Fan rotor kinetic energy control power optimization method based on variable parameter Kalman filtering

Abstract

The invention provides a fan rotor kinetic energy control power optimization method based on variable parameter Kalman filtering, and belongs to the technical field of wind power generation. The technical problem of phase delay existing in smoothing of a traditional low-pass filter is solved. According to the technical scheme, the method comprises the following steps: S1, designing a Kalman filter equation for a wind turbine generator, and smoothing an output power reference value of the wind turbine generator; s2, wind turbine generator rotor kinetic energy state variables are introduced, and wind turbine generator rotor kinetic energy is subjected to optimization control; s3, designing fuzzy control considering the wind turbine generator rotor kinetic energy demand quantity and the wind turbine generator rotor kinetic energy state variable; and S4, reference smooth power is obtained through variable parameter Kalman filtering, a reference rotating speed is obtained through wind turbine generator rotor kinetic energy control, and stabilization of the output power of the wind turbine generator is achieved. According to the invention, smoothing processing can be carried out on the output electromagnetic power at the turbulent wind speed.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of wind power generation technology, and particularly relates to a wind turbine rotor kinetic energy control power optimization method based on variable parameter Kalman filtering. BACKGROUND

[0002] With the continuous promotion of large-scale wind power grid connection and the large-scale of single machine capacity, the wind power fluctuation caused by the intermittence and randomness of wind energy is increasingly serious, which threatens the power quality and transient stability of the power grid. In a low-inertia weak power grid system, the risk of system frequency out-of-limit caused by such fluctuations will be particularly prominent. It is of great practical significance to effectively smooth the output power of wind turbine while ensuring the stable operation of the unit.

[0003] At present, indirect power control is used for long-time scale power smoothing of large-scale wind power grid connection system through energy storage system, which has problems such as high cost in practical application. Therefore, scholars propose to use the resources of large-scale wind turbine for direct power control, among which the method of controlling and smoothing output power based on wind turbine rotor inertia kinetic energy has become a research hotspot. However, the low-pass filter used in the general rotor kinetic energy control strategy will cause the technical problem of phase delay, and the speed will deviate from the optimal power control curve, resulting in operating load or even shutdown accident. Therefore, it is still necessary to improve the output power optimization strategy based on rotor kinetic energy control. SUMMARY

[0004] The present application aims to provide a wind turbine rotor kinetic energy control power optimization method based on variable parameter Kalman filtering, derive a power smoothing strategy based on wind turbine rotor kinetic energy control according to the power feedback maximum power tracking control PSF-MPPT strategy framework, design a Kalman filter equation for wind turbine, smooth the wind turbine output power reference value, and optimize the control of wind turbine rotor kinetic energy by introducing wind turbine rotor kinetic energy state variable , prevent speed out-of-limit and shutdown accident; design a fuzzy control considering the wind turbine rotor kinetic energy demand and wind turbine rotor kinetic energy state variable , dynamically adjust the Kalman gain K k, reduce fatigue load caused by wind turbine rotor kinetic energy smoothing control strategy, get reference smoothing power through variable parameter Kalman filtering, get reference speed through wind turbine rotor kinetic energy control, realize the smoothing of wind turbine output power; the application can smooth the output electromagnetic power under turbulent wind speed, solve the phase delay problem existing in the traditional low-pass filter smoothing, prevent wind turbine speed overrun and shutdown risk by considering the wind turbine rotor kinetic energy state, reduce the operating load caused by wind turbine rotor kinetic energy control, and improve the wind energy utilization efficiency to a certain extent.

[0005] In order to achieve the above application purpose, the technical scheme of the application is as follows: a wind turbine rotor kinetic energy control power optimization method based on variable parameter Kalman filtering, comprising the following steps: S1: deriving a power smoothing strategy based on wind turbine rotor kinetic energy control according to the power feedback maximum power tracking control PSF-MPPT strategy framework, designing a Kalman filter equation for wind turbine, and smoothing the wind turbine output power reference value: S2: introducing wind turbine rotor kinetic energy state variable , optimizing the control of wind turbine rotor kinetic energy, preventing wind turbine speed overrun and shutdown accident; S3: designing fuzzy control considering wind turbine rotor kinetic energy demand and wind turbine rotor kinetic energy state variable , adjusting the Kalman gain K k , reducing fatigue load caused by wind turbine rotor kinetic energy smoothing control strategy; S4: getting reference smoothing power through variable parameter Kalman filtering, getting reference speed through wind turbine rotor kinetic energy control, realizing the smoothing of wind turbine output power.

[0006] Further, in step S1, the wind turbine rotor kinetic energy demand stored or released by the wind turbine rotor is represented as: (1) In the formula, J is the moment of inertia of the wind turbine; is the wind turbine rotor speed; is the wind turbine input aerodynamic power; is the wind turbine output electromagnetic power.

[0007] Replace the wind turbine output electromagnetic power with the smoothing power reference value, and rewrite formula (1) as: (2) Where, It is the reference value of the smoothed electromagnetic power output by the wind turbine.

[0008] Put formula (2) in to Performing integration during this period, we obtain equations (3) and (4): (3) (4) Where: and Represented in and The rotor speed at time It can be expressed as: , yes to The speed change within a period of time.

[0009] Will Substituting into formula (4) we can get: (5) Solve equation (5), discard the negative value, and the solution is for: (6) but It can be expressed as formula (7): (7) According to formula (7), It is the reference speed output under the smooth control of the wind turbine rotor kinetic energy. .

[0010] As a modern filtering technology, Kalman filter can solve the time delay problem of traditional low-pass filter. When the power of wind turbine changes suddenly, the Kalman filter gain can also be adjusted in real time. K k Make the filter performance stable.

[0011] The input power signal of the Kalman filter is selected as the power signal value calculated according to the optimal power curve , whose expression is: (8) In the formula It is the optimal power coefficient of the wind turbine, which is set according to the parameters of the wind turbine itself and the power signal value As the power input to be smoothed by the wind turbine, it can enable the power control link to maintain the smoothing characteristics of the optimal power feedback method.

[0012] The Kalman filter time update equation is: (9) The Kalman filter state update equation is: (10) Where, It is a wind turbine k The power signal value calculated by the PSF-MPPT algorithm at all times; It is the smoothed power reference signal of the wind turbine generator obtained by Kalman filtering at the previous moment; It is a wind turbine k Time-smoothed power reference signal; It is the prior estimate of the current state of the wind turbine obtained at the previous moment; It is a wind turbine k The covariance of the prior estimate of time, is the covariance of the prior estimate of the wind turbine at the previous moment; is the gain of the Kalman filter. In the standard Kalman filter system, both process noise and measurement noise obey the Gaussian white noise distribution. Q and R are the covariance of process noise and the covariance of measurement noise, respectively.

[0013] Furthermore, in step S2, the wind turbine rotor kinetic energy state variable is introduced , optimize the control of the wind turbine rotor kinetic energy to prevent the wind turbine speed from exceeding the limit and causing shutdown accidents.

[0014] The rotor kinetic energy of a wind turbine is expressed as: (11) According to formula (11), the kinetic energy storage of the wind turbine rotor at the current moment is proportional to the square of the wind turbine rotor speed. In order to characterize the kinetic energy storage of the wind turbine rotor, the relevant variables are introduced: the kinetic energy state variable of the wind turbine rotor , whose expression is: (12) In the formula Indicates the kinetic energy stored in the wind turbine rotor at the current moment; Represents the maximum kinetic energy storage of the wind turbine rotor; Represents the rotor speed of the wind turbine at the current moment; Represents the upper limit of the wind turbine rotor speed, that is, the upper limit of the wind turbine speed.

[0015] In the case of a sudden drop in wind speed, due to the decrease in output electromagnetic power, the wind turbine rotor kinetic energy control requires the wind turbine to slow down and release rotor kinetic energy to support power. This action will cause the wind turbine to continue to slow down and even cause a shutdown accident. In order to prevent the wind turbine from shutting down, the wind turbine speed control lower limit is selected as 0.6rad / s, and the wind turbine speed control upper limit is selected as 1.134 rad / s; the corresponding wind turbine rotor kinetic energy state variable The lower limit is 0.28 and the upper limit is 1.

[0016] Furthermore, in step S3, the design takes into account the kinetic energy demand of the wind turbine rotor. And the wind turbine rotor kinetic energy state variables Fuzzy control, by dynamically adjusting the Kalman gain K k , reducing the fatigue load caused by the wind turbine rotor kinetic energy smoothing control strategy.

[0017] In the face of turbulent wind speed, the traditional fixed parameter Kalman filter cannot achieve the ideal filtering effect, and the speed of the wind turbine rotor kinetic energy smooth control will deviate significantly from the maximum power control curve, causing operating load. Therefore, the wind turbine rotor kinetic energy state variable is used and the wind turbine rotor kinetic energy demand As input, the covariance of the measurement noise R As the output, a dual-input single-output fuzzy control algorithm is constructed, and the Kalman gain The basic idea of ​​fuzzy reasoning design is as follows: when the wind turbine rotor kinetic energy state variable When the wind turbine rotor kinetic energy demand is low, If it is a negative value (kinetic energy release command), the Kalman gain is increased. (Decrease R ), to prevent over-discharge of stored energy; if it is a kinetic energy storage instruction, reduce the Kalman gain (Increase R ), increase the rotor kinetic energy of the stored energy, so that the rotor speed Quickly recover to the middle area; when the wind turbine rotor speed reaches the upper limit and is still a kinetic energy storage instruction, or reaches the lower limit and is still a kinetic energy release instruction, the control will no longer act until the power is released / stored until the speed returns to normal. Among them, the wind turbine rotor kinetic energy state variable The fuzzy domain is {0.28, 0.424, 0.64, 0.856, 1.0}, and the fuzzy linguistic variables are {S, SM, M, BM, B}; the kinetic energy demand of the wind turbine rotor The fuzzy domain of the fuzzy field is {-2e6, -1.5e6, -1e6, 0, 1e6, 1.5e6, 2e6}, and the fuzzy language variable is {NB, NM, NS, ZO, PS, PM, PB}; the covariance of the measurement noise R The fuzzy domain of the fuzzy field is {0.01, 10, 25, 40, 60, 80, 100}, and the fuzzy language variable is {VS, S, SM, M, BM, B, VB}.

[0018] Further, in step S4, the reference smooth power is obtained through the variable parameter Kalman filter, the reference speed of the wind turbine is obtained through the wind turbine rotor kinetic energy control, the output power of the wind turbine is smoothed, the wind turbine rotor kinetic energy storage is optimized and the operation load of the wind turbine is reduced.

[0019] Compared with the prior art, the beneficial effects of the present application are: 1. Power smoothing and cost optimization under turbulent wind speed: the present application directly optimizes the active power instruction by changing the control link and using the variable parameter Kalman filter , and generates a wind turbine speed reference signal based on wind turbine rotor kinetic energy control ; a) without external energy storage devices, the energy storage devices and their maintenance costs are saved, and the wind turbine rotor kinetic energy is used E k As a "virtual energy storage", the stored power is absorbed or released by adjusting the speed to smooth the power fluctuation; b) reduce the impact on the power grid: the electromagnetic power fluctuation of the present application is obviously improved compared with the traditional MPPT control, the first-order low-pass filter control rotor kinetic energy control and the fixed parameter Kalman filter rotor kinetic energy control.

[0020] 2. Variable parameter Kalman filter eliminates phase delay: the variable parameter Kalman filter in the present application uses the wind turbine rotor kinetic energy state variable and the wind turbine rotor kinetic energy demand as input to adjust the Kalman gain in real time K k . The phase delay problem existing in the traditional low-pass filter smoothing is solved, and the control dynamic performance and accuracy are improved.

[0021] 3. Wind turbine rotor kinetic energy state monitoring prevents over-limit shutdown: the present application introduces the wind turbine rotor kinetic energy state variable , and quantizes the speed boundary as a continuous state variable as the fuzzy control input value; when approaching the lower limit and needing to release kinetic energy, the K kThe smoothing effect is reduced to prevent continuous deceleration from causing instability of the wind turbine. Without measuring the real-time wind speed, the probability of speed exceeding the limit and the risk of shutdown are reduced by considering the rotor kinetic energy state.

[0022] 4. Reducing the operating load and improving the wind energy utilization rate: In the present application, the Kalman filter is used to As input, the power is smoothed while being as close to the maximum power point as possible; at the same time, the rotor speed of the wind turbine is considered and the fuzzy control of the rotor kinetic energy state variable of the wind turbine, the Kalman gain is dynamically adjusted K k The speed fluctuation range is reduced. The operating load caused by the rotor kinetic energy control is reduced, and the wind energy utilization efficiency is improved to a certain extent. BRIEF DESCRIPTION OF DRAWINGS

[0023] The accompanying drawings are included to provide a further understanding of the present application, and constitute a part of the specification, illustrate the present application and are used to explain the present application, and do not constitute a limitation of the present application.

[0024] Figure 1 The present application is based on the variable parameter Kalman filter wind turbine rotor kinetic energy control power optimization method machine side control block diagram.

[0025] Figure 2 The present application is a fuzzy inference result diagram of the fuzzy controller.

[0026] Figure 3 The present application is a given 60s turbulent wind speed sequence diagram.

[0027] Figure 4 The present application is a comparison diagram of the method and the MPPT control, the traditional low-pass filter LPF (Low-pass filter, LPF), and the constant parameter Kalman filter Kalman output electromagnetic power.

[0028] Figure 5 The present application is a comparison diagram of the method and the output electromagnetic power rate of change of power RoCoP under the MPPT control.

[0029] Figure 6 The present application is a comparison diagram of the method and the rotor speed of the traditional low-pass filter LPF and the constant parameter Kalman filter Kalman.

[0030] Figure 7 The present application is a comparison diagram of the method and the rotor kinetic energy of the traditional low-pass filter LPF and the constant parameter Kalman filter Kalman.

[0031] Figure 8The flow chart of the fan rotor kinetic energy control power optimization method based on variable parameter Kalman filtering for the method of the application. DETAILED DESCRIPTION

[0032] In order to make the purpose, technical scheme and advantages of the application more clear, the application is further described in detail below in combination with the drawings, tables and examples. Of course, the specific examples described herein are only used to explain the application and not to limit the application.

[0033] Example 1: see Figure 8 , Example 1 proposes a fan rotor kinetic energy control power optimization method based on variable parameter Kalman filtering. The method smoothes the output electromagnetic power by cascading a variable parameter Kalman filter under the framework of rotor kinetic energy control power smoothing of a wind turbine generator; and adjusts the Kalman gain by fuzzy control to optimize the rotor kinetic energy of the wind turbine generator to reduce the operating load of the wind turbine generator, including the following steps: S1: derive a power smoothing strategy based on rotor kinetic energy control according to the power feedback maximum power tracking control (PSF-MPPT) strategy framework, design a Kalman filter equation for the wind turbine generator, and smooth the wind turbine generator output power reference value; S2: introduce the wind turbine rotor kinetic energy state variable , optimize the control of the wind turbine rotor kinetic energy, prevent the wind turbine rotor speed from exceeding the limit and the shutdown accident; S3: design a fuzzy control considering the wind turbine rotor kinetic energy demand and the wind turbine rotor kinetic energy state variable , adjust the Kalman gain K k dynamically to reduce the fatigue load caused by the wind turbine rotor kinetic energy smoothing control strategy; S4: obtain the reference smoothing power through variable parameter Kalman filtering, obtain the wind turbine reference speed through wind turbine rotor kinetic energy control, and realize the suppression of the wind turbine output power.

[0034] Specifically, in step S1, the wind turbine rotor kinetic energy demand stored or released by the wind turbine rotor can be represented as: (1) In the formula, J is the moment of inertia of the wind turbine; is the wind turbine rotor speed; is the wind turbine input aerodynamic power; is the wind turbine output electromagnetic power.

[0035] The wind turbine output electromagnetic power Substitute the smooth power reference value, formula (1) is rewritten as: (2) In the formula, is the smooth electromagnetic power reference value of the wind turbine output.

[0036] Formula (2) is integrated in to to obtain formula (3) and formula (4): (3) (4) In the formula, and represent the rotor speed at and , respectively, so can be expressed as: , is the rotor speed change in to .

[0037] Substitute into formula (4) to obtain: (5) Solve formula (5), and discard the negative solution. The solution is : (6) So can be expressed as formula (7): (7) The calculated according to formula (7) is the reference speed output by the wind turbine rotor kinetic energy control smoothing.

[0038] As a modern filtering technology, the Kalman filter can solve the time delay problem of traditional low-pass filters. When the wind turbine power suddenly changes, the filter performance can be stabilized by adjusting the Kalman filter gain in real time.

[0039] The input power signal of the Kalman filter is the power signal value calculated according to the optimal power curve, and its expression is: (8) In the formula, is the optimal power coefficient of the wind turbine, which is set according to the parameters of the wind turbine itself. The power signal value As the wind turbine to be smoothed power input quantity, the power control link can make the optimal power feedback method itself has the damping characteristics.

[0040] The Kalman filter time update equation is: (9) The Kalman filter state update equation is: (10) In the formula, is the wind turbine k power signal value calculated at the moment according to the PSF-MPPT algorithm; is the smoothed power reference signal obtained after Kalman filtering at the previous moment of the wind turbine; is the wind turbine k smoothed power reference signal at the moment; is the prior estimate of the current state obtained at the previous moment of the wind turbine; is the covariance of the prior estimate at the moment of the wind turbine k , and is the covariance of the prior estimate at the previous moment of the wind turbine; is the gain of the Kalman filter. In the standard Kalman filtering system, the process noise and the measurement noise are subject to Gaussian white noise distribution, Q and R are the covariance of the process noise and the covariance of the measurement noise, respectively.

[0041] Specifically, in step S2, the wind turbine rotor kinetic energy state variable is introduced,

[0042] The rotor kinetic energy of the wind turbine is represented as: (11) According to formula (11), the wind turbine rotor kinetic energy storage at the current moment is proportional to the square of the wind turbine rotor speed. In order to depict the wind turbine rotor kinetic energy storage, the relevant variable, wind turbine rotor kinetic energy state variable is introduced, and its expression is: (12) In the formula , represents the current moment wind turbine rotor kinetic energy storage; represents the maximum kinetic energy storage of the wind turbine rotor; represents the current moment rotor speed of the wind turbine; represents the upper limit of the wind turbine rotor speed, i.e. the upper limit of the wind turbine rotor speed.

[0043] In the case of sudden wind speed drop, due to the decrease of output electromagnetic power, the wind turbine needs to slow down to release the rotor kinetic energy to support the power, which will cause the wind turbine to continue to slow down and even cause shutdown accident. In order to prevent the wind turbine from shutting down, the lower limit of the speed control is selected as 0.6 rad / s, the upper limit of the speed control is selected as the upper limit of the wind turbine speed, which is 1.134 rad / s; the lower limit value of the corresponding wind turbine rotor kinetic energy state variable is 0.28, and the upper limit value is 1.

[0044] Specifically, in step S3, the fuzzy control of the wind turbine rotor kinetic energy demand and the wind turbine rotor kinetic energy state variable is designed, and the Kalman gain K k is dynamically adjusted to reduce the fatigue load caused by the wind turbine rotor kinetic energy smooth control strategy.

[0045] In the case of turbulent wind speed, the traditional Kalman filter with fixed parameters cannot achieve ideal filtering effect, and the speed under the wind turbine rotor kinetic energy smooth control will deviate from the maximum power control curve by a large margin, causing operating load. Therefore, the wind turbine rotor kinetic energy state variable and the wind turbine rotor kinetic energy demand are used as input quantities, the covariance of the measurement noise R is used as output quantity, and a double-input single-output fuzzy control algorithm is constructed to adjust and control the Kalman gain online. The basic idea of designing fuzzy reasoning is as follows: when the wind turbine rotor kinetic energy state variable is low, if the wind turbine rotor kinetic energy demand is negative (kinetic energy release instruction), the Kalman gain is increased (the R is reduced) to prevent over-discharge of the energy storage; if it is a kinetic energy storage instruction, the Kalman gain is reduced (the R is increased) to increase the rotor kinetic energy of the energy storage, so that the rotor speed quickly recovers to the middle region; when the wind turbine rotor speed reaches the upper limit and is still a kinetic energy storage instruction, or reaches the lower limit and is still a kinetic energy release instruction, the control no longer acts until the release / storage power recovers to normal. Among them, the fuzzy domain of the wind turbine rotor kinetic energy state variable is {0.28, 0.424, 0.64, 0.856, 1.0}, and the fuzzy language variable is {S, SM, M, BM, B}; the wind turbine rotor kinetic energy demand The fuzzy domain of the fuzzy variable is {-2e6, -1.5e6, -1e6, 0, 1e6, 1.5e6, 2e6}, and the fuzzy language variable is {NB, NM, NS, ZO, PS, PM, PB}; the covariance of the measurement noise R The fuzzy domain of the fuzzy variable is {0.01, 10, 25, 40, 60, 80, 100}, and the fuzzy language variable is {VS, S, SM, M, BM, B, VB}.

[0046] Specifically, in step S4, the reference smooth power is obtained through the variable parameter Kalman filter, the reference speed is obtained through the wind turbine rotor kinetic energy control, the output power of the wind turbine is smoothed, the wind turbine rotor kinetic energy storage is optimized, and the operating load of the wind turbine is reduced.

[0047] Embodiment 2: see Figure 1 and Figure 2 Embodiment 2 proposes a wind turbine rotor kinetic energy control power optimization method based on variable parameter Kalman filter. Among them Figure 1 is the overall control strategy block diagram, which smoothes the output electromagnetic power by cascading a variable parameter Kalman filter in the wind turbine rotor kinetic energy control loop. Among them, the input power signal of the Kalman filter is selected as the power signal value calculated according to the optimal power curve , so that the smooth power is as close as possible to the maximum power point, and the economic benefit is guaranteed. The energy that needs to be stored or released by the wind turbine rotor is the wind turbine rotor kinetic energy demand , and the wind turbine rotor kinetic energy state variable is introduced. The wind turbine rotor kinetic energy state variable and the wind turbine rotor kinetic energy demand R are used as input quantities, and the covariance of the measurement noise is used as an output quantity to construct a double-input single-output fuzzy control algorithm to control the online adjustment of the Kalman gain . In order to prevent the wind turbine from stopping, the lower limit of the wind turbine speed control is selected as 0.6 rad / s, and the upper limit of the wind turbine speed control is selected as 1.134 rad / s; the lower limit value of the corresponding wind turbine rotor kinetic energy state variable is 0.28, and the upper limit value is 1. In the face of turbulent wind speed, the traditional fixed parameter Kalman filter cannot achieve ideal filtering effect, and the rotor kinetic energy smoothing control will deviate from the maximum power control curve by a large margin, causing the operating load. Therefore, the wind turbine rotor kinetic energy state variable and the wind turbine rotor kinetic energy demand RAs the output, a dual-input single-output fuzzy control algorithm is constructed, and the Kalman gain The basic idea of ​​fuzzy reasoning design is as follows: when the wind turbine rotor kinetic energy state variable When the wind turbine rotor kinetic energy demand is low, If it is a negative value (kinetic energy release command), the Kalman gain is increased. (Decrease R ), to prevent over-discharge of stored energy; if it is a kinetic energy storage instruction, reduce the Kalman gain (Increase R ), increase the rotor kinetic energy of the stored energy, so that the rotor speed Quickly recover to the middle area; when the rotor speed reaches the upper limit and is still a kinetic energy storage instruction, or reaches the lower limit and is still a kinetic energy release instruction, the control will no longer act until the power is released / stored until the speed returns to normal.

[0048] like Figure 2 The figure shows the fuzzy reasoning result of the fuzzy controller in the present invention, wherein the wind turbine rotor kinetic energy state variable The fuzzy domain is {0.28, 0.424, 0.64, 0.856, 1.0}, and the fuzzy linguistic variables are {S, SM, M, BM, B}; the kinetic energy demand of the wind turbine rotor The fuzzy domain is {-2e6, -1.5e6, -1e6, 0,1e6, 1.5e6, 2e6}, and the fuzzy linguistic variables are {NB, NM, NS, ZO, PS, PM, PB}; the covariance of the measurement noise is R The fuzzy domain is {0.01, 10, 25, 40, 60, 80, 100}, and the fuzzy linguistic variables are {VS, S, SM, M, BM, B, VB}.

[0049] Example 3: See Figures 3-7 ,Example 3 proposes a fan rotor kinetic energy control power optimization method based on variable parameter Kalman filtering. Figure 3 The 60-second turbulent wind speed curve used in the simulation experiment verifies the smoothing effect of the proposed strategy on the electromagnetic output of the wind turbine. In Example 3, the wind turbine rotor kinetic energy control power optimization method based on variable parameter Kalman filtering proposed in the present invention is compared with the traditional MPPT control method, low-pass filter rotor kinetic energy control method, and fixed parameter Kalman filter rotor kinetic energy control method.

[0050] like Figure 4Figure 2 shows a comparison of the output electromagnetic power under different control strategies. Compared to traditional MPPT control methods, low-pass filter rotor kinetic energy control methods, and fixed-parameter Kalman filter rotor kinetic energy control methods, the wind turbine rotor kinetic energy control power optimization method based on a variable parameter Kalman filter achieves significant power leveling optimization without excessive loss of wind energy, ensuring economic benefits. Furthermore, it avoids the phase delay issues and oscillation in the low-frequency wind speed range often associated with the low-pass filter rotor kinetic energy control method.

[0051] Table 1 shows the output electromagnetic power data for different control strategies. As shown in Table 1, the wind turbine rotor kinetic energy control power optimization method using a variable parameter Kalman filter achieves an average output electromagnetic power of 3.431 MW, second only to MPPT control. The standard deviation of the output electromagnetic power is 0.662 MW, the lowest among all control strategies. This shows that the present invention stabilizes the output electromagnetic power without excessive energy loss, ensuring economic benefits.

[0052] Table 1 compares the output electromagnetic power data of the method of the present invention with those of MPPT control, traditional first-order low-pass filter LPF, and fixed parameter Kalman filter Kalman:

[0053] like Figure 5 As shown in the figure, the wind turbine rotor kinetic energy control power optimization method based on variable parameter Kalman filtering is compared with the output electromagnetic power change rate (RoCoP) under the traditional MPPT control strategy. It can be seen that the use of the strategy of the present invention effectively reduces the output electromagnetic power fluctuation and has a good stabilization effect.

[0054] like Figure 6 As shown in Figure 2, the wind turbine rotor speed is compared under different control strategies. It can be seen that under the wind turbine rotor kinetic energy control power optimization method based on variable parameter Kalman filter, the wind turbine rotor kinetic energy state variable is used to calculate the wind turbine rotor kinetic energy state variable. and the wind turbine rotor kinetic energy demand Kalman gain adjusted by fuzzy control K k , effectively reducing the operating load caused by the rotor kinetic energy power smoothing method, and reducing the probability of speed limit exceeding and the risk of shutdown.

[0055] like Figure 7 The figure shows the comparison of wind turbine rotor kinetic energy under different control strategies. , the speed boundary is quantified into a continuous state variable as the fuzzy control input value. Figure 7It can be seen that, in the fan rotor kinetic energy control power optimization method based on the variable parameter Kalman filter, the rotor energy is fully utilized while avoiding overcharging / over-discharging of the rotor energy storage, ensuring the smoothness of the wind turbine and preventing the instability of the wind turbine.

[0056] The above merely describes the preferred embodiments of the present application and is not intended to limit the present application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A variable parameter Kalman filter based power optimization method for fan rotor kinetic energy control, characterized in that, Comprising the following steps: S1: Deriving a power smoothing strategy based on wind turbine rotor kinetic energy control according to a power feedback maximum power tracking control PSF-MPPT strategy framework, designing a Kalman filter equation for the wind turbine, and smoothing the wind turbine output power reference value; S2: Introducing wind turbine rotor kinetic energy state variable Optimizing control of wind turbine rotor kinetic energy to prevent wind turbine speed overrun and shutdown accidents; S3: Designing a fuzzy control of the wind turbine rotor kinetic energy demand and the wind turbine rotor kinetic energy state variable by dynamically adjusting the Kalman gain to reduce the fatigue loads caused by the wind turbine rotor kinetic energy smoothing control strategy and the wind turbine rotor kinetic energy state variable by dynamically adjusting the Kalman gain K k ​ S4: Obtaining a reference smoothed power through variable parameter Kalman filtering, obtaining a reference speed through wind turbine rotor kinetic energy control, and achieving suppression of the wind turbine output power.

2. The variable parameter Kalman filter based power optimization method for fan rotor kinetic energy control according to claim 1, wherein, The power feedback maximum power tracking control PSF-MPPT strategy framework derives a power smoothing strategy based on wind turbine rotor kinetic energy control, which includes the amount of wind turbine rotor kinetic energy required to be stored or released by the wind turbine rotor which is expressed as: （1） wherein J is the wind turbine rotational inertia; is the wind turbine rotor speed; is the wind turbine input aerodynamic power; is the wind turbine output electromagnetic power; Converting the wind turbine output electromagnetic power Substituting the smoothed power reference value, equation (1) is rewritten as: （2） In the formula, is the smoothed electromagnetic power reference value of the wind turbine output; Substituting equation (2) into equation (1) gives to Integrating over this time gives equations (3) and (4): （3） （4） In the formula: and respectively represent the rotor speed at the time and respectively represent the rotor speed at the time is expressed as: , is the rotor speed change amount in the period from to . Substituting equation (4) into equation (3) gives: Substituting equation (4) into equation (3) gives （5） Solving equation (5) and discarding the negative solution, the solution is is: （6） then is expressed as formula (7): （7） According to formula (7), It is the reference speed output under the smooth control of the wind turbine rotor kinetic energy. ; The input power signal of the Kalman filter is selected as the power signal value calculated from the optimal power curve whose expression is: （8） In the formula is the optimal power coefficient of the wind turbine, which is set according to the parameters of the wind turbine itself, and the power signal value is taken as the power to be smoothed of the wind turbine. The Kalman filter time update equation is: （9） The Kalman filter state update equation is: （10） wherein, is the wind turbine k is the power signal value calculated by the PSF-MPPT strategy according to the power feedback at the moment; is the smoothed power reference signal obtained by Kalman filtering at the previous moment of the wind turbine; is the wind turbine k is the smoothed power reference signal at the moment; is the priori estimation of the current state obtained at the previous moment of the wind turbine; is the wind turbine k is the covariance of the priori estimation at the moment, is the covariance of the priori estimation at the previous moment of the wind turbine; is the gain of the Kalman filter, in the standard Kalman filtering system, the process noise and the measurement noise obey Gaussian white noise distribution, Q and R are the covariance of the process noise and the covariance of the measurement noise, respectively.

3. The variable parameter Kalman filter based power optimization method for fan rotor kinetic energy control according to claim 1, wherein, In the step S2, the wind turbine rotor kinetic energy state variable is introduced The wind turbine rotor kinetic energy is optimized and controlled, The rotor kinetic energy of the wind turbine is represented as: （11） According to formula (11), the wind turbine rotor kinetic energy storage at the current time is proportional to the square of the wind turbine rotor speed, and a relevant variable is introduced: a wind turbine rotor kinetic energy state variable The expression is: （12） In the formula represents the current moment wind turbine rotor storage kinetic energy; represents the maximum kinetic energy storage of the wind turbine rotor; represents the current moment wind turbine rotor speed; represents the upper limit of the wind turbine rotor speed, that is, the upper limit of the wind turbine rotor speed.

4. The variable parameter Kalman filter based power optimization method for fan rotor kinetic energy control according to claim 1, wherein, The step S3 specifically comprises designing a fuzzy control considering the wind turbine rotor kinetic energy demand and the wind turbine rotor kinetic energy state variable by dynamically adjusting Kalman gain K k ; Facing the turbulent wind speed, the kinetic energy state variable of wind turbine rotor and the kinetic energy demand of wind turbine rotor As an input quantity, the covariance of measurement noise R As an output quantity, a double-input single-output fuzzy control algorithm is constructed to control the Kalman gain online The fuzzy inference process is designed as follows: when the wind turbine rotor kinetic energy state variable is low, if the wind turbine rotor kinetic energy demand is negative, then the Kalman gain is increased; If kinetic energy storage instruction, reduce the kalman gain Increase the rotor kinetic energy of the energy storage wind turbine, so that the rotor speed of the wind turbine Quickly recover to the middle area; When the wind turbine rotor speed reaches the upper limit and is still the kinetic energy storage instruction, or reaches the lower limit and is still the kinetic energy release instruction, the control is no longer active until the release or storage power is restored to normal.

5. The variable parameter Kalman filter based power optimization method for fan rotor kinetic energy control according to claim 1, wherein, In the step S4, the reference smoothed power is obtained through variable parameter Kalman filtering, the reference speed is obtained through wind turbine rotor kinetic energy control, the wind turbine output power is suppressed, the wind turbine rotor kinetic energy storage is optimized, and the wind turbine operating load is reduced.

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