CN110417032A - A Multi-objective Optimal Control Method for Doubly-fed Fans Participating in System Frequency Modulation - Google Patents

Abstract

The invention provides a multi-objective optimization control method for double-fed wind turbines participating in system frequency modulation, including: maximum power tracking strategy for wind turbines to restore speed; design variable power curves; and optimize variable power curve parameters based on Pareto algorithm. The technical scheme of the invention realizes the multi-objective optimization of the speed recovery process, can reduce the secondary drop of the frequency and the speed recovery time, improve the stability of the operation of the wind power generation system, and prolong the operation life.

Description

A Multi-objective Optimal Control Method for Doubly-fed Fans Participating in System Frequency Modulation

technical field

The invention belongs to the technical field of wind power generation systems, and in particular relates to a multi-objective optimal control method for double-fed wind turbines participating in system frequency modulation.

Background technique

Wind energy has many advantages such as cleanness, wide sources, and renewable. Wind power generation has become one of the most potential new energy generation methods at present. With the large-scale grid connection of wind power, the stability of grid frequency is facing increasingly severe challenges. On the one hand, due to the application of doubly-fed asynchronous generators, power and frequency are completely decoupled, resulting in wind turbines being unable to track frequency fluctuations in time, and the equivalent inertia of the system is 0; on the other hand, wind turbines generally adopt maximum power tracking control strategy , unable to provide additional active power to cope with fluctuations in grid frequency. Therefore, wind turbines cannot respond to fluctuations in grid system frequency as efficiently as conventional thermal power plants. Experts at home and abroad have made many innovative breakthroughs in wind power frequency regulation. There are two types of frequency modulation technologies for wind turbines participating in the system: reserved and non-reserved.

Participating in frequency regulation of the power system in a reserve way is to change the pitch angle or rotor speed, reduce the steady-state output power value of the wind turbine, and make it in an unloaded state, thereby obtaining a part of reserve capacity. When the system frequency decreases, increase the output power of the wind farm and use the spare capacity to participate in the frequency regulation of the system. Although the reserve mode has the same frequency regulation performance as the thermal power unit, the wind turbine is in a state of load reduction for a long time due to the reserve mode, which greatly reduces the economic benefits of wind power generation.

The method of not leaving a backup is to change the output power of the wind turbine after the disturbance occurs through the virtual rotor inertia control, prompting the wind turbine to release the kinetic energy stored in the rotor, simulating the inertia characteristics of the synchronous machine, and reducing the disadvantages brought by wind power grid connection to the system effect to help improve system frequency response. The rotor inertia control makes reasonable use of the kinetic energy stored in the wind turbine rotor. When the system frequency decreases, the rotor kinetic energy is released based on the electromagnetic torque control of the doubly-fed induction generator for short-term power support. In the process of power support, as the kinetic energy of the rotor is gradually released, when the generator speed drops to the lower limit of the speed, the wind turbine exits the frequency modulation control, restores the MPPT control, and re-absorbs the energy in the wind to restore the speed. At this time, A large drop in electromagnetic torque leads to a sudden drop in active power output, which causes a second drop in system frequency.

For example, the Chinese patent application number is CN201210037763.4, which discloses a combined control method for primary frequency modulation of double-fed wind turbines. The continuous Hopfield neural network is used to optimize the parameters of the PD controller online, and a neural controller with strong self-adaptive ability is established. It can realize the joint control of rotor kinetic energy and reserve power, take the minimum frequency change as the objective function, make the network weight correspond to the system state variable, and use the output of the neuron as the parameter of the PD controller, through the expression of the objective function and the energy function The combination of expressions obtains the change law of the parameters, and then finds the steady-state output according to the law. The invention uses the PSCAD/EMTDC simulation platform to carry out detailed simulation research on the neural joint control strategy, and compares it with the traditional frequency control strategy. The result shows that the Hopfield neural joint control has a better primary frequency modulation control effect. However, this invention does not focus on the analysis of the secondary drop of the system frequency and the recovery time for speed recovery when the primary frequency modulation control is withdrawn.

Another example is that the Chinese patent application number is 201810062931.2, which discloses a coordinated control method for double-fed fan auxiliary synchronous generators participating in the secondary frequency regulation of the power grid. Under the premise of stability, the double-fed wind turbine can actively respond to the AGC control signal and change its own output, and the double-fed wind turbine assists the synchronous generator to participate in the secondary frequency regulation of the power grid to realize the priority dispatch of new energy and save the cost of the secondary frequency regulation of the power grid. However, this invention does not propose a detailed analysis of the main target parameters that need to be coordinated and optimized for the wind power system to participate in system frequency regulation, nor does it reduce the secondary drop of frequency and speed recovery time, and improve the stability and extension of wind power system operation. The problem of operating life.

Contents of the invention

In view of the lack of focus on the analysis of the secondary drop of the system frequency and the recovery time for speed recovery when exiting the primary frequency regulation control in the prior art, the purpose of the present invention is to provide a multi-objective system for double-fed fans to participate in system frequency regulation Optimal control method.

The technical scheme that the present invention solves the problem is:

The multi-objective optimal control method includes:

Step 1. Maximum power tracking strategy for wind turbines to restore speed;

Step 2. Design variable power curve;

Step 3. Optimizing the variable power curve parameters based on the Pareto algorithm.

Further, step 1 maximum power tracking strategy wind turbine speed recovery includes:

Step 11. When the wind turbine is running in a steady state, the wind turbine runs at point A of the maximum power point tracking state; when the grid frequency decreases, the wind turbine performs frequency modulation, and the electromagnetic power output by the wind turbine is from the point A of the maximum power point tracking state. Increase from point A to point B in the state of maximum power tracking point; then run along a straight line from point B in the state of maximum power tracking point to point C of electromagnetic power for power support, and at the same time reduce the speed of the wind turbine to point C of electromagnetic power The corresponding speed lower limit;

Step 12. During the process of running the wind turbine from point B of the maximum power tracking point state to the electric power point C along a straight line for power support, when the speed of the wind turbine reaches the lower limit of the speed, the wind turbine quits frequency regulation.

Further, step 2 design variable power curve includes:

Step 21. When the speed ω _r of the wind turbine drops to the lower limit of the speed ω _rmin , the wind turbine quits frequency regulation, and the coefficient K of the variable power curve is set, which is calculated according to the following formula (1):

In the formula, ω _r is the speed of the wind turbine, ω _ref is the reference value of the speed, and ω _rmin is the speed when the wind turbine participates in frequency regulation;

Step 22. Design variable power function, calculate according to the following formula (2):

In the formula: f(t) is variable power function, t is time, first parameter a, second parameter b, third parameter t ₁ , fourth parameter t ₂ ;

Step 23. Design the variable power curve according to the formula (1) of the variable power curve coefficient and the formula (2) of the variable power function, calculate according to the following formula (3):

P _ac =K×f(t)...(3)

Where: P _ac is the variable power curve.

Further, step 3 is based on the Pareto algorithm to optimize the variable power curve parameters including:

Step 31. According to the design of the variable power curve, the range values of the first parameter a, the second parameter b, the third parameter t ₁ and the fourth parameter t ₂ are set by performing a simulation test on the variable power function f(t), As shown in the following equations (4) to (7):

-0.07≤a≤-0.06...(4)

0.1≤b≤0.3...(5)

t ₁ =173...(6)

220≤t ₂ ≤230...(7)

Step 32. By selecting the third parameter _t1 as 173 and selecting the first parameter, the second parameter and the fourth parameter in the value range of the first parameter, the second parameter and the fourth parameter to calculate different variable power curves;

Step 33. Taking the grid frequency’s secondary drop depth and speed recovery time when the wind turbine quits frequency regulation as the optimization target, determine the grid frequency’s secondary drop depth and speed recovery time according to different variable power curves, and use the grid frequency’s secondary Draw a graph with the drop depth as the abscissa and the speed recovery time as the ordinate, and use the Pareto algorithm to analyze the results and select the Pareto optimal parameters.

Compared with the prior art, the beneficial effects of the present invention are:

1. The technical solution of the present invention restores the speed of the wind turbine through the maximum power tracking strategy; designs the variable power curve; optimizes the parameters of the variable power curve based on the Pareto algorithm, thereby maintaining the stability of the grid frequency and ensuring the safety of the power system Effect.

2. The technical solution of the present invention focuses on the analysis of the secondary drop of the system frequency and the recovery time of the speed recovery when the primary frequency regulation control is withdrawn, which is of great significance for improving the wind power grid connection capability.

3. The technical solution of the present invention mainly clarifies the main target parameters that need to be coordinated and optimized for the wind power generation system participating in system frequency regulation. A detailed analysis is performed for situations where multiple objective optimizations exist. Based on the Pareto optimization theory, the Pareto optimization boundary problem of multi-objective optimization is processed, and the multi-objective optimization of the speed recovery process is realized. And it can reduce the secondary drop of the frequency and the speed recovery time, improve the stability of the wind power generation system operation, and prolong the operation life.

Description of drawings

Fig. 1 is a schematic flow chart of the multi-objective optimal control method of the present invention;

Fig. 2 is a schematic diagram of the principle of the speed recovery method of the wind turbine operating in the MPPT interval of the present invention;

Fig. 3 is the structural diagram that the optimized variable power curve of the present invention carries out speed recovery;

Fig. 4 is the frequency comparison chart of adopting MPPT curve restoration of the present invention to carry out frequency modulation and wind power not participating in frequency modulation;

Fig. 5 is the comparison diagram of the frequency change of b in the determined value range of the present invention;

Fig. 6 is a comparison chart of frequency changes of a in a certain value range of the present invention; Fig. 7 is a comparison chart of frequency changes of t ₂ in a certain value range of the present invention.

Detailed ways

The specific implementation of the multi-objective optimization control method of the present invention will be further described in detail below in conjunction with the accompanying drawings.

As shown in Figure 1, the multi-objective optimal control method includes:

Step 1. Maximum power tracking strategy wind turbine for speed recovery:

Step 11. The curve shown in Fig. 2 comprises maximum power point tracking state (MPPT) curve, dotted line and CA curve, point E, G, A and B are all located on the maximum power point tracking state (MPPT) curve, and dotted line is a certain The corresponding mechanical power-speed curve under wind speed, the CA curve is the variable power curve, and ΔP _e1 , ΔP _e2 and ΔP _e3 are the electromagnetic power drop values respectively. In order to improve the secondary drop problem of the grid frequency, the method of designing the variable power curve makes When the wind turbine of the doubly-fed fan quits frequency regulation, the electromagnetic power drops slowly along the CA curve until it intersects with the MPPT tracking curve at point A. When the speed comprehensive recovery method of the variable power curve is adopted, the output power of the wind turbine runs along the ABCA trajectory, and the electromagnetic power drop range is ΔP _e1 when the wind turbine quits frequency modulation, which greatly improves the problem of the secondary drop of the grid frequency. When the wind turbine is running in a stable state, the wind turbine runs at point A of the maximum power point tracking state; when the grid frequency decreases, the wind turbine performs frequency modulation, and the electromagnetic power output by the wind turbine is from point A of the maximum power point tracking state Increase to point B of the maximum power tracking point state; then run along a straight line from point B of the maximum power tracking point state to point C of electromagnetic power for power support, and at the same time the speed of the wind turbine decreases to the corresponding Lower speed limit;

Step 12. During the process of the wind turbine running from point B of the maximum power tracking point state to point C of electromagnetic power along a straight line for power support, when the speed of the wind turbine reaches the lower limit of the speed, the wind turbine quits frequency regulation. It can be seen from Fig. 2 that if the traditional maximum power tracking strategy is used for speed recovery, the electromagnetic power of the wind turbine will drop from point C to point E, and then the output power of the wind turbine will be recovered by MPPT along the route ABCEA. When the wind turbine quits frequency regulation, the electromagnetic power The drop value is ΔP _e1 +ΔP _e2 , and the frequency of the power grid undergoes a severe secondary drop.

Step 2. Design variable power curve;

Step 21. The structure diagram of the design strategy is shown in Figure 3. When the speed ω _r of the wind turbine drops to the lower limit of the speed ω _rmin , the wind turbine quits frequency regulation, and the coefficient K of the variable power curve is set, which is calculated according to the following formula (1):

In the formula, ω _r is the speed of the wind turbine, ω _ref is the reference value of the speed, and ω _rmin is the speed when the wind turbine participates in frequency regulation.

Set the variable power curve coefficient K so that the wind turbine output electromagnetic power will not drop too much when the wind turbine exits the frequency regulation at the beginning stage, the wind turbine speed ω _r gradually returns to ω _ref , and the variable power curve coefficient gradually decreases from 1 to 0, the variable power curve Pac gradually changes from negative to 0.

Step 22. Design variable power function, calculate according to the following formula (2):

In the formula, f(t) is a variable power function, t is time, the first parameter a, the second parameter b, the third parameter t ₁ , and the fourth parameter t ₂ .

Step 23. Design the variable power curve according to the formula (1) of the variable power curve coefficient and the formula (2) of the variable power function, calculate according to the following formula (3):

P _ac =K×f(t)...(3)

In the formula, P _ac is the variable power curve.

Step 3. Optimizing the variable power curve parameters based on the Pareto algorithm.

Step 31. According to the design of the variable power curve, the range values of the first parameter a, the second parameter b, the third parameter t ₁ and the fourth parameter t ₂ are set by performing a simulation test on the variable power function f(t), As shown in (4)~(7):

-0.07≤a≤-0.06...(4)

0.1≤b≤0.3...(5)

t ₁ =173...(6)

220≤t ₂ ≤230...(7)

Step 32. By selecting the third parameter _t1 as 173 and selecting the first parameter, the second parameter and the fourth parameter in the value range of the first parameter, the second parameter and the fourth parameter to calculate different variable power curves;

Step 33. Taking the grid frequency’s secondary drop depth and speed recovery time when the wind turbine quits frequency regulation as the optimization target, determine the grid frequency’s secondary drop depth and speed recovery time according to different variable power curves, and use the grid frequency’s secondary Draw a graph with the drop depth as the abscissa and the speed recovery time as the ordinate, and use the Pareto algorithm to analyze the results and select the Pareto optimal parameters.

Specific examples:

Constructing a simulation system consisting of a conventional synchronous unit (250MVA/13.8kV) and a certain type of wind power virtual synchronous machine (2MW/690V×31) for simulation analysis, combining the above-mentioned parameter optimization method includes the following steps:

Step 1. Maximum power tracking strategy for speed recovery

In the system simulation model constructed including the wind power virtual synchronous machine and the conventional synchronous unit, the frequency drop problem of the wind power virtual synchronous machine based on the rotor inertia control is simulated, as shown in Figure 4, and the classic MPPT restoration Research and verify the frequency change of system frequency regulation and wind power not participating in system frequency regulation.

Step 2. Optimal design of variable power curve

The simulation test is carried out for the variable power function f(t), the variable power curve is adjusted by changing the function parameter value, and the variable power curve strategy is added at the time of exiting the frequency modulation, t ₁ =173, and the influence of the change of each parameter value on the speed recovery is studied.

Step 2.1, the impact of the change of b in f(t) on the speed recovery, keep a, t ₁ and t ₂ unchanged, take different values for the parameter b in f(t), conduct simulation analysis, and compare the frequency changes respectively As shown in Figure 5;

Step 2.2, the influence of the change of a in f(t) on the speed recovery, keep b, t ₁ and t ₂ unchanged, take different values for the parameter a in f(t), conduct simulation analysis, and compare the frequency changes respectively As shown in Figure 6;

Step 2.3, the impact of the change of t ₂ in f(t) on the speed recovery, keep a, b and t ₁ unchanged, take different values for the parameter t ₂ in f(t), conduct simulation analysis, and compare the frequency changes Respectively as shown in Figure 7;

Step 3. Optimizing based on Pareto algorithm

Step 3.1, the control object of the present invention is the parameters a, b and _t2 in the variable power curve, the parameter range is set as the inequality (4), (5) and (7) setting, a certain number is taken within the above parameter range The parameters of different variable power curves are formed, and the parameters of different variable power curves are marked with numbers such as 1, 2, 3... and so on. This is simulated separately.

In step 3.2, the present invention takes the second drop depth of the frequency and the speed recovery time when the frequency regulation of wind power is out of frequency regulation as the optimization target.

Step 3.3, through the variable power curve parameters and optimization goals selected in steps 3.1 and 3.2. For different variable power curve parameters, according to the simulation or test results, the frequency secondary drop depth and speed recovery time are used to draw curves on the abscissa and ordinate, and the Pareto algorithm is used to analyze the results, and the Pareto optimum is selected. Excellent parameters.

Based on the above steps, there is a coordination problem between the secondary drop problem in the speed recovery stage and the time of the speed recovery process when the wind turbine adopts the rotor inertia control to participate in the primary frequency modulation of the system.

The present invention is not limited to the above-mentioned embodiments, and without departing from the essence of the present invention, any deformation, improvement, and replacement conceivable by those skilled in the art fall within the protection scope of the present invention.

Claims (4)

1. A kind of multi-objective optimal control method that double-fed fan participates in system frequency modulation, is characterized in that, comprises: Step 1. Maximum power tracking strategy for wind turbines to restore speed; Step 2. Design variable power curve; Step 3. Optimizing the variable power curve parameters based on the Pareto algorithm. 2. The multi-objective optimization control method for DFIG participating in system frequency modulation according to claim 1, characterized in that, step 1. The maximum power tracking strategy of the wind turbine to restore the speed comprises: Step 11. When the wind turbine is running in a steady state, the wind turbine runs at point A of the maximum power point tracking state; when the grid frequency decreases, the wind turbine performs frequency modulation, and the electromagnetic power output by the wind turbine is from the point A of the maximum power point tracking state. Increase from point A to point B in the state of maximum power tracking point; then run along a straight line from point B in the state of maximum power tracking point to point C of electromagnetic power for power support, and at the same time reduce the speed of the wind turbine to point C of electromagnetic power The corresponding speed lower limit; Step 12. During the process of running the wind turbine from point B of the maximum power tracking point state to the electric power point C along a straight line for power support, when the speed of the wind turbine reaches the lower limit of the speed, the wind turbine quits frequency regulation. 3. The multi-objective optimal control method of the double-fed fan participating in system frequency modulation according to claim 1, characterized in that, step 2. The design variable power curve comprises: Step 21. When the speed ω _r of the wind turbine drops to the lower limit of the speed ω _rmin , the wind turbine quits frequency regulation, and the coefficient K of the variable power curve is set, which is calculated according to the following formula (1): In the formula, ω _r is the speed of the wind turbine, ω _ref is the reference value of the speed, and ω _rmin is the speed when the wind turbine participates in frequency regulation; Step 22. Design variable power function, calculate according to the following formula (2): In the formula, f(t) is a variable power function, t is time, the first parameter a, the second parameter b, the third parameter t ₁ , and the fourth parameter t ₂ ; Step 23. Design the variable power curve according to the formula (1) of the variable power curve coefficient and the formula (2) of the variable power function, calculate according to the following formula (3): P _ac =K×f(t)...(3) In the formula, P _ac is the variable power curve. 4. The multi-objective optimization control method for the double-fed fan to participate in system frequency modulation according to claim 1, characterized in that, step 3. The optimization of variable power curve parameters based on the Pareto algorithm comprises: Step 31. According to the design of the variable power curve, the range values of the first parameter a, the second parameter b, the third parameter t ₁ and the fourth parameter t ₂ are set by performing a simulation test on the variable power function f(t), As shown in (4)~(7): -0.07≤a≤-0.06...(4) 0.1≤b≤0.3...(5) t ₁ =173...(6) 220≤t ₂ ≤230...(7) Step 32. By selecting the third parameter _t1 as 173 and selecting the first parameter, the second parameter and the fourth parameter in the value range of the first parameter, the second parameter and the fourth parameter to calculate different variable power curves; Step 33. Taking the grid frequency’s secondary drop depth and speed recovery time when the wind turbine quits frequency regulation as the optimization target, determine the grid frequency’s secondary drop depth and speed recovery time according to different variable power curves, and use the grid frequency’s secondary Draw a graph with the drop depth as the abscissa and the speed recovery time as the ordinate, and use the Pareto algorithm to analyze the results and select the Pareto optimal parameters.