Wind turbine comprehensive inertia frequency control method based on fuzzy adaptive parameters

Abstract

The present application relates to the technical field of smart grid, and provides a wind turbine comprehensive inertia frequency control method based on fuzzy adaptive parameters. In the method, the adaptive parameter method based on frequency and speed is adopted in the wind turbine frequency support stage, the first fuzzy adaptive control law is used to dynamically correct the comprehensive inertia frequency control parameters according to the proportion of the remaining adjustable kinetic energy of the wind turbine to the total adjustable kinetic energy and the first system frequency deviation, the maximum frequency deviation is reduced, and the frequency secondary drop is relieved; in the speed recovery stage, the wind turbine output power reference value is dynamically corrected by the fuzzy adaptive control according to the proportion of the remaining adjustable kinetic energy of the wind turbine to the total adjustable kinetic energy and the second system frequency deviation, the frequency secondary drop and the speed recovery time are comprehensively considered, the output power reference value dynamically changes with the wind turbine frequency approaching the steady-state frequency after the system disturbance, the frequency secondary drop is relieved, the speed recovery time is avoided to be too long, and the system operation stability is improved.

Description

Technical Field

[0001] This invention relates to the field of smart grid technology, and in particular to integrated inertial frequency control technology, specifically to an integrated inertial frequency control method for wind turbine generators based on fuzzy adaptive parameters. Background Technology

[0002] Because wind turbines typically operate in maximum power point tracking (MPPT) mode, the large-scale grid connection of wind power and the decreasing proportion of traditional synchronous turbines lead to a decline in the equivalent inertia and primary frequency regulation capability of the power system, posing a significant challenge to system frequency stability. Therefore, new requirements have been placed on wind power grid connection, particularly requiring wind turbines to possess active grid frequency support capabilities similar to synchronous turbines. According to the "Technical Regulations for Wind Farm Grid Connection," grid-connected wind and solar power should have primary frequency regulation capabilities to ensure frequency system safety.

[0003] Currently, the main methods for active frequency support in wind turbines include rotor kinetic energy control and power reserve control. Power reserve control aims to operate the wind turbine away from its maximum power output, reserving some power to respond to system frequency changes. However, this can lead to wind curtailment, reduced wind energy utilization efficiency, and decreased economic benefits for wind farms. Rotor kinetic energy control, on the other hand, uses additional control to reduce turbine speed, release stored kinetic energy, and increase turbine output power to provide frequency support for the system. Existing additional control strategies mainly include droop control, virtual inertia control, and a combination of both, called synthetic inertia (SI) control. To ensure stable turbine operation, when the speed drops to the minimum allowable value, the wind turbine will exit frequency regulation, which will trigger a secondary frequency drop (SFD) phenomenon.

[0004] When wind turbines use integrated inertial control for frequency regulation, it can be divided into two stages: the frequency support stage and the speed recovery stage. In the frequency support stage, using fixed integrated inertial control parameters means that smaller parameters cannot fully utilize the turbine's frequency regulation capability, while larger parameters will cause a significant secondary frequency drop. Current technologies propose mitigating this secondary frequency drop by adaptively adjusting the integrated inertial control parameters based on turbine speed, but these do not consider the system's frequency regulation requirements. Furthermore, current technologies using system frequency deviation and frequency change rate as inputs for fuzzy control and dynamically changing the droop control coefficient for frequency control do not consider the turbine's own frequency regulation capability. When the turbine enters the speed recovery stage after the frequency support stage, it will revert to MPPT mode. At this point, power deficit will cause SFD (Slow Speed ​​Delay). To reduce SFD during speed recovery, the turbine's output power can be reduced by a constant value at the end of the frequency support stage, but this approach still results in a considerable SFD. Using a smooth, accelerated recovery curve in the speed recovery stage can reduce SFD, but it will prolong the speed recovery time. Summary of the Invention

[0005] In view of the defects and shortcomings of existing technologies, especially the lack of sufficient consideration of system frequency regulation requirements and the wind turbine's own frequency regulation capabilities, and the problem of frequency control that reduces SFD but results in excessively long speed recovery time, the purpose of this invention is to provide a comprehensive inertial frequency control method for wind turbines based on fuzzy adaptive parameters. In the frequency support phase, this method comprehensively considers the system's frequency regulation requirements and the wind turbine's own frequency regulation potential, using fuzzy control to dynamically change the comprehensive inertial parameters, reducing the maximum frequency deviation and mitigating the secondary frequency drop, exhibiting good adaptability. In the speed recovery phase, this method comprehensively considers the secondary frequency drop and the speed recovery time, using fuzzy control to dynamically adjust the wind turbine's output power reference value, which can mitigate the secondary frequency drop while avoiding the problem of excessively long speed recovery time, thus improving system operational stability.

[0006] According to a first aspect of the present invention, a method for integrated inertial frequency control of wind turbine generators based on fuzzy adaptive parameters is proposed, comprising the following steps:

[0007] Obtain parameters of wind farms and their wind turbines;

[0008] Based on the frequency and speed of the wind turbine, adaptive frequency regulation control of the wind turbine is carried out through the comprehensive inertial control equation of the wind turbine consisting of virtual inertia control and droop control. In this process, the fuzzy adaptive part of the droop control coefficient and inertial control coefficient of the wind turbine is dynamically corrected according to the first fuzzy adaptive control law based on the proportion of the remaining adjustable kinetic energy of the wind turbine to the total adjustable kinetic energy and the frequency deviation of the first system.

[0009] Furthermore, during the speed recovery phase, based on the proportion of the remaining adjustable kinetic energy of the wind turbine to the total adjustable kinetic energy and the frequency deviation of the second system, the output power reference value of the wind turbine is dynamically corrected according to the second fuzzy adaptive control law, so that the output power reference value changes dynamically as the wind turbine frequency approaches the steady-state frequency after the system disturbance.

[0010] In combination with the implementation of the above technical solutions, compared with the prior art, the significant advantages of the wind turbine integrated inertial frequency control method based on fuzzy adaptive parameters proposed in this invention are as follows:

[0011] In the frequency support stage of wind turbine, an adaptive parameter method based on frequency and speed is adopted. According to the proportion of the remaining adjustable kinetic energy of the wind turbine to the total adjustable kinetic energy and the frequency deviation of the first system, the frequency regulation requirements of the system and the frequency regulation potential of the wind turbine itself are comprehensively considered. The fuzzy adaptive part of the droop control coefficient and inertia control coefficient of the wind turbine is dynamically corrected according to the first fuzzy adaptive control law. The comprehensive inertia parameter is dynamically changed by using the first fuzzy adaptive control law, which can reduce the maximum frequency deviation and alleviate the secondary frequency drop.

[0012] During the speed recovery phase, based on the proportion of the remaining adjustable kinetic energy of the wind turbine to the total adjustable kinetic energy and the frequency deviation of the second system, and taking into account the secondary frequency drop and the speed recovery time, the output power reference value of the wind turbine is dynamically corrected according to the second fuzzy adaptive control law. This makes the output power reference value change dynamically as the wind turbine frequency approaches the steady-state frequency after the system disturbance, which can alleviate the secondary frequency drop and avoid excessive speed recovery time, thereby improving the system's operational stability. Attached Figure Description

[0013] Figure 1 A flowchart of a wind turbine integrated inertial frequency control method based on fuzzy adaptive parameters provided in an embodiment of the present invention.

[0014] Figure 2 The schematic diagram of a 3-unit, 9-node wind farm access system provided in an embodiment of the present invention.

[0015] Figure 3a The frequency support stage fuzzy control input provided in the embodiments of the present invention x f A schematic diagram of the membership function curve.

[0016] Figure 3b The frequency support stage fuzzy control input provided in the embodiments of the present invention x i A schematic diagram of the membership function curve.

[0017] Figure 3c The frequency support stage fuzzy control output provided in the embodiments of the present invention kad,i , k ain,i A schematic diagram of the membership function curve.

[0018] Figure 4a Comprehensive inertial parameters provided for embodiments of the present invention k ad,i The result of the reasoning is shown in the diagram.

[0019] Figure 4b Comprehensive inertial parameters provided for embodiments of the present invention k ain,i The result of the reasoning is shown in the diagram.

[0020] Figure 5a Fuzzy control input for the speed recovery stage provided in this embodiment of the invention x fss A schematic diagram of the membership function curve.

[0021] Figure 5b Fuzzy control output for the speed recovery stage provided in this embodiment of the invention P i A schematic diagram of the membership function curve.

[0022] Figure 6 The diagram shows the inference results of the wind turbine output power reference value provided in the embodiments of the present invention.

[0023] Figure 7a A comparison diagram of system frequencies for different control methods of a fan at the same wind speed, provided in an embodiment of the present invention.

[0024] Figure 7b This is a comparison chart of the output power of different control methods for a fan at the same wind speed, provided in an embodiment of the present invention.

[0025] Figure 7c A comparison chart of the rotational speeds of different control methods for a fan under the same wind speed, provided in an embodiment of the present invention.

[0026] Figure 8a This is a comparison chart of system frequencies for different control methods of a fan at different wind speeds, provided in an embodiment of the present invention.

[0027] Figure 8b This is a comparison chart of the output power of different control methods for fans at different wind speeds, provided in an embodiment of the present invention.

[0028] Figure 8c This is a comparison chart of the rotational speed of a fan under different wind speeds and different control methods, provided in an embodiment of the present invention. Detailed Implementation

[0029] To better understand the technical content of the present invention, specific embodiments are described below in conjunction with the accompanying drawings.

[0030] Various aspects of the invention are described in this disclosure with reference to the accompanying drawings, which illustrate numerous illustrative embodiments. The embodiments of this disclosure are not necessarily intended to encompass all aspects of the invention. It should be understood that the various concepts and embodiments described above, as well as those described in more detail below, can be implemented in any of many ways, because the concepts and embodiments disclosed herein are not limited to any particular implementation. Furthermore, some aspects of the invention disclosed may be used alone or in any suitable combination with other aspects of the invention disclosed.

[0031] Combination Figure 1 The example of the integrated inertial frequency control method for wind turbines based on fuzzy adaptive parameters, as shown, includes the following process:

[0032] Step S101: Obtain the parameters of the wind farm and its wind turbines;

[0033] Step S102: Based on the frequency and speed of the wind turbine, adaptive frequency regulation control of the wind turbine is carried out through the comprehensive inertial control equation of the wind turbine consisting of virtual inertial control and droop control. In this process, according to the proportion of the remaining adjustable kinetic energy of the wind turbine to the total adjustable kinetic energy and the frequency deviation of the first system, the fuzzy adaptive part of the droop control coefficient and the inertial control coefficient of the wind turbine is dynamically corrected according to the first fuzzy adaptive control law.

[0034] Step S103: During the speed recovery phase, based on the proportion of the remaining adjustable kinetic energy of the wind turbine to the total adjustable kinetic energy and the frequency deviation of the second system, the output power reference value of the wind turbine is dynamically corrected according to the second fuzzy adaptive control law, so that the output power reference value changes dynamically as the wind turbine frequency approaches the steady-state frequency after the system disturbance.

[0035] As an optional implementation, in step S101, obtaining the parameters of the wind farm and its wind turbines includes:

[0036] Obtain the wiring within the wind farm l-k impedance between Z lk ,in, l =1,2,…,m; k =1,2,…,m; l , k These are the node numbers within the wind farm, and m is the total number of nodes in the wind farm.

[0037] Obtain the wind turbine G in the wind farm wi capacity S wi Inertial time constant H wi and generator terminal transformer capacity STi Short-circuit impedance Z Ti and wind speed v i , i =1,2,…, n w , n w This represents the total number of wind turbines in the wind farm.

[0038] As an optional implementation, in step S102, to simulate the inertial support and primary frequency regulation function of the synchronous generator, the comprehensive inertial control equation of the wind turbine, consisting of virtual inertia control and droop control, is expressed as follows:

[0039] Δ P WT = - k in ×(d f / d t ) + k d ×( f 0 – f );

[0040] In the formula, Δ P WT This represents the additional power generated during frequency regulation of the wind turbine. f System frequency, f 0 represents the steady-state frequency of the system before the disturbance. k in For inertial control coefficients, k d This is the droop control coefficient;

[0041] Therefore, the comprehensive inertia control coefficient of the wind turbine is determined as follows:

[0042] k d,i = k d0,i + k ad,i ;

[0043] k in,i = k in0,i + k ain,i ;

[0044] In the formula, k d,i , k in,i Wind turbine G wi The droop control coefficient and the inertia control coefficient;k d0,i , k in0,i Wind turbine G wi The fixed portions of the sag control coefficient and the inertia control coefficient. k ad,i , k ain,i Wind turbine G wi The fuzzy adaptive components of the droop control coefficient and the inertia control coefficient.

[0045] Because wind turbines have different wind speeds and therefore different frequency regulation potentials, in the examples of this invention, the wind turbine G... wi The fixed values ​​for the sag control coefficient and the inertia control coefficient are as follows:

[0046] ;

[0047] Where A1 and A2 represent the fixed portion of the droop control coefficient and the fixed portion of the inertia control coefficient under the reference wind speed, respectively; ω nom This represents the steady-state speed of the wind turbine before disturbance at the reference wind speed; ω 0,i For wind turbine G wi Steady-state speed before the disturbance.

[0048] As an optional example, the reference wind speed is taken as 10.5 m / s, A1=15, and A2=1. ω min Indicates wind turbine G wi Minimum speed limit, ω min =0.75.

[0049] In an embodiment of the present invention, during the frequency support stage of the wind turbine, variables related to frequency and rotational speed are used as inputs for fuzzy control, and the fuzzy adaptive part is dynamically changed, thereby fully utilizing the frequency regulation potential of the wind turbine, reducing the maximum frequency deviation, and mitigating the secondary frequency drop.

[0050] As an optional implementation, in step S102, based on the proportion of the remaining adjustable kinetic energy of the wind turbine to the total adjustable kinetic energy and the first system frequency deviation, the fuzzy adaptive portion of the droop control coefficient and inertia control coefficient of the wind turbine is dynamically corrected according to the first fuzzy adaptive control law, including:

[0051] Obtain wind turbine operating state factors x i :

[0052] ;

[0053] The wind turbine operating status factor x i The proportion of the remaining adjustable kinetic energy to the total adjustable kinetic energy of the wind turbine is defined to characterize the wind turbine's frequency regulation capability. ω r,i Indicates the rotational speed of the wind turbine;

[0054] Obtain the first system frequency deviation x f :

[0055] ;

[0056] The first system frequency deviation x f Used to characterize the kinetic energy required by the system during frequency modulation, among which f min This indicates the minimum allowed frequency value of the system;

[0057] Configure the first fuzzy controller based on the ratio of the remaining adjustable kinetic energy of the fan to the total adjustable kinetic energy. x i and the first system frequency deviation x f As input, the fuzzy adaptive part of the wind turbine's droop control coefficient and inertia control coefficient is used. k ad,i , k ain,i As output; where, as input x f It includes three fuzzy subsets, from smallest to largest: S, M, and B; as input... x i And as output k ad,i , k ain,i Each includes 7 fuzzy subsets, which are listed from smallest to largest as: SS, MS, S, M, SB, MB, B;

[0058] The first fuzzy controller is configured to dynamically correct the fuzzy adaptive portions of the wind turbine's droop control coefficient and inertia control coefficient according to a first fuzzy adaptive control law. k ad,i , k ain,i The first fuzzy adaptive control law is as follows:

[0059] For the fuzzy adaptive part of the wind turbine droop control coefficient k ad,i :

[0060] When the remaining kinetic energy of the wind turbine is sufficient, the proportion of the remaining adjustable kinetic energy of the wind turbine to the total adjustable kinetic energy is... x i When the frequency is S~B, providing frequency support is the priority control objective, and a larger frequency range is used. k ad,i ;

[0061] When the remaining kinetic energy of the fan is insufficient, the proportion of the fan's remaining adjustable kinetic energy to the total adjustable kinetic energy is... x i When the frequency is SS~MS, the priority control objective is to reduce the frequency of secondary drops, and a smaller [control method] is used. k ad,i ;

[0062] For the fuzzy adaptive part of the inertial control coefficient of wind turbine generators k ain,i :

[0063] k ain,i It decreases as the frequency deviation of the first system increases, and it decreases as the remaining kinetic energy of the wind turbine decreases.

[0064] In embodiments of the present invention, the fuzzy controller mainly consists of four parts: fuzzification, fuzzy rules, fuzzy inference, and defuzzification. Through membership functions, fuzzy concepts in fuzzy linguistic variables can be transformed into specific numerical values. The essence of fuzzy control is to "describe the actual working conditions using fuzzy language (such as "large / medium / small") and then transform them into specific control parameters."

[0065] In an embodiment of the present invention, during the frequency support stage corresponding to step S102, the first system frequency deviation... x f It characterizes the system frequency deviation, and its fuzzy subset describes the fuzzy language of the working conditions, including three: S (small, indicating that the system lacks support), M (medium, indicating that the system needs medium support), and B (large, indicating that the system urgently needs strong support), which are used to judge the urgency of the system side.

[0066] The proportion of the remaining adjustable kinetic energy of the wind turbine to the total adjustable kinetic energy x i The fuzzy subsets used to characterize the extent of support a wind turbine can provide describe the fuzzy language of the operating conditions, including: SS (very small, indicating almost no kinetic energy), MS (small, indicating little kinetic energy), S (relatively small, indicating less kinetic energy), M (medium, indicating moderate kinetic energy), SB (relatively large, indicating more kinetic energy), MB (large, indicating more kinetic energy), and B (extremely large, indicating sufficient kinetic energy), which are used to determine the upper limit of the wind turbine's capacity.

[0067] The purpose of droop control is to increase the power output of the wind turbine as the system frequency decreases. kad,i The larger the frequency, the more power the wind turbine generates (the stronger the support), but excessive generation will consume too much kinetic energy, causing the frequency to drop twice (after the first drop, it drops again during the recovery).

[0068] In an example of the present invention, the fuzzy control rule for the fuzzy adaptive portion of the droop control coefficient is as follows:

[0069] When the wind turbine has sufficient remaining kinetic energy (S~B), the primary function is to provide frequency support, and a larger [energy level] is used. k ad,i Prioritize meeting the system's frequency regulation needs, using a larger... k ad,i Provide strong support;

[0070] When the remaining kinetic energy of the wind turbine is very small (SS~MS), the focus is on reducing the secondary frequency drop, and a smaller frequency is used. k ad,i Prioritize protecting wind kinetic energy, using smaller... k ad,i Avoid excessive consumption and prevent the frequency from dropping again.

[0071] The role of inertial control is to simulate the inertia of a synchronous generator—when the frequency changes abruptly, the kinetic energy released by the wind turbine slows down the rate of frequency decrease (i.e., reduces the initial rate of change of frequency). k ain,i The larger the frequency, the slower it decreases, but it will still consume kinetic energy.

[0072] In an example of the present invention, the fuzzy control rule for the fuzzy adaptive part of the inertial control coefficients is as follows:

[0073] k ain,i It decreases as the frequency deviation increases and the remaining kinetic energy of the wind turbine decreases.

[0074] Therefore, the established fuzzy control rules are used to... k ad,i , k ain,i After performing fuzzy inference, the defuzzified result multiplied by the corresponding scaling factor can be obtained. k ad,i , k ain,i Actual value.

[0075] It should be understood that, in the embodiments of the present invention, as described in the aforementioned calculation process, the wind turbine operating state factor x i Frequency deviation from system x f The domain of discourse is [0,1].

[0076] To ensure good adaptability of fuzzy control, the output universe of discourse of the first fuzzy controller is set to [0,1]. After fuzzy inference and defuzzification using the established first fuzzy adaptive control law, the output is multiplied by the corresponding scaling factor. k 1,i , k 2,i And obtain k ad,i , k ain,i Actual value:

[0077] ;

[0078] in, k 1,i , k 2,i A1 and A2 represent the scaling factors of fuzzy control droop and inertia coefficient, respectively. A3 and A4 represent the scaling factors of fuzzy control droop and inertia coefficient at the reference wind speed, respectively.

[0079] As an optional example, A3 has a value of 25; A4 has a value of 10.

[0080] As an optional example, in step S103, based on the proportion of the remaining adjustable kinetic energy of the wind turbine to the total adjustable kinetic energy and the frequency deviation of the second system, the output power reference value of the wind turbine is dynamically corrected according to the second fuzzy adaptive control law, so that the output power reference value changes dynamically as the wind turbine frequency approaches the steady-state frequency after the system disturbance, including:

[0081] Obtain the second system frequency deviation during the speed recovery phase x fss :

[0082] ;

[0083] In the formula, f ss The steady-state frequency of the system after disturbance, and the frequency deviation of the second system. x fss Characterizing system frequency deviation f ss The degree;

[0084] Configure a second fuzzy controller based on the ratio of the remaining adjustable kinetic energy of the fan to the total adjustable kinetic energy. x i and the frequency deviation of the second system x fss As input, the wind turbine's output power reference value P i As output;

[0085] Among them, the input x fss It includes three fuzzy subsets, from smallest to largest: S, M, and B; as input... x i It includes 7 fuzzy subsets, from smallest to largest: SS, MS, S, M, SB, MB, B; as the output P i It includes five fuzzy subsets, from smallest to largest: VS, RS, M, RB, and VB.

[0086] The second fuzzy controller is configured to dynamically correct the output power reference value of the wind turbine according to the second fuzzy adaptive control law. P i The second fuzzy adaptive control law is as follows:

[0087] In the initial stage of speed recovery (when the frequency deviation is large and the risk of secondary frequency drop is extremely high), a larger output power reference value is adopted in order to mitigate the secondary frequency drop.

[0088] As the frequency gradually approaches the steady-state frequency after the system disturbance... f ss (As the frequency deviation gradually decreases, the risk of a secondary drop gradually decreases.) A reduced output power reference value is used to accelerate power recovery and reduce the speed recovery time.

[0089] It should be understood that reducing the output power of the wind turbine by a constant value or using a smooth acceleration recovery curve during the speed recovery phase essentially aims to alleviate the secondary frequency drop by sacrificing the speed recovery time. Therefore, in order to reduce SFD and avoid excessively long speed recovery time, variables related to frequency and speed are also used as inputs for fuzzy control during the speed recovery phase. The output power reference value of the wind turbine is dynamically changed for fuzzy control to balance the contradiction between alleviating the secondary frequency drop and shortening the speed recovery time.

[0090] In this embodiment, the second system frequency deviation x fss It characterizes the degree to which the system frequency deviates from the steady-state frequency after the disturbance, reflecting the risk of a secondary drop. Its fuzzy subset describes the fuzzy language of the operating conditions, including three: S (small, frequency close to) f ss M (low risk of secondary fall), M (medium risk), B (large, frequency far from fss, high risk of secondary fall).

[0091] As mentioned above, the proportion of the remaining adjustable kinetic energy of the wind turbine to the total adjustable kinetic energy. x iUsed to characterize the extent of support a wind turbine can provide, its fuzzy subsets describe the fuzzy language of the operating conditions, including: SS (very small, indicating almost no kinetic energy), MS (small, indicating little kinetic energy), S (relatively small, indicating less kinetic energy), M (medium, indicating moderate kinetic energy), SB (relatively large, indicating more kinetic energy), MB (large, indicating more kinetic energy), and B (extremely large, indicating sufficient kinetic energy), used to determine the upper limit of the wind turbine's capacity, i.e., the wind turbine's acceleration capacity.

[0092] Output P i The (output power reference value) represents how much power should be generated, which determines whether the wind turbine generates more power to prevent secondary power drops or generates less power to accelerate recovery. Its fuzzy subset describes the fuzzy language of the operating conditions, including five: VS (very small, less power generation, priority to accelerate recovery), RS (small), M (medium, balanced), RB (large), and VB (very large, more power generation, priority to prevent secondary power drops).

[0093] It should be understood that, in the embodiments of the present invention, the output universe of discourse of the second fuzzy controller in the speed recovery stage is also set to [0,1]. After fuzzy inference and defuzzification through the established second fuzzy adaptive control law, the output universe is multiplied by the corresponding scaling factor. k 3,i The actual value of the output power reference value is obtained:

[0094] k 3,i = P m,i ( ω off );

[0095] In the formula, P m,i ( ω off ) indicates wind turbine G wi Mechanical power at the moment of frequency modulation exit. ω off Indicates wind turbine G wi The rotational speed at the moment of exiting frequency modulation.

[0096] In this embodiment of the invention, each wind turbine has a capacity of 25 MVA and an inertial time constant of 5.04 s, with a total wind farm capacity of 150 MVA. Two scenarios were set up: in scenario 1, the wind speed of all six wind turbines is the same, 10.5 m / s; in scenario 2, the wind turbine units and their wind speeds are as follows:

[0097] G w1 ~G w6 The wind speeds are as follows: 12.0; 10.0; 9.5; 11.0; 10.5; 8.5; the unit of wind speed is m / s.

[0098] exist Figure 2 In the example shown, the turns ratio of T1-T6 is 575V / 25kV, the capacity is 30MVA, and the short-circuit impedance is 0.04pu.

[0099] The impedance of lines 10-17, 17-18, 18-19, 10-20, 20-21, and 21-22 is all 0.126 + 1.066. J Ohm.

[0100] As mentioned above, during the frequency support phase, variables related to frequency and rotational speed are used as inputs for fuzzy control. Based on the proportion of the remaining adjustable kinetic energy of the wind turbine to the total adjustable kinetic energy and the frequency deviation of the first system, the fuzzy adaptive part of the droop control coefficient and inertia control coefficient of the wind turbine is dynamically corrected according to the first fuzzy adaptive control law, so as to give full play to the frequency regulation potential of the wind turbine, reduce the maximum frequency deviation and alleviate the secondary frequency drop.

[0101] enter x f It contains three fuzzy subsets: S (small), M (medium), and B (large).

[0102] enter x i and output k ad,i , k ain,i Each contains seven fuzzy subsets:

[0103] SS (extremely small), MS (small), S (relatively small), M (medium), SB (relatively large), MB (large), B (extremely large).

[0104] As an example, x f , x i , k ad,i , k ain,i The membership function curves are as follows: Figure 3a , 3b As shown in 3c.

[0105] enter x f , x i The universe of discourse of all fuzzy controllers is [0,1]. To ensure good adaptability of the fuzzy control, the output universe of discourse of the first fuzzy controller is set to [0,1]. This can be obtained by multiplying by a scaling factor. k ad,i , k ain,i The actual value.

[0106] The core of fuzzy control is its fuzzy control rules. For the droop control coefficient, the fuzzy rules are shown in Table 1 below.

[0107] Table 1 - Fuzzy Control Rules for Sag Control Coefficients

[0108]

[0109] When the frequency deviation is small (S) and the remaining kinetic energy is large (B), in order to provide a certain amount of active power support while retaining some kinetic energy, a medium-frequency method is adopted. k ad,i (M);

[0110] When the frequency deviation is large (B) and the remaining kinetic energy is moderate (M), a larger [frequency] is used to increase the minimum frequency. k ad,i (SB);

[0111] When the frequency deviation is large (B) and the remaining kinetic energy is small (SS), a small frequency drop is used to reduce the secondary frequency drop. k ad,i (MS).

[0112] The general rule is: when the wind turbine has sufficient remaining kinetic energy (S~B), the primary focus is on providing frequency support, and a larger frequency is adopted. k ad,i When the remaining kinetic energy of the wind turbine is very small (SS~MS), the focus is on reducing the secondary frequency drop, and a smaller [energy level] is used. k ad,i The reasoning result is as follows Figure 4a As shown.

[0113] For the inertia coefficient, the fuzzy rules are shown in Table 2 below:

[0114] Table 2 - Fuzzy Control Rules for Inertia Coefficient

[0115]

[0116] When the frequency deviation is small (S) and the remaining kinetic energy is large (B), in order to provide greater active power support and reduce the rate of frequency change, the maximum frequency deviation is adopted. k ad,i (B);

[0117] When the frequency deviation is moderate (M) and the remaining kinetic energy is moderate (M), in order to increase the minimum frequency, a moderate approach is still used. k ad,i (M);

[0118] When the frequency deviation is large (B) and the remaining kinetic energy is very small (SS), a minimal frequency drop is used to reduce the secondary frequency drop. k ad,i(SS), the reasoning result is as follows Figure 4b As shown.

[0119] Furthermore, during the speed recovery phase, based on the proportion of the remaining adjustable kinetic energy of the wind turbine to the total adjustable kinetic energy and the frequency deviation of the second system, the output power reference value of the wind turbine is dynamically corrected according to the second fuzzy adaptive control law, so that the output power reference value changes dynamically as the wind turbine frequency approaches the steady-state frequency after the system disturbance.

[0120] As mentioned above, reducing the unit's output power by a constant value or using a smooth acceleration recovery curve during the speed recovery phase essentially aims to mitigate the secondary frequency drop by sacrificing the speed recovery time. Therefore, in order to reduce SFD and avoid excessively long speed recovery time, variables related to frequency and speed are also used as inputs for fuzzy control during the speed recovery phase. Dynamic adaptive control is performed by dynamically changing the wind turbine's output power reference value to mitigate the secondary frequency drop and avoid excessively long speed recovery time, thereby improving the system's operational stability.

[0121] During the speed recovery phase, the wind turbine operating state factor will be... x i Second system frequency deviation x fss As input for fuzzy control.

[0122] enter x fss It includes three fuzzy subsets: S (small), M (medium), and B (large).

[0123] Output power reference value P i It contains five fuzzy subsets: VS (very small), RS (smaller), M (medium), RB (larger), and VB (very large).

[0124] As an example, such as Figure 5a , 5b The diagram shows x fss , P i Membership function curve. Similar to the frequency support stage, the output universe of discourse of the fuzzy controller in the speed recovery stage is also [0,1], and the scaling factor can be expressed as... k 3,i : k 3,i = P m,i ( ω off ), by multiplying by the corresponding scaling factor k 3,i The actual value of the output power reference value is obtained. Wherein, Pm,i ( ω off ) equals wind turbine G wi Mechanical power at the moment of frequency modulation exit. ω off Indicates wind turbine G wi The rotational speed at the moment of exiting frequency modulation.

[0125] During the speed recovery phase, for the output power reference value P i The fuzzy rules are shown in Table 3 below:

[0126] Table 3 - Fuzzy Control Rules for Output Power Reference Values

[0127]

[0128] Among them, when the rotational speed begins to recover (SS), the frequency deviates. f ss To reduce the unbalanced power at the time of frequency withdrawal and thus reduce the secondary frequency drop, a very large (B) value is adopted. P i (VB);

[0129] As the frequency approaches f ss In order to reduce the speed recovery time (S), a smaller time is used. P i (VS~RS).

[0130] Therefore, the overall fuzzy rule for the speed recovery phase is: in the initial stage of speed recovery, with the aim of mitigating the secondary frequency drop, a larger... P i As the frequency gradually approaches f ss , reduce P i To accelerate power recovery and reduce speed recovery time, the reasoning is as follows: Figure 6 As shown.

[0131] Furthermore, in this embodiment, the effectiveness of the integrated inertial frequency control method based on fuzzy adaptive parameters is further verified through multiple evaluation indicators, specifically including:

[0132] (1) Initial rate of change of frequency R cof This reflects the rate of frequency change in the initial time period after the disturbance occurs. Due to measurement and action delays, the rate of frequency change within 1 second after the disturbance is generally taken. The calculation formula is as follows: R cof =( f 1- f 0) / Δ t In the formula, f 1 represents the system frequency 1 second after the disturbance occurs, and Δ t It is 1 second;

[0133] (2) The lowest frequency of the two frequency drops f nad1 , f nad2 ;

[0134] (3) Steady-state frequency after fault f ss ;

[0135] (4) Rotational speed recovery time t rec .

[0136] In conjunction with this embodiment, a three-machine nine-node system model including a wind farm was built in Matlab / Simulink software. The integrated inertial frequency control method based on fuzzy adaptive parameters proposed in this invention was compared with traditional integrated inertial control and speed-based adaptive parameter control methods. The corresponding system frequency response curve, output power curve and speed curve were obtained, and various evaluation indicators were calculated to verify the effectiveness of the method.

[0137] Method A: Each unit in the field receives the system frequency signal and performs traditional integrated inertial control.

[0138] Method B: During the kinetic energy release phase, each unit in the field adopts an adaptive parameter method based on fuzzy control of rotational speed, and during the rotational speed recovery phase, a preset acceleration recovery curve is used.

[0139] The method proposed in this invention is as follows: each unit in the field adopts fuzzy adaptive control, and the droop control coefficient and inertia control coefficient change with the frequency and speed (dynamically adjusting their adaptive parameters). During the speed recovery phase, the power compensation is performed according to the frequency and speed to dynamically correct the output power reference value.

[0140] At 10 seconds, the load suddenly increases by 50MW. In method A, k d =30, k in =10; In Method B, the droop coefficient varies in the range of [25,45], and the inertia coefficient varies in the range of [5,10].

[0141] In the method proposed in this invention, different variation ranges are determined according to different wind speeds of the fan, and the aforementioned two scenarios are set: for scenario 1 where the wind speed is the same, Figures 7a-7cA comparison of system frequency, output power, and speed curves for different control methods is presented. As can be seen from the figure, the method proposed by the present invention can significantly improve the two minimum frequency points of the system. Compared with method B, the method proposed by the present invention can significantly shorten the speed recovery time. The evaluation indicators for different methods in scenario 1 are shown in Table 4 below.

[0142] Table 4 - Evaluation Metrics for Different Methods in Scenario 1

[0143]

[0144] For scenario 2 with different wind speeds Figures 8a-8c The system frequency, output power, and G are given for different control methods. w1 The speed curve comparison chart shows that, similar to scenario 1, the method proposed according to this invention can significantly improve the two frequency minimum points of the system. Furthermore, compared to method B, the method proposed according to this invention can significantly shorten the speed recovery time. This demonstrates that the integrated inertial frequency control method based on fuzzy adaptive parameters proposed in this invention has good adaptability. The speed recovery time of wind turbines varies with different wind speeds. (The last sentence appears to be incomplete and possibly refers to G...) w1 The rotational speed recovery time is recorded as t rec1 The evaluation metrics for different methods in scenario 2 are shown in Table 5 below.

[0145] Table 5 - Evaluation Metrics for Different Methods in Scenario 2

[0146]

[0147] In summary, the wind turbine integrated inertial frequency control method based on fuzzy adaptive parameters proposed in this invention first acquires wind farm network parameters and wind turbine wind speed data. During the wind turbine frequency support phase, an adaptive parameter method based on frequency and rotational speed is employed. Considering the proportion of the remaining adjustable kinetic energy of the turbine to the total adjustable kinetic energy and the first system frequency deviation, the system's frequency regulation requirements and the turbine's own frequency regulation potential are comprehensively considered. The fuzzy adaptive portion of the droop control coefficient and inertial control coefficient of the wind turbine is dynamically corrected according to the first fuzzy adaptive control law. The integrated inertial parameters are dynamically changed using the first fuzzy adaptive control law, which can reduce the maximum frequency deviation and alleviate the second frequency drop. During the rotational speed recovery phase, considering the proportion of the remaining adjustable kinetic energy of the turbine to the total adjustable kinetic energy and the second system frequency deviation, the second frequency drop and rotational speed recovery time are comprehensively considered. The output power reference value of the wind turbine is dynamically corrected according to the second fuzzy adaptive control law, so that the output power reference value dynamically changes as the turbine frequency approaches the steady-state frequency after system disturbance. This can alleviate the second frequency drop while avoiding excessively long rotational speed recovery times, thus improving system operational stability.

[0148] Furthermore, the fuzzy adaptive integrated inertial frequency control method was evaluated using evaluation indicators such as the initial rate of change of frequency, the steady-state frequency after a fault, and the speed recovery time. Simulation verification was conducted in different scenarios. The verification results show that, compared with conventional methods in the prior art, the fuzzy adaptive integrated inertial frequency control method proposed in this invention has good applicability and stability, and is of great significance for improving the frequency stability of new power systems.

[0149] While the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the invention. Those skilled in the art can make various modifications and refinements without departing from the spirit and scope of the invention. Therefore, the scope of protection of the present invention shall be determined by the claims.

Claims

1. A method for integrated inertial frequency control of wind turbine generators based on fuzzy adaptive parameters, characterized in that, Includes the following steps: Obtain parameters of wind farms and their wind turbines; Based on the frequency and speed of the wind turbine, adaptive frequency regulation control of the wind turbine is carried out through the comprehensive inertial control equation of the wind turbine consisting of virtual inertia control and droop control. In this process, the fuzzy adaptive part of the droop control coefficient and inertial control coefficient of the wind turbine is dynamically corrected according to the first fuzzy adaptive control law based on the proportion of the remaining adjustable kinetic energy of the wind turbine to the total adjustable kinetic energy and the frequency deviation of the first system. Furthermore, during the speed recovery phase, based on the proportion of the remaining adjustable kinetic energy of the wind turbine to the total adjustable kinetic energy and the frequency deviation of the second system, the output power reference value of the wind turbine is dynamically corrected according to the second fuzzy adaptive control law, so that the output power reference value changes dynamically as the wind turbine frequency approaches the steady-state frequency after the system disturbance. The integrated inertial control equation for the wind turbine, which consists of virtual inertia control and droop control, is expressed as follows: D P WT = - k in ×(d f / d t ) + k d ×( f 0 – f ); In the formula, Δ P WT This indicates the additional power generated during frequency regulation of the wind turbine. f For system frequency, f 0 represents the steady-state frequency of the system before the disturbance. k in For inertial control coefficients, k d This is the droop control coefficient; Therefore, the comprehensive inertia control coefficient of the wind turbine is determined as follows: k d,i = k d0,i + k ad,i ； k in,i = k in0,i + k ain,i ； In the formula, k d,i , k in,i Wind turbine G wi The droop control coefficient and the inertia control coefficient; k d0,i , k in0,i Wind turbine G wi The fixed portions of the sag control coefficient and the inertia control coefficient. k ad,i , k ain,i Wind turbine G wi The fuzzy adaptive components of the droop control coefficient and the inertia control coefficient.

2. The integrated inertial frequency control method for wind turbines based on fuzzy adaptive parameters according to claim 1, characterized in that, The acquisition of wind farm and wind turbine parameters includes: Obtain the wiring within the wind farm l-k impedance between Z lk ,in, l =1,2,…,m; k =1,2,…,m; l , k These are the node numbers within the wind farm, and m is the total number of nodes in the wind farm. Obtain the wind turbine G in the wind farm wi capacity S wi Inertial time constant H wi and generator terminal transformer capacity S Ti Short-circuit impedance Z Ti and wind speed v i , i =1,2,…, n w , n w This represents the total number of wind turbines in the wind farm.

3. The integrated inertial frequency control method for wind turbines based on fuzzy adaptive parameters according to claim 1, characterized in that, The wind turbine G wi The fixed values ​​for the sag control coefficient and the inertia control coefficient are as follows: ； Where A1 and A2 represent the fixed portion of the droop control coefficient and the fixed portion of the inertia control coefficient under the reference wind speed, respectively; ω nom G represents the wind turbine unit at the reference wind speed. wi Steady-state speed before disturbance; ω min Indicates wind turbine G wi Minimum speed limit, ω 0,i For wind turbine G wi Steady-state speed before the disturbance.

4. The wind turbine integrated inertial frequency control method based on fuzzy adaptive parameters according to claim 3, characterized in that, The reference wind speed was set to 10.5 m / s; The fixed portion A1 of the sag control coefficient under the reference wind speed is set to 15; The inertial control coefficient A2 at the reference wind speed is set to 1. Wind turbine G wi Minimum speed limit ω min Set it to 0.

75.

5. The integrated inertial frequency control method for wind turbines based on fuzzy adaptive parameters according to claim 3, characterized in that, The fuzzy adaptive part, which dynamically corrects the droop control coefficient and inertia control coefficient of the wind turbine according to the first fuzzy adaptive control law based on the proportion of the remaining adjustable kinetic energy of the wind turbine to the total adjustable kinetic energy and the frequency deviation of the first system, includes: Obtain wind turbine operating state factors x i : ； The wind turbine operating status factor x i The proportion of the remaining adjustable kinetic energy to the total adjustable kinetic energy of the wind turbine is defined to characterize the wind turbine's frequency regulation capability. ω r,i Indicates the rotational speed of the wind turbine; Obtain the first system frequency deviation x f : ； The first system frequency deviation x f Used to characterize the kinetic energy required by the system during frequency modulation, among which f min This indicates the minimum frequency allowed by the system. Configure the first fuzzy controller based on the ratio of the remaining adjustable kinetic energy of the fan to the total adjustable kinetic energy. x i and the first system frequency deviation x f As input, the fuzzy adaptive part of the wind turbine's droop control coefficient and inertia control coefficient is used. k ad,i , k ain,i As output; where, as input x f It includes three fuzzy subsets, from smallest to largest: S, M, and B; as input... x i And as output k ad,i , k ain,i Each includes 7 fuzzy subsets, which are listed from smallest to largest as: SS, MS, S, M, SB, MB, B; The first fuzzy controller is configured to dynamically correct the fuzzy adaptive portions of the wind turbine droop control coefficient and inertia control coefficient according to the first fuzzy adaptive control law. k ad,i , k ain,i The first fuzzy adaptive control law is as follows: For the fuzzy adaptive part of the wind turbine droop control coefficient k ad,i : When the remaining kinetic energy of the wind turbine is sufficient, the proportion of the remaining adjustable kinetic energy of the wind turbine to the total adjustable kinetic energy is... x i When the frequency is S~B, providing frequency support is the priority control objective, and a larger frequency range is used. k ad,i ; When the remaining kinetic energy of the fan is insufficient, the proportion of the fan's remaining adjustable kinetic energy to the total adjustable kinetic energy is... x i When the frequency is SS~MS, the priority control objective is to reduce the frequency of secondary drops, and a smaller [control method] is used. k ad,i ; For the fuzzy adaptive part of the inertial control coefficient of wind turbine generators k ain,i : k ain,i It decreases as the frequency deviation of the first system increases, and it decreases as the remaining kinetic energy of the wind turbine decreases; Among them, the wind turbine operating state factor x i Frequency deviation from the first system x f The domain of discourse is [0,1]. The output universe of discourse of the first fuzzy controller is [0,1]. After fuzzy inference and defuzzification through the established first fuzzy adaptive control law, it is multiplied by the corresponding scaling factor. k 1,i , k 2,i And obtain k ad,i , k ain,i Actual value: ； in, k 1,i , k 2,i A1 and A2 represent the scaling factors of fuzzy control droop and inertia coefficient, respectively. A3 and A4 represent the scaling factors of fuzzy control droop and inertia coefficient at the reference wind speed, respectively.

6. The wind turbine integrated inertial frequency control method based on fuzzy adaptive parameters according to claim 5, characterized in that, The value of A3 is 25; the value of A4 is 10.

7. The wind turbine integrated inertial frequency control method based on fuzzy adaptive parameters according to claim 4, characterized in that, The step of dynamically correcting the wind turbine's output power reference value according to the proportion of the wind turbine's remaining adjustable kinetic energy to the total adjustable kinetic energy and the second system frequency deviation, based on the second fuzzy adaptive control law, so that the output power reference value dynamically changes as the wind turbine frequency approaches the steady-state frequency after the system disturbance, includes: Obtain the second system frequency deviation during the speed recovery phase x fss : ； In the formula, f ss The steady-state frequency of the system after disturbance, and the frequency deviation of the second system. x fss Characterizing system frequency deviation f ss The degree; Configure a second fuzzy controller based on the ratio of the remaining adjustable kinetic energy of the fan to the total adjustable kinetic energy. x i and the frequency deviation of the second system x fss As input, the wind turbine's output power reference value P i As output; Among them, the input x fss It includes three fuzzy subsets, from smallest to largest: S, M, and B; as input... x i It includes 7 fuzzy subsets, from smallest to largest: SS, MS, S, M, SB, MB, B; as the output P i It includes five fuzzy subsets, from smallest to largest: VS, RS, M, RB, and VB; The second fuzzy controller is configured to dynamically correct the output power reference value of the wind turbine according to the second fuzzy adaptive control law. P i The second fuzzy adaptive control law is as follows: In the initial stage of speed recovery, a larger output power reference value is adopted in order to mitigate the secondary drop in frequency; As the frequency gradually approaches the steady-state frequency after the system disturbance... f ss A reduced output power reference value is used to accelerate power recovery, thereby reducing the speed recovery time; In the speed recovery phase, the output universe of the second fuzzy controller is [0,1]. After fuzzy inference and defuzzification using the established second fuzzy adaptive control law, the output is multiplied by the corresponding scaling factor. k 3,i The actual value of the output power reference value is obtained: k 3,i = P m,i ( ω off )； In the formula, P m,i ( ω off ) indicates wind turbine G wi Mechanical power at the moment of frequency modulation exit. ω off Indicates wind turbine G wi The rotational speed at the moment of exiting frequency modulation.

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