An optimization method and device for virtual inertia control of a wind turbine generator

Abstract

The application relates to the technical field of new energy optimization control, and particularly provides a wind turbine virtual inertia control optimization method and device, which comprises the following steps: substituting relevant parameters of a wind-thermal combined frequency modulation linear discretization model into a pre-constructed target optimization model and solving the target optimization model to obtain an optimal wind-thermal combined frequency modulation linear discretization model control domain control quantity increment; and adjusting parameters of wind turbine virtual inertia control based on the optimal wind-thermal combined frequency modulation linear discretization model control domain control quantity increment; wherein the relevant parameters comprise state quantity, disturbance quantity, system frequency deviation output quantity and wind turbine active power output change quantity, and the parameters of the wind turbine virtual inertia control at least comprise one of the following: a deviation term coefficient and a differential term coefficient. The scheme can adaptively adjust the deviation term coefficient and the differential term coefficient by sensing the change of the frequency, so that a better inertia frequency modulation effect is obtained.

Description

Technical Field

[0001] The present invention relates to the technical field of new energy optimization control, and in particular to an optimization method and device for virtual inertia control of a wind turbine generator set. Background Art

[0002] Traditional hydropower and thermal power plants use synchronous generators as power generation equipment. Their inherent inertia-responsive frequency modulation is characteristic of synchronous generators. In wind power systems, the isolation of wind turbine converters decouples the turbine rotor motion from the system frequency, rendering the turbine virtually inertial. This reduces the system's equivalent inertia and severely impacts frequency stability.

[0003] To mitigate the adverse effects of insufficient grid inertia due to increased wind power grid-connected capacity, wind turbines employ a virtual inertia control scheme. By simulating the inertia response of synchronous motors, wind turbines output similarly variable power during grid-connected operation, suppressing grid frequency fluctuations and improving the grid's ability to operate stably. The deviation coefficient and the differential coefficient are two core parameters in the inertia control method, influencing the system's equivalent inertia and damping, and thus the performance of the virtual inertia control method. Constant deviation and differential coefficients, as they cannot sense changes in system frequency, have limited frequency regulation. Therefore, a technical solution for online adjustment of inertia control parameters is urgently needed. Summary of the Invention

[0004] In order to overcome the above-mentioned defects, the present invention proposes an optimization method and device for virtual inertia control of a wind turbine generator set.

[0005] In a first aspect, a method for optimizing virtual inertia control of a wind turbine is provided, the method comprising:

[0006] Substitute the relevant parameters of the wind-fire joint frequency modulation linear discretization model into the pre-built target optimization model and solve it to obtain the optimal control quantity increment of the control domain of the wind-fire joint frequency modulation linear discretization model;

[0007] Incrementally adjusting the parameters of the wind turbine virtual inertia control based on the optimal wind-fire combined frequency regulation linear discretization model control domain control quantity;

[0008] The relevant parameters include: state quantity, disturbance quantity, system frequency deviation output quantity and wind turbine active power output change quantity, and the parameters of wind turbine virtual inertia control include at least one of the following: deviation term coefficient and differential term coefficient.

[0009] Preferably, the mathematical expression of the state quantity is: x(k)=[Δf(k) ΔX g(k) ΔP r (k) ΔP g (k)] T The mathematical expression of the disturbance is: d(k) = ΔP D (k); The mathematical expression of the system frequency deviation output is: y p (k) = Δf(k); the mathematical expression of the change in the active power output of the wind turbine is: pe (k)=ΔP e (k);

[0010] Among them, x(k) is the state quantity at time k, Δf(k) is the system frequency deviation value at time k, ΔX g (k) is the governor valve position deviation value at time k, ΔP r (k) is the output power deviation of the reheat unit at time k, ΔP g (k) is the output power deviation value of the thermal power unit at time k, d(k) is the disturbance at time k, ΔP D (k) is the system load deviation value at time k, y p (k) is the system frequency deviation output at time k, y pe (k) is the change in active power output of the wind turbine at time k, ΔP e (k) is the output power of the wind turbine at time k.

[0011] Furthermore, the calculation formula of the wind-fire combined frequency modulation linear discretization model is as follows:

[0012]

[0013] In the above formula, x(k+1) is the state quantity at time k+1, y p (k+1) is the system frequency deviation output at time k+1, A is the frequency linear discretization state parameter, B u is the frequency linear discretization control parameter, u(k) is the parameter adjustment of the wind turbine virtual inertia control, u(k)=[ΔK p (k) ΔK d (k)] T , ΔK p (k) is the deviation coefficient of the deviation term at time k, ΔK d (k) is the differential coefficient deviation at time k, B d is the frequency linear discretization disturbance parameter, C is the frequency linear discretization output parameter, A pe is the power linear discretization state parameter, B u_pe is the power linear discretization control parameter, B d_pe is the power linear discretization disturbance parameter.

[0014] Furthermore, the A and B u 、B d , C is calculated as follows:

[0015]

[0016]

[0017]

[0018] C=[1 0 0 0]

[0019] In the above formula, K p_op is the initial value of the deviation coefficient, D is the equivalent damping coefficient, T s is the sampling period, M is the equivalent inertia time constant, K d_op is the initial value of the differential term coefficient, T G is the time constant of the speed regulator, R is the adjustment coefficient, F HP is the reheat coefficient, T RH is the reheat time constant, T CH is the time constant of the steam turbine, Δf op is the frequency initial value deviation, is the initial frequency slope.

[0020] Furthermore, the A pe 、B u_pe and B d_pe The calculation formula is as follows:

[0021]

[0022]

[0023]

[0024] Furthermore, the calculation formula of the objective function in the pre-built target optimization model is as follows:

[0025]

[0026] In the above formula, J is the target value, Γ y Optimize the target weight matrix for frequency deviation, Γ pe The target weight matrix for optimizing the output power change of the wind turbine inertia frequency regulation is Y p (k+1|k) is the forward prediction expression of the system frequency deviation, Y pe (k+1|k) is the forward prediction expression for the change in wind turbine output power.

[0027] Furthermore, the forward prediction expression of the system frequency deviation is:

[0028] Y P (k+1|k)=S x Δx(k)+Iy p (k)+S d Δd(k)+S u ΔU(k)

[0029] The forward prediction expression of the wind turbine output power change is:

[0030] Y pe (k+1|k)=S x_pe Δx(k)+Iy pe (k)+S d_pe Δd(k)+S u_pe ΔU(k)

[0031] In the above formula, S x is the frequency deviation forward prediction state coefficient, I is the p×1 dimensional unit matrix, p is the prediction time domain, S d is the frequency deviation forward prediction disturbance coefficient, S u is the frequency deviation forward prediction control coefficient, ΔU(k) ​​is the control quantity increment of the control domain of the wind-fire joint frequency modulation linear discretization model at time k, S x_pe is the forward prediction state coefficient of the output power change, S d_pe is the forward prediction disturbance coefficient of output power change, S u_pe is the forward prediction control coefficient of the output power change, Δx(k) is the deviation of the state quantity at time k, and Δd(k) is the deviation of the disturbance quantity at time k.

[0032] Furthermore, the S x , S d , S u The calculation formula is as follows:

[0033]

[0034]

[0035]

[0036] Among them, c is the control time domain;

[0037] The S x_pe , S d_pe , S u_pe The calculation formula is as follows:

[0038]

[0039]

[0040]

[0041] Furthermore, the calculation formula for the control quantity increment of the control domain of the wind-fire joint frequency modulation linear discretization model at time k is as follows:

[0042]

[0043] In the above formula, Δu(k+c-1) is the cth control quantity increment in the control quantity increment of the wind-fire joint frequency regulation linear discretization model control domain at time k, ΔΔK p (k+c-1) is the deviation increment of the cth control domain in the control increment of the wind-fire joint frequency modulation linear discretization model at time k, ΔΔK d (k+c-1) is the increment of the differential coefficient deviation of the cth control domain in the control quantity increment of the wind-fire joint frequency modulation linear discretization model control domain at time k, and c is the control time domain.

[0044] Preferably, the calculation formula of the constraint conditions in the pre-built target optimization model is as follows:

[0045] K p min ≤K p (k)≤K p max

[0046] K d min ≤K d (k)≤K d max

[0047] In the above formula, K p (k) is the number of deviation terms at time k, K d (k) is the differential term coefficient at time k, K p min is the lower limit of the number of deviation terms, K p max is the upper limit of the number of deviation terms, K d min is the lower limit of the differential term coefficient, K d max is the upper limit of the differential term coefficient.

[0048] Preferably, the parameters of the wind turbine virtual inertia control are adjusted as follows:

[0049]

[0050]

[0051] In the above formula, K p (k) is the number of deviation terms at time k, K d (k) is the differential term coefficient at time k, is the deviation increment of the first control domain in the control increment of the optimal k-time wind-fire joint frequency regulation linear discretization model control domain, ΔK p (k-1) is the deviation of the deviation coefficient at time k-1, K p_op is the initial value of the deviation term coefficient, is the differential coefficient deviation increment of the first control domain in the control quantity increment of the control domain of the optimal k-time wind-fire joint frequency regulation linear discretization model, ΔK d (k-1) is the deviation of the differential coefficient at time k-1, K d_op is the initial value of the differential term coefficient.

[0052] In a second aspect, a device for optimizing virtual inertia control of a wind turbine is provided, wherein the device comprises:

[0053] A solution module is used to substitute the relevant parameters of the wind-fire joint frequency modulation linear discretization model into the pre-built target optimization model and solve it to obtain the optimal control quantity increment of the control domain of the wind-fire joint frequency modulation linear discretization model;

[0054] An adjustment module, configured to adjust parameters of a virtual inertia control of a wind turbine generator set based on an incremental control quantity of a control domain of the optimal wind-fire combined frequency regulation linear discretization model;

[0055] The relevant parameters include: state quantity, disturbance quantity, system frequency deviation output quantity and wind turbine active power output change quantity, and the parameters of wind turbine virtual inertia control include at least one of the following: deviation term coefficient and differential term coefficient.

[0056] Furthermore, the calculation formula of the objective function in the pre-built target optimization model is as follows:

[0057]

[0058] In the above formula, J is the target value, Γ y Optimize the target weight matrix for frequency deviation, Γ pe The target weight matrix for optimizing the output power change of the wind turbine inertia frequency regulation is Y p (k+1|k) is the forward prediction expression of the system frequency deviation, Y pe (k+1|k) is the forward prediction expression for the change in wind turbine output power.

[0059] Furthermore, the forward prediction expression of the system frequency deviation is:

[0060] Y P (k+1|k)=S x Δx(k)+Iy p (k)+S dΔd(k)+S u ΔU(k)

[0061] The forward prediction expression of the wind turbine output power change is:

[0062] Y pe (k+1|k)=S x_pe Δx(k)+Iy pe (k)+S d_pe Δd(k)+S u_pe ΔU(k)

[0063] In the above formula, S x is the frequency deviation forward prediction state coefficient, I is the p×1 dimensional unit matrix, p is the prediction time domain, S d is the frequency deviation forward prediction disturbance coefficient, S u is the frequency deviation forward prediction control coefficient, ΔU(k) ​​is the control quantity increment of the control domain of the wind-fire joint frequency modulation linear discretization model at time k, S x_pe is the forward prediction state coefficient of the output power change, S d_pe is the forward prediction disturbance coefficient of output power change, S u_pe is the output power change forward prediction control coefficient, Δx(k) is the deviation of the state quantity at time k, Δd(k) is the deviation of the disturbance quantity at time k, y p (k) is the system frequency deviation output at time k, y pe (k) is the change in active power output of the wind turbine at time k.

[0064] Furthermore, the calculation formula for the control quantity increment of the control domain of the wind-fire joint frequency modulation linear discretization model at time k is as follows:

[0065]

[0066] In the above formula, Δu(k+c-1) is the cth control quantity increment in the control quantity increment of the wind-fire joint frequency regulation linear discretization model control domain at time k, ΔΔK p (k+c-1) is the deviation increment of the cth control domain in the control increment of the wind-fire joint frequency modulation linear discretization model at time k, ΔΔK d (k+c-1) is the increment of the differential coefficient deviation of the cth control domain in the control quantity increment of the wind-fire joint frequency modulation linear discretization model control domain at time k, and c is the control time domain.

[0067] Preferably, the calculation formula of the constraint conditions in the pre-built target optimization model is as follows:

[0068] K p min ≤K p (k)≤Kp max

[0069] K d min ≤K d (k)≤K d max

[0070] In the above formula, K p (k) is the number of deviation terms at time k, K d (k) is the differential term coefficient at time k, K p min is the lower limit of the number of deviation terms, K p max is the upper limit of the number of deviation terms, K d min is the lower limit of the differential term coefficient, K d max is the upper limit of the differential term coefficient.

[0071] Preferably, the adjustment module is specifically used to adjust the parameters of the wind turbine virtual inertia control according to the following formula:

[0072]

[0073]

[0074] In the above formula, K p (k) is the number of deviation terms at time k, K d (k) is the differential term coefficient at time k, is the deviation increment of the first control domain in the control increment of the optimal k-time wind-fire joint frequency regulation linear discretization model control domain, ΔK p (k-1) is the deviation of the deviation coefficient at time k-1, K p_op is the initial value of the deviation term coefficient, is the differential coefficient deviation increment of the first control domain in the control quantity increment of the control domain of the optimal k-time wind-fire joint frequency regulation linear discretization model, ΔK d (k-1) is the deviation of the differential coefficient at time k-1, K d_op is the initial value of the differential term coefficient.

[0075] In a third aspect, a computer device is provided, comprising: one or more processors;

[0076] The processor is configured to store one or more programs;

[0077] When the one or more programs are executed by the one or more processors, the optimization method for virtual inertia control of a wind turbine generator system is implemented.

[0078] In a fourth aspect, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed, the optimization method for virtual inertia control of a wind turbine generator system is implemented.

[0079] The above one or more technical solutions of the present invention have at least one or more of the following beneficial effects:

[0080] The present invention provides a method and device for optimizing virtual inertia control of a wind turbine, comprising: substituting relevant parameters of a wind-fire combined frequency modulation linear discretization model into a pre-constructed target optimization model and solving the model to obtain an optimal control quantity increment for the control domain of the wind-fire combined frequency modulation linear discretization model; and adjusting parameters of the wind turbine virtual inertia control based on the optimal control quantity increment for the control domain of the wind-fire combined frequency modulation linear discretization model. The relevant parameters include: state quantity, disturbance quantity, system frequency deviation output quantity, and wind turbine active power output change; and the parameters of the wind turbine virtual inertia control include at least one of the following: a deviation term coefficient and a differential term coefficient. The pre-constructed target optimization model in this solution uses the system frequency deviation and the wind turbine inertia frequency modulation output power change as optimization targets, thereby reducing the system frequency deviation while also reducing the fluctuation of the wind turbine inertia frequency modulation power output. Through a model predictive control method, the inertia frequency modulation method can sense changes in the system frequency, achieve online optimization and update of the differential term coefficient and the deviation term coefficient, and achieve better wind turbine inertia frequency modulation performance. BRIEF DESCRIPTION OF THE DRAWINGS

[0081] Figure 1 1 is a flow chart of main steps of a method for optimizing virtual inertia control of a wind turbine generator system according to an embodiment of the present invention;

[0082] Figure 2 This is a main structural block diagram of a device for optimizing virtual inertia control of a wind turbine generator system according to an embodiment of the present invention. DETAILED DESCRIPTION

[0083] The specific embodiments of the present invention will be further described in detail below with reference to the accompanying drawings.

[0084] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts shall fall within the scope of protection of the present invention.

[0085] Example 1

[0086] The differential term coefficient and deviation term coefficient in the traditional wind turbine inertia frequency regulation system cannot be adaptively adjusted according to changes in the system frequency and the operating conditions of the wind turbine, resulting in certain limitations in the wind turbine inertia frequency regulation method. To overcome the above-mentioned shortcomings of the prior art, the present invention provides an optimization method for virtual inertia control of a wind turbine. The real-time sampling values ​​of the wind-fire combined frequency regulation system are input into the Model Predictive Control (MPC) model, and the optimal deviation term coefficient and differential term coefficient at the current moment are output in real time through online calculation, thereby realizing adaptive adjustment of the deviation term coefficient and differential term coefficient to improve the inertia frequency regulation performance of the wind turbine.

[0087] See attached Figure 1 , Figure 1 FIG. 1 is a flow chart showing the main steps of a method for optimizing virtual inertia control of a wind turbine generator system according to an embodiment of the present invention. Figure 1 As shown, the optimization method for virtual inertia control of a wind turbine generator system in an embodiment of the present invention mainly includes the following steps:

[0088] Step S101: Substitute the relevant parameters of the wind-fire joint frequency modulation linear discretization model into the pre-built target optimization model and solve it to obtain the optimal control quantity increment of the control domain of the wind-fire joint frequency modulation linear discretization model;

[0089] Step S102: Incrementally adjusting the parameters of the wind turbine virtual inertia control based on the optimal wind-thermal combined frequency regulation linear discretization model control domain control quantity;

[0090] The relevant parameters include: state quantity, disturbance quantity, system frequency deviation output quantity and wind turbine active power output change quantity, and the parameters of wind turbine virtual inertia control include at least one of the following: deviation term coefficient and differential term coefficient.

[0091] In this embodiment, the mathematical expression of the state quantity is: x(k)=[Δf(k) ΔX g (k) ΔP r (k) ΔP g (k)] T The mathematical expression of the disturbance is: d(k) = ΔP D (k); The mathematical expression of the system frequency deviation output is: y p (k) = Δf(k); the mathematical expression of the change in the active power output of the wind turbine is: pe (k)=ΔP e (k);

[0092] Among them, x(k) is the state quantity at time k, Δf(k) is the system frequency deviation value at time k, ΔX g(k) is the governor valve position deviation value at time k, ΔP r (k) is the output power deviation of the reheat unit at time k, ΔP g (k) is the output power deviation value of the thermal power unit at time k, d(k) is the disturbance at time k, ΔP D (k) is the system load deviation value at time k, y p (k) is the system frequency deviation output at time k, y pe (k) is the change in active power output of the wind turbine at time k, ΔP e (k) is the output power of the wind turbine at time k.

[0093] In one embodiment, given a wind power inertia frequency regulation model, a thermal power frequency regulation model, and a system frequency characteristic model, a linear discretization model of wind-thermal combined frequency regulation is established, which takes the wind turbine inertia frequency regulation deviation coefficient and the differential term coefficient as input and takes the system frequency deviation and the wind turbine output power change as output. The calculation formula is as follows:

[0094]

[0095] In the above formula, x(k+1) is the state quantity at time k+1, y p (k+1) is the system frequency deviation output at time k+1, A is the frequency linear discretization state parameter, B u is the frequency linear discretization control parameter, u(k) is the parameter adjustment of the wind turbine virtual inertia control, u(k)=[ΔK p (k) ΔK d (k)] T , ΔK p (k) is the deviation coefficient of the deviation term at time k, ΔK d (k) is the differential coefficient deviation at time k, B d is the frequency linear discretization disturbance parameter, C is the frequency linear discretization output parameter, A pe is the power linear discretization state parameter, B u_pe is the power linear discretization control parameter, B d_pe is the power linear discretization disturbance parameter.

[0096] In one embodiment, the A, B u 、B d , C is calculated as follows:

[0097]

[0098]

[0099]

[0100] C=[1 0 0 0]

[0101] In the above formula, K p_op is the initial value of the deviation coefficient, D is the equivalent damping coefficient, T s is the sampling period, M is the equivalent inertia time constant, K d_op is the initial value of the differential term coefficient, T G is the time constant of the speed regulator, R is the adjustment coefficient, F HP is the reheat coefficient, T RH is the reheat time constant, T CH is the time constant of the steam turbine, Δf op is the frequency initial value deviation, is the initial frequency slope.

[0102] In one embodiment, the A pe 、B u_pe and B d_pe The calculation formula is as follows:

[0103]

[0104]

[0105]

[0106] In one embodiment, the minimum system frequency deviation and the minimum wind turbine output power change are used as optimization targets, and the calculation formula of the objective function in the pre-built target optimization model is as follows:

[0107]

[0108] In the above formula, J is the target value, Γ y Optimize the target weight matrix for frequency deviation, Γ pe The target weight matrix for optimizing the output power change of the wind turbine inertia frequency regulation is Y p (k+1|k) is the forward prediction expression of the system frequency deviation, Y pe (k+1|k) is the forward prediction expression for the change in wind turbine output power.

[0109] In one embodiment, the system frequency deviation forward prediction expression is:

[0110] Y P (k+1|k)=S x Δx(k)+Iy p (k)+S d Δd(k)+S u ΔU(k)

[0111] The forward prediction expression of the wind turbine output power change is:

[0112] Y pe (k+1|k)=S x_pe Δx(k)+Iy pe (k)+S d_pe Δd(k)+S u_pe ΔU(k)

[0113] In the above formula, S x is the frequency deviation forward prediction state coefficient, I is the p×1 dimensional unit matrix, p is the prediction time domain, S d is the frequency deviation forward prediction disturbance coefficient, S u is the frequency deviation forward prediction control coefficient, ΔU(k) ​​is the control quantity increment of the control domain of the wind-fire joint frequency modulation linear discretization model at time k, S x_pe is the forward prediction state coefficient of the output power change, S d_pe is the forward prediction disturbance coefficient of output power change, S u_pe is the forward prediction control coefficient of the output power change, Δx(k) is the deviation of the state quantity at time k, and Δd(k) is the deviation of the disturbance quantity at time k.

[0114] In one embodiment, the S x , S d , S u The calculation formula is as follows:

[0115]

[0116]

[0117]

[0118] Among them, c is the control time domain;

[0119] The S x_pe , S d_pe , S u_pe The calculation formula is as follows:

[0120]

[0121]

[0122]

[0123] In one embodiment, the calculation formula for the control quantity increment of the control domain of the wind-fire combined frequency modulation linear discretization model at time k is as follows:

[0124]

[0125] In the above formula, Δu(k+c-1) is the cth control quantity increment in the control quantity increment of the wind-fire joint frequency regulation linear discretization model control domain at time k, ΔΔK p (k+c-1) is the deviation increment of the cth control domain in the control increment of the wind-fire joint frequency modulation linear discretization model at time k, ΔΔK d (k+c-1) is the increment of the differential coefficient deviation of the cth control domain in the control quantity increment of the wind-fire joint frequency modulation linear discretization model control domain at time k, and c is the control time domain.

[0126] In this embodiment, the calculation formula of the constraint conditions in the pre-built target optimization model is as follows:

[0127] K p min ≤K p (k)≤K p max

[0128] K d min ≤K d (k)≤K d max

[0129] In the above formula, K p (k) is the number of deviation terms at time k, K d (k) is the differential term coefficient at time k, K p min is the lower limit of the number of deviation terms, K p max is the upper limit of the number of deviation terms, K d min is the lower limit of the differential term coefficient, K d max is the upper limit of the differential term coefficient.

[0130] Finally, the parameters of the wind turbine virtual inertia control are adjusted as follows:

[0131]

[0132]

[0133] In the above formula, K p (k) is the number of deviation terms at time k, K d (k) is the differential term coefficient at time k, is the deviation increment of the first control domain in the control increment of the optimal k-time wind-fire joint frequency regulation linear discretization model control domain, ΔK p (k-1) is the deviation of the deviation coefficient at time k-1, K p_op is the initial value of the deviation term coefficient, is the differential coefficient deviation increment of the first control domain in the control quantity increment of the control domain of the optimal k-time wind-fire joint frequency regulation linear discretization model, ΔK d(k-1) is the deviation of the differential coefficient at time k-1, K d_op is the initial value of the differential term coefficient.

[0134] Example 2

[0135] Based on the same inventive concept, the present invention provides an optimization device for virtual inertia control of a wind turbine generator set, such as Figure 2 As shown, the optimization device for virtual inertia control of a wind turbine generator set includes:

[0136] A solution module is used to substitute the relevant parameters of the wind-fire joint frequency modulation linear discretization model into the pre-built target optimization model and solve it to obtain the optimal control quantity increment of the control domain of the wind-fire joint frequency modulation linear discretization model;

[0137] An adjustment module, configured to adjust parameters of a virtual inertia control of a wind turbine generator set based on an incremental control quantity of a control domain of the optimal wind-fire combined frequency regulation linear discretization model;

[0138] The relevant parameters include: state quantity, disturbance quantity, system frequency deviation output quantity and wind turbine active power output change quantity, and the parameters of wind turbine virtual inertia control include at least one of the following: deviation term coefficient and differential term coefficient.

[0139] Preferably, the mathematical expression of the state quantity is: x(k)=[Δf(k) ΔX g (k) ΔP r (k) ΔP g (k)] T The mathematical expression of the disturbance is: d(k) = ΔP D (k); The mathematical expression of the system frequency deviation output is: y p (k) = Δf(k); the mathematical expression of the change in the active power output of the wind turbine is: pe (k)=ΔP e (k);

[0140] Among them, x(k) is the state quantity at time k, Δf(k) is the system frequency deviation value at time k, ΔX g (k) is the governor valve position deviation value at time k, ΔP r (k) is the output power deviation of the reheat unit at time k, ΔP g (k) is the output power deviation value of the thermal power unit at time k, d(k) is the disturbance at time k, ΔP D (k) is the system load deviation value at time k, y p (k) is the system frequency deviation output at time k, y pe (k) is the change in active power output of the wind turbine at time k, ΔP e(k) is the output power of the wind turbine at time k.

[0141] Furthermore, the calculation formula of the wind-fire combined frequency modulation linear discretization model is as follows:

[0142]

[0143] In the above formula, x(k+1) is the state quantity at time k+1, y p (k+1) is the system frequency deviation output at time k+1, A is the frequency linear discretization state parameter, B u is the frequency linear discretization control parameter, u(k) is the parameter adjustment of the wind turbine virtual inertia control, u(k)=[ΔK p (k) ΔK d (k)] T , ΔK p (k) is the deviation coefficient of the deviation term at time k, ΔK d (k) is the differential coefficient deviation at time k, B d is the frequency linear discretization disturbance parameter, C is the frequency linear discretization output parameter, A pe is the power linear discretization state parameter, B u_pe is the power linear discretization control parameter, B d_pe is the power linear discretization disturbance parameter.

[0144] Furthermore, the A and B u 、B d , C is calculated as follows:

[0145]

[0146]

[0147]

[0148] C=[1 0 0 0]

[0149] In the above formula, K p_op is the initial value of the deviation coefficient, D is the equivalent damping coefficient, T s is the sampling period, M is the equivalent inertia time constant, K d_op is the initial value of the differential term coefficient, T G is the time constant of the speed regulator, R is the adjustment coefficient, F HP is the reheat coefficient, T RH is the reheat time constant, T CH is the time constant of the steam turbine, Δf op is the frequency initial value deviation, is the initial frequency slope.

[0150] Furthermore, the A pe 、B u_pe and B d_pe The calculation formula is as follows:

[0151]

[0152]

[0153]

[0154] Furthermore, the calculation formula of the objective function in the pre-built target optimization model is as follows:

[0155]

[0156] In the above formula, J is the target value, Γ y Optimize the target weight matrix for frequency deviation, Γ pe The target weight matrix for optimizing the output power change of the wind turbine inertia frequency regulation is Y p (k+1|k) is the forward prediction expression of the system frequency deviation, Y pe (k+1|k) is the forward prediction expression for the change in wind turbine output power.

[0157] Furthermore, the forward prediction expression of the system frequency deviation is:

[0158] Y P (k+1|k)=S x Δx(k)+Iy p (k)+S d Δd(k)+S u ΔU(k)

[0159] The forward prediction expression of the wind turbine output power change is:

[0160] Y pe (k+1|k)=S x_pe Δx(k)+Iy pe (k)+S d_pe Δd(k)+S u_pe ΔU(k)

[0161] In the above formula, S x is the frequency deviation forward prediction state coefficient, I is the p×1 dimensional unit matrix, p is the prediction time domain, S d is the frequency deviation forward prediction disturbance coefficient, S u is the frequency deviation forward prediction control coefficient, ΔU(k) ​​is the control quantity increment of the control domain of the wind-fire joint frequency modulation linear discretization model at time k, S x_peis the forward prediction state coefficient of the output power change, S d_pe is the forward prediction disturbance coefficient of output power change, S u_pe is the forward prediction control coefficient of the output power change, Δx(k) is the deviation of the state quantity at time k, and Δd(k) is the deviation of the disturbance quantity at time k.

[0162] Furthermore, the S x , S d , S u The calculation formula is as follows:

[0163]

[0164]

[0165]

[0166] Among them, c is the control time domain;

[0167] The S x_pe , S d_pe , S u_pe The calculation formula is as follows:

[0168]

[0169]

[0170]

[0171] Furthermore, the calculation formula for the control quantity increment of the control domain of the wind-fire joint frequency modulation linear discretization model at time k is as follows:

[0172]

[0173] In the above formula, Δu(k+c-1) is the cth control quantity increment in the control quantity increment of the wind-fire joint frequency regulation linear discretization model control domain at time k, ΔΔK p (k+c-1) is the deviation increment of the cth control domain in the control increment of the wind-fire joint frequency modulation linear discretization model at time k, ΔΔK d (k+c-1) is the increment of the differential coefficient deviation of the cth control domain in the control quantity increment of the wind-fire joint frequency modulation linear discretization model control domain at time k, and c is the control time domain.

[0174] Preferably, the calculation formula of the constraint conditions in the pre-built target optimization model is as follows:

[0175] K p min ≤K p (k)≤K p max

[0176] K d min ≤K d (k)≤K d max

[0177] In the above formula, K p (k) is the number of deviation terms at time k, K d (k) is the differential term coefficient at time k, K p min is the lower limit of the number of deviation terms, K p max is the upper limit of the number of deviation terms, K d min is the lower limit of the differential term coefficient, K d max is the upper limit of the differential term coefficient.

[0178] Preferably, the parameters of the wind turbine virtual inertia control are adjusted as follows:

[0179]

[0180]

[0181] In the above formula, K p (k) is the number of deviation terms at time k, K d (k) is the differential term coefficient at time k, is the deviation increment of the first control domain in the control increment of the optimal k-time wind-fire joint frequency regulation linear discretization model control domain, ΔK p (k-1) is the deviation of the deviation coefficient at time k-1, K p_op is the initial value of the deviation term coefficient, is the differential coefficient deviation increment of the first control domain in the control quantity increment of the control domain of the optimal k-time wind-fire joint frequency regulation linear discretization model, ΔK d (k-1) is the deviation of the differential coefficient at time k-1, K d_op is the initial value of the differential term coefficient.

[0182] Example 3

[0183] Based on the same inventive concept, the present invention also provides a computer device, which includes a processor and a memory, wherein the memory is used to store a computer program, the computer program includes program instructions, and the processor is used to execute the program instructions stored in the computer storage medium. The processor may be a central processing unit (CPU), or may be 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. It is the computing core and control core of the terminal, which is suitable for implementing one or more instructions, specifically suitable for loading and executing one or more instructions in the computer storage medium to implement the corresponding method flow or corresponding function, so as to implement the steps of the optimization method of virtual inertia control of a wind turbine in the above embodiment.

[0184] Example 4

[0185] Based on the same inventive concept, the present invention also provides a storage medium, specifically a computer-readable storage medium (Memory), which is a memory device in a computer device for storing programs and data. It can be understood that the computer-readable storage medium here can include both built-in storage media in the computer device and, of course, extended storage media supported by the computer device. The computer-readable storage medium provides a storage space, which stores the operating system of the terminal. In addition, one or more instructions suitable for being loaded and executed by the processor are also stored in the storage space. These instructions can be one or more computer programs (including program codes). It should be noted that the computer-readable storage medium here can be a high-speed RAM memory or a non-volatile memory, such as at least one disk memory. The processor can load and execute one or more instructions stored in the computer-readable storage medium to implement the steps of the optimization method for virtual inertia control of a wind turbine set in the above embodiment.

[0186] It will be understood by those skilled in the art 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. 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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 wind turbine virtual inertia control optimization method, characterized in that: The method comprises: Substitute the relevant parameters of the wind-fire joint frequency modulation linear discretization model into the pre-built target optimization model and solve it to obtain the optimal control quantity increment of the control domain of the wind-fire joint frequency modulation linear discretization model; Incrementally adjusting the parameters of the wind turbine virtual inertia control based on the optimal wind-fire combined frequency regulation linear discretization model control domain control quantity; The relevant parameters include: state quantity, disturbance quantity, system frequency deviation output quantity and wind turbine active power output change quantity, and the parameters of wind turbine virtual inertia control include at least one of the following: deviation term coefficient and differential term coefficient; The mathematical expression of the state quantity is: x(k)=[Δf(k)ΔX g (k)ΔP r (k)ΔP g (k)] T The mathematical expression of the disturbance is: d(k) = ΔP D (k); The mathematical expression of the system frequency deviation output is: y p (k) = Δf(k); the mathematical expression of the change in the active power output of the wind turbine is: pe (k)=ΔP e (k); Among them, x(k) is the state quantity at time k, Δf(k) is the system frequency deviation value at time k, ΔX g (k) is the governor valve position deviation value at time k, ΔP r (k) is the output power deviation of the reheat unit at time k, ΔP g (k) is the output power deviation value of the thermal power unit at time k, d(k) is the disturbance at time k, ΔP D (k) is the system load deviation value at time k, y p (k) is the system frequency deviation output at time k, y pe (k) is the change in active power output of the wind turbine at time k, ΔP e (k) is the output power of the wind turbine at time k; The calculation formula of the wind-fire combined frequency modulation linear discretization model is as follows: In the above formula, x(k+1) is the state quantity at time k+1, y p (k+1) is the system frequency deviation output at time k+1, A is the frequency linear discretization state parameter, B u is the frequency linear discretization control parameter, u(k) is the parameter adjustment of the wind turbine virtual inertia control, u(k)=[ΔK p (k)ΔK d (k)] T , ΔK p (k) is the deviation coefficient of the deviation term at time k, ΔK d (k) is the differential coefficient deviation at time k, B d is the frequency linear discretization disturbance parameter, C is the frequency linear discretization output parameter, A pe is the power linear discretization state parameter, B u_pe is the power linear discretization control parameter, B d_pe is the power linear discretization disturbance parameter; A and B u 、B d , C is calculated as follows: C＝[1 0 0 0] In the above formula, K p_op is the initial value of the deviation coefficient, D is the equivalent damping coefficient, T s is the sampling period, M is the equivalent inertia time constant, K d_op is the initial value of the differential term coefficient, T G is the time constant of the speed regulator, R is the adjustment coefficient, F HP is the reheat coefficient, T RH is the reheat time constant, T CH is the time constant of the steam turbine, Δf op is the frequency initial value deviation, is the frequency initial slope; The A pe 、B u_pe and B d_pe The calculation formula is as follows: The calculation formula of the objective function in the pre-built target optimization model is as follows: In the above formula, J is the target value, Γ y Optimize the target weight matrix for frequency deviation, Γ pe The target weight matrix for optimizing the output power change of the wind turbine inertia frequency regulation is Y p (k+1|k) is the forward prediction expression of the system frequency deviation, Y pe (k+1|k) is the forward prediction expression for the change in wind turbine output power.

2. The method according to claim 1, wherein The forward prediction expression of the system frequency deviation is: Y P (k+1|k)=S x Δx(k)+Iy p (k)+S d Δd(k)+S u ΔU(k) The forward prediction expression of the wind turbine output power change is: Y pe (k+1|k)=S x_pe Δx(k)+Iy pe (k)+S d_pe Δd(k)+S u_pe ΔU(k) In the above formula, S x is the frequency deviation forward prediction state coefficient, I is the p×1 dimensional unit matrix, p is the prediction time domain, S d is the frequency deviation forward prediction disturbance coefficient, S u is the frequency deviation forward prediction control coefficient, ΔU(k) ​​is the control quantity increment of the control domain of the wind-fire joint frequency modulation linear discretization model at time k, S x_pe is the forward prediction state coefficient of the output power change, S d_pe is the forward prediction disturbance coefficient of output power change, S u_pe is the forward prediction control coefficient of the output power change, Δx(k) is the deviation of the state quantity at time k, and Δd(k) is the deviation of the disturbance quantity at time k.

3. The method according to claim 2, wherein The S x , S d , S u The calculation formula is as follows: Among them, c is the control time domain; The S x_pe , S d_pe , S u_pe The calculation formula is as follows:

4. The method according to claim 2, wherein The calculation formula for the control quantity increment of the control domain of the wind-fire combined frequency modulation linear discretization model at time k is as follows: In the above formula, Δu(k+c-1) is the cth control quantity increment in the control quantity increment of the wind-fire joint frequency regulation linear discretization model control domain at time k, ΔΔK p (k+c-1) is the deviation increment of the cth control domain in the control increment of the wind-fire joint frequency modulation linear discretization model at time k, ΔΔK d (k+c-1) is the increment of the differential coefficient deviation of the cth control domain in the control quantity increment of the wind-fire joint frequency modulation linear discretization model control domain at time k, and c is the control time domain.

5. The method according to claim 1, wherein The calculation formula of the constraint conditions in the pre-built target optimization model is as follows: K pmin ≤K p (k)≤K pmax K dmin ≤K d (k)≤K dmax In the above formula, K p (k) is the number of deviation terms at time k, K d (k) is the differential term coefficient at time k, K pmin is the lower limit of the number of deviation terms, K pmax is the upper limit of the number of deviation terms, K dmin is the lower limit of the differential term coefficient, K dmax is the upper limit of the differential term coefficient.

6. The method according to claim 1, wherein The parameters of the wind turbine virtual inertia control are adjusted as follows: In the above formula, K p (k) is the number of deviation terms at time k, K d (k) is the differential term coefficient at time k, is the deviation increment of the first control domain in the control increment of the optimal k-time wind-fire joint frequency regulation linear discretization model control domain, ΔK p (k-1) is the deviation of the deviation coefficient at time k-1, K p_op is the initial value of the deviation term coefficient, is the differential coefficient deviation increment of the first control domain in the control quantity increment of the control domain of the optimal k-time wind-fire joint frequency regulation linear discretization model, ΔK d (k-1) is the deviation of the differential coefficient at time k-1, K d_op is the initial value of the differential term coefficient.

7. An optimization device for virtual inertia control of a wind turbine generator set, characterized in that: The device comprises: A solution module is used to substitute the relevant parameters of the wind-fire joint frequency modulation linear discretization model into the pre-built target optimization model and solve it to obtain the optimal control quantity increment of the control domain of the wind-fire joint frequency modulation linear discretization model; An adjustment module, configured to adjust parameters of a virtual inertia control of a wind turbine generator set based on an incremental control quantity of a control domain of the optimal wind-fire combined frequency regulation linear discretization model; The relevant parameters include: state quantity, disturbance quantity, system frequency deviation output quantity and wind turbine active power output change quantity, and the parameters of wind turbine virtual inertia control include at least one of the following: deviation term coefficient and differential term coefficient; The mathematical expression of the state quantity is: x(k)=[Δf(k)ΔX g (k)ΔP r (k)ΔP g (k)] T The mathematical expression of the disturbance is: d(k) = ΔP D (k); The mathematical expression of the system frequency deviation output is: y p (k) = Δf(k); the mathematical expression of the change in the active power output of the wind turbine is: pe (k)=ΔP e (k); Among them, x(k) is the state quantity at time k, Δf(k) is the system frequency deviation value at time k, ΔX g (k) is the governor valve position deviation value at time k, ΔP r (k) is the output power deviation of the reheat unit at time k, ΔP g (k) is the output power deviation value of the thermal power unit at time k, d(k) is the disturbance at time k, ΔP D (k) is the system load deviation value at time k, y p (k) is the system frequency deviation output at time k, y pe (k) is the change in active power output of the wind turbine at time k, ΔP e (k) is the output power of the wind turbine at time k; The calculation formula of the wind-fire combined frequency modulation linear discretization model is as follows: In the above formula, x(k+1) is the state quantity at time k+1, y p (k+1) is the system frequency deviation output at time k+1, A is the frequency linear discretization state parameter, B u is the frequency linear discretization control parameter, u(k) is the parameter adjustment of the wind turbine virtual inertia control, u(k)=[ΔK p (k)ΔK d (k)] T , ΔK p (k) is the deviation coefficient of the deviation term at time k, ΔK d (k) is the differential coefficient deviation at time k, B d is the frequency linear discretization disturbance parameter, C is the frequency linear discretization output parameter, A pe is the power linear discretization state parameter, B u_pe is the power linear discretization control parameter, B d_pe is the power linear discretization disturbance parameter; A and B u 、B d , C is calculated as follows: C＝[1 0 0 0] In the above formula, K p_op is the initial value of the deviation coefficient, D is the equivalent damping coefficient, T s is the sampling period, M is the equivalent inertia time constant, K d_op is the initial value of the differential term coefficient, T G is the time constant of the speed regulator, R is the adjustment coefficient, F HP is the reheat coefficient, T RH is the reheat time constant, T CH is the time constant of the steam turbine, Δf op is the frequency initial value deviation, is the frequency initial slope; The A pe 、B u_pe and B d_pe The calculation formula is as follows: The calculation formula of the objective function in the pre-built target optimization model is as follows: In the above formula, J is the target value, Γ y Optimize the target weight matrix for frequency deviation, Γ pe The target weight matrix for optimizing the output power change of the wind turbine inertia frequency regulation is Y p (k+1|k) is the forward prediction expression of the system frequency deviation, Y pe (k+1|k) is the forward prediction expression for the change in wind turbine output power.

8. The device according to claim 7, wherein The forward prediction expression of the system frequency deviation is: Y P (k+1|k)=S x Δx(k)+Iy p (k)+S d Δd(k)+S u ΔU(k) The forward prediction expression of the wind turbine output power change is: Y pe (k+1|k)=S x_pe Δx(k)+Iy pe (k)+S d_pe Δd(k)+S u_pe ΔU(k) In the above formula, S x is the frequency deviation forward prediction state coefficient, I is the p×1 dimensional unit matrix, p is the prediction time domain, S d is the frequency deviation forward prediction disturbance coefficient, S u is the frequency deviation forward prediction control coefficient, ΔU(k) ​​is the control quantity increment of the control domain of the wind-fire joint frequency modulation linear discretization model at time k, S x_pe is the forward prediction state coefficient of the output power change, S d_pe is the forward prediction disturbance coefficient of output power change, S u_pe is the output power change forward prediction control coefficient, Δx(k) is the deviation of the state quantity at time k, Δd(k) is the deviation of the disturbance quantity at time k, y p (k) is the system frequency deviation output at time k, y pe (k) is the change in active power output of the wind turbine at time k.

9. The device according to claim 8, wherein The calculation formula for the control quantity increment of the control domain of the wind-fire combined frequency modulation linear discretization model at time k is as follows: In the above formula, Δu(k+c-1) is the cth control quantity increment in the control quantity increment of the wind-fire joint frequency regulation linear discretization model control domain at time k, ΔΔK p (k+c-1) is the deviation increment of the cth control domain in the control increment of the wind-fire joint frequency modulation linear discretization model at time k, ΔΔK d (k+c-1) is the increment of the differential coefficient deviation of the cth control domain in the control quantity increment of the wind-fire joint frequency modulation linear discretization model control domain at time k, and c is the control time domain.

10. The device according to claim 7, wherein The calculation formula of the constraint conditions in the pre-built target optimization model is as follows: K pmin ≤K p (k)≤K pmax K dmin ≤K d (k)≤K dmax In the above formula, K p (k) is the number of deviation terms at time k, K d (k) is the differential term coefficient at time k, K pmin is the lower limit of the number of deviation terms, K pmax is the upper limit of the number of deviation terms, K dmin is the lower limit of the differential term coefficient, K dmax is the upper limit of the differential term coefficient.

11. The device according to claim 7, wherein The adjustment module is specifically used to adjust the parameters of the wind turbine virtual inertia control according to the following formula: In the above formula, K p (k) is the number of deviation terms at time k, K d (k) is the differential term coefficient at time k, is the deviation increment of the first control domain in the control increment of the optimal k-time wind-fire joint frequency regulation linear discretization model control domain, ΔK p (k-1) is the deviation of the deviation coefficient at time k-1, K p_op is the initial value of the deviation term coefficient, is the differential coefficient deviation increment of the first control domain in the control quantity increment of the control domain of the optimal k-time wind-fire joint frequency regulation linear discretization model, ΔK d (k-1) is the deviation of the differential coefficient at time k-1, K d_op is the initial value of the differential term coefficient.

12. A computer device, characterized in that: include: one or more processors; The processor is configured to store one or more programs; When the one or more programs are executed by the one or more processors, the optimization method for virtual inertia control of a wind turbine generator set according to any one of claims 1 to 6 is implemented.

13. A computer-readable storage medium, characterized in that A computer program is stored thereon, and when the computer program is executed, the optimization method for virtual inertia control of a wind turbine generator set according to any one of claims 1 to 6 is implemented.

Images