Fuzzy frequency droop control method considering wind speed change

Abstract

The invention discloses a fuzzy frequency droop control method considering wind speed change, which introduces fuzzy frequency control parameters on the basis of traditional droop control of a fan, can effectively relieve the problem of excessive deceleration possibly occurring in a deceleration stage in the traditional droop control, and enhances the safety of the droop control. The wind turbine generator set can still obtain satisfactory frequency support performance under the condition that the operation condition changes greatly or different frequency interferences occur. Meanwhile, fuzzy control parameters considering wind speed changes are introduced, a traditional droop control method of the wind turbine is further optimized, the fuzzy control parameters are obtained through the wind speed and the wind speed change rate, when the wind speed rises rapidly, the wind turbine generator can further release more rotor kinetic energy to participate in the frequency modulation process, and therefore the wind turbine generator control efficiency is improved. When the wind speed drops quickly, the kinetic energy release of the wind turbine generator is slowed down to prevent the problem of excessive speed reduction, so that the wind turbine generator can stably return to the MPPT control mode to prepare for next frequency modulation control.

Description

Technical Field

[0001] The invention belongs to the technical field of wind power generation, and in particular relates to a fuzzy frequency droop control method considering wind speed changes. Background Art

[0002] Variable-speed wind turbines are the mainstream model in wind farms at present. They realize the functional exchange between the rotor side and the grid side through the converter, adopt vector control technology, and have the advantages of power decoupling control and maximum power point tracking (MPPT). However, MPPT operation decouples the frequency of wind turbines from the system frequency, making it lack inertial response capability. With the increasing penetration rate of wind turbines, the inertia and frequency control capabilities of the power system will decrease significantly, deteriorating the system frequency safety characteristics and restricting the level of wind power consumption. When the system frequency deviation is too large, it will trigger protection actions such as low-frequency load reduction, thereby endangering the frequency safety and stability of the entire system.

[0003] The control strategies for variable-speed wind turbines participating in frequency regulation mainly include rotor kinetic energy control and power reserve control. The power reserve control strategy, such as the literature [WANG Xin, YANG Dejian, JIN Enshu, et al. Virtual inertiacontrol optimization strategy of doubly-fed wind turbine[J]. Smart Power, 2022, 50(8):1-6, 81.WANG Xin, YANG Dejian, JIN Enshu, et al. Virtual inertiacontrol optimization strategy of doubly-fed wind turbine[J]. Smart Power, 2022, 50(8):1-6, 81], reserves a reserve through pitch control and overspeed control to support system frequency regulation. Pitch control reduces active output by controlling the pitch angle of the wind turbine, but frequent switching of the pitch angle will increase wind turbine wear. Overspeed control is restricted by the wind turbine speed and is only applicable to low wind speed conditions. The rotor kinetic energy control strategy, such as the literature [Chen Changqing, Li Xinran, Yang Yang, et al. Energy storage configuration method and coordinated operation strategy based on wind power frequency regulation characteristics [J]. Electric Power Construction, 2022, 43 (1): 96-103. Chen Changqing, Li Xinran, Yang Yang, et al. Energy storage configuration method and coordinated operation strategy based on wind power frequency regulation characteristics [J]. Electric Power Construction, 2022, 43 (1): 96-103] utilizes the kinetic energy released by the wind turbine rotor to participate in the system frequency regulation, which mainly includes inertial control and droop control. Inertial control and droop control use the measured frequency information as input, adjust their own output through the control loop, and compensate for the system power shortage by releasing the kinetic energy in the rotor. However, these controls no longer consider the impact of wind speed changes after the frequency regulation begins; in fact, during the frequency regulation period, the wind speed input to the wind turbine may change significantly, and continuous wind speed changes may have a significant impact on the wind turbine during the frequency regulation period. Summary of the invention

[0004] In order to enable wind turbines to adapt to the continuous changes in wind speed during the frequency regulation process and thus obtain better frequency regulation effects, the present invention provides a fuzzy frequency droop control method that takes wind speed changes into consideration. The method combines fuzzy logic control with droop control and uses fuzzy logic control to directly modify droop control parameters according to wind speed changes, which can effectively improve the adaptability of the control strategy to wind speed changes.

[0005] A fuzzy frequency droop control method considering wind speed changes is proposed. For a grid-connected system including a wind turbine, an output active power reference value of a converter control unit on the wind turbine side is improved to improve the system's adaptability to wind speed changes during frequency regulation.

[0006] Furthermore, the specific process of improving the design of the output active power reference value is as follows:

[0007] (1) Obtain the grid frequency and wind speed through monitoring, and then calculate the frequency change rate, frequency change amount and wind speed change rate;

[0008] (2) Calculate and determine the frequency adjustment coefficient k based on the frequency change rate and frequency change amount f ;

[0009] (3) Calculate and determine the frequency control parameter C based on the wind speed and wind speed change rate;

[0010] (4) According to the frequency adjustment coefficient k f The output active power reference value is determined by calculating and calculating the frequency control parameter C.

[0011] Furthermore, the frequency change rate calculated in step (1) is The frequency change is αΔf s |, wind speed change rate is where Δf s =f s -f 0 , f s is the actual grid frequency, f 0 is the rated frequency of the power grid, v is the wind speed, t represents the time, and α is the regulation coefficient.

[0012] Furthermore, the size of the control coefficient α is determined by the wind power penetration rate (i.e., the proportion of wind power generation capacity in the system to all power generation capacity). When the wind power penetration rate is low (by setting a threshold for comparison), increasing the value of α is conducive to the wind turbine to fully utilize the rotor kinetic energy of wind power to accelerate the operation recovery of the wind turbine; when the wind power penetration rate is high, reducing the value of α is conducive to reducing the burden of the remaining synchronous generators in the system to output more active power to adjust the frequency by controlling and slowing down the MPPT operation recovery speed of the wind turbine, thereby preventing the occurrence of secondary frequency drop.

[0013] Furthermore, in step (2), the frequency adjustment coefficient k is determined by using a fuzzy controller. fThe specific implementation method is as follows: first, normalize the frequency change rate and the frequency change amount to the range of [0, 1], then evenly divide the entire frequency change rate value range into 6 fuzzy sets, from small to large, namely NB, NM, NS, PS, PM, PB, and divide the frequency change amount and the frequency adjustment coefficient k into 6 fuzzy sets. f The entire value range of is evenly divided into 9 fuzzy sets, from small to large, they are NL, NM, NS, NZ, Z, PZ, PS, PM, PL; finally, according to the frequency adjustment coefficient k f The frequency adjustment coefficient k is determined by the triangular membership relationship between the frequency change rate and the frequency change amount. f The fuzzy set where the specific value is located, and then the frequency adjustment coefficient k is calculated through the defuzzification algorithm f The specific value of .

[0014] Furthermore, in the step (3), the frequency control parameter C is calculated and determined by using a fuzzy controller. The specific implementation method is as follows: first, the wind speed and the wind speed change rate are normalized to the range of [0, 1], and then the entire value range of the wind speed change rate is evenly divided into 4 fuzzy sets, which are NS, NB, PS, and PB from small to large. The entire value range of the wind speed is evenly divided into 4 fuzzy sets, which are NFR_L, Mid, High, and NFR_H from small to large. The entire value range of the frequency control parameter C is evenly divided into 7 fuzzy sets, which are NB, NM, NS, ZE, PS, PM, and PB from small to large. Finally, according to the triangular membership relationship between the frequency control parameter C and the wind speed and the wind speed change rate, the fuzzy set where the specific value of the frequency control parameter C is located is determined, and then the specific value of the frequency control parameter C is calculated by a defuzzification algorithm.

[0015] Furthermore, in step (4), the output active power reference value is calculated by the following formula:

[0016]

[0017] Where: P ref is the output active power reference value, P MPPT is the active power output of the wind turbine in the MPPT operation mode, Δf s =f s -f 0 , f s is the actual grid frequency, f 0 is the rated frequency of the power grid, K is the intermediate variable, and ΔP is the power compensation amount.

[0018] Preferably, a start condition is set for the fuzzy frequency droop control method of the present invention, that is, when the actual grid frequency f s Exceeding the set frequency range [f min , fmax ], so that the wind turbine side converter control unit adopts the output active power reference value with improved design.

[0019] A computer device comprises a memory and a processor, wherein the memory stores a computer program, and the processor is used to execute the computer program to implement the above-mentioned fuzzy frequency droop control method considering wind speed changes.

[0020] A computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the fuzzy frequency droop control method considering wind speed changes is implemented.

[0021] Based on the above technical solution, the present invention has the following beneficial technical effects:

[0022] 1. The present invention introduces fuzzy frequency control parameters into the traditional droop control of the wind turbine, which can effectively alleviate the problem of excessive deceleration that may occur in the deceleration stage of the traditional droop control and enhance the safety of the droop control. The superiority of fuzzy control enables the wind turbine to obtain satisfactory frequency support performance even when the operating conditions vary greatly or when different frequency interferences occur.

[0023] 2. The present invention introduces fuzzy control parameters that take wind speed changes into consideration, and further optimizes the traditional droop control method of the wind turbine. The fuzzy control parameters obtained through real-time wind speed and wind speed change rate can enable the wind turbine to further release more rotor kinetic energy to participate in the frequency modulation process when the wind speed rises rapidly, and slow down the kinetic energy release of the wind turbine to prevent the problem of excessive deceleration when the wind speed drops rapidly, so that the wind turbine can smoothly return to the MPPT control mode and prepare for the next frequency modulation control. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 is the frequency adjustment coefficient k f Schematic diagram of the triangular membership function between the frequency change rate and the frequency change amount.

[0025] Figure 2 k is the output frequency adjustment coefficient of the fuzzy controller f Schematic diagram of the results. DETAILED DESCRIPTION

[0026] In order to describe the present invention more specifically, the technical solution of the present invention is described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0027] The fuzzy frequency droop control method considering wind speed change in the present invention aims at the shortcomings of the traditional fan frequency droop control, such as weak dynamic adjustment ability and lack of consideration of wind speed change. The fuzzy frequency droop control is used to adjust the reference power of the fan in the frequency adjustment process in real time. That is, in the frequency adjustment control process, the fuzzy control parameter C is obtained according to the real-time wind speed and wind speed change rate, and the fuzzy control parameter C is obtained according to the frequency change rate. and frequency change αΔf s |Get the fuzzy frequency control parameter k f , and finally by changing the reference power P ref Influence the fan frequency modulation process, the specific implementation process is as follows:

[0028] Step 1: Based on the frequency event detection of the wind turbine, the parameter k used to implement the fuzzy frequency droop control is designed. f , fuzzy controller 1 according to the frequency change rate and frequency change αΔf s |, the parameter k of fuzzy frequency droop control is obtained through fuzzy controller f , where α is an adjustable parameter. When the wind power penetration rate is low, α can be increased, which is conducive to the wind turbine to fully utilize the rotor kinetic energy of wind power to accelerate the operation recovery of the wind turbine; when the wind power penetration rate is high, α can be reduced, which makes the fuzzy control slow down the MPPT operation recovery speed of the wind turbine, which is conducive to reducing the burden of the remaining synchronous generators in the network to output more active power to adjust the frequency and prevent the occurrence of secondary frequency drop.

[0029] Fuzzy frequency adjustment parameter k f The specific process is as follows: When the system frequency exceeds the frequency interval [f min ,f max ], the starting condition is met; the frequency adjustment coefficient k f From the fuzzy controller 1, the two input αΔf s |Normalized in the range [0, 1], αΔf s The membership function of | is assigned to 9 fuzzy sets. The membership function of 6 fuzzy sets is assigned. The specific fuzzy logic rules and membership functions are shown in Table 1 and Figure 1 As shown, the frequency adjustment coefficient k output by fuzzy controller 1 is f like Figure 2 shown.

[0030] Table 1

[0031]

[0032] Step 2: Design the frequency control parameter C considering the wind speed. When the start-up condition is detected, the reference output power of the wind turbine is determined by P in MPPT mode. MPPT Change to P ref , P ref The fuzzy control parameter C is obtained from the real-time wind speed and wind speed change rate, and the frequency change rate is and frequency change αΔf s |Get k f Together constitute.

[0033] Wind turbine reference output power P ref The expression is as follows:

[0034]

[0035] Where: k f and C are the outputs of the two fuzzy controllers respectively.

[0036] Similar to fuzzy controller 1, fuzzy controller 2 combines wind speed v with wind speed change rate Normalized and assigned to the fuzzy set using the triangular membership function, the control logic of fuzzy controller 2 is shown in Table 2:

[0037] Table 2

[0038]

[0039] In order to obtain accurate parameters for defuzzification, this implementation adopts the area centroid method to output the calculation results of the triangular membership function. The area centroid method is to find the center of the fuzzy set membership function curve and the area enclosed by the horizontal coordinate, and select the horizontal coordinate value corresponding to this center as the representative value of this fuzzy set. Suppose the membership function of the fuzzy set A on the domain U is A(μ), μ∈U, and the horizontal coordinate corresponding to the area center is μ c , according to the definition of the area center method, the formula is:

[0040]

[0041] The above description of the embodiments is to facilitate the understanding and application of the present invention by those skilled in the art. It is obvious that those skilled in the art can easily make various modifications to the above embodiments and apply the general principles described herein to other embodiments without creative work. Therefore, the present invention is not limited to the above embodiments. Improvements and modifications made by those skilled in the art to the present invention based on the disclosure of the present invention should be within the protection scope of the present invention.

Claims

1. A fuzzy frequency droop control method considering wind speed variation, characterized in that: For the grid-connected system including wind turbines, the output active power reference value of the wind turbine-side converter control unit is improved and designed to improve the system's adaptability to wind speed changes during frequency regulation.

2. The fuzzy frequency droop control method considering wind speed variation according to claim 1 is characterized in that: The specific process of improving the design of the output active power reference value is as follows: (1) Obtain the grid frequency and wind speed through monitoring, and then calculate the frequency change rate, frequency change amount and wind speed change rate; (2) Calculate and determine the frequency adjustment coefficient k based on the frequency change rate and frequency change amount f ; (3) Calculate and determine the frequency control parameter C based on the wind speed and wind speed change rate; (4) According to the frequency adjustment coefficient k f The output active power reference value is determined by calculating and calculating the frequency control parameter C.

3. The fuzzy frequency droop control method considering wind speed variation according to claim 2, characterized in that: The frequency change rate calculated in step (1) is The frequency change is αΔf s |, wind speed change rate is where Δf s =f s -f0,f s is the actual grid frequency, f0 is the rated grid frequency, v is the wind speed, t represents the time, and α is the regulation coefficient.

4. The fuzzy frequency droop control method considering wind speed variation according to claim 3 is characterized in that: The size of the control coefficient α is determined by the wind power penetration rate. When the wind power penetration rate is low, increasing the value of α is conducive to the wind turbine to fully utilize the rotor kinetic energy of wind power to accelerate the operation recovery of the wind turbine; when the wind power penetration rate is high, reducing the value of α, by controlling and slowing down the MPPT operation recovery speed of the wind turbine, is conducive to reducing the burden of the remaining synchronous generators in the system to output more active power to adjust the frequency, and preventing the occurrence of secondary frequency drop.

5. The fuzzy frequency droop control method considering wind speed variation according to claim 2 is characterized in that: In step (2), the frequency adjustment coefficient k is calculated and determined by using a fuzzy controller f The specific implementation method is as follows: first, normalize the frequency change rate and the frequency change amount to the range of [0, 1], then evenly divide the entire frequency change rate value range into 6 fuzzy sets, from small to large, namely NB, NM, NS, PS, PM, PB, and divide the frequency change amount and the frequency adjustment coefficient k into 6 fuzzy sets. f The entire value range of is evenly divided into 9 fuzzy sets, from small to large, they are NL, NM, NS, NZ, Z, PZ, PS, PM, PL; finally, according to the frequency adjustment coefficient k f The frequency adjustment coefficient k is determined by the triangular membership relationship between the frequency change rate and the frequency change amount. f The fuzzy set where the specific value is located, and then the frequency adjustment coefficient k is calculated through the defuzzification algorithm f The specific value of .

6. The fuzzy frequency droop control method considering wind speed variation according to claim 2, characterized in that: In the step (3), the frequency control parameter C is calculated and determined by using a fuzzy controller. The specific implementation method is as follows: first, the wind speed and the wind speed change rate are normalized to the range of [0, 1], and then the entire value interval of the wind speed change rate is evenly divided into 4 fuzzy sets, which are NS, NB, PS, and PB from small to large. The entire value interval of the wind speed is evenly divided into 4 fuzzy sets, which are NFR_L, Mid, High, and NFR_H from small to large. The entire value interval of the frequency control parameter C is evenly divided into 7 fuzzy sets, which are NB, NM, NS, ZE, PS, PM, and PB from small to large. Finally, according to the triangular membership relationship between the frequency control parameter C and the wind speed and the wind speed change rate, the fuzzy set where the specific value of the frequency control parameter C is located is determined, and then the specific value of the frequency control parameter C is calculated by a defuzzification algorithm.

7. The fuzzy frequency droop control method considering wind speed variation according to claim 2, characterized in that: In step (4), the output active power reference value is calculated by the following formula: Where: P ref is the output active power reference value, P MPPT is the active power output of the wind turbine in the MPPT operation mode, Δf s =f s -f0,f s is the actual grid frequency, f0 is the rated grid frequency, K is the intermediate variable, and ΔP is the power compensation amount.

8. The fuzzy frequency droop control method considering wind speed variation according to claim 1, characterized in that: Set the starting condition for the fuzzy frequency droop control method, that is, when the actual grid frequency f s Exceeding the set frequency range [f min , f max ], so that the wind turbine side converter control unit adopts the output active power reference value with improved design.

9. A computer device comprising a memory and a processor, characterized in that: The memory stores a computer program, and the processor is used to execute the computer program to implement the fuzzy frequency droop control method considering wind speed changes as claimed in any one of claims 1 to 8.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the fuzzy frequency droop control method considering wind speed changes as claimed in any one of claims 1 to 8 is implemented.

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