Wind power plant frequency active support method and system based on frequency modulation capability dynamic estimation

Abstract

The invention belongs to the technical field of wind power plant control, and discloses a wind power plant frequency active support method and system based on frequency modulation capability dynamic estimation. The method comprises the following steps: evaluating the active power regulation capability of each wind turbine generator in a wind power plant in real time; based on the active power regulation capability of each wind turbine generator, solving a pre-established target function of a wind power plant frequency modulation active power prediction model under a constraint condition, and obtaining a frequency modulation active power instruction of each wind turbine generator in the wind power plant; and controlling each wind turbine generator in the wind power plant to execute the frequency modulation active instruction. When frequency fluctuation occurs in the system, the frequency modulation capability of each unit is dynamically estimated according to the operation state and the prediction model of each unit, and the frequency modulation active capability of the wind turbine generator is fully excavated to realize rapid frequency modulation response and active support of the frequency of the wind power plant under the condition that the frequency requirement of the system after high-proportion new energy is accessed to the power grid is met.

Description

Technical Field

[0001] The present invention belongs to the technical field of wind farm control, and in particular relates to a method and system for actively supporting wind farm frequency based on dynamic estimation of frequency regulation capability. Background Art

[0002] my country's renewable energy sector is developing rapidly. By the end of December 2024, the cumulative installed power generation capacity reached approximately 3.35 billion kilowatts, a year-on-year increase of 14.6%. Of this total, solar power generation capacity reached approximately 890 million kilowatts, a year-on-year increase of 45.2%, and wind power capacity reached approximately 520 million kilowatts, a year-on-year increase of 18.0%. As wind power installations continue to expand and power generation rapidly increases, the system's inertia capacity continues to decline. Power disturbances can easily lead to frequency security issues, necessitating the ability for wind power to actively participate in power system frequency regulation. Consequently, countries around the world are requiring wind power grid-connected technologies to include grid-friendly active support capabilities such as inertia and active frequency regulation.

[0003] In practice, wind power's active participation in system frequency is primarily achieved through inertia response and primary frequency regulation. The overall process involves calculating the change in active power required for frequency regulation based on the grid frequency change and rate of change. This active power, along with the active power required for primary frequency regulation and inertia frequency regulation, is then distributed to each unit according to a specific strategy. However, this frequency control approach is relatively simplistic, simply assigning a fixed active power change based on frequency fluctuations. This approach fails to fully tap the potential of each unit to achieve precise grid frequency regulation. Summary of the Invention

[0004] The purpose of the present invention is to provide a method and system for active frequency support of a wind farm based on dynamic estimation of frequency regulation capability, so as to solve the technical problem that the existing technology has a single control mode when performing frequency support of a wind farm and cannot achieve accurate frequency regulation.

[0005] In order to achieve the above object, the present invention adopts the following technical solutions: In a first aspect, the present invention provides a method for actively supporting wind farm frequency based on dynamic estimation of frequency regulation capability, comprising: Real-time evaluation of the active power regulation capability of each wind turbine in the wind farm; Based on the active power regulation capability of each wind turbine, and under the constraints, the objective function of the pre-established wind farm frequency regulation active power prediction model is solved to obtain the frequency regulation active power instructions of each wind turbine in the wind farm; Control each wind turbine in the wind farm to execute the frequency modulation active power instruction.

[0006] The present invention is further improved in that: in the step of real-time evaluation of the active power regulation capability of each wind turbine in the wind farm, the specific evaluation is obtained: iChanges in active energy per second Hedi i Changes in the maximum frequency modulation energy contained in the kinetic energy of the second rotor .

[0007] A further improvement of the present invention is that: i Changes in active energy per second for: (8) No. i Changes in the maximum frequency modulation energy contained in the kinetic energy of the second rotor for: (9) in, and are the active power calculated based on the wind speed prediction and the active power corresponding to the current known wind speed; is the total moment of inertia of the wind turbine transmission chain converted to the high-speed shaft, Indicates the i The predicted speed in seconds, ω gen It is the speed of the wind turbine set from the current speed to the speed after the rotor is released.

[0008] A further improvement of the present invention is that, in the step of solving the objective function of a pre-established wind farm frequency regulation active power prediction model based on the active power regulation capability of each wind turbine generator set and satisfying the constraint conditions to obtain the frequency regulation active power instructions of each wind turbine generator set in the wind farm, the wind farm frequency regulation active power prediction model is: (3) in: ,

[0009] in, x (k) and x (k-1) are the speed regulator output, reheat boiler output, thermal power active power, grid frequency, and active power of each wind turbine at time k and k-1 respectively. u (k) and u (k-1) are the active power instructions of each wind turbine in the wind farm at time k and k-1 respectively, d (k) and d (k-1) is the active power of the load disturbance at time k and k-1 respectively.

[0010] A further improvement of the present invention is that, in the step of solving the objective function of a pre-established wind farm frequency regulation active power prediction model based on the active power regulation capability of each wind turbine generator set and satisfying the constraint conditions to obtain the frequency regulation active power instructions of each wind turbine generator set in the wind farm, the objective function of the wind farm frequency regulation active power prediction model is: (7) Among them, J is the objective function, k and j are the moments, Wind turbine frequency regulation cost coefficient, The change in active power of the i-th wind turbine group, n p The prediction time domain of the wind farm frequency regulation active power prediction model predictive control, n c To control the time domain, λ 1. λ 2. λ 3 is the weight coefficient, △ f is the grid frequency variation.

[0011] A further improvement of the present invention lies in that: in the step of solving the objective function of a pre-established wind farm frequency regulation active power prediction model based on the active power regulation capability of each wind turbine generator set and satisfying constraint conditions to obtain the frequency regulation active power instructions of each wind turbine generator set in the wind farm, the constraint conditions include: wind farm regulation capability constraint, system frequency change amount and frequency change rate constraint.

[0012] A further improvement of the present invention is that: the wind farm regulation capability constraint is: (12) Where, For the i The lower limit of active power regulation of unit j at the moment, For the i Unit No. j The upper limit of active regulation energy at all times; For the i The active power regulation of the unit at the jth moment; For the station j The lower limit of active power regulation at the moment, Station No. j The upper limit of active power regulation at all times, n p is the prediction time domain of the wind farm frequency regulation active power prediction model predictive control, and n is the total number of wind turbines in the wind farm.

[0013] A further improvement of the present invention is that the constraints on the system frequency change amount and frequency change rate include: (13) (14) Among them, △ f is the frequency variation of the power grid, △ P L is the active power variation of the system load disturbance, D eq is the equivalent damping coefficient of the power system; (15) (16) in, df max is the maximum frequency change speed of the power grid; D eqmin is the minimum equivalent damping coefficient of the power system; (17) Among them, Δ f max is the maximum value of the grid frequency variation; (18).

[0014] In a second aspect, the present invention provides a wind farm frequency active support system based on dynamic estimation of frequency regulation capability, comprising: Dynamic evaluation module, used to evaluate the active power regulation capability of each wind turbine in the wind farm in real time; A solution module is used to solve the objective function of the pre-established wind farm frequency regulation active power prediction model based on the active power regulation capability of each wind turbine and under the constraints, and obtain the frequency regulation active power instructions of each wind turbine in the wind farm; The control module is used to control each wind turbine in the wind farm to execute the frequency modulation active power instruction.

[0015] In a third aspect, the present invention provides an electronic device comprising a processor and a memory, wherein the processor is configured to execute a computer program stored in the memory to implement the method for actively supporting the frequency of a wind farm based on dynamic estimation of frequency regulation capability.

[0016] In a fourth aspect, the present invention provides a computer-readable storage medium storing at least one instruction, which, when executed by a processor, implements the method for active frequency support of a wind farm based on dynamic estimation of frequency regulation capability.

[0017] Compared with the prior art, the present invention has the following beneficial effects: The present invention provides a method for actively supporting wind farm frequency based on dynamic frequency regulation capability estimation, comprising: real-time evaluation of the active power regulation capability of each wind turbine in the wind farm; solving the objective function of a pre-established wind farm frequency regulation active power prediction model based on the active power regulation capability of each wind turbine, subject to constraints, to obtain active power frequency regulation instructions for each wind turbine in the wind farm; and controlling each wind turbine in the wind farm to execute the active power frequency regulation instructions. The present invention provides a predictive optimization control method for wind farm frequency regulation models based on dynamic frequency regulation capability estimation. Based on the establishment of a wind farm frequency regulation active power prediction model, the frequency regulation capability of each turbine is dynamically estimated according to the operating status of each turbine and the prediction model. Furthermore, considering the system frequency regulation requirements constrained by the system frequency change rate and change amount, a model is established with the optimization objectives of minimizing frequency regulation cost and maximizing frequency regulation performance. This method achieves on-site wind turbine frequency regulation active power control that balances economic efficiency and rapid frequency response performance while fully utilizing the active power regulation capability of each turbine.

[0018] When the system frequency fluctuates, the present invention fully taps the frequency regulation capability of the wind turbine to achieve rapid frequency regulation response and realize active frequency support of the wind farm while meeting the frequency demand of the system after a high proportion of new energy is connected to the power grid. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] The accompanying drawings, which constitute part of the present invention, are intended to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are intended to explain the present invention and do not constitute an undue limitation of the present invention. In the accompanying drawings: Figure 1 This is a schematic diagram of the frequency response model of the wind-thermal combined power system; Figure 2 This is a flowchart for second-level wind speed / rotation speed prediction based on prediction error modal decomposition; Figure 3 This is a schematic diagram of the framework of the MPC-based wind turbine rapid frequency regulation control model; Figure 4 This is a flow chart of a method for actively supporting wind farm frequency based on dynamic estimation of frequency regulation capability according to an embodiment of the present invention; Figure 5 This is a structural block diagram of a wind farm frequency active support system based on dynamic estimation of frequency regulation capability according to an embodiment of the present invention.

[0020] Figure 6 The figure is a structural block diagram of an electronic device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0021] The present invention will be described in detail below with reference to the accompanying drawings and in combination with embodiments. It should be noted that, in the absence of conflict, the embodiments and features of the embodiments of the present invention can be combined with each other.

[0022] The following detailed description is an exemplary description and is intended to provide further detailed description of the present invention. Unless otherwise indicated, all technical terms used in the present invention have the same meaning as those generally understood by those skilled in the art to which the present invention belongs. The terms used in the present invention are only for describing specific embodiments and are not intended to limit the exemplary embodiments according to the present invention.

[0023] An embodiment of the present invention provides a method for actively supporting wind farm frequency based on dynamic estimation of frequency regulation capability, comprising the following steps: S1. Establish a mathematical model of frequency response of wind-thermal combined power system: See also Figure 1 As shown in Figure 2, a mathematical model of the frequency response of a wind-thermal combined power system is established. In this mathematical model, wind power is composed of n wind turbines, and thermal power is replaced by one thermal power unit, where the output ratios of wind power and thermal power are respectively R W With 1- R W .

[0024] In the picture T Jeq 、 D eq are the equivalent inertia time constant and equivalent damping coefficient of the power system, △ f is the frequency variation of the power grid, △ P G ,△ P wi ,△ P L are the active power changes of thermal power, wind power and system load disturbance, Δ X g is the output change of the speed regulator, Δ P r is the change in reheat boiler output.

[0025] When the system power generation and load power are balanced, the system frequency is the rated value. When the two are not matched, the system frequency will deviate from the rated value. The mathematical model of the frequency response of the wind-thermal combined power system can be obtained as follows: (1) Among them, R is the regulation coefficient of thermal power unit, T GT is the time constant of the thermal power unit speed regulator, F HP is the steam turbine reheat constant of the thermal power unit, T RH is the reheat time constant of the thermal power unit, T CHis the time constant of the steam turbine of the thermal power unit, n is the number of wind turbine units, is the active power control time constant of each wind turbine, is the frequency modulation active power instruction of each wind turbine, and s represents the Laplace transform.

[0026] S2. Design a wind turbine frequency regulation active power control algorithm based on Model Predictive Control (MPC): Get state variables x f =[Δ X g ,Δ P r ,Δ P G ,Δ f ,Δ P wi ] T , control variables u f =[Δ P Wi_ref ] T , disturbance variable d f =Δ P L , output variable y f =[Δ f ,Δ P wi ], the system frequency response model for model predictive control can be obtained, namely: (2) in:

[0027]

[0028] In order to reduce the static error, the above formula is written as an incremental model, and the sampling period is set to T s , we can get sampling k The discretized state space model of the frequency response of the wind-storage-thermal combined power system at time t is: (3) in: ,

[0029] in, x (k) and x(k-1) are the speed regulator output, reheat boiler output, thermal power active power, grid frequency, and active power of each wind turbine at time k and k-1 respectively. u (k) and u (k-1) are the active power instructions of each wind turbine in the wind farm at time k and k-1 respectively, d (k) and d (k-1) is the active power of the load disturbance at time k and k-1 respectively.

[0030] The discretized state space model of formula (3) can be used for the frequency response prediction model of wind-thermal combined power.

[0031] S3, optimization control objectives based on MPC wind turbine frequency regulation active power control algorithm; When wind power actively participates in frequency support, on the one hand, it is necessary to quickly adjust the active output of each unit to minimize the deviation of the control frequency from the system's natural frequency. On the other hand, it is necessary to reduce the frequency regulation cost of the wind turbine. The frequency regulation cost of the wind turbine mainly lies in the fact that the increase in power requires changing the wind turbine rotor speed, causing it to deviate from the fixed mechanical torque provided by the current wind speed. The generation of additional torque requires corresponding energy supply, which will also increase the mechanical wear of the unit. In order to achieve the fast frequency response performance of the wind turbine and reduce the frequency regulation cost of the unit, the optimization objectives can be obtained by combining these two goals as follows: 1) During the period from system frequency fluctuation to system frequency stabilization, the grid frequency deviation is minimized, and the frequency regulation active power of wind turbines is distributed in real time.

[0032] (4) Wherein, obj1 is the first objective function; 2) During the frequency regulation process, the frequency regulation cost of wind turbines should be kept low, specifically: (5) Among them, Obj2 is the second objective function, is the frequency regulation cost of each wind turbine, is the frequency regulation cost coefficient of wind turbines, is the active power variation of the i-th wind turbine; 3) In addition, in order to prevent the control variable, i.e., the unit frequency regulation active power, from changing suddenly, the frequency regulation active power change should be set small, specifically: (6) Where, obj3 is the third objective function; λ 1~ λ 3 is the weight coefficient.

[0033] In summary, assuming that the prediction time domain of model predictive control is n p, the control time domain is n c , the optimization control objective function of the frequency modulation system can be constructed as shown below: (7) Among them, J is the objective function, k and j are the moments, Wind turbine frequency regulation cost coefficient, The change in active power of the i-th wind turbine group, n p The prediction time domain of the wind farm frequency regulation active power prediction model predictive control, n c To control the time domain, λ 1. λ 2. λ 3 is the weight coefficient, △ f is the grid frequency variation.

[0034] S4. Constraints of the MPC-based wind turbine frequency modulation active power control algorithm: During the frequency regulation process, in order to tap the frequency regulation capability of each unit, an embodiment of the present invention proposes an improved long short-term memory (LSTM) neural network second-level wind speed and rotation speed prediction method based on prediction error modal decomposition. By extracting effective information from the error, the predicted wind speed and rotation speed are corrected in real time, thereby improving the wind speed and rotation speed prediction accuracy; furthermore, the active frequency regulation capability of each wind turbine is established by comprehensively considering the changes in wind speed and rotation speed, and dynamic estimation of the active support capability and change trend of wind power frequency is realized, and it is used as a constraint condition of the model prediction optimization control algorithm, so as to more efficiently exert the regulation capability of each unit and realize rapid regulation of the system power grid frequency.

[0035] In order to improve the trend lag problem in the forecast and improve the forecast accuracy, the wind speed / speed prediction error signal is subjected to empirical mode decomposition based on the preliminary wind speed / speed prediction sequence obtained through the LSTM network. The appropriate components are selected for LSTM network training and prediction respectively. The prediction results of each component are reconstructed and the wind speed / speed prediction sequence is corrected to improve the overall wind speed / speed prediction accuracy. The second-level wind speed and speed prediction process based on prediction error mode decomposition is as follows: Figure 2 shown.

[0036] Based on the above predicted wind speed and rotation speed results, and combined with the current state dynamic estimation of wind power frequency active support capability, the impact of future short-term operating conditions changes on active frequency regulation capability can be divided into two parts: one is the change in active power generation energy caused by wind speed changes. , and secondly, the maximum frequency modulation energy change contained in the rotor kinetic energy , the specific calculation is shown in formula (8)-(9).

[0037] (8) (9) Where, Indicates the current time to the i The change of active energy added per second, and are the active power calculated based on the wind speed prediction and the active power corresponding to the current known wind speed; Indicates the i The change of the maximum frequency modulation energy contained in the kinetic energy of the second rotor, is the total moment of inertia of the wind turbine transmission chain converted to the high-speed shaft, Indicates the i The speed prediction value in seconds, the wind turbine speed changes from the current speed to the speed after the rotor release is completed ω gen .

[0038] Therefore, during the entire frequency regulation process, the active power regulation boundary of each unit can be calculated in real time based on the dynamic estimation of the frequency regulation capability of the unit.

[0039] (10) Active change of rotor kinetic energy Δ P J Plan according to duration Δ t It can be calculated that (in order to connect the variable pitch to release energy and consider the unit speed to switch smoothly to the primary frequency modulation mode, Δ t =5~10s).

[0040] (11) in, It is the predicted adjustable change of active power of each unit.

[0041] Therefore, the constraints of the model predictive frequency modulation control algorithm can be obtained as follows: 1) Wind farm regulation capacity constraints; According to the real-time calculated active regulation boundaries of each unit and the upper and lower bounds of the wind farm regulation capacity, the frequency regulation active variation constraint conditions can be obtained as follows: (12) Where, For the i The lower limit of active power regulation of unit j at the moment, For the i Unit No. j The upper limit of active regulation energy at all times; For the i The active power regulation of the unit at the jth moment; For the station jThe lower limit of active power regulation at the moment, Station No. j The upper limit of active power regulation at all times.

[0042] 2) Constraints on the system frequency change amount and frequency change rate; In the early stage of load disturbance, the frequency change rate is the largest because the generator speed governor and new energy rapid frequency regulation have not yet taken action, and it can be used as a system frequency constraint indicator.

[0043] (13) (14) At present, in order to ensure the normal operation of power generation equipment, the frequency change rate is generally set to no more than 0.4 Hz / s. In actual application, the appropriate frequency change rate can be flexibly selected as the maximum frequency change speed constraint of the system according to the actual needs of the power grid. df max , the minimum inertia requirement of the system is: (15) (16) When the frequency modulation source performs a frequency modulation action, it will increase or decrease power to reduce the system power deviation, thereby suppressing the increase in frequency variation. At this time, the frequency variation is related to the synchronous machine regulator parameters, wind power reserve power, wind storage frequency modulation parameters and other parameters. The maximum frequency variation value Δ is set. f max , the minimum damping requirement of the system is: (17) In summary, when the system is subject to load fluctuations, in order to ensure frequency stability, the system must meet the requirements of minimum inertia and minimum damping. That is, the sum of the inertia of the synchronous units in the system and the virtual inertia of the wind turbines should not be less than the minimum inertia requirement, and the sum of the damping of the synchronous units in the system and the damping of the wind turbines should not be less than the minimum damping requirement. It can be obtained that: (18).

[0044] The above constructs the optimization objective function J and constraints of the wind farm frequency regulation MPC control. In order to conveniently solve the minimum value of the objective function J, it is necessary to convert the objective function J into a quadratic programming problem and then obtain the optimal solution of the optimization objective function J.

[0045] Therefore, the MPC-based wind turbine rapid frequency regulation control model framework can be obtained as follows: Figure 3 shown.

[0046] To explore the frequency regulation capabilities of each wind turbine, this paper proposes a second-level wind speed and rotational speed prediction method using an improved long-short-term memory (LSTM) neural network based on prediction error modal decomposition. Using the predicted wind speed and rotational speed, the method evaluates the active frequency regulation capabilities of each wind turbine, taking into account variations in wind speed and rotational speed. Based on this, an optimization model is established with the goals of minimizing frequency regulation costs and optimizing frequency regulation performance. The frequency regulation capabilities of each wind turbine are considered as dynamic constraints. This model, while fully leveraging the active regulation capabilities of each wind turbine, achieves on-site wind turbine frequency regulation control that balances economic efficiency with rapid frequency response performance, effectively improving the overall frequency regulation effectiveness of the wind farm.

[0047] See also Figure 4 As shown, an embodiment of the present invention provides a method for actively supporting wind farm frequency based on dynamic estimation of frequency regulation capability, including: S100: Real-time evaluation of the active power regulation capability of each wind turbine in the wind farm; S200, based on the active power regulation capability of each wind turbine generator set and subject to satisfying constraint conditions, solving the objective function of a pre-established wind farm frequency regulation active power prediction model to obtain frequency regulation active power instructions for each wind turbine generator set in the wind farm; S300: Control each wind turbine in the wind farm to execute the frequency modulation active power instruction.

[0048] In a specific embodiment, in the step of real-time evaluation of the active power regulation capability of each wind turbine in the wind farm, the specific evaluation is obtained: i Changes in active energy per second Hedi i Changes in the maximum frequency modulation energy contained in the kinetic energy of the second rotor ; From the previous moment to the i Changes in active energy per second for: (8) No. i Changes in the maximum frequency modulation energy contained in the kinetic energy of the second rotor for: (9) in, and are the active power calculated based on the wind speed prediction and the active power corresponding to the current known wind speed; is the total moment of inertia of the wind turbine transmission chain converted to the high-speed shaft, Indicates the i The predicted speed in seconds, ω gen It is the speed of the wind turbine set from the current speed to the speed after the rotor is released.

[0049] During the entire frequency regulation process, the active power regulation boundary of each unit can be calculated in real time based on the dynamic estimation of the frequency regulation capability of the unit.

[0050] (10) Active change of rotor kinetic energy Δ P J Plan according to duration Δ t It can be calculated that (in order to connect the variable pitch to release energy and consider the unit speed to switch smoothly to the primary frequency modulation mode, Δ t =5~10s).

[0051] (11) in, It is the predicted adjustable change of active power of each unit.

[0052] In a specific embodiment, based on the active power regulation capability of each wind turbine, under the constraint condition, the objective function of a pre-established wind farm frequency regulation active power prediction model is solved to obtain the frequency regulation active power instructions of each wind turbine in the wind farm. The wind farm frequency regulation active power prediction model is: (3) in: ,

[0053] in, x (k) and x (k-1) are the governor output, reheat boiler output, thermal power active power, grid frequency, and active power of each wind turbine at time k and k-1 respectively. u (k) and u (k-1) are the active power instructions of each wind turbine in the wind farm at time k and k-1 respectively, d (k) and d (k-1) is the active power of the load disturbance at time k and k-1 respectively.

[0054] In a specific embodiment, based on the active power regulation capability of each wind turbine, under the constraint condition, the objective function of a pre-established wind farm frequency regulation active power prediction model is solved to obtain the frequency regulation active power instruction of each wind turbine in the wind farm. The objective function of the wind farm frequency regulation active power prediction model is: (7) Among them, J is the objective function, k and j are the moments, Wind turbine frequency regulation cost coefficient, The change in active power of the i-th wind turbine group, np The prediction time domain of the wind farm frequency regulation active power prediction model predictive control, n c To control the time domain, λ 1. λ 2. λ 3 is the weight coefficient, △ f is the grid frequency variation.

[0055] The constraints include: wind farm regulation capability constraints, system frequency change and frequency change rate constraints. The wind farm regulation capability constraints are: (12) Where, For the i The lower limit of active power regulation of unit j at the moment, For the i Unit No. j The upper limit of active regulation energy at all times; For the i The active power regulation of the unit at the jth moment; For the station j The lower limit of active power regulation at the moment, Station No. j The upper limit of active power regulation at all times, n p is the prediction time domain of the wind farm frequency regulation active power prediction model predictive control, and n is the total number of wind turbines in the wind farm.

[0056] The constraints on the system frequency change amount and frequency change rate include: (13) (14) Among them, △ f is the frequency variation of the power grid, △ P L is the active power variation of the system load disturbance, D eq is the equivalent damping coefficient of the power system; (15) (16) in, df max is the maximum frequency change speed of the power grid; D eqmin is the minimum equivalent damping coefficient of the power system; (17) Among them, Δ f maxThe maximum value of the frequency change of the sub-point unit; (18).

[0057] Please refer to Figure 5 As shown, an embodiment of the present invention provides a wind farm frequency active support system based on dynamic estimation of frequency regulation capability, comprising: Dynamic evaluation module, used to evaluate the active power regulation capability of each wind turbine in the wind farm in real time; A solution module is used to solve the objective function of the pre-established wind farm frequency regulation active power prediction model based on the active power regulation capability of each wind turbine and under the constraints, and obtain the frequency regulation active power instructions of each wind turbine in the wind farm; The control module is used to control each wind turbine in the wind farm to execute the frequency modulation active power instruction.

[0058] In a specific embodiment, in the step of real-time evaluation of the active power regulation capability of each wind turbine in the wind farm, the specific evaluation is as follows: i Changes in active energy per second Hedi i Changes in the maximum frequency modulation energy contained in the kinetic energy of the second rotor . From the moment before to the i Changes in active energy per second for: (8) No. i Changes in the maximum frequency modulation energy contained in the kinetic energy of the second rotor for: (9) in, and are the active power calculated based on the wind speed prediction and the active power corresponding to the current known wind speed; is the total moment of inertia of the wind turbine transmission chain converted to the high-speed shaft, Indicates the i The predicted speed in seconds, ω gen It is the speed of the wind turbine set from the current speed to the speed after the rotor is released.

[0059] In a specific embodiment, in the step of solving the objective function of a pre-established wind farm frequency regulation active power prediction model based on the active power regulation capability of each wind turbine and satisfying the constraint conditions to obtain the frequency regulation active power instructions of each wind turbine in the wind farm, the wind farm frequency regulation active power prediction model is: (3) in: ,

[0060] in, x (k) and x (k-1) are the governor output, reheat boiler output, thermal power active power, grid frequency, and active power of each wind turbine at time k and k-1 respectively. u (k) and u (k-1) are the active power instructions of each wind turbine in the wind farm at time k and k-1 respectively, d (k) and d (k-1) is the active power of the load disturbance at time k and k-1 respectively.

[0061] The objective function of the wind farm frequency regulation active power prediction model is: (7) Among them, J is the objective function, k and j are the moments, Wind turbine frequency regulation cost coefficient, The change in active power of the i-th wind turbine group, n p The prediction time domain of the wind farm frequency regulation active power prediction model predictive control, n c To control the time domain, λ 1. λ 2. λ 3 is the weight coefficient, △ f is the frequency variation of the power grid. The constraints include: wind farm regulation capacity constraint, system frequency variation and frequency variation rate constraint.

[0062] The wind farm regulation capacity constraint is: (12) Where, For the i The lower limit of active power regulation of unit j at the moment, For the i Unit No. j The upper limit of active regulation energy at all times; For the i The active power regulation of the unit at the jth moment; For the station j The lower limit of active power regulation at the moment, Station No. j The upper limit of active power regulation at all times, n p is the prediction time domain of the wind farm frequency regulation active power prediction model predictive control, and n is the total number of wind turbines in the wind farm.

[0063] The constraints on the system frequency change amount and frequency change rate include: 5 (14) Among them, △ f is the frequency variation of the power grid, △ P L is the active power variation of the system load disturbance, D eq is the equivalent damping coefficient of the power system; (15) (16) in, df max is the maximum frequency change speed of the power grid; D eqmin is the minimum equivalent damping coefficient of the power system; (17) Among them, Δ f max The maximum value of the frequency change of the sub-point unit; (18).

[0064] An embodiment of the present invention provides a method for actively supporting wind farm frequency based on dynamic estimation of frequency regulation capability, comprising: (1) Establish a mathematical model of frequency response of wind-thermal combined power system: A frequency response model of the combined wind and thermal power system is established. It is analyzed that when the system power generation and load power are unbalanced, the system frequency will deviate from the rated value, and the system load frequency dynamic response model is obtained.

[0065] (2) Design of a wind turbine frequency regulation active power control algorithm based on model predictive control (MPC): The active power control of a wind turbine is a time-varying nonlinear model related to multiple variables such as wind speed, rotational speed, and pitch angle. The active power control algorithm of the wind turbine cannot be designed directly. Therefore, the small signal increment method is used to linearize it. That is, after taking a small disturbance on each state variable at the operating point, Taylor expansion is performed and the first-order terms are retained to obtain a linearized wind turbine model.

[0066] (3) Design of control algorithm based on model prediction optimization: When wind power actively participates in frequency support, it's necessary to rapidly adjust the active output of each turbine to minimize the deviation of the controlled frequency from the system's natural frequency. Furthermore, it's necessary to reduce the frequency regulation costs of the wind turbines. This cost stems primarily from the fact that increasing power requires changing the rotor speed, deviating from the fixed mechanical torque provided by the current wind speed. This additional torque requires a corresponding energy supply, which increases mechanical wear on the turbine. Therefore, a model predictive optimization control algorithm was designed to achieve rapid frequency response performance for wind turbines and reduce frequency regulation costs.

[0067] In the frequency regulation process, in order to tap the active regulation capability of each unit, an improved long short-term memory (LSTM) neural network second-level wind speed and speed prediction method based on prediction error modal decomposition is proposed. By extracting effective information from the error, the predicted wind speed and speed are corrected in real time, thereby improving the prediction accuracy of wind speed and speed. Furthermore, the active frequency regulation capability of each wind turbine is established by comprehensively considering the changes in wind speed and speed, and the dynamic estimation of the active support capability and change trend of wind power frequency is realized. It is used as a constraint condition of the model prediction optimization control algorithm, so as to more efficiently exert the regulation capability of each unit and realize rapid regulation of the system grid frequency.

[0068] In addition, considering that the power generation equipment in the system needs to work normally within a certain range of the grid frequency, the appropriate frequency change rate and system frequency change are flexibly selected as constraints based on the actual needs of the power grid in different regions.

[0069] (4) Optimal solution for frequency modulation active power distribution: The wind farm control system adopts the model predictive control algorithm to solve the above optimization objective function, and under the constraints of various parameters, each unit executes the frequency regulation active power value for the frequency regulation active power command issued to each unit, thereby realizing frequency regulation response control.

[0070] The present invention establishes a wind farm frequency active support active power control model, takes the active frequency regulation capability evaluation results of each wind turbine as a dynamic constraint condition, considers the system frequency regulation demand with the system frequency change rate and change amount as static constraints, and establishes a model with the minimum frequency regulation cost and the best frequency regulation performance as the optimization goals, thereby realizing the frequency regulation active power control of wind turbines in the station that takes into account both economy and fast frequency response performance.

[0071] See also Figure 6 As shown, an embodiment of the present invention provides an electronic device 100 for implementing a method for actively supporting wind farm frequency based on dynamic estimation of frequency regulation capability; the electronic device 100 includes a memory 101, at least one processor 102, a computer program 103 stored in the memory 101 and executable on the at least one processor 102, and at least one communication bus 104.

[0072] The memory 101 can be used to store the computer program 103. The processor 102 implements the steps of the method for active frequency support of a wind farm based on dynamic estimation of frequency regulation capability described in the embodiment by running or executing the computer program stored in the memory 101 and calling the data stored in the memory 101. The memory 101 can mainly include a program storage area and a data storage area. The program storage area can store an operating system and application programs required for at least one function (such as a sound playback function, an image playback function, etc.); the data storage area can store data (such as audio data) created based on the use of the electronic device 100. In addition, the memory 101 can include non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, at least one disk storage device, a flash memory device, or other non-volatile solid-state storage device.

[0073] The at least one processor 102 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The processor 102 may be a microprocessor or any conventional processor, etc. The processor 102 is the control center of the electronic device 100 and connects various parts of the entire electronic device 100 using various interfaces and lines.

[0074] The memory 101 in the electronic device 100 stores a plurality of instructions to implement a method for actively supporting the frequency of a wind farm based on dynamic estimation of frequency regulation capability. The processor 102 may execute the plurality of instructions to implement: Real-time evaluation of the active power regulation capability of each wind turbine in the wind farm; Based on the active power regulation capability of each wind turbine, and under the constraints, the objective function of the pre-established wind farm frequency regulation active power prediction model is solved to obtain the frequency regulation active power instructions of each wind turbine in the wind farm; Control each wind turbine in the wind farm to execute the frequency modulation active power instruction.

[0075] If the module / unit integrated in the electronic device 100 is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present invention implements all or part of the process in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned method embodiments when executed by the processor. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form, etc. The computer-readable medium may include: any entity or device that can carry the computer program code, recording medium, U disk, mobile hard disk, magnetic disk, optical disk, computer memory and read-only memory (ROM, Read-Only Memory).

[0076] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0077] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0078] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0079] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0080] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered by the scope of protection of the claims of the present invention.

Claims

1. A method for actively supporting wind farm frequency based on dynamic estimation of frequency regulation capability, characterized in that: include: Real-time evaluation of the active power regulation capability of each wind turbine in the wind farm; Based on the active power regulation capability of each wind turbine, and under the constraints, the objective function of the pre-established wind farm frequency regulation active power prediction model is solved to obtain the frequency regulation active power instructions of each wind turbine in the wind farm; Control each wind turbine in the wind farm to execute the frequency modulation active power instruction.

2. The method for actively supporting wind farm frequency based on dynamic estimation of frequency regulation capability according to claim 1, characterized in that: In the step of real-time evaluation of the active power regulation capability of each wind turbine in the wind farm, the specific evaluation is as follows: i Changes in active energy per second Hedi i Changes in the maximum frequency modulation energy contained in the kinetic energy of the second rotor .

3. The method for actively supporting wind farm frequency based on dynamic estimation of frequency regulation capability according to claim 2, characterized in that: From the previous moment to the i Changes in active energy per second for: （8） No. i Changes in the maximum frequency modulation energy contained in the kinetic energy of the second rotor for: （9） in, and are the active power calculated based on the wind speed prediction and the active power corresponding to the current known wind speed; is the total moment of inertia of the wind turbine transmission chain converted to the high-speed shaft, Indicates the i The predicted speed in seconds, ω gen It is the speed of the wind turbine set from the current speed to the speed after the rotor is released.

4. The method for actively supporting wind farm frequency based on dynamic estimation of frequency regulation capability according to claim 1, characterized in that: In the step of solving the objective function of the pre-established wind farm frequency regulation active power prediction model based on the active power regulation capability of each wind turbine generator set and satisfying the constraint conditions to obtain the frequency regulation active power instructions of each wind turbine generator set in the wind farm, the wind farm frequency regulation active power prediction model is: （3） in: ， in, x (k) and x (k-1) are the speed regulator output, reheat boiler output, thermal power active power, grid frequency, and active power of each wind turbine at time k and k-1 respectively. u (k) and u (k-1) are the active power instructions of each wind turbine in the wind farm at time k and k-1 respectively, d (k) and d (k-1) is the active power of the load disturbance at time k and k-1 respectively.

5. The method for active frequency support of a wind farm based on dynamic estimation of frequency regulation capability according to claim 1, characterized in that: In the step of solving the objective function of the pre-established wind farm frequency regulation active power prediction model based on the active power regulation capability of each wind turbine generator set and satisfying the constraint conditions to obtain the frequency regulation active power instructions of each wind turbine generator set in the wind farm, the objective function of the wind farm frequency regulation active power prediction model is: （7） Among them, J is the objective function, k and j are the moments, Wind turbine frequency regulation cost coefficient, The change in active power of the i-th wind turbine group, n p The prediction time domain of the wind farm frequency regulation active power prediction model predictive control, n c To control the time domain, λ 1. λ 2. λ 3 is the weight coefficient, △ f is the grid frequency variation.

6. The method for actively supporting wind farm frequency based on dynamic estimation of frequency regulation capability according to claim 1, characterized in that: In the step of solving the objective function of the pre-established wind farm frequency regulation active power prediction model based on the active power regulation capability of each wind turbine and satisfying the constraint conditions to obtain the frequency regulation active power instructions of each wind turbine in the wind farm, the constraint conditions include: wind farm regulation capability constraint, system frequency change amount and frequency change rate constraint.

7. The method for actively supporting wind farm frequency based on dynamic estimation of frequency regulation capability according to claim 6, characterized in that: The wind farm regulation capability constraint is: （12） Where, For the i The lower limit of active power regulation of unit j at the moment, For the i Unit No. j The upper limit of active regulation energy at all times; For the i The active power regulation of the unit at the jth moment; For the station j The lower limit of active power regulation at the moment, Station No. j The upper limit of active power regulation at all times, n p is the prediction time domain of the wind farm frequency regulation active power prediction model predictive control, and n is the total number of wind turbines in the wind farm.

8. The method for actively supporting wind farm frequency based on dynamic estimation of frequency regulation capability according to claim 6, characterized in that: The constraints on the system frequency change amount and frequency change rate include: （13） （14） Among them, △ f is the frequency variation of the power grid, △ P L is the active power variation of the system load disturbance, D eq is the equivalent damping coefficient of the power system; （15） （16） in, df max is the maximum frequency change speed of the power grid; D eqmin is the minimum equivalent damping coefficient of the power system; （17） Among them, Δ f max The maximum value of the frequency change of the sub-point unit; （18）。 9. The wind farm frequency active support system based on dynamic estimation of frequency regulation capability is characterized by: include: Dynamic evaluation module, used to evaluate the active power regulation capability of each wind turbine in the wind farm in real time; A solution module is used to solve the objective function of the pre-established wind farm frequency regulation active power prediction model based on the active power regulation capability of each wind turbine and under the constraints, and obtain the frequency regulation active power instructions of each wind turbine in the wind farm; The control module is used to control each wind turbine in the wind farm to execute the frequency modulation active power instruction.

10. The wind farm frequency active support system based on dynamic estimation of frequency regulation capability according to claim 9, characterized in that: In the step of real-time evaluation of the active power regulation capability of each wind turbine in the wind farm, the specific evaluation is as follows: i Changes in active energy per second Hedi i Changes in the maximum frequency modulation energy contained in the kinetic energy of the second rotor .

11. The wind farm frequency active support system based on dynamic estimation of frequency regulation capability according to claim 10, characterized in that: From the previous moment to the i Changes in active energy per second for: （8） No. i Changes in the maximum frequency modulation energy contained in the kinetic energy of the second rotor for: （9） in, and are the active power calculated based on the wind speed prediction and the active power corresponding to the current known wind speed; is the total moment of inertia of the wind turbine transmission chain converted to the high-speed shaft, Indicates the i The predicted speed in seconds, ω gen It is the speed of the wind turbine set from the current speed to the speed after the rotor is released.

12. The wind farm frequency active support system based on dynamic estimation of frequency regulation capability according to claim 9, characterized in that: In the step of solving the objective function of the pre-established wind farm frequency regulation active power prediction model based on the active power regulation capability of each wind turbine generator set and satisfying the constraint conditions to obtain the frequency regulation active power instructions of each wind turbine generator set in the wind farm, the wind farm frequency regulation active power prediction model is: （3） in: ， in, x (k) and x (k-1) are the speed regulator output, reheat boiler output, thermal power active power, grid frequency, and active power of each wind turbine at time k and k-1 respectively. u (k) and u (k-1) are the active power instructions of each wind turbine in the wind farm at time k and k-1 respectively, d (k) and d (k-1) is the active power of the load disturbance at time k and k-1 respectively.

13. The wind farm frequency active support system based on dynamic estimation of frequency regulation capability according to claim 9, characterized in that: In the step of solving the objective function of the pre-established wind farm frequency regulation active power prediction model based on the active power regulation capability of each wind turbine generator set and satisfying the constraint conditions to obtain the frequency regulation active power instructions of each wind turbine generator set in the wind farm, the objective function of the wind farm frequency regulation active power prediction model is: （7） Among them, J is the objective function, k and j are the moments, Wind turbine frequency regulation cost coefficient, The change in active power of the i-th wind turbine group, n p The prediction time domain of the wind farm frequency regulation active power prediction model predictive control, n c To control the time domain, λ 1. λ 2. λ 3 is the weight coefficient, △ f is the grid frequency variation.

14. The wind farm frequency active support system based on dynamic estimation of frequency regulation capability according to claim 9, characterized in that: In the step of solving the objective function of the pre-established wind farm frequency regulation active power prediction model based on the active power regulation capability of each wind turbine and satisfying the constraint conditions to obtain the frequency regulation active power instructions of each wind turbine in the wind farm, the constraint conditions include: wind farm regulation capability constraint, system frequency change amount and frequency change rate constraint.

15. The wind farm frequency active support system based on dynamic estimation of frequency regulation capability according to claim 14, characterized in that: The wind farm regulation capability constraint is: （12） Where, For the i The lower limit of active power regulation of unit j at the moment, For the i Unit No. j The upper limit of active regulation energy at all times; For the i The active power regulation of the unit at the jth moment; For the station j The lower limit of active power regulation at the moment, Station No. j The upper limit of active power regulation at all times, n p is the prediction time domain of the wind farm frequency regulation active power prediction model predictive control, and n is the total number of wind turbines in the wind farm.

16. The wind farm frequency active support system based on dynamic estimation of frequency regulation capability according to claim 14, characterized in that: The constraints on the system frequency change amount and frequency change rate include: （13） （14） Among them, △ f is the frequency variation of the power grid, △ P L is the active power variation of the system load disturbance, D eq is the equivalent damping coefficient of the power system; （15） （16） in, df max is the maximum frequency change speed of the power grid; D eqmin is the minimum equivalent damping coefficient of the power system; （17） Among them, Δ f max The maximum value of the frequency change of the sub-point unit; （18）。 17. An electronic device, characterized in that: The method comprises a processor and a memory, wherein the processor is used to execute a computer program stored in the memory to implement the method for active frequency support of a wind farm based on dynamic estimation of frequency regulation capability as claimed in any one of claims 1 to 8.

18. A computer-readable storage medium, characterized in that The computer-readable storage medium stores at least one instruction, and when the at least one instruction is executed by a processor, the method for actively supporting the frequency of a wind farm based on dynamic estimation of frequency regulation capability according to any one of claims 1 to 8 is implemented.

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