CN114465287B - Wind farm rapid active power optimization method and system - Google Patents
Wind farm rapid active power optimization method and system Download PDFInfo
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- H—ELECTRICITY
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- H02J3/46—Controlling of the sharing of output between the generators, converters, or transformers
- H02J3/48—Controlling the sharing of the in-phase component
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Abstract
The invention discloses a method and a system for optimizing the rapid active power of a wind farm, wherein the method comprises the steps of respectively constructing an incremental space state equation of the increment of an active power reference instruction of a fan and the increment of wind thrust F t and bearing torsion T s fatigue load; establishing an objective function of wind power plant fast active power optimization based on the constructed incremental space state equation; and solving a solution for obtaining the minimum value of the objective function based on the equal-microincrement criterion to obtain the increment of the optimal fan active power reference instruction so as to control the fan active power output. According to the invention, the cost function of the fatigue load of the wind turbine is constructed by the incremental space equation of the wind turbine, and the optimization objective function is solved by adopting the equal-micro rate-increasing criterion, so that the solving complexity of the objective function can be simplified, the calculation burden of the rapid active power optimization of the wind power plant is reduced, and the solving efficiency of the rapid active power optimization of the wind power plant is improved.
Description
Technical Field
The invention relates to a wind power plant active power control technology, in particular to a wind power plant rapid active power optimization method and system.
Background
Wind energy is one of the rapidly growing renewable energy sources. Due to natural geographical environment factors, there will be a dramatic increase in both installed scale and installed capacity of future offshore wind farms. The wind speed distribution and fluctuation in the wind power plant are more complex and changeable, so that the load of the wind turbine generator is increased rapidly and the fatigue load is increased rapidly, and accidents such as damage to a fan gear box, cracking of blades, collapse of a tower and the like are caused when the wind power plant is serious: the blower is subjected to the combined action of turbulence and external random load, and the dynamic behavior shows extremely strong high-order nonlinearity; and the coupling degree between the generator sets is increased in geometric quantity, and the large-scale wind turbine group model has the characteristic of ultra-high order. The traditional small wind power plant is slow in calculation by optimizing an active power control mode through a high-order mathematical model of a wind motor group in a centralized manner, and the requirement of rapid control of the load of a large-scale wind turbine group is difficult to meet. The fatigue load rapid and accurate control of the fans is a technical development direction of active optimal control of the wind power plant in the future, and is oriented to the ultra-large scale wind motor group in the future.
Disclosure of Invention
The invention aims to solve the technical problems: aiming at the problems in the prior art, the invention provides a method and a system for optimizing the rapid active power of a wind farm, which apply an equal-micro-increment criterion to the active power optimization control problem of the wind farm, firstly construct a cost function of fatigue load of a fan by an increment space equation of the fan, simplify the complexity of an optimization objective function, reduce the calculation burden of a central controller, improve the solving efficiency and solve the optimization objective function by adopting the equal-micro-increment criterion.
In order to solve the technical problems, the invention adopts the following technical scheme:
a wind farm rapid active power optimization method comprises the following steps:
1) Respectively constructing increment of active power reference instruction of fan Incremental space state equations for fatigue loads with wind thrust force F t and bearing torsion force T s respectively;
2) Based on the constructed incremental space state equation, establishing an objective function of each fan on fatigue load;
3) Setting an active scheduling instruction and power generation constraint conditions of fans by taking total fatigue load of N fans in a wind power plant as a target, and establishing a wind power plant rapid active power optimization model;
4) Solving a wind power plant rapid active power optimization model based on an equal-microincrement criterion to obtain a solution of an extremely small value of an objective function, and obtaining an increment of an optimal fan active power reference instruction To control the active power output of the blower.
Optionally, step 1) includes:
1.1 Delta for respectively constructing active power reference instructions of fan Incremental space state equations for fatigue loads with wind thrust force F t and bearing torsion force T s respectively;
1.2 Respectively determining the increment of the active power reference instruction of the fan The first-order deflection of the incremental space state equation of the fatigue load of the wind thrust F t and the bearing torsion T s is respectively carried out.
Optionally, step 1.1) includes:
1.1.1 Constructing an incremental model of wind thrust F t and bearing torsion T s as shown in the following formula:
ΔFt＝KβFΔβ+KωFΔωg， (2)
In the above formula, eta g is the transmission ratio coefficient of a gear box, J g is the inertia of a generator, K ωT、KβT is the coefficient of first-order Taylor expansion of bearing torsion T s at an initial time T 0, K βF and K ωF are the coefficient of first-order Taylor expansion of wind thrust F t at an initial time T 0, J t is the equivalent inertia, J r is the inertia of an impeller rotor, For the value of the generator output power at initial time t 0, mu g is generator efficiency,For the value of the filtering speed at the initial time t 0, Δβ is the increment of the pitch angle, Δω g is the increment of the rotation speed of the generator, and Δω f is the increment of the rotation speed of the generator after filtering the higher harmonic wave;
1.1.2 Based on the incremental model of wind thrust force F t and bearing torsion force T s, establishing a continuous state space equation near the working point as follows:
In the above formula, x is a state variable, the increment of the state variable x is Δx= [ Δβ ref,Δβ,Δωg,Δωf ], and the increment of the control variable u is Δβ ref is the increment of the pitch angle reference value, a Ι,BΙ,EΙ is the continuous state space matrix;
1.1.3 Discretizing a continuous state space equation near the working point by using a sampling period t s to obtain a discrete state space equation as follows:
x(k+1)＝Adx(k)+BdΔu+Ed，(4)
in the above formula, x (k+1) and x (k) are state variables at k+1 and k time, and a d,Bd,Ed is a discrete state space matrix;
1.1.4 Respectively constructing the increment of the active power reference instruction of the fan according to a discrete state space equation near the working point The incremental space state equations for the fatigue loads with wind thrust force F t and bearing torsion force T s respectively are shown in the following formulas:
In the above formula, deltaF t is the increment of wind thrust F t, deltaT s is the increment of bearing torque force T s, deltax= [ DeltaF t,ΔTs ] is the increment of control variable, The method comprises the steps of (1) obtaining an increment of an active power reference instruction of a fan to be solved; the matrix of the incremental spatial state equations for wind thrust force F t and bearing torque force T s, respectively, and has:
In the above formula, eta g is the transmission ratio coefficient of a gear box, J g is the inertia of a generator, K ωT、KβT is the coefficient of first-order Taylor expansion of bearing torsion T s at an initial time T 0, K βF and K ωF are the coefficient of first-order Taylor expansion of wind thrust F t at an initial time T 0, J t is the equivalent inertia, J r is the inertia of an impeller rotor, The value of the output power of the generator at the initial time t 0; mu g is the efficiency of the generator,Is the value of the filtering speed at the initial instant t 0.
Optionally, the increment of the fan active power reference command determined in step 1.2)The first-order partial guide equation of the incremental space state equation of the fatigue load of the wind thrust force F t and the bearing torsion force T s is shown as follows:
In the above-mentioned method, the step of, For all fan active power reference instructions of the wind power plant,For the increment of the fan active power reference instruction to be solved,AndIs an intermediate variable of the matrix of the incremental spatial state equations of wind thrust F t,AndIs an intermediate variable of an incremental space state equation matrix of the bearing torsion T s, and comprises:
In the above formula, B d is a discrete state space matrix, The intermediate variables of the wind thrust force F t and the bearing torsion force T s are respectively, and E d is a discrete state space matrix.
Optionally, the objective function of each fan with respect to fatigue load is established in step 2) as:
In the above formula, x i is P W.i of the fan active power reference instruction, Representing the fatigue load of the output active power P W.i of the ith fan,As a weighting factor of the wind thrust force F t in the objective function,Incremental for wind thrust F t with respect to fan active power reference commandIs a function of (a) and (b),As a weight coefficient of the bearing torsion T s in the objective function,Delta for bearing torsion T s with respect to fan active power reference commandIs a function of (2).
Optionally, the functional expression of the wind farm rapid active power optimization model established in step 3) is:
in the above formula, N represents the number of fans in the wind power plant, Represents the fatigue load of the output active power P W.i of the ith fan, x i is P W.i of the active power reference instruction of the fan,As the available wind power of the wind turbine,Is a scheduling instruction from a wind farm.
Optionally, step 4) includes:
4.1 Solving a wind power plant fast active power optimization model by using a Lagrange method to obtain a corresponding Lagrange function:
In the above formula, L (x 1,x2…xN) represents a Lagrangian function, N represents the number of fans in the wind farm, Represents the fatigue load of the output active power P W.i of the ith fan, x i is P W.i of the active power reference instruction of the fan,For scheduling instructions from a wind farm,The maximum value of the output active power P W.i of the ith fan is represented, lambda, u 1,u2 are Lagrange multipliers, and u 1,u2 are all more than or equal to 0; the requirements for extremum of the first-order partial derivative of the formula (15) include:
According to the extremum theory, the interval where the output active power P W.i of the ith fan is positioned is discussed by formulas (16) - (19), the extremum of the interval where the output active power P W.i of the ith fan is positioned is judged, so that the solution of the minimum value of the objective function is obtained by solving, and the solved function expression is as follows:
In the above formula, lambda i represents a feasible solution of the objective function, lambda * is an optimal solution of the objective function, P i.max is the maximum available wind power of the ith fan, and x i is P W.i of the active power reference instruction of the ith fan; For the first-order deflection of the fatigue load of each fan with respect to the output active power P W.i of the ith fan, the expression is as follows:
In the above-mentioned method, the step of, Is the first-order partial derivative of the incremental space state equation of the fatigue load of the wind thrust F t,Is the first-order partial derivative of the incremental space state equation of the bearing torsion T s fatigue load,As a weighting factor of the wind thrust force F t in the objective function,Is a weight coefficient of the bearing torsion T s in the objective function.
Optionally, in step 4), when solving the solution for obtaining the minimum value of the objective function based on the equal-microincrement criterion, the method further comprises the step of referencing the increment of the fan active power reference instructionThe step of simplifying the first-order partial derivative of the incremental space state equation of the fatigue load with the wind thrust force F t and the bearing torsion force T s respectively into discrete incremental forms, wherein the increment of the active power reference instruction of the fan is simplifiedThe first-order partial derivative of the incremental space state equation of the fatigue load with the wind thrust force F t and the bearing torsion force T s is simplified into a discrete incremental form, and the functional expression is as follows:
In the above formula, x i is denoted as P W.i of the i-th fan active power reference instruction, An objective function of P W.i representing an active power reference instruction of the ith wind turbine of wind power,For all fan active power reference instructions of the wind power plant,N represents the number of fans in the wind power plant.
In addition, the invention also provides a wind farm rapid active power optimization system, which comprises a microprocessor and a memory which are connected with each other, wherein the microprocessor is programmed or configured to execute the steps of the wind farm rapid active power optimization method.
Furthermore, the invention also provides a computer readable storage medium, wherein the computer readable storage medium stores a computer program for being executed by a computer device to implement the wind farm rapid active power optimization method.
Compared with the prior art, the invention has the following advantages:
1. The method comprises respectively constructing the increment of the active power reference instruction of the fan Incremental space state equations for fatigue loads with wind thrust force F t and bearing torsion force T s respectively; establishing an objective function of wind power plant fast active power optimization based on the constructed incremental space state equation; solving to obtain a solution of a minimum value of an objective function based on an equal-microincrement criterion to obtain an increment of an optimal fan active power reference instructionAccording to the invention, the cost function of the fatigue load of the wind turbine is constructed by using the incremental space equation of the wind turbine and the optimization objective function is solved by adopting the equal-microincreasing criterion, so that the solving complexity of the objective function can be simplified, the calculation burden of the rapid active power optimization of the wind power plant is reduced, and the solving efficiency of the rapid active power optimization of the wind power plant is improved.
2. The method takes the total fatigue load of N fans in the wind power plant as a target, sets the active scheduling instruction and the power generation constraint condition of the fans, establishes a wind power plant rapid active power optimization model, solves the wind power plant rapid active power optimization model based on an equal micro-increment criterion to obtain the solution of the minimum value of the objective function, and obtains the increment of the optimal fan active power reference instructionThe active power output of the fan is controlled, and the wind power plant is better in robustness and stability based on the distributed wind power plant power optimization control.
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 graph comparing the bearing torque T s of the 5 th fan according to the embodiment of the present invention.
FIG. 3 is a graph showing the comparison of the wind thrust force F t of the 5 th fan according to the embodiment of the present invention.
Detailed Description
As shown in fig. 1, the method for optimizing the fast active power of the wind farm according to the embodiment includes:
1) Respectively constructing increment of active power reference instruction of fan Incremental space state equations for fatigue loads with wind thrust force F t and bearing torsion force T s respectively;
2) Based on the constructed incremental space state equation, establishing an objective function of each fan on fatigue load;
3) Setting an active scheduling instruction and power generation constraint conditions of fans by taking total fatigue load of N fans in a wind power plant as a target, and establishing a wind power plant rapid active power optimization model;
4) Solving a wind power plant rapid active power optimization model based on an equal-microincrement criterion to obtain a solution of an extremely small value of an objective function, and obtaining an increment of an optimal fan active power reference instruction To control the active power output of the blower.
In this embodiment, step 1) includes:
1.1 Delta for respectively constructing active power reference instructions of fan Incremental space state equations for fatigue loads with wind thrust force F t and bearing torsion force T s respectively;
1.2 Respectively determining the increment of the active power reference instruction of the fan The first-order deflection of the incremental space state equation of the fatigue load of the wind thrust F t and the bearing torsion T s is respectively carried out.
In this embodiment, step 1.1) includes:
1.1.1 Constructing an incremental model of wind thrust F t and bearing torsion T s as shown in the following formula:
ΔFt＝KβFΔβ+KωFΔωg， (2)
In the above formula, eta g is the transmission ratio coefficient of a gear box, J g is the inertia of a generator, K ωT、KβT is the coefficient of first-order Taylor expansion of bearing torsion T s at an initial time T 0, K βF and K ωF are the coefficient of first-order Taylor expansion of wind thrust F t at an initial time T 0, J t is the equivalent inertia, J r is the inertia of an impeller rotor, For the value of the generator output power at initial time t 0, mu g is generator efficiency,For the value of the filtering speed at the initial time t 0, θ is the pitch angle and is definedK 1 and K 2 are constants, delta beta is the increment of the pitch angle, delta beta ref is the increment of the pitch angle reference value, delta omega g is the increment of the rotating speed of the generator, and delta omega f is the increment of the rotating speed of the generator after higher harmonic wave is filtered;
1.1.2 Based on the incremental model of wind thrust force F t and bearing torsion force T s, establishing a continuous state space equation near the working point as follows:
In the above formula, x is a state variable, the increment of the state variable x is Δx= [ Δβ ref,Δβ,Δωg,Δωf ], and the increment of the control variable u is Δβ ref is the increment of the pitch angle reference value, a Ι,BΙ,EΙ is the continuous state space matrix, and the expression is:
Wherein θ is the pitch angle and is defined K 1 and K 2 are constants, Δβ is the increment of the pitch angle, K P is the proportional gain of the pitch angle controller, K i is the integral gain of the pitch angle controller, T f is the time constant of the low pass filter,K c＝K0+K1θ,K0 and K 1 are constants, T θ is a time constant,As the value of the pitch angle at time t 0,For the value of the generator rotor speed at time t 0,For the rotational speed of the generator at time t,For the pitch angle at time t, θ 0 is the pitch angle at time t 0,For the aerodynamic torque at the instant t 0,Which is the generator torque at time t.
1.1.3 Discretizing a continuous state space equation near the working point by using a sampling period t s to obtain a discrete state space equation as follows:
x(k+1)＝Adx(k)+BdΔu+Ed， (4)
In the above formula, x (k+1) and x (k) are state variables at k+1 and k time, a d,Bd,Ed is a discrete state space matrix, and the expression is:
In the above formula, a Ι,BΙ,EΙ is a continuous state space matrix, t is an independent variable, and t s is a sampling period;
1.1.4 Respectively constructing the increment of the active power reference instruction of the fan according to a discrete state space equation near the working point The incremental space state equations for the fatigue loads with wind thrust force F t and bearing torsion force T s respectively are shown in the following formulas:
In the above formula, deltaF t is the increment of wind thrust F t, deltaT s is the increment of bearing torque force T s, deltax= [ DeltaF t,ΔTs ] is the increment of control variable, The method comprises the steps of (1) obtaining an increment of an active power reference instruction of a fan to be solved; the matrix of the incremental spatial state equations for wind thrust force F t and bearing torque force T s, respectively, and has:
In the above formula, eta g is the transmission ratio coefficient of a gear box, J g is the inertia of a generator, K ωT、KβT is the coefficient of first-order Taylor expansion of bearing torsion T s at an initial time T 0, K βF and K ωF are the coefficient of first-order Taylor expansion of wind thrust F t at an initial time T 0, J t is the equivalent inertia, J r is the inertia of an impeller rotor, The value of the output power of the generator at the initial time t 0; mu g is the efficiency of the generator,Is the value of the filtering speed at the initial instant t 0.
In this embodiment, the increment of the fan active power reference command determined in step 1.2)The first-order partial guide equation of the incremental space state equation of the fatigue load of the wind thrust force F t and the bearing torsion force T s is shown as follows:
In the above-mentioned method, the step of, For all fan active power reference instructions of the wind power plant,For the increment of the fan active power reference instruction to be solved,AndIs an intermediate variable of the matrix of the incremental spatial state equations of wind thrust F t,AndIs an intermediate variable of an incremental space state equation matrix of the bearing torsion T s, and comprises:
In the above formula, B d is a discrete state space matrix, The intermediate variables of the wind thrust force F t and the bearing torsion force T s are respectively, and E d is a discrete state space matrix.
In this embodiment, the objective function of each fan set up in step 2) with respect to fatigue load is:
In the above formula, x i is P W.i of the fan active power reference instruction, Representing the fatigue load of the output active power P W.i of the ith fan,As a weighting factor of the wind thrust force F t in the objective function,Incremental for wind thrust F t with respect to fan active power reference commandIs a function of (a) and (b),As a weight coefficient of the bearing torsion T s in the objective function,Delta for bearing torsion T s with respect to fan active power reference commandIs a function of (2).
In this embodiment, the functional expression of the wind farm fast active power optimization model established in step 3) is:
in the above formula, N represents the number of fans in the wind power plant, Represents the fatigue load of the output active power P W.i of the ith fan, x i is P W.i of the active power reference instruction of the fan,As the available wind power of the wind turbine,Is a scheduling instruction from a wind farm.
In this embodiment, step 4) includes:
4.1 Solving a wind power plant fast active power optimization model by using a Lagrange method to obtain a corresponding Lagrange function:
In the above formula, L (x 1,x2…xN) represents a Lagrangian function, N represents the number of fans in the wind farm, Represents the fatigue load of the output active power P W.i of the ith fan, x i is P W.i of the active power reference instruction of the fan,For scheduling instructions from a wind farm,The maximum value of the output active power P W.i of the ith fan is represented, lambda, u 1,u2 are Lagrange multipliers, and u 1,u2 are all more than or equal to 0; the requirements for extremum of the first-order partial derivative of the formula (15) include:
According to the extremum theory, the interval where the output active power P W.i of the ith fan is positioned is discussed by formulas (16) - (19), the extremum of the interval where the output active power P W.i of the ith fan is positioned is judged, so that the solution of the minimum value of the objective function is obtained by solving, and the solved function expression is as follows:
In the above formula, lambda i represents a feasible solution of the objective function, lambda * is an optimal solution of the objective function, P i.max is the maximum available wind power of the ith fan, and x i is P W.i of the active power reference instruction of the ith fan; For the first-order deflection of the fatigue load of each fan with respect to the output active power P W.i of the ith fan, the expression is as follows:
In the above-mentioned method, the step of, Is the first-order partial derivative of the incremental space state equation of the fatigue load of the wind thrust F t,Is the first-order partial derivative of the incremental space state equation of the bearing torsion T s fatigue load,As a weighting factor of the wind thrust force F t in the objective function,Is a weight coefficient of the bearing torsion T s in the objective function.
In this embodiment, when the solution of the minimum value of the objective function is obtained by solving the solution in step 4) based on the equal-microincrement criterion, the method further includes the step of adding the increment of the fan active power reference instructionThe step of simplifying the first-order partial derivative of the incremental space state equation of the fatigue load with the wind thrust force F t and the bearing torsion force T s respectively into discrete incremental forms, wherein the increment of the active power reference instruction of the fan is simplifiedThe first-order partial derivative of the incremental space state equation of the fatigue load with the wind thrust force F t and the bearing torsion force T s is simplified into a discrete incremental form, and the functional expression is as follows:
In the above formula, x i is denoted as P W.i of the i-th fan active power reference instruction, An objective function of P W.i representing an active power reference instruction of the ith wind turbine of wind power,For all fan active power reference instructions of the wind power plant,N represents the number of fans in the wind power plant.
In order to further verify the effectiveness of the wind farm rapid active power optimization method, a wind farm simulation model of 105 MW fans is built in the embodiment. A fan model SIM WIND FARM for the wind power plant is generated, and a control algorithm of the simulation model is respectively compared with two control schemes of a rapid active power optimization method (OPT) and an available wind power proportional distribution algorithm (Proportional Dispatch, PD) of the wind power plant.
The simulation results of the 5 th fan as an analysis case are shown in fig. 2 and 3. The wind speed changes greatly within 0-300s, and the scheduling instruction is about 50% of the available wind power of the output fan, namely, the fan is in a load shedding operation mode. At this time, when the instruction of a wind farm instruction dispatcher TSO (transmission system operator) is met, the control algorithm adopts two control schemes of a wind farm rapid active power optimization method (OPT for short) and an available wind power proportional distribution algorithm (Proportional Dispatch for short) respectively, so that the fatigue load of the fan is different in terms of wind thrust F t and bearing torque T s. As can be seen from fig. 2 and 3: at 0s-300s, the method of the present embodiment is more gentle in bearing torque F t and wind thrust T s fluctuations than the PD method. In 300s-500s, the bearing torque F t is substantially consistent with the fluctuating trend of the wind thrust T s in the fan operating and maximum power tracking (MPPT) operating modes. Therefore, the rapid active power optimization method (OPT) of the wind farm in the embodiment has the best effect on the consistency of the bearing torque F t and the wind thrust T s when the fan operates in the load shedding mode. From the simulation results, the rapid active power optimization method (OPT for short) of the wind farm can restrain the large fluctuation of the bearing torque F t and the wind thrust T s, so that the stress fatigue load of the unit is slowed down, and the service time of the unit equipment is prolonged.
In addition, the embodiment also provides a wind farm rapid active power optimization system, which comprises a microprocessor and a memory which are connected with each other, wherein the microprocessor is programmed or configured to execute the steps of the wind farm rapid active power optimization method.
Furthermore, the present embodiment also provides a computer readable storage medium having stored therein a computer program for execution by a computer device to implement the aforementioned wind farm fast active power optimization method.
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 (8)
1. The rapid active power optimization method for the wind farm is characterized by comprising the following steps of:
1) Respectively constructing increment of active power reference instruction of fan Incremental space state equations for fatigue loads with wind thrust force F t and bearing torsion force T s respectively;
2) Based on the constructed incremental space state equation, establishing an objective function of each fan on fatigue load;
3) Setting an active scheduling instruction and power generation constraint conditions of fans by taking total fatigue load of N fans in a wind power plant as a target, and establishing a wind power plant rapid active power optimization model;
4) Solving a wind power plant rapid active power optimization model based on an equal-microincrement criterion to obtain a solution of an extremely small value of an objective function, and obtaining an increment of an optimal fan active power reference instruction To control the active power output of the blower;
in the step 2), the objective function of each fan related to fatigue load is established as follows:
，(12)
In the above, x i is the active power reference instruction of the fan ，Representing the output active power of the ith fanIs used for the fatigue load of the steel plate,As a weighting factor of the wind thrust force F t in the objective function,Incremental for wind thrust F t with respect to fan active power reference commandIs a function of (a) and (b),As a weight coefficient of the bearing torsion T s in the objective function,Delta for bearing torsion T s with respect to fan active power reference commandIs a function of (2);
the functional expression of the wind power plant rapid active power optimization model established in the step 3) is as follows:
，(13)
，(14)
in the above formula, N represents the number of fans in the wind power plant, Representing the output active power of the ith fanX i is the fatigue load of the fan active power reference command，As the available wind power of the wind turbine,Is a scheduling instruction from a wind farm.
2. The method for optimizing fast active power of a wind farm according to claim 1, wherein step 1) comprises:
1.1 Delta for respectively constructing active power reference instructions of fan Incremental space state equations for fatigue loads with wind thrust force F t and bearing torsion force T s respectively;
1.2 Respectively determining the increment of the active power reference instruction of the fan The first-order deflection of the incremental space state equation of the fatigue load of the wind thrust F t and the bearing torsion T s is respectively carried out.
3. The method of optimizing fast active power of a wind farm according to claim 2, wherein step 1.1) comprises:
1.1.1 Constructing an incremental model of wind thrust F t and bearing torsion T s as shown in the following formula:
，(1)
，(2)
In the above-mentioned method, the step of, For an increment of the bearing torque T s,For the increment of the wind thrust force F t,J g is the generator inertia, K ωT、KβT is the coefficient of first-order Taylor expansion of the bearing torque force T s at the initial time T 0, K βF and K ωF are the coefficients of first-order Taylor expansion of the wind thrust force F t at the initial time T 0, J t is the equivalent inertia, J r is the impeller rotor inertia,For the value of the generator output power at the initial instant t 0,For the efficiency of the generator,For the value of the filtering speed at the initial instant t 0,As an increment of the pitch angle,Is the increment of the rotation speed of the generator,The increment of the rotating speed after filtering the higher harmonic wave for the generator,The increment of the active power reference instruction of the fan;
1.1.2 Based on the incremental model of wind thrust force F t and bearing torsion force T s, establishing a continuous state space equation near the working point as follows:
，(3)
In the above-mentioned method, the step of, As state variablesIs increased by an increment of (2)Control variableIs increased by an increment of (2), For the increment of the pitch angle reference value,,,Respectively a continuous state space matrix;
1.1.3 Discretizing a continuous state space equation near the working point by using a sampling period t s to obtain a discrete state space equation as follows:
，(4)
In the above-mentioned method, the step of, AndThe state variables at times k +1 and k respectively,,,Is a discrete state space matrix;
1.1.4 Respectively constructing the increment of the active power reference instruction of the fan according to a discrete state space equation near the working point The incremental space state equations for the fatigue loads with wind thrust force F t and bearing torsion force T s respectively are shown in the following formulas:
，(5)
，(6)
In the above-mentioned method, the step of, For the increment of the wind thrust force F t,For an increment of the bearing torque T s,In order to control the increment of the variable,The method comprises the steps of (1) obtaining an increment of an active power reference instruction of a fan to be solved;,,, the matrix of the incremental spatial state equations for wind thrust force F t and bearing torque force T s, respectively, and has:
，(7)
In the above-mentioned method, the step of, J g is the generator inertia, K ωT、KβT is the coefficient of first-order Taylor expansion of the bearing torque force T s at the initial time T 0, K βF and K ωF are the coefficients of first-order Taylor expansion of the wind thrust force F t at the initial time T 0, J t is the equivalent inertia, J r is the impeller rotor inertia,The value of the output power of the generator at the initial time t 0; For the efficiency of the generator, Is the value of the filtering speed at the initial instant t 0.
4. A method of fast active power optimisation of a wind farm according to claim 3, wherein the fan active power reference command increment determined in step 1.2) is increasedThe first-order partial guide equation of the incremental space state equation of the fatigue load of the wind thrust force F t and the bearing torsion force T s is shown as follows:
，(8)
，(9)
In the above-mentioned method, the step of, For all fan active power reference instructions of the wind power plant,For the increment of the fan active power reference instruction to be solved,AndIs an intermediate variable of the matrix of the incremental spatial state equations of wind thrust F t,AndIs an intermediate variable of an incremental space state equation matrix of the bearing torsion T s, and comprises:
，(10)
，(11)
In the above-mentioned method, the step of, In the form of a matrix of discrete state space,,,,The intermediate variables of the wind thrust force F t and the bearing torsion force T s respectively,Is a discrete state space matrix.
5. The method of optimizing fast active power of a wind farm according to claim 4, wherein step 4) comprises:
4.1 Solving a wind power plant fast active power optimization model by using a Lagrange method to obtain a corresponding Lagrange function:
，(15)
In the above-mentioned method, the step of, Represents Lagrangian function, N represents the number of fans in the wind power plant,Representing the output active power of the ith fanX i is the fatigue load of the fan active power reference command，For scheduling instructions from a wind farm,Representing the output active power of the ith fanIs used for the control of the maximum value of (c),U 1,u2 is a Lagrangian multiplier, and u 1,u2 is equal to or greater than 0; the requirements for extremum of the first-order partial derivative of the formula (15) include:
，(16)
，(17)
，(18)
，(19)
In the above-mentioned method, the step of, Representing a lagrangian function; according to the extreme value theory, the output active power of the ith fan is discussed by formulas (16) - (19)Judging the output active power of the ith fan in the intervalThe extremum of the interval is solved to obtain the solution of the minimum value of the objective function, and the solved function expression is as follows:
，(20)
In the above-mentioned method, the step of, A feasible solution to the objective function is represented,For the optimal solution of the objective function,For the maximum available wind power of the ith fan, x i is the active power reference instruction of the ith fan；For the first-order deflection of the fatigue load of each fan with respect to the output active power P W.i of the ith fan, the expression is as follows:
，(21)
In the above-mentioned method, the step of, Is the first-order partial derivative of the incremental space state equation of the fatigue load of the wind thrust F t,Is the first-order partial derivative of the incremental space state equation of the bearing torsion T s fatigue load,As a weighting factor of the wind thrust force F t in the objective function,Is a weight coefficient of the bearing torsion T s in the objective function.
6. The method for optimizing fast active power of a wind farm according to claim 5, wherein when the solution of the minimum value of the objective function is obtained in step 4) based on the equal-minute incremental criterion, the method further comprises the step of referencing the increment of the fan active power reference commandThe step of simplifying the first-order partial derivative of the incremental space state equation of the fatigue load with the wind thrust force F t and the bearing torsion force T s respectively into discrete incremental forms, wherein the increment of the active power reference instruction of the fan is simplifiedThe first-order partial derivative of the incremental space state equation of the fatigue load with the wind thrust force F t and the bearing torsion force T s is simplified into a discrete incremental form, and the functional expression is as follows:
， ，(22)
In the above-mentioned method, the step of, Denoted as the i-th fan active power reference instruction，Representing active power reference instruction of ith wind turbineIs used for the function of the object of (a),For all fan active power reference instructions of the wind power plant,N represents the number of fans in the wind power plant.
7. A wind farm rapid active power optimization system comprising a microprocessor and a memory connected to each other, characterized in that the microprocessor is programmed or configured to perform the steps of the wind farm rapid active power optimization method according to any of claims 1-6.
8. A computer readable storage medium, characterized in that it has stored therein a computer program for being executed by a computer device to implement the fast active power optimization method of a wind farm according to any of the claims 1-6.
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