wind power units, a collecting circuit, a medium-voltage transformer, a high-voltage transformer and an external power grid, a real wake flow effect exists in the real wind power plant, a certain time is required for the real wake flow effect to propagate from an upstream real wind power unit to a downstream real wind power unit, namely, the real wake flow effect has a time delay characteristic, the real wind power plant also comprises the state information measuring system and is used for measuring inflow wind speed and thrust coefficients of all the real wind power units in real time, and the real wind power plant is controlled by receiving the control instructions issued by the active wake flow control module.
As shown in fig. 2 and 3, the virtual wind farm includes virtual wind turbines, and is configured to provide status information of each virtual wind turbine for the data acquisition and storage module, where the status information includes, but is not limited to, generator rotation speed, electromagnetic torque, pitch angle, electromagnetic power, and the like, and receive a control command of the active wake control module. The virtual wind power plant comprises virtual wind power units, wherein the virtual wind power units are high-precision digital models of real wind power units, the number and the spatial arrangement of the virtual wind power units are consistent with those of the real wind power units, virtual wake effects exist in the virtual wind power plant and are simulated into virtual wind speeds provided by an active wake control module, the virtual wind speeds are calculated through a corrected engineering wake model without considering wake delay time, namely, the virtual wake effects have no time delay characteristic, and the state information of the downstream virtual wind power units at the current moment can reflect the future state information of the real wind power units corresponding to the virtual wind power units due to the fact that the virtual wake effects have no time delay characteristic, namely, the virtual wind power units have the following states:,
Wherein, In the virtual wind power plant, the ith virtual wind power unit is arranged in the virtual wind power plantStatus information of the time of day,For the ith real wind turbine generator in the real wind power plantStatus information of the time of day,Is wake delay time, and has:
,
Wherein, For the geographic distance between the 1 st real wind turbine generator and the i th real wind turbine generator along the wind direction in the real wind power plant,Is the average inflow wind speed. The virtual wind power plant in the step S1 comprises virtual wind power units formed by a digital model corresponding to real wind power units, the number and the spatial arrangement of the virtual wind power units are consistent with those of the real wind power units, a simulated virtual wake effect exists in the virtual wind power plant, the virtual wake effect is simulated to be a wind speed predicted value calculated according to an engineering wake model after correction parameters and is used as a virtual wind speed without considering wake delay time, and based on the above formula, the state information of the downstream virtual wind power units at the current moment can be determined to reflect the future state information of the real wind power units corresponding to the virtual wind power units.
Fig. 2 is a schematic diagram of a simulation system of a simulation case in this embodiment, where the simulation system further includes a wake model correction module configured to correct parameters in an engineering wake model and provide the optimized parameters to an active wake control module to improve wind speed prediction accuracy of the engineering wake model, the active wake control module is configured to solve a wind farm dynamic maximum power generation control problem, calculate a wind speed predicted value according to the corrected engineering wake model, issue control instructions for a virtual wind farm and a real wind farm, and the data acquisition and storage module is configured to acquire and store data of the real wind farm and the virtual wind farm, provide wind speed measurement values and thrust coefficients of each real wind turbine to the wake model correction module, and provide state information of each virtual wind turbine to the active wake control module.
Calculating a wind speed predicted value V pre according to the thrust coefficient of each real wind turbine provided by the data acquisition and storage module and the Jensen wake model, and then constructing an optimization model according to a wind speed measured value V meas of each real wind turbine provided by the data acquisition and storage module, wherein in the step S2 of the embodiment, when parameters in the engineering wake model are corrected, a function expression of the adopted wake model for correcting and optimizing problems is as follows:
,
In the above-mentioned method, the step of, For the number of real wind turbines in a real wind farm,For the ith real wind turbine generator calculated according to the thrust coefficient of each real wind turbine generator and the Jensen wake modelA predicted value of the wind speed at the moment in time,In order to acquire the ith real wind turbine generator systemA measurement of the wind speed at a moment in time, whereinAnd correcting parameters in the engineering wake model according to wind speed measured values of all the real wind turbines refers to correcting and optimizing problems aiming at the wake model, and correcting the optimal wake expansion coefficient of a Jensen wake model in the engineering wake model. And solving an upper wake model correction optimization problem interface to obtain an optimal wake expansion coefficient k of the Jensen wake model, and providing the optimal wake expansion coefficient k to the active wake control module.
In this embodiment, a function expression for performing delay processing on wind speed measurement values of each real wind turbine is:
,
,
Wherein, For the ith real wind turbine generator in the real wind power plantThe thrust coefficient of the moment of time,In order to correct parameters in an engineering wake model, an ith real wind turbine generator set in a real wind power plantThe thrust coefficient of the moment of time,Obtaining the ith real wind turbine generator in the real wind power plant after delay treatmentThe wind speed measurement at the moment in time,In order to correct parameters in an engineering wake model, an ith real wind turbine generator set in a real wind power plantThe wind speed measurement at the moment in time,For wake delay time, the superscript R represents measurement data of a real wind farm, and the superscript C represents data used when modifying parameters in an engineering wake model.
In step S3 of this embodiment, solving a control problem of dynamic maximum power generation of a wind farm and issuing a control command to a real wind farm according to a solving result, so as to generate power according to the maximum power generation includes:
S3.1, establishing a state space model of the virtual wind turbine shown in the following formula:
,
Wherein, For the state matrix of the ith virtual wind turbineThe derivative of (i) th virtual wind turbine generator system state matrixIs thatControl matrix of ith virtual wind turbine generator system,The generator rotating speed variation quantity of the ith virtual wind turbine generator system,The electromagnetic torque variation quantity of the ith virtual wind turbine generator system,For the pitch angle variation of the ith virtual wind turbine,In order to issue a control command of the electromagnetic torque variation quantity of the ith virtual wind turbine generator,In order to issue a control instruction of the pitch angle variation of the ith virtual wind turbine generator,The inflow wind speed variation of the ith virtual wind turbine generator system,、、AndThe coefficient matrix of the ith virtual wind turbine generator system is:
,,
,,
Wherein, In order to achieve the gear box ratio change,Is the equivalent mass of the wind wheel and the generator,The mechanical torque of the impeller of the ith virtual wind turbine generator set,For the generator rotating speed of the ith virtual wind turbine,Is the time constant of the generator and,For the time constant of the pitch mechanism,For the pitch angle of the ith virtual wind turbine,The mechanical torque of the ith virtual wind turbine generator system,For the ith virtual wind power electromagnetic torque of the unit;
,
Wherein, State matrix for virtual wind farmIs the derivative of the virtual wind farm state matrixControl matrix of virtual wind power plant for matrix formed by state matrix of each virtual wind motorFor the matrix formed by the control matrix of each virtual wind motor, the wind speed variation of the virtual wind power plantA matrix formed by the inflow wind speed variation of each virtual wind turbine,、、AndThe matrix is formed by coefficient matrixes of the virtual wind motors respectively;
,
,
,
,
,
,
,
Wherein, ~For the state matrix of each virtual wind motor,~For the control matrix of each virtual wind motor,~For the inflow wind speed variation of each virtual wind motor,~For the state coefficient matrix of each virtual wind motor,~For the control coefficient matrix of each virtual wind motor,~For the coefficient matrix of each virtual wind motor,~Wind speed coefficient matrix of each virtual wind motor;
s3.3, using thrust coefficient of the ith virtual wind turbine generator set And inflow wind speedThe delta form of constructing the Jensen wake model for the variables is shown as follows:
,
Wherein, The variation of wake wind speed caused by the ith virtual wind generator set to the nth virtual wind generator set,The wake wind speed caused by the ith virtual wind generator set to the nth virtual wind generator set,The thrust coefficient variation of the ith virtual wind turbine generator system,Inflow wind speed for ith virtual wind turbineAnd has:
,
,
Wherein, To correct the optimal wake expansion coefficient of the Jensen wake model in the engineering wake model,For the distance between the ith virtual wind turbine generator and the nth virtual wind turbine generator along the wind direction,For the radius of the impeller wheel,In order to cover the area for the wake,The swept area of the impeller;
s3.4, using the inflow wind speed of the ith virtual wind turbine generator set Wake wind speed caused by the ith virtual wind turbine generator to the nth virtual wind turbine generatorWriting an increment form of an energy conservation wake superposition model for the variable, and erecting the increment form of the Jensen wake model in parallel to obtain an inflow wind speed predicted value of the nth virtual wind turbine generator setIn increments of:
,
Wherein, The virtual wind turbine generator set is a virtual wind turbine generator set which causes wake flow influence on the virtual wind turbine generator set n;
S3.5, according to the inflow wind speed predicted value of the nth virtual wind turbine generator set Determining the incremental form of the inflow wind speed predicted value of the ith virtual wind turbine unit to the nth virtual wind turbine unit:
,
Wherein, AndIs a coefficient matrix, and has:
,
,
Wherein the coefficient matrix AndThe expression of any element in (a) is:
,
;
Based on coefficient matrix For the lower triangular matrix, and all diagonal line elements are 0, simplifying the increment form of the inflow wind speed predicted value from the ith virtual wind turbine generator to the nth virtual wind turbine generator into:
;
S3.6, substituting the increment form of the inflow wind speed predicted value of the ith virtual wind turbine generator set to the nth virtual wind turbine generator set into the state space model of the virtual wind power plant to obtain a control equation of the virtual wind power plant:
;
S3.7, controlling the maximum power generation amount of the wind power plant according to the following mode:
,
Wherein N is the total number of virtual wind turbines in the wind farm, P e,i is the electromagnetic power emitted by wind turbine i, and has:
,
Wherein, In order for the power generation efficiency to be high,For the rotational speed of the generator,The increment of the electromagnetic power emitted by the wind turbine generator i can be obtained as the electromagnetic torqueThe method comprises the following steps:
,
Wherein, w e and T e are initial values of the rotating speed and the electromagnetic torque of the generator, AndBased on the increment of the rotating speed and the electromagnetic torque of the generator, the maximum generating capacity control problem can be built based on a control equation of the virtual wind power plant:
,
Wherein, For the rated power of the wind farm,For the number of real wind turbines in a real wind farm,Is the electromagnetic power initial value of the virtual wind turbine generator set i,Is the initial value change rate of the electromagnetic power of the virtual wind turbine generator set i,AndDetermining constraints of a maximum power generation amount control problem, wherein the constraints comprise constraints for preventing the overaction of the wind turbine and constraints for avoiding overload or shutdown risks, and the functional expressions of the constraints for preventing the overaction of the wind turbine are as follows:
,
Wherein, AndRespectively the electromagnetic torque increment and the pitch angle increment of the ith real wind turbine generator,AndRespectively the initial values of the electromagnetic torque and the pitch angle of the ith real wind turbine generator,AndThe upper limit of each control change of the electromagnetic torque and the pitch angle is respectively,AndThe function expression of the constraint for avoiding overload or shutdown risk is as follows:
Wherein, In the form of a matrix of lower power limits,For the power matrix to be rated for,,WhereinAs a lower power limit of the power supply,For the rated power of the electric motor,A matrix of all 1 elements in dimension n x 1; In the form of a power sensitivity matrix, ,;
S3.8, converting the increment form and the maximum power generation control problem of the virtual wind power plant into a standard quadratic programming problem for quick solving, and solving the obtained optimal control quantityAndDirectly distributed to the virtual wind power plant, and distributed to the real wind power plant after being processed by combining wake delay time.
In the embodiment, when the wake delay time delay processing is combined and then distributed to the real wind farm in step S3.8, the function expression of the wake delay time delay processing is as follows:
,
,
Wherein, For the ith virtual wind turbine generator systemThe amount of change in the electromagnetic torque at the moment,For the ith virtual wind turbine generator systemThe amount of change in the electromagnetic torque at the moment,For the ith virtual wind turbine generator systemThe amount of change in pitch angle at the moment in time,For the ith virtual wind turbine generator systemThe amount of change in pitch angle at time.
In step S3 of this embodiment, calculating a wind speed predicted value according to the engineering wake model after the parameters are corrected and issuing the wind speed predicted value to the virtual wind farm means that the ith real wind turbine calculated according to the thrust coefficient of each real wind turbine and the Jensen wake model is located in the virtual wind farmPredicted value of wind speed at timeAnd issuing the virtual wind power plant. The method comprises the steps of collecting and storing actual wind speed measured values and thrust coefficients of real wind turbines and state information of virtual wind turbines, carrying out delay processing on the actual wind speed measured values and thrust coefficient data of the real wind turbines according to wake delay time, transmitting the real wind turbine data after the delay processing to a wake model optimization module, and transmitting the virtual wind turbine data to an active wake control module.
In order to verify the effectiveness of the virtual-real combined wind farm maximum power output control method of the embodiment, SIMWINDFARM software and MATLAB/Simulink software build an engineering wake model dynamic correction simulation system based on wind farm wind speed actual measurement data, as shown in fig. 2, and the working principle is shown in fig. 3. The simulation system regards SIMWINDFARM as a real offshore wind farm, and comprises 6 multiplied by 6 NREL 5MW wind turbines arranged along the wind direction, wherein the distance between the front and the back of each wind turbine and the lateral direction is 630 meters. The simulated environment was set to a time-varying wind regime with an average wind speed of 10m/s and a turbulence intensity of 10%.
Fig. 4 shows the comparison effect of the total power generation amount of the wind farm and the other three control methods according to the method of the present embodiment, where the "proposed strategy" is the maximum power output control method of the wind farm according to the present embodiment, the "no-correction" strategy is the maximum power output control method of the wind farm without wake correction, the "static control" is the constant power coefficient control method solved by the particle swarm algorithm, and the "greedy control" is that all wind turbines are operated in the maximum power point tracking mode. Based on a greedy control method, it can be seen that the proposed strategy and the uncorrected strategy both bring about an obvious improvement of the total power generation of the wind farm, and the improvement of the power generation brought about by the uncorrected strategy is lower than the proposed strategy because the engineering wake model used by the uncorrected strategy is not accurate enough, which are respectively improved by 7.74% and 4.75%. The static control strategy cannot match the wind speed changing at the moment, so that the total power generation amount of the wind power plant is reduced by 2.72 percent.
Fig. 5 shows the power generation amount of the wind turbine generators 1-6 according to the method of the embodiment. It can be seen that, compared with the greedy control strategy, the generated energy of the first two wind turbines is actively reduced by the proposed strategy, and the generated energy of the last four wind turbines is improved. Under the static control strategy, the generated energy of the wind turbine generator generates serious fluctuation, because the static control controls a constant power coefficient for the wind turbine generator at a variable wind speed, and the control command cannot be matched with the wind speed for most of the time.
Fig. 6 and 7 show fatigue load conditions of the wind turbines 1 and 6, respectively, according to the method of the present embodiment. From the figure, it can be seen that under the greedy control strategy, the spindle torque and tower bending moment amplitude of the wind turbine 1 are the largest, the wind turbine 6 is the smallest, and the provided control strategy and the correction-free strategy both reduce the load amplitude of the wind turbine 1 and increase the load amplitude of the wind turbine 6, because the wind turbine 1 actively reduces the output power, and the output power of the wind turbine 6 is improved. In addition, since the uncorrected strategy uses an inaccurate engineering wake model, the output power of the wind turbine 1 is reduced too much. Because the control command cannot be matched with the wind speed under the static control strategy, the fluctuation of the load is quite serious, and the safe operation of the wind turbine generator can be seriously influenced.
Fig. 8 shows a comparison chart of mechanical states of wind turbines 1 and 6 between a virtual wind farm and a real wind farm according to the method of the present embodiment, where (a) is comparison of rotational speeds of generators of the real wind turbines 1 and 6 and the virtual wind turbines 1 and 6 after the wake model is modified, (b) is comparison of thrust coefficients of the real wind turbines 1 and 6 and the virtual wind turbines 1 and 6 after the wake model is modified, (c) is comparison of rotational speeds of generators of the real wind turbines 1 and 6 and the virtual wind turbines 1 and 6 after the wake model is unmodified, and (d) is comparison of thrust coefficients of the real wind turbines 1 and 6 and the virtual wind turbines 1 and 6 after the wake model is modified. It can be seen that, before the wake model is corrected, the difference between the rotation speed and the thrust coefficient of the generator is also larger due to the larger difference between the inflow wind speed of the real wind turbine and the inflow wind speed of the virtual wind turbine, and after the wake model is corrected, the difference is obviously reduced, so that the control mode of distributing the control instruction to the real wind power plant is more reasonable by solving the maximum power output problem of the virtual wind power plant. This is also why the proposed strategy improves the overall power efficiency of the wind farm better than the unmodified strategy.
In summary, the virtual-actual combined wind farm maximum power output control method comprises the steps of providing wind speed measured values and thrust coefficients of all real wind turbines to a wake model correction module and providing state information of all virtual wind turbines to an active wake control module based on the real wind farm and corresponding virtual wind farm and data acquired and stored by a data acquisition and storage module, wherein the real wind turbines are used for providing wind speed measured values and thrust coefficients of all real wind turbines for the data acquisition and storage module and receiving control instructions of the active wake control module, the virtual wind farm comprises the virtual wind turbines and is used for providing state information of all virtual wind turbines for the data acquisition and storage module, the state information comprises but is not limited to generator rotating speed, electromagnetic torque, pitch angle, electromagnetic power and the like, and receiving control instructions of the active wake control module, correcting parameters in an engineering wake model by using the wake model correction module and providing the optimized parameters to the active wake control module so as to improve wind speed prediction accuracy of the engineering wake model, solving the problem of the dynamic wake control module according to the wind farm, calculating the wind power generation capacity of the active wake control module and the wind farm under the consideration of the overall wind farm, and the wind power generation capacity can be more effective in view of the wind farm, and the wind farm can be more economical efficiency and the wind farm.
In addition, the embodiment also provides a virtual-real combined wind farm maximum power output control system, which comprises a microprocessor and a memory which are connected with each other, wherein the microprocessor is programmed or configured to execute the virtual-real combined wind farm maximum power output control method. In addition, the embodiment also provides a computer readable storage medium, and a computer program or instructions are stored in the computer readable storage medium, and the computer program or instructions are programmed or configured to execute the virtual-real combined wind farm maximum power output control method through a processor. Furthermore, the present embodiment provides a computer program product comprising a computer program or instructions programmed or configured to execute the virtual-real combined wind farm maximum power output control method by a processor.
It will be appreciated by those skilled in the art that embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-readable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein. The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks. These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks. These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The above description is only a preferred embodiment of the present invention, and the protection scope of the present invention is not limited to the above examples, and all technical solutions belonging to the concept of the present invention belong to the protection scope of the present invention. It should be noted that modifications and adaptations to the present invention may occur to one skilled in the art without departing from the principles of the present invention and are intended to be within the scope of the present invention.
Claims (10)
1. The virtual-real combined wind farm maximum power output control method is characterized by comprising the following steps of:
S1, acquiring and storing wind speed measured values and thrust coefficients of all real wind turbines of a real wind power plant and state information of all virtual wind turbines in a virtual wind power plant corresponding to the real wind power plant;
s2, correcting parameters in the engineering wake flow model according to wind speed measured values of all real wind turbines;
And S3, solving the control problem of the dynamic maximum power generation of the wind power plant, issuing a control instruction to the real wind power plant according to the solving result to generate power according to the maximum power generation, calculating a wind speed predicted value according to the engineering wake model after the parameters are corrected, and issuing the control instruction to the virtual wind power plant.
2. The virtual-actual combined wind farm maximum power output control method according to claim 1 is characterized in that the virtual wind farm in the step S1 comprises virtual wind turbines formed by a digital model corresponding to real wind turbines, the number and the spatial arrangement of the virtual wind turbines are consistent with those of the real wind turbines, a simulated virtual wake effect exists in the virtual wind farm, the virtual wake effect is simulated to be a wind speed predicted value calculated according to an engineering wake model after correction parameters, the wind speed predicted value is used as a virtual wind speed without considering wake delay time, and the state information of the downstream virtual wind turbines at the current moment can reflect the future state information of the real wind turbines corresponding to the virtual wind turbines based on the following formula:
,
Wherein, In the virtual wind power plant, the ith virtual wind power unit is arranged in the virtual wind power plantStatus information of the time of day,For the ith real wind turbine generator in the real wind power plantStatus information of the time of day,Is wake delay time, and has:
,
Wherein, For the geographic distance between the 1 st real wind turbine generator and the i th real wind turbine generator along the wind direction in the real wind power plant,Is the average inflow wind speed.
3. The virtual-real combined wind farm maximum power output control method according to claim 1, wherein when parameters in the engineering wake model are corrected in step S2, a functional expression of the wake model correction optimization problem is adopted as follows:
,
In the above-mentioned method, the step of, For the number of real wind turbines in a real wind farm,For the ith real wind turbine generator calculated according to the thrust coefficient of each real wind turbine generator and the Jensen wake modelA predicted value of the wind speed at the moment in time,In order to acquire the ith real wind turbine generator systemA measurement of the wind speed at a moment in time, whereinAnd correcting parameters in the engineering wake model according to wind speed measured values of all the real wind turbines refers to correcting and optimizing problems aiming at the wake model, and correcting the optimal wake expansion coefficient of a Jensen wake model in the engineering wake model.
4. The virtual-actual combined wind farm maximum power output control method according to claim 3, wherein a function expression for performing delay processing on wind speed measurement values of each real wind turbine is:
,
,
Wherein, For the ith real wind turbine generator in the real wind power plantThe thrust coefficient of the moment of time,In order to correct parameters in an engineering wake model, an ith real wind turbine generator set in a real wind power plantThe thrust coefficient of the moment of time,Obtaining the ith real wind turbine generator in the real wind power plant after delay treatmentThe wind speed measurement at the moment in time,In order to correct parameters in an engineering wake model, an ith real wind turbine generator set in a real wind power plantThe wind speed measurement at the moment in time,For wake delay time, the superscript R represents measurement data of a real wind farm, and the superscript C represents data used when modifying parameters in an engineering wake model.
5. The virtual-actual combined wind farm maximum power output control method according to claim 1, wherein solving the wind farm dynamic maximum power generation control problem in step S3 and issuing a control command to the real wind farm according to the solving result to generate power according to the maximum power generation comprises:
S3.1, establishing a state space model of the virtual wind turbine shown in the following formula:
,
Wherein, For the state matrix of the ith virtual wind turbineThe derivative of (i) th virtual wind turbine generator system state matrixIs thatControl matrix of ith virtual wind turbine generator system,The generator rotating speed variation quantity of the ith virtual wind turbine generator system,The electromagnetic torque variation quantity of the ith virtual wind turbine generator system,For the pitch angle variation of the ith virtual wind turbine,In order to issue a control command of the electromagnetic torque variation quantity of the ith virtual wind turbine generator,In order to issue a control instruction of the pitch angle variation of the ith virtual wind turbine generator,The inflow wind speed variation of the ith virtual wind turbine generator system,、、AndThe coefficient matrix of the ith virtual wind turbine generator system is:
,,
,,
Wherein, In order to achieve the gear box ratio change,Is the equivalent mass of the wind wheel and the generator,The mechanical torque of the impeller of the ith virtual wind turbine generator set,For the generator rotating speed of the ith virtual wind turbine,Is the time constant of the generator and,For the time constant of the pitch mechanism,For the pitch angle of the ith virtual wind turbine,The mechanical torque of the ith virtual wind turbine generator system,For the ith virtual wind power electromagnetic torque of the unit;
s3.2, constructing a state space model of the established virtual wind power plant according to the state space model of the virtual wind turbine generator set:
,
Wherein, State matrix for virtual wind farmIs the derivative of the virtual wind farm state matrixControl matrix of virtual wind power plant for matrix formed by state matrix of each virtual wind motorFor the matrix formed by the control matrix of each virtual wind motor, the wind speed variation of the virtual wind power plantA matrix formed by the inflow wind speed variation of each virtual wind turbine,、、AndThe matrix is formed by coefficient matrixes of the virtual wind motors respectively;
s3.3, using thrust coefficient of the ith virtual wind turbine generator set And inflow wind speedThe delta form of constructing the Jensen wake model for the variables is shown as follows:
,
Wherein, The variation of wake wind speed caused by the ith virtual wind generator set to the nth virtual wind generator set,The wake wind speed caused by the ith virtual wind generator set to the nth virtual wind generator set,The thrust coefficient variation of the ith virtual wind turbine generator system,Inflow wind speed for ith virtual wind turbineAnd has:
,
,
Wherein, To correct the optimal wake expansion coefficient of the Jensen wake model in the engineering wake model,For the distance between the ith virtual wind turbine generator and the nth virtual wind turbine generator along the wind direction,For the radius of the impeller wheel,In order to cover the area for the wake,The swept area of the impeller;
s3.4, using the inflow wind speed of the ith virtual wind turbine generator set Wake wind speed caused by the ith virtual wind turbine generator to the nth virtual wind turbine generatorWriting an increment form of an energy conservation wake superposition model for the variable, and erecting the increment form of the Jensen wake model in parallel to obtain an inflow wind speed predicted value of the nth virtual wind turbine generator setIn increments of:
,
Wherein, The virtual wind turbine generator set is a virtual wind turbine generator set which causes wake flow influence on the virtual wind turbine generator set n;
S3.5, according to the inflow wind speed predicted value of the nth virtual wind turbine generator set Determining the incremental form of the inflow wind speed predicted value of the ith virtual wind turbine unit to the nth virtual wind turbine unit:
,
Wherein, AndIs a coefficient matrix, and has:
,,
Wherein the coefficient matrix AndThe expression of any element in (a) is:
,
;
Based on coefficient matrix For the lower triangular matrix, and all diagonal line elements are 0, simplifying the increment form of the inflow wind speed predicted value from the ith virtual wind turbine generator to the nth virtual wind turbine generator into:
;
S3.6, substituting the increment form of the inflow wind speed predicted value of the ith virtual wind turbine generator set to the nth virtual wind turbine generator set into the state space model of the virtual wind power plant to obtain a control equation of the virtual wind power plant:
;
s3.7, constructing a maximum power generation amount control problem based on a control equation of the virtual wind power plant:
,
Wherein, For the rated power of the wind farm,For the number of real wind turbines in a real wind farm,Is the electromagnetic power initial value of the virtual wind turbine generator set i,Is the initial value change rate of the electromagnetic power of the virtual wind turbine generator set i,AndDetermining constraints of a maximum power generation amount control problem, wherein the constraints comprise constraints for preventing the overaction of the wind turbine and constraints for avoiding overload or shutdown risks, and the functional expressions of the constraints for preventing the overaction of the wind turbine are as follows:
,
Wherein, AndRespectively the electromagnetic torque increment and the pitch angle increment of the ith real wind turbine generator,AndRespectively the initial values of the electromagnetic torque and the pitch angle of the ith real wind turbine generator,AndThe upper limit of each control change of the electromagnetic torque and the pitch angle is respectively,AndThe function expression of the constraint for avoiding overload or shutdown risk is as follows:
Wherein, In the form of a matrix of lower power limits,For the power matrix to be rated for,,WhereinAs a lower power limit of the power supply,For the rated power of the electric motor,A matrix of all 1 elements in dimension n x 1; In the form of a power sensitivity matrix, ,;
S3.8, converting the increment form and the maximum power generation control problem of the virtual wind power plant into a standard quadratic programming problem for quick solving, and solving the obtained optimal control quantityAndDirectly distributed to the virtual wind power plant, and distributed to the real wind power plant after being processed by combining wake delay time.
6. The method for controlling maximum power output of a virtual-real combined wind farm according to claim 5, wherein when the combined wake delay time post-processing is allocated to a real wind farm in step S3.8, the combined wake delay time post-processing has a functional expression as follows:
,
,
Wherein, For the ith virtual wind turbine generator systemThe amount of change in the electromagnetic torque at the moment,For the ith virtual wind turbine generator systemThe amount of change in the electromagnetic torque at the moment,For the ith virtual wind turbine generator systemThe amount of change in pitch angle at the moment in time,For the ith virtual wind turbine generator systemThe amount of change in pitch angle at time.
7. The virtual-actual combined wind farm maximum power output control method according to claim 3, wherein in step S3, a wind speed predicted value is calculated according to the engineering wake model after the parameters are corrected, and the wind speed predicted value is issued to the virtual wind farm, which means that the ith real wind turbine generator calculated according to the thrust coefficient of each real wind turbine generator and the Jensen wake model is calculated in the virtual wind farmPredicted value of wind speed at timeAnd issuing the virtual wind power plant.
8. A virtual-real combined wind farm maximum power output control system comprising a microprocessor and a memory connected to each other, wherein the microprocessor is programmed or configured to perform the virtual-real combined wind farm maximum power output control method of any of claims 1 to 7.
9. A computer readable storage medium having a computer program or instructions stored therein, wherein the computer program or instructions is programmed or configured to execute the virtual-real combined wind farm maximum power output control method according to any one of claims 1 to 7 by a processor.
10. A computer program product comprising a computer program or instructions, characterized in that the computer program or instructions is programmed or configured to execute the virtual-real combined wind farm maximum power output control method according to any of claims 1 to 7 by a processor.
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