Permanent magnet wind turbine wind farm primary frequency modulation capability evaluation method

Abstract

The application provides a permanent magnet wind turbine wind power plant primary frequency modulation capability evaluation method, which comprises the following steps: obtaining rotor kinetic energy of the wind turbine available for frequency modulation, obtaining maximum power of the wind turbine in the primary frequency modulation stage by using a model prediction control method, and obtaining maximum droop coefficient and maximum inertia coefficient of the wind field. The application not only enables the power grid dispatching party to correctly know the frequency modulation capability of the wind power plant, gives the wind power plant a suitable frequency modulation coefficient, ensures safe operation of equipment in the wind field, but also can fully tap the frequency modulation capability of the wind power plant and improve the frequency stability of the power grid.

Description

TECHNICAL FIELD

[0001] The application belongs to the field of power system state evaluation, and particularly relates to a primary frequency modulation capacity evaluation method for a permanent magnet wind turbine wind farm. BACKGROUND

[0002] In recent decades, due to the rapidly developing world economy and productivity level, the energy demand of modern society is increasing, and fossil fuels have caused environmental problems such as climate warming and air pollution, and the traditional energy structure dominated by fossil fuels is facing unprecedented challenges. Therefore, in the whole world, more environmentally friendly and resource-rich renewable energy has been widely used. Among the current renewable energy generation technologies, wind power technology is more mature, has a larger development scale and better commercial prospects, and is increasingly valued by countries around the world, and has been widely used and developed. In terms of large-scale development of wind power, China has good wind energy resource conditions, a relatively mature industrial foundation, and advanced technical support for power grids, especially the future economic development prospects and the increasing energy demand of China, making it feasible and necessary to build large-scale wind power. With the continuous expansion of wind turbine capacity, the penetration rate of wind power in the power system is continuously increasing, and the influence of wind power on the power system is becoming larger and deeper.

[0003] Among the various problems brought about by wind power integration, the power grid frequency fluctuation problem caused by wind power generation has attracted much attention. Currently, there are two types of wind turbines mainly used in wind farms, one is a doubly-fed wind turbine, and the other is a permanent magnet direct-drive wind turbine, both of which are variable-speed constant-frequency wind turbines. Variable-speed constant-frequency wind turbines operate as asynchronous motors in the power grid, connecting to the grid through a converter, which isolates the power generation side of the wind farm from the grid. The mechanical power and electromagnetic power are decoupled, and the speed and grid frequency are decoupled. The rotor of the wind turbine cannot respond quickly and effectively to changes in system frequency, so the wind turbine cannot provide inertia to the power grid. With a large number of wind turbines connected to the grid, some conventional generators will inevitably be replaced, thereby reducing the overall rotational inertia of the power system, making it more difficult to control the system frequency. To improve system controllability, most wind farms use wind turbine rotor kinetic energy control methods to simulate the inertia characteristics and droop characteristics of conventional power stations to participate in primary frequency modulation. However, under the rotor kinetic energy control method, if the estimation of wind turbine frequency modulation capacity is unreasonable, the wind turbine may be disconnected due to excessively low speed. Therefore, it is very important to establish a wind farm frequency modulation capacity evaluation system and a frequency modulation capacity evaluation method. However, the current evaluation method cannot determine the virtual droop coefficient, virtual inertia coefficient and maximum output of the wind farm under the condition of ensuring the safe operation of the equipment. SUMMARY

[0004] In order to overcome the above prior art deficiencies, the application provides a permanent magnet fan wind farm primary frequency modulation capability evaluation method, which not only enables the power grid dispatcher to correctly know the frequency modulation capability of the wind farm, gives the wind farm a suitable frequency modulation coefficient, ensures the safe operation of the equipment in the wind farm, but also can fully tap the frequency modulation capability of the wind farm and improve the frequency stability of the power grid.

[0005] The technical scheme adopted by the application is: it includes obtaining rotor kinetic energy available for frequency modulation of the fan, using model predictive control method to optimize obtaining maximum power of the fan in the primary frequency modulation stage, and maximum droop coefficient and maximum inertia coefficient of the wind farm. i ;

[0006]

[0007] In the formula, SOE i represents the energy state evaluation index of the kinetic energy stored in the blade of the i-th fan in the wind farm, ω i represents the unit value of the rotating speed of the blade of the i-th fan in the wind farm, ω i,min represents the lower limit of the unit value of the rotating speed of the blade of the i-th permanent magnet fan in the wind farm to ensure normal power generation, and ω i,max represents the upper limit of the unit value of the rotating speed of the blade of the i-th permanent magnet fan in the wind farm to ensure normal power generation.

[0008] Further, according to the energy state evaluation index SOE i of the kinetic energy stored in the blade of the fan in the wind farm, the power generation level evaluation index x i of the permanent magnet fan in the wind farm is established.

[0009]

[0010] In the formula, P e,i represents the unit value of the electromagnetic power output by the i-th fan in the wind farm, P m,i represents the unit value of the mechanical power captured by the i-th permanent magnet fan in the wind farm.

[0011] Further, the energy available for frequency modulation of the fan is:

[0012]

[0013] Wherein, J C is the moment of inertia of the fan. The energy available for frequency modulation of the entire wind farm is:

[0014]

[0015] Further, the model predictive control method is used to optimize the maximum power of the wind turbine in the primary frequency modulation stage, which comprises the following steps:

[0016] (1) performing short-term prediction on the wind speed of the wind turbine;

[0017] (2) assuming that the rotating speed of the wind turbine linearly decreases to the minimum value at the end of the primary frequency modulation, the rotating speed of the wind turbine at each control point is ω p,i ;

[0018] (3) calculating the predicted mechanical power P m,i of the wind turbine by using the following wind turbine power formula:

[0019]

[0020] wherein ρ is the air density, R is the blade radius, C p,i is the wind energy capture coefficient, β i is the pitch angle of the wind turbine, λ i is the tip speed ratio of the wind turbine, and v s,i is the wind speed.

[0021] (4) using the MPC model to optimize the maximum frequency modulation output P e,i of the wind turbine;

[0022] (5) using the obtained maximum frequency modulation output P e,i of the wind turbine and the following formula to continuously iterate to obtain the predicted rotating speed and mechanical power of the wind turbine at each control point:

[0023] ω i (t)=(P m,i (t-1) / J C -P e,i (t-1) / J C )·T / ω i (t-1)+ω i (t-1)

[0024] wherein T is the reference time, and t is the time point in the dynamic sequence of the wind turbine power;

[0025] (6) repeating steps (4) and (5) until the difference between the maximum frequency modulation powers of the wind turbine obtained before and after is less than a threshold value, and the maximum power obtained is the solution;

[0026] and the maximum frequency modulation power of the entire wind farm is:

[0027]

[0028] Further, step (4) in the calculation steps comprises the following steps:

[0029] (4-1) Objective function is the maximum frequency modulation power of the wind turbine:

[0030] (4-2) Set constraints, constraints are:

[0031] P i,min <P e,i (j)<P i,max

[0032] ω i,min <ω i (j)<ω i,max

[0033] ω i (j+1)=(P m,p,i (j) / J C -P e,i / J C )·T / ω p,i (j)+ω i (j)

[0034] Where, ω p,i is the predicted speed of the wind turbine i, P m,p,i is the predicted mechanical power of the wind turbine i, ω i,min , ω i,max is the maximum minimum speed of the wind turbine, P i,min , P i,max is the maximum minimum active power of the wind turbine.

[0035] Further, the maximum droop coefficient and the maximum inertia coefficient of the wind farm are calculated by the following steps:

[0036] (1) The wind speed of the wind turbine is ultra-short-term predicted;

[0037] (2) Assume the droop coefficient and the virtual inertia coefficient of the wind farm;

[0038] (3) After obtaining the droop coefficient and the inertia coefficient of the wind farm, the overall output of the wind farm and the system frequency are calculated;

[0039] (4) The electromagnetic power of the wind turbine is calculated by the following formula:

[0040] x i =x j ,i,j∈{1,2,L,l};

[0041] (5) The predicted speed and the mechanical power of the wind turbine at each control point are obtained by continuously iterating the maximum frequency modulation output P e,i of the wind turbine and the following formula:

[0042] ω i (t)=(P m,i(t-1) / J C -P e,i (t-1) / J C )·T / ω i (t-1)+ω i (t-1)

[0043] wherein T is a reference time, t is a time point in a wind turbine power dynamic sequence;

[0044] (6) using the MPC model, optimizing to solve the frequency modulation coefficient of the wind farm;

[0045] (7) repeating steps (3), (4), (5), (6), the difference between the frequency modulation coefficients of the wind farm before and after is less than a threshold value, and outputting the maximum frequency modulation coefficient of the wind farm.

[0046] Further, step (3) therein, after obtaining the droop coefficient and inertia coefficient of the wind farm, calculates the overall output of the wind farm and the system frequency, including the following steps:

[0047] (3-1) calculating the wind turbine output using the following virtual inertia control formula of the wind farm:

[0048]

[0049] wherein P in represents the inertia response of the wind farm, P f represents the droop response of the wind farm, K f is the droop coefficient of the wind turbine, K in is the inertia coefficient of the wind farm, f is the system frequency, and f n is the rated frequency of the system;

[0050] (3-2) solving the system frequency at the next time point using the frequency dynamic equation;

[0051]

[0052] -f n ·K in / (2H·T)·(f(t)-f(t-1))+P UB / 2H·f n ·T

[0053] iteratively calculating (4-3-1) and (4-3-2) until the wind farm power of all control points is calculated.

[0054] Further, step (6) therein, using the MPC model, optimizing to solve the frequency modulation coefficient of the wind farm includes the following steps:

[0055] (6-1) establishing an objective function:

[0056]

[0057] wherein, a1 is a frequency drop depth coefficient, indicating the lowest frequency in the frequency modulation process of the power system, J z is a frequency drop slope coefficient, indicating the average rate of change of frequency from the occurrence of the frequency disturbance of the power system to the lowest point of the frequency, K1, K2 are target function weight coefficients;

[0058] (6-2) Set the constraint condition, the constraint condition is:

[0059] P i,min <P e,i (j)<P i,max

[0060] ω min <ω i (j)<ω max

[0061] -ΔP max ≤P i (j)-P i (j-1)≤ΔP max

[0062] ω i (j+1)=(P m,p,i (j) / J C -P e,i / J C )·T / ω p,i (j)+ω i (j)

[0063]

[0064] -f n ·K in / (2H·T)·(f(t)-f(t-1))+P UB / 2H·f n ·T

[0065] x i =x j ,i,j∈{1,2,L,l}

[0066]

[0067] wherein, P UB is a preset system unbalanced power, P WF is the wind farm power calculated by the frequency and the frequency modulation coefficient.

[0068] The beneficial effects of the present application are:

[0069] The method can calculate the rotor kinetic energy available for frequency modulation of the fan, set the maximum average power output of the fan during frequency modulation under the condition of ensuring the safe operation of the fan, combine the operation conditions of all fans in the entire wind farm, combine the virtual inertia control strategy of the fan frequency modulation, set the maximum droop coefficient and the maximum inertia coefficient of the wind farm under the condition of ensuring the safe operation of the equipment in the wind farm, and maximize the frequency modulation capacity of the wind farm to improve the frequency stability of the system. Since the evaluation index and evaluation method proposed by the method can better reflect the maximum frequency modulation capacity of the wind farm, the method can play an important role in the process of participating in the primary frequency modulation of the power system. BRIEF DESCRIPTION OF DRAWINGS

[0070] Figure 1 Virtual inertia control logic diagram for wind turbine;

[0071] Figure 2 Frequency characteristic diagram for wind farm;

[0072] Figure 3 Fan operation state prediction value updating step;

[0073] Figure 4 Wind farm and system state prediction value updating step. DETAILED DESCRIPTION

[0074] In order to make the technical solutions of the present application clearer, the present application will be further described below with reference to the drawings.

[0075] The present application provides a kind of permanent magnet fan wind farm primary frequency modulation capacity evaluation method, this method will according to fan parameter state, for three wind power frequency modulation indexes are evaluated, i.e. the rotor kinetic energy available for frequency modulation of fan, the maximum frequency modulation power of fan, wind farm maximum droop coefficient and inertia coefficient, this method includes the acquisition of the rotor kinetic energy available for frequency modulation of fan, the maximum power of fan in primary frequency modulation stage is optimized and acquired using model predictive control method, and wind farm maximum droop coefficient and maximum inertia coefficient.

[0076] The evaluation means of the present application has three evaluation indexes: rotor kinetic energy available for frequency modulation of fan, maximum frequency modulation power of fan and maximum droop coefficient and maximum inertia coefficient of wind farm.

[0077] 1, rotor kinetic energy available for frequency modulation of fan

[0078] (1) establish the energy state evaluation index SOE of the kinetic energy stored by the blades of the fan in the wind farm i :

[0079]

[0080] In the above formula, SOE iω represents the energy state evaluation index of the kinetic energy stored in the blade of the i-th wind turbine in a wind farm. i ω represents the per-unit value of the rotational speed of the i-th wind turbine blade in the wind farm. i,min ω represents the lower limit of the per-unit blade rotational speed of the i-th permanent magnet wind turbine in a wind farm to ensure normal power generation. i,max This represents the upper limit of the per-unit blade rotational speed of the i-th permanent magnet wind turbine in the wind farm to ensure normal power generation.

[0081] Based on the above-mentioned energy state evaluation index SOE for storing kinetic energy in wind turbine blades in wind farms i Establish an evaluation index x for the power generation level of permanent magnet wind turbines in wind farms. i :

[0082]

[0083] In the above formula, P e,i P represents the per-unit value of the electromagnetic power output by the i-th wind turbine in the wind farm. m,i This represents the per-unit value of the mechanical power captured by the i-th permanent magnet wind turbine in the wind farm.

[0084] During a single frequency regulation process, due to its short duration, the kinetic energy control method of the fan rotor can be used to temporarily release the rotor kinetic energy in the fan, causing an imbalance between the fan's electromagnetic power and mechanical power, thus allowing the fan to participate in the single frequency regulation. Therefore, this system needs to evaluate the kinetic energy stored in the fan rotor that can be used for the system's single frequency regulation to demonstrate the fan's frequency regulation capability. The kinetic energy stored in the fan blades is proportional to the square of its rotational speed:

[0085]

[0086] Among them, J C Let ω be the moment of inertia of the wind turbine. i The fan speed is ω, which represents the upper and lower limits of the fan speed. i,min ,ω i,max Subsequently, the energy available for frequency regulation by the wind turbine is:

[0087]

[0088] The rotor kinetic energy available for frequency regulation in the entire wind farm is:

[0089]

[0090] Once the kinetic energy available for frequency regulation in the entire wind farm is obtained, the frequency regulation margin of the wind farm can be obtained for wind farms using rotor kinetic energy control, thereby ensuring the safe operation of the wind farm during frequency regulation and laying the foundation for calculating the maximum frequency regulation power and the maximum frequency regulation coefficient of the wind farm.

[0091] 2, maximum frequency modulation power of the wind turbine

[0092] The wind turbine needs to evaluate the maximum frequency modulation power of the wind turbine according to the operating state of the wind turbine when participating in frequency modulation, to ensure the operating safety of the wind turbine when participating in frequency modulation. The virtual inertia control is adopted in the frequency modulation process of wind power, and the control diagram is as shown in Figure 1 .

[0093] According to the frequency characteristics of the wind farm Figure 2 , it is assumed that the most adverse situation is encountered during the participation of the wind turbine in the frequency modulation process: the system frequency remains at the minimum value of the frequency modulation frequency of the wind farm during the frequency modulation process, at which time the wind turbine will remain unchanged during the frequency modulation process. In order to ensure the operating safety of the wind turbine, the maximum frequency modulation power under the above condition is obtained more conservatively. In order to obtain the maximum frequency modulation power of the wind turbine, the MPC method is adopted, and the steps are as shown in Figure 3 .

[0094] (2-1) First, the wind speed of the wind turbine in the future period of time is super-short-term predicted.

[0095] (2-2) Then, in order to lay the foundation for MPC, the electromechanical transient model of the wind turbine needs to be obtained and discretized.

[0096] The mechanical power obtained by the turbine blades of the wind turbine in the wind farm is related to the pitch angle, the tip speed ratio and the wind speed, which can be expressed as the predicted mechanical power P m,i of the wind turbine.

[0097]

[0098] Where ρ is the air density, R is the blade radius, C p,i is the wind energy capture coefficient, β i is the pitch angle of the wind turbine, λ i is the tip speed ratio of the wind turbine, and v s,i is the wind speed.

[0099] For easy calculation, the top MPC of the system adopts the first-order electromechanical transient model of the wind turbine, and the subsequent linearization process of the wind turbine transient model is discretized. The mechanical power P m,i can be converted to the motor shaft, and the rotating dynamic process can be expressed as:

[0100]

[0101] Where J C is the moment of inertia, ω i is the mechanical speed, T m,i is the mechanical torque of the wind turbine, and T e,i is the electromagnetic torque of the wind turbine. It can be seen from the above formula that the electromagnetic torque of the wind turbine is related to the electromagnetic power Pe,i Directly related.

[0102] Considering the solution of the wind turbine MPC model, the electromechanical transient model (7) needs to be discretized and linearized. For the nonlinear problem of the electromechanical transient process prediction model, a complete discretization technique can be used to obtain the discrete model at time t:

[0103] ω i (t)=(P m,i (t-1) / J C -P e,i (t-1) / J C )·T / ω i (t-1)+ω i (t-1) (8)

[0104] Where T is the reference time and t is the time point in the wind turbine electromechanical dynamic sequence.

[0105] (2-2) First, assume that the fan can reach the minimum speed in the final stage, and assume that the fan speed decreases at a linear rate, and obtain the speed value ω of each fan at each control point. p,i And calculate the mechanical power P of the fan according to the formula (6) for the mechanical power of the fan. m,p,i .

[0106] (2-3) Using the predicted wind speed and fan speed at the fan, calculate the maximum frequency regulation power of the fan through the following objective function, constraints and MPC optimization iterative calculation method.

[0107] 1) Objective function

[0108] The goal of this frequency regulation capability assessment system is to obtain the maximum frequency regulation power of the fan.

[0109]

[0110] 2) Constraints

[0111] The maximum frequency-regulating capacity (MPC) calculation for a wind turbine needs to consider the turbine's operational safety and electromechanical transient processes, thus requiring the consideration of certain constraints. These constraints include turbine power constraints, speed constraints, and the turbine's electromechanical transient equations.

[0112] P i,min <P e,i (j) < P i,max (10)

[0113] ω i,min <ω i (j)<ω i,max (11)

[0114] ωi (j+1) = (P m,p,i (j) / J C -P e,i / J C )·T / ω p,i (j)+ω i (j) (12)

[0115] where ω p,i is the predicted speed of the wind turbine i, P m,p,i is the predicted mechanical power of the wind turbine i, ω i,min , ω i,max are the maximum and minimum speed of the wind turbine, P i,min , P i,max are the maximum and minimum active power of the wind turbine. The nonlinear constraint (12) is linearized by substituting the predicted value of the wind turbine speed and the power value of the wind turbine power.

[0116] (2-4) Substitute the optimization result of MPC into equation (8) to calculate the wind turbine speed at the next time, and calculate the wind turbine power at the next time through equation (6).

[0117] (2-5) Repeat steps (2-3), (2-4), and the difference between the maximum frequency modulation power of the wind turbine before and after is less than the threshold, and the maximum wind turbine power obtained is the solution. And the sum of the maximum frequency modulation power of the entire wind farm is:

[0118]

[0119] After obtaining the maximum frequency modulation power of the wind farm, it can be used as a constraint condition to ensure the safe operation of the wind turbine in the primary frequency modulation process of the power system, to ensure the safe operation of the wind turbine.

[0120] 3、Maximum droop coefficient and maximum inertia coefficient of wind farm

[0121] After obtaining the maximum frequency modulation power of all wind turbines, the frequency modulation capability evaluation system first evaluates the maximum imbalance power that the power grid can withstand through the grid frequency curve and the grid imbalance power in the historical frequency modulation event, and sets the maximum frequency modulation coefficient of the wind farm through the imbalance power to prevent the safe operation of the equipment in the wind farm from being threatened during frequency modulation. For example, Figure 4 , the solving steps of the maximum droop coefficient and the maximum inertia coefficient of the wind farm are as follows:

[0122] (3-1) First, the frequency transient model is derived. In the power system, when the power generation and load do not match, the AC grid frequency deviates from the standard value and starts to oscillate. In this paper, the wind farm will be simulated as a whole to imitate the inertia characteristics and droop characteristics of conventional power stations.

[0123]

[0124] where P in represents the inertial response of the wind farm, P f represents the droop response of the wind farm, K f is the droop coefficient of the wind farm, K in is the inertial coefficient of the wind farm, f is the system frequency, f n is the rated frequency of the system. In this paper, the power system frequency dynamic equation can be calculated as a first-order inertia equation:

[0125]

[0126] where H is the system inertia coefficient, represents the droop coefficient of the conventional units, P G is the power generation of the system conventional units, P L is the system load, P WF is the normal power generation of the wind farm.

[0127] P UB = P G -P L + P WF (16)

[0128] Combining (15) and (16), (15) is discretized as:

[0129]

[0130] (3-2) Then the wind turbine rotor kinetic energy state index and the wind farm power generation level index are established

[0131] (3-2-1) The wind turbine rotor kinetic energy state index is established as follows:

[0132]

[0133] where SOE i represents the rotor kinetic energy state index of the wind turbine i.

[0134] (3-2-2) The wind farm power generation level evaluation index is established as follows

[0135]

[0136] where x i is the power generation level index of the wind turbine in the wind farm.

[0137] (3-3) Assuming the droop coefficient and the inertia coefficient of the wind farm, the overall output of the wind farm is calculated using equation (14), and the system frequency under the droop coefficient is calculated using equation (15).

[0138] (3-4) According to the obtained wind farm power, according to the wind turbine power generation level index (19), taking the wind turbine power generation level index consistent as the power distribution target (20), the power of each wind turbine is calculated

[0139] x i =x j ,i,j∈{1,2,L,l} (20)

[0140] (3-5) The wind turbine speed at the next time is predicted and calculated by using formula (8), and the mechanical power of the wind turbine is calculated by using formula (6). Repeat (3-4) and (3-5) until the predicted wind turbine speed and the predicted wind turbine power of all control points are obtained.

[0141] (3-6) The wind speed at the wind turbine and the wind turbine speed are predicted, and the maximum droop coefficient and the maximum inertia coefficient of the wind farm are calculated by using the following objective function, constraint condition and MPC optimization iterative calculation method.

[0142] 1) Objective function

[0143]

[0144] Wherein, α1 is the frequency drop depth coefficient, indicating the lowest frequency in the power system frequency modulation process, J z is the frequency drop slope coefficient, indicating the average frequency change rate from the occurrence of power system frequency disturbance to the lowest frequency point, K1, K2 are the objective function weight coefficients;

[0145] 2) Constraint condition

[0146] The MPC of the maximum frequency modulation output of the wind turbine needs to consider the operation safety of the wind turbine and the electromechanical transient process of the wind turbine, so some constraint conditions need to be considered. Among them, the constraint conditions include wind turbine power constraint, wind turbine power climbing constraint, speed constraint, wind turbine electromechanical transient equation, system frequency transient equation.

[0147] P i,min <P e,i (j)<P i,max (22)

[0148] ω min <ω i (j)<ω max (23)

[0149] -ΔP max ≤P i (j)-P i (j-1)≤ΔP max (24)

[0150] ω i(j+1) = (P m,p,i (j) / J C -P e,i / J C )·T / ω p,i (j)+ω i (j) (25)

[0151]

[0152]

[0153] where P UB is the preset system unbalanced power, P WF is the wind farm power calculated by the frequency and the frequency modulation coefficient, when the wind farm participates in frequency modulation, the power distribution among the wind turbines in the wind farm will be obtained by formula (18) according to the rotor kinetic energy state evaluation index SOE of the wind turbine, therefore the constraint condition needs to be added to formula (18). The nonlinear constraint (25) is substituted by the predicted value of the wind turbine speed and the power value of the wind turbine power, and the nonlinear quantity is replaced, so as to linearize.

[0154] By calculating the maximum droop coefficient and the maximum inertia coefficient of the wind farm, the frequency modulation potential of the wind farm can be fully tapped under the premise of ensuring the safe operation of the wind turbines in the wind farm, the frequency fluctuation of the power system in the frequency modulation process is minimized, and the purpose of frequency stability is achieved.

[0155] The permanent magnet wind turbine wind farm primary frequency modulation capacity evaluation system and evaluation method are designed by the model predictive control technology. The wind farm central controller predicts the state of the wind turbine in the frequency modulation process by using the electromechanical transient model of the wind turbine based on the historical measurement data of the wind turbine and the parameters of the wind turbine, and obtains the frequency modulation available energy, the maximum frequency modulation power of the wind turbine in the future period of time, and the maximum droop coefficient and the maximum inertia coefficient of the wind farm.

[0156] The specific characteristics of the method are as follows:

[0157] 1. The wind power frequency modulation control method considered in the method is a virtual inertia control method based on rotor kinetic energy control, the rotor kinetic energy available for frequency modulation of the wind turbine, the maximum frequency modulation power of the wind turbine, and the maximum droop coefficient and the inertia coefficient of the wind farm are proposed, and a set of wind farm frequency modulation capacity evaluation system is formed.

[0158] 2. The method considers the rotor kinetic energy frequency modulation control of the wind turbine, and under the premise of ensuring the safe operation of the wind turbine, the maximum power output of the wind turbine during frequency modulation is calculated by using the model predictive control method, so that the frequency modulation output of the wind turbine under certain wind resources can be tapped to the maximum extent.

[0159] 3. The method can calculate the maximum droop coefficient and the maximum inertia coefficient of the wind farm during the frequency modulation by using the model predictive control method under the premise of ensuring the safe operation of the equipment in the wind farm, can maximize the frequency modulation capability of the wind farm, and improve the frequency stability of the system.

[0160] The above merely describes preferred embodiments of the present application, and equivalent changes or modifications made to the structure, features and principles described in the scope of the present application are included in the scope of the present application.

Claims

1. A method for evaluating the primary frequency regulation capability of a permanent magnet wind turbine wind farm, characterized in that, This includes obtaining the rotor kinetic energy of the wind turbine that can be used for frequency regulation, optimizing the wind turbine's maximum power during the primary frequency regulation stage, and the wind field's maximum droop coefficient and maximum inertia coefficient using model predictive control. The calculation of the maximum sag coefficient and maximum inertia coefficient of the wind field includes the following steps: (1) To make ultra-short-term predictions of wind turbine speed; (2) Assume the droop coefficient and virtual inertia coefficient of the wind field; (3) After obtaining the wind field droop coefficient and inertia coefficient, calculate the overall power output of the wind farm and the system frequency; (3-1) Calculate the wind turbine output using the following virtual inertial control formula for wind farms: Among them, P in P represents the inertial response of the wind field. f K represents the droop response of the wind field. f K is the fan sag coefficient. in Let f be the wind field inertia coefficient, and f be the system frequency. n The system's rated frequency; (3-2) Solve for the system frequency at the next moment using the frequency dynamic equation; Among them, f n Where H is the system's rated frequency, and H is the system's inertia coefficient. K is the droop coefficient for conventional units. in Where P is the wind field inertia coefficient, T is the reference time, and P is the reference time. UB This is the preset system imbalance power; Iteratively calculate (3-1) and (3-2) until the wind power at all control points is calculated; (4) Calculate the electromagnetic power of the fan using the following formula; x i =x j ,i,j∈{1,2,L,l}; Where, x i As an evaluation index for the power generation level of permanent magnet wind turbines; (5) Using the obtained maximum frequency-regulating output P of the fan e,i The predicted speed and mechanical power of the fan at each control point are obtained by iteratively applying the following formula. ω i (t)=(P m,i (t-1) / J C -P e,i (t-1) / J C )·T / ω i (t-1)+ω i (t-1); Among them, P m,i To predict the mechanical power of the wind turbine, J C P is the moment of inertia of the wind turbine. e,i For the maximum frequency regulation output of the wind turbine, ω i This represents the per-unit value of the rotational speed of the i-th wind turbine blade in the wind farm, where T is the reference time and t is the time point in the dynamic sequence of wind turbine power. (6) The frequency regulation coefficient of the wind field is optimized and solved using the MPC model; (7) Repeat steps (3), (4), (5), and (6). The difference between the frequency modulation coefficients of the wind field in the two consecutive steps is less than the threshold, and the maximum frequency modulation coefficient of the wind field is output.

2. The method for evaluating the primary frequency regulation capability of a permanent magnet wind turbine wind farm according to claim 1, characterized in that, Harnessing the rotor kinetic energy of wind turbines for frequency regulation includes establishing an energy state evaluation index (SOE) for the kinetic energy stored in the turbine blades within a wind farm. i ; In the formula, SOE i ω represents the energy state evaluation index of the kinetic energy stored in the blade of the i-th wind turbine in a wind farm. i ω represents the per-unit value of the rotational speed of the i-th wind turbine blade in the wind farm. i,min ω represents the lower limit of the per-unit blade rotational speed of the i-th permanent magnet wind turbine in a wind farm to ensure normal power generation. i,max This represents the upper limit of the per-unit blade rotational speed of the i-th permanent magnet wind turbine in the wind farm to ensure normal power generation.

3. The method for evaluating the primary frequency regulation capability of a permanent magnet wind turbine wind farm according to claim 2, characterized in that, According to the energy state evaluation index SOE of the wind turbine blades in the wind farm i Establish an evaluation index x for the power generation level of permanent magnet wind turbines in wind farms. i : In the formula, P e,i P represents the per-unit value of the electromagnetic power output by the i-th wind turbine in the wind farm. m,i SOE represents the per-unit value of the mechanical power captured by the i-th permanent magnet wind turbine in the wind farm. i This represents the energy state evaluation index for the kinetic energy stored in the blade of the i-th wind turbine in a wind farm.

4. The method for evaluating the primary frequency regulation capability of a permanent magnet wind turbine wind farm according to claim 3, characterized in that, The energy available for frequency regulation by the fan is: Among them, J C Let ω be the moment of inertia of the wind turbine. i ω represents the per-unit value of the rotational speed of the i-th wind turbine blade in the wind farm. i,min This represents the lower limit of the per-unit blade rotational speed of the i-th permanent magnet wind turbine in the wind farm to ensure normal power generation; the energy available for frequency regulation in the entire wind farm is: E f,i This refers to the energy that the fan can use for frequency regulation.

5. The method for evaluating the primary frequency regulation capability of a permanent magnet wind turbine wind farm according to claim 1, characterized in that, The optimization of obtaining the maximum power of the wind turbine during the primary frequency regulation stage using model predictive control includes the following steps: (1) To make ultra-short-term predictions of wind turbine speed; (2) Assuming the fan speed decreases linearly and reaches its minimum value at the end of the first frequency regulation, the fan speed values ​​ω at each control point are obtained. p,i ; (3) Calculate the predicted mechanical power P of the wind turbine using the following wind turbine power formula. m,i ; Where ρ is the air density, R is the blade radius, and C p,i β is the wind energy capture factor. i λ is the pitch angle of the wind turbine blades. i v is the tip speed ratio of the fan blades. s,i Wind speed; (4) Using the MPC model, the maximum frequency-regulating output P of the fan is optimized and solved. e,i ; (5) Using the obtained maximum frequency-regulating output P of the fan e,i The predicted speed and mechanical power of the fan at each control point are obtained by iteratively applying the following formula. ω i (t)=(P m,i (t-1) / J C -P e,i (t-1) / J C )·T / ω i (t-1)+ω i (t-1); Among them, P m,o For predicting the mechanical power of the wind turbine, P e,i For the maximum frequency regulation output of the wind turbine, J C Let ω be the moment of inertia of the wind turbine. i This represents the per-unit value of the rotational speed of the i-th wind turbine blade in the wind farm, where T is the reference time and t is the time point in the dynamic sequence of wind turbine power. (6) Repeat steps (4) and (5). After the difference between the maximum frequency regulation power of the fan in the two steps is less than the threshold, the maximum power of the fan is the solution obtained. The maximum frequency modulation power of the entire wind farm is: P e,i This is the maximum frequency regulation output of the wind turbine.

6. The method for evaluating the primary frequency regulation capability of a permanent magnet wind turbine wind farm according to claim 5, characterized in that, Step (4) of its calculation process includes the following steps: (4-1) The objective function is the maximum frequency regulation power of the wind turbine: Among them, P e,i This is the maximum frequency-regulating output of the fan; (4-2) Set the constraints as follows: P i,min <P e,i (j)<P i,max , oh i,min <oh i (j)<ω i,max , ω i (j+1)=(P m,p,i (j) / J C -P e,i / J C )·T / ω p,i (j)+ω i (j); Where, ω p,i For the predicted rotational speed of fan i, P m,p,i ω is the predicted mechanical power of wind turbine i. i,min ω i,max P represents the minimum and maximum speeds of the fan. i,min P i,max This represents the minimum and maximum active power of the wind turbine.

7. The method for evaluating the primary frequency regulation capability of a permanent magnet wind turbine wind farm according to claim 1, characterized in that, The calculation of the maximum droop coefficient and maximum inertia coefficient of the wind field includes the following steps (6): Using the MPC model, the frequency modulation coefficient of the wind field is optimized and solved. (6-1) Establish the objective function: Where α1 is the frequency sag depth coefficient, indicating the lowest frequency during power system frequency regulation, J z K1 and K2 are the frequency drop slope coefficients, which indicate the average rate of change of frequency from the occurrence of frequency disturbance in the power system to the lowest frequency point. K1 and K2 are the weight coefficients of the objective function. (6-2) Set constraints.

8. The method for evaluating the primary frequency regulation capability of a permanent magnet wind turbine wind farm according to claim 7, characterized in that, In step (6-2), the constraints are: P i,min <P e,i (j)<P i,max ; oh min <oh i (j)<ω max ; -ΔP max ≤P i (j)-P i (j-1)≤ΔP max ; ω i (j+1)=(P m,p,i (j) / J C -P e,i / J C )·T / ω p,i (j)+ω i (j); x i =x j ,i,j∈{1,2,L,l}; In the formula, P i,min P i,max P represents the minimum and maximum active power of the wind turbine. e,i For the maximum frequency regulation output of the wind turbine, ω i Let P be the per-unit value of the rotational speed of the i-th wind turbine blade in the wind farm. m,p,i For the predicted mechanical power of wind turbine i, J C Let ω be the moment of inertia of the fan, T be the reference time, and ω be the rotational inertia of p,i f is the predicted rotational speed of fan i. n K is the system's rated frequency. f This is the fan sag coefficient. Where H is the sag coefficient for conventional units, H is the system inertia coefficient, and K is the droop coefficient. in P is the wind field inertia coefficient. UB x is the preset system unbalanced power. i P is an evaluation index for the power generation level of permanent magnet wind turbines. WF This is the wind power calculated using the frequency and frequency modulation coefficient.

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