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

Abstract

The application provides a fan rotor kinetic energy control power optimization method based on a variable parameter Kalman filter, and belongs to the technical field of wind power generation. The technical scheme is as follows: S1: designing a Kalman filter equation for a wind turbine to perform smooth processing on a wind turbine output power reference value; S2: introducing a wind turbine rotor kinetic energy state variable to perform optimization control on the wind turbine rotor kinetic energy; S3: designing a fuzzy control considering the wind turbine rotor kinetic energy demand and the wind turbine rotor kinetic energy state variable; and S4: obtaining a reference smooth power through variable parameter Kalman filtering, obtaining a reference rotating speed through wind turbine rotor kinetic energy control, and realizing the suppression of the wind turbine output power. The application can perform smooth processing on the output electromagnetic power under a 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 traditional low-pass filter smoothing, prevent wind turbine speed overrun and shutdown risk by considering wind turbine rotor kinetic energy state, reduce operation load caused by wind turbine rotor kinetic energy control, and improve wind energy utilization efficiency to a certain extent.

[0005] In order to realize the above-mentioned application purpose, the technical scheme adopted by the application is specifically as follows: a wind turbine rotor kinetic energy control power optimization method based on variable parameter Kalman filtering, comprising the following steps:

[0006] 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:

[0007] 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;

[0008] S3: designing fuzzy control considering wind turbine rotor kinetic energy demand and wind turbine rotor kinetic energy state variable , adjusting Kalman gain K k dynamically, reducing fatigue load caused by wind turbine rotor kinetic energy smoothing control strategy;

[0009] 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.

[0010] Further, in step S1, the wind turbine rotor kinetic energy demand stored or released by the wind turbine rotor is represented as:

[0011] (1)

[0012] 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.

[0013] The wind turbine output electromagnetic power Substitute the smooth power reference value, formula (1) is rewritten as:

[0014] (2)

[0015] In the formula, is the smooth electromagnetic power reference value of the wind turbine output.

[0016] Formula (2) is integrated in to This time, formula (3) and formula (4) are obtained:

[0017] (3)

[0018] (4)

[0019] In the formula: and Respectively represent the rotor speed at and Time, then Can be expressed as: , Is the rotor speed change in to Time period.

[0020] Substitute Into formula (4) can be obtained:

[0021] (5)

[0022] Solve formula (5), eliminate the negative solution, the solution is As:

[0023] (6)

[0024] Then Can be expressed as formula (7):

[0025] (7)

[0026] The Calculated according to formula (7) It is the reference speed of the wind turbine rotor kinetic energy control smooth output .

[0027] As a modern filtering technology, Kalman filter can solve the time delay problem of traditional low-pass filter. When the wind turbine power suddenly changes, the Kalman filter gain K k Can be adjusted in real time to stabilize the performance of the filter.

[0028] The input power signal of the Kalman filter is selected as the power signal value calculated according to the optimal power curve , the expression of which is:

[0029] (8)

[0030] 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 wind turbine power input to be smoothed, so that the power control link can maintain the damping characteristics of the optimal power feedback method itself.

[0031] The time update equation of the Kalman filter is:

[0032] (9)

[0033] The state update equation of the Kalman filter is:

[0034] (10)

[0035] In the formula, is the power signal value calculated at the moment according to the PSF-MPPT algorithm; k is the smoothed power reference signal obtained after the Kalman filter smoothing at the previous moment of the wind turbine; is the smoothed power reference signal at the moment of the wind turbine; is the smoothed power reference signal at the moment of the wind turbine; k 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, is the covariance of the prior estimate at the previous moment of the wind turbine; k is the gain of the Kalman filter. In a standard Kalman filter system, the process noise and the measurement noise both obey Gaussian white noise distribution, and are the covariance of the process noise and the covariance of the measurement noise, respectively. Q R

[0036] Further, in step S2, the wind turbine rotor kinetic energy state variable is introduced to optimize the control of the wind turbine rotor kinetic energy and prevent the wind turbine from overrunning and shutting down.

[0037] The rotor kinetic energy of the wind turbine is represented as:

[0038] (11) ​​

[0039] As can be seen from equation (11), the current kinetic energy storage of the wind turbine rotor is proportional to the square of the wind turbine rotor speed. To characterize the kinetic energy storage of the wind turbine rotor, relevant variables are introduced: wind turbine rotor kinetic energy state variables. Its expression is:

[0040] (12)

[0041] In the formula This indicates the kinetic energy stored in the rotor of the wind turbine at the current moment; This represents the maximum kinetic energy storage capacity of the wind turbine rotor; This represents the current rotor speed of the wind turbine. This represents the upper limit of the rotor speed of the wind turbine, i.e., the upper limit of the wind turbine speed.

[0042] Under sudden wind speed drops, the output electromagnetic power decreases. According to the wind turbine rotor kinetic energy control, the wind turbine needs to decelerate to release rotor kinetic energy to support power output. This action can cause the wind turbine to continuously decelerate and even lead to a shutdown. To prevent wind turbine shutdown, a lower limit of 0.6 rad / s is selected for wind turbine speed control, and an upper limit of 1.134 rad / s is selected. The corresponding wind turbine rotor kinetic energy state variables... The lower limit is 0.28, and the upper limit is 1.

[0043] Furthermore, in step S3, the design takes into account the kinetic energy requirements of the wind turbine rotor. and the kinetic energy state variables of the wind turbine rotor Fuzzy control, through dynamic adjustment of Kalman gain K k This reduces the fatigue load caused by the smooth control strategy for rotor kinetic energy of wind turbine units.

[0044] Under turbulent wind speeds, traditional constant-parameter Kalman filters cannot achieve ideal filtering results, and the rotor speed of wind turbines deviates significantly from the maximum power control curve under smooth rotor kinetic energy control, causing operational loads. Therefore, using the rotor kinetic energy state variable of the wind turbine is necessary. The kinetic energy demand of wind turbine rotor As an input, the covariance of the measurement noise R As the output, a two-input single-output fuzzy control algorithm is constructed, and the Kalman gain is adjusted. Online adjustment and control are implemented. The basic idea of ​​fuzzy inference is as follows: when the rotor kinetic energy state variable of the wind turbine is... When the value is low, if the rotor kinetic energy demand of the wind turbine is low... If the value is negative (kinetic energy release command), then increase the Kalman gain. (reducing R ), preventing over-discharge of the energy storage; if it is a kinetic energy storage instruction, reducing the Kalman gain (increasing R ), increasing the rotor kinetic energy of the energy storage, so that the rotor speed quickly recovers to the middle region; when the rotor speed of the wind turbine 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. Wherein, 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 fuzzy domain of the wind turbine rotor kinetic energy demand is {-2e6, -1.5e6, -1e6, 0, 1e6, 1.5e6, 2e6}, and the fuzzy language variable is {NB, NM, NS, ZO, PS, PM, PB}; the fuzzy domain of the covariance of the measurement noise R is {0.01, 10, 25, 40, 60, 80, 100}, and the fuzzy language variable is {VS, S, SM, M, BM, B, VB}.

[0045] Further, in step S4, the reference smoothed power is obtained through the variable parameter Kalman filter, and the reference rotor speed of the wind turbine is obtained through the wind turbine rotor kinetic energy control, so as to realize the suppression of the output power of the wind turbine, the optimization of the wind turbine rotor kinetic energy storage, and the reduction of the operating load of the wind turbine.

[0046] Compared with the prior art, the present application has the following beneficial effects:

[0047] 1. Power suppression and cost optimization under turbulent wind speed: the present application directly optimizes the active power instruction by changing the control link and using a 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 rotor kinetic energy of the wind turbine itself is used E k as a "virtual energy storage" to absorb or release storage power to suppress power fluctuations; b) reducing 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.

[0048] 2. Variable parameter Kalman filter eliminates phase delay: the variable parameter Kalman filter in the application takes the wind turbine rotor kinetic energy state variable and the wind turbine rotor kinetic energy demand as input, and adjusts 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.

[0049] 3. Wind turbine rotor kinetic energy state monitoring prevents over-limit shutdown: the application introduces the wind turbine rotor kinetic energy state variable , quantizes the speed boundary as a continuous state variable as the fuzzy control input value; when approaching the lower limit and the kinetic energy needs to be released, the K k smoothing effect is reduced to prevent continuous deceleration from causing the wind turbine to lose stability. Without measuring the real-time wind speed, the rotor kinetic energy state is considered to reduce the speed over-limit probability and shutdown risk.

[0050] 4. Reduce operating load and improve wind energy utilization rate: the Kalman filter in the application takes as input, so that the power is smoothed while being as close to the maximum power point as possible; at the same time, the fuzzy control of the wind turbine speed and the wind turbine rotor kinetic energy state variable is considered to reduce the speed fluctuation range by dynamically adjusting the Kalman gain K k . 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

[0051] The accompanying drawings are included to provide a further understanding of the application, and constitute a part of the specification, together with the embodiments of the application, to explain the application, and do not constitute a limitation on the application.

[0052] Figure 1 is the machine side control block diagram of the wind turbine rotor kinetic energy control power optimization method based on variable parameter Kalman filter of the application.

[0053] Figure 2 is a schematic diagram of the fuzzy inference result of the fuzzy controller in the application.

[0054] Figure 3 is a schematic diagram of a given 60s turbulent wind speed sequence in the application.

[0055] Figure 4The method of the present application and the MPPT control, the traditional low-pass filter LPF (Low-pass filter, LPF), the parameter-fixed Kalman filter output electromagnetic power comparison chart.

[0056] Figure 5 The method of the present application and the MPPT control output electromagnetic power rate of change (Rate of change of power, RoCoP) comparison chart.

[0057] Figure 6 The method of the present application and the traditional low-pass filter LPF, the parameter-fixed Kalman filter rotor speed comparison chart.

[0058] Figure 7 The method of the present application and the traditional low-pass filter LPF, the parameter-fixed Kalman filter rotor kinetic energy comparison chart.

[0059] Figure 8 The method of the present application and the method of the present application based on the variable parameter Kalman filter fan rotor kinetic energy control power optimization method flow chart. DETAILED DESCRIPTION

[0060] In order to make the purpose, technical scheme and advantages of the present application more clear and obvious, the present application is further described in detail below in combination with the drawings, tables and examples. Of course, the specific examples described here are only used to explain the present application, and are not used to limit the present application.

[0061] Example 1: see Figure 8 , Example 1 proposes a variable parameter Kalman filter based fan rotor kinetic energy control power optimization method, which smoothes the output electromagnetic power through cascading variable parameter Kalman filter under the frame of wind turbine rotor kinetic energy control power smoothing; and adjusts the Kalman gain by fuzzy control to optimize the wind turbine rotor kinetic energy and reduce the wind turbine operating load, including the following steps:

[0062] S1: According to the power feedback maximum power tracking control (PSF-MPPT) strategy framework, derive the power smoothing strategy based on rotor kinetic energy control, design the Kalman filter equation for wind turbine, and smooth the wind turbine output power reference value;

[0063] S2: Introduce the wind turbine rotor kinetic energy state variable , optimize the control of wind turbine rotor kinetic energy, prevent wind turbine speed from exceeding the limit and shutdown accident;

[0064] S3: Design considering the demand of wind turbine rotor kinetic energy And the wind turbine rotor kinetic energy state variable fuzzy control, by dynamically adjusting Kalman gain K k , reduce fatigue load caused by wind turbine rotor kinetic energy smoothing control strategy;

[0065] S4: get the reference smoothing power through variable parameter Kalman filtering, get the reference speed of wind turbine through wind turbine rotor kinetic energy control, realize the smoothing of wind turbine output power.

[0066] Specifically, in step S1, the amount of wind turbine rotor kinetic energy required to be stored or released by the wind turbine rotor can be expressed as:

[0067] (1)

[0068] In the formula, J is the moment of inertia of the wind turbine; is the rotor speed of the wind turbine; is the input aerodynamic power of the wind turbine; is the output electromagnetic power of the wind turbine.

[0069] Replace the output electromagnetic power of the wind turbine with the smoothing power reference value, and rewrite formula (1) as:

[0070] (2)

[0071] In the formula, is the reference value of the smoothing electromagnetic power output by the wind turbine.

[0072] Integrate formula (2) in the time period to to obtain formula (3) and formula (4):

[0073] (3)

[0074] (4)

[0075] In the formula: and represent the rotor speed at time and , then can be expressed as: , is the change in rotor speed in the time period to .

[0076] Substitute into formula (4) to obtain:

[0077] (5)

[0078] Solving equation (5), the negative solution is discarded, and the solution is :

[0079] (6)

[0080] Then can be expressed as equation (7):

[0081] (7)

[0082] According to equation (7), the is the reference speed of the wind turbine rotor output under the smooth control of the kinetic energy of the rotor .

[0083] As a modern filtering technology, the Kalman filter can solve the time delay problem of the traditional low-pass filter. When the power of the wind turbine suddenly changes, the performance of the filter can be stabilized by adjusting the Kalman filter gain in real time.

[0084] The input power signal of the Kalman filter is the power signal value calculated according to the optimal power curve , and its expression is:

[0085] (8)

[0086] In the formula, is the optimal power coefficient of the wind turbine, which is set according to the parameters of the wind turbine itself. Taking the power signal value as the input power of the wind turbine to be smoothed can make the power control link maintain the inherent damping characteristics of the optimal power feedback method.

[0087] The time update equation of the Kalman filter is:

[0088] (9)

[0089] The state update equation of the Kalman filter is:

[0090] (10)

[0091] In the formula, is the power signal value calculated at time k according to the PSF-MPPT algorithm; is the smoothed power reference signal obtained by the Kalman filter at the previous time of the wind turbine; is the smoothed power reference signal at time k of the wind turbine; is the prior estimation of the current state of the wind turbine at the previous time instant; is the wind turbine k at the previous time instant, is the covariance of the prior estimation of the wind turbine at the previous time instant; is the gain of the Kalman filter. In a standard Kalman filter system, both 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 measurement noise, respectively.

[0092] Specifically, in step S2, the wind turbine rotor kinetic energy state variable is introduced, and the wind turbine rotor kinetic energy is optimized and controlled to prevent wind turbine speed from exceeding the limit and to prevent shutdown accidents.

[0093] The wind turbine rotor kinetic energy is expressed as:

[0094] (11)

[0095] According to formula (11), the wind turbine rotor kinetic energy storage at the current time instant is proportional to the square of the wind turbine rotor speed. In order to characterize the wind turbine rotor kinetic energy storage, the wind turbine rotor kinetic energy state variable is introduced, and its expression is:

[0096] (12)

[0097] In formula (12), represents the wind turbine rotor kinetic energy storage at the current time instant; represents the maximum wind turbine rotor kinetic energy storage; represents the wind turbine rotor speed at the current time instant; represents the upper limit of the wind turbine rotor speed, i.e., the upper limit of the wind turbine rotor speed.

[0098] Under the condition of sudden wind speed drop, due to the decrease in output electromagnetic power, the wind turbine needs to slow down to release rotor kinetic energy to support power in accordance with wind turbine rotor kinetic energy control. This action will cause the wind turbine to continue to slow down and even cause shutdown accidents. In order to prevent the wind turbine from shutting down, the lower limit of the rotor speed control is selected as 0.6 rad / s, and the upper limit of the rotor speed control is the upper limit of the wind turbine rotor speed, which is 1.134 rad / s; the corresponding lower limit value of the wind turbine rotor kinetic energy state variable is 0.28, and the upper limit value is 1.

[0099] Specifically, in step S3, the wind turbine rotor kinetic energy demand and the wind turbine rotor kinetic energy state variable fuzzy control, by dynamically adjusting Kalman gain K k , reduce fatigue load caused by rotor kinetic energy smoothing control strategy of wind turbine.

[0100] In the face of turbulent wind speed, the traditional fixed parameter Kalman filter can not achieve the desired filtering effect, and the rotor speed of wind turbine kinetic energy smoothing 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 the 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 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), increase the Kalman gain (decrease R ), prevent over-discharge of energy storage; if it is a kinetic energy storage instruction, decrease the Kalman gain (increase R ), increase the rotor kinetic energy of 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 universe 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 fuzzy universe of the wind turbine rotor kinetic energy demand is {-2e6, -1.5e6, -1e6, 0, 1e6, 1.5e6, 2e6}, and the fuzzy language variable is {NB, NM, NS, ZO, PS, PM, PB}; the fuzzy universe of the measurement noise covariance R is {0.01, 10, 25, 40, 60, 80, 100}, and the fuzzy language variable is {VS, S, SM, M, BM, B, VB}.

[0101] Specifically, in step S4, the reference smoothing power is obtained through the variable parameter Kalman filter, and the reference speed is obtained through the wind turbine rotor kinetic energy control, which realizes the suppression of the wind turbine output power, the optimization of the wind turbine rotor kinetic energy storage, and the reduction of the wind turbine operating load.

[0102] Embodiment 2: see Figure 1 With 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 diagram, through the cascade variable parameter Kalman filter in the wind turbine rotor kinetic energy control loop to smooth the output electromagnetic power. Among them, the input power signal of Kalman filter is selected as the power signal value calculated according to the optimal power curve , so that the power is smoothed while being as close to the maximum power point as possible to ensure economic benefits. The energy that needs to be stored or released by the wind turbine rotor is the wind turbine rotor kinetic energy demand , while introducing the wind turbine rotor kinetic energy state variable , the wind turbine rotor kinetic energy state variable and the wind turbine rotor kinetic energy demand as input, the covariance of measurement noise R as output, a double-input single-output fuzzy control algorithm is constructed to adjust and control the Kalman gain online. 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 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 operating load. Therefore, the wind turbine rotor kinetic energy state variable and the wind turbine rotor kinetic energy demand are used as input, the covariance of measurement noise R is used as output, a double-input single-output fuzzy control algorithm is constructed to adjust and control the Kalman gain online. The basic idea of fuzzy reasoning design 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), increase the Kalman gain (decrease R ) to prevent over-discharge of energy storage; if it is a kinetic energy storage instruction, reduce the Kalman gain (increase R ) to increase the rotor kinetic energy of energy storage, so that the rotor speed quickly recovers 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 not act until the release / storage power recovers to normal.

[0103] As Figure 2 shown in the figure is a fuzzy inference result diagram of the fuzzy controller in the application, wherein the fuzzy universe of the wind turbine rotor kinetic energy state variable is {0.28, 0.424, 0.64, 0.856, 1.0}, the fuzzy language variable is {S, SM, M, BM, B}; the fuzzy universe of the wind turbine rotor kinetic energy demand is {-2e6, -1.5e6, -1e6, 0, 1e6, 1.5e6, 2e6}, the fuzzy language variable is {NB, NM, NS, ZO, PS, PM, PB}; the fuzzy universe of the measurement noise covariance R is {0.01, 10, 25, 40, 60, 80, 100}, the fuzzy language variable is {VS, S, SM, M, BM, B, VB}.

[0104] Example 3: see Figures 3-7 , Example 3 proposes a wind turbine rotor kinetic energy control power optimization method based on variable parameter Kalman filtering. Figure 3 The figure is a 60s turbulent wind speed curve used for simulation experiment, verifying the smoothing effect of the proposed strategy on the output electromagnetic power 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 application is compared with the traditional MPPT control method, the low-pass filter rotor kinetic energy control method, and the fixed parameter Kalman filter rotor kinetic energy control method.

[0105] As Figure 4 shown in the figure, it is the output electromagnetic power comparison under different control strategies. The wind turbine rotor kinetic energy control power optimization method based on variable parameter Kalman filtering realizes obvious power suppression optimization compared with the traditional MPPT control method, the low-pass filter rotor kinetic energy control method, and the fixed parameter Kalman filter rotor kinetic energy control method, and does not lose too much wind energy, ensuring economic benefits. At the same time, it prevents the phase delay problem of the low-pass filter rotor kinetic energy control method and the oscillation phenomenon in the low frequency band of wind speed.

[0106] As shown in Table 1, it is the relevant data of the output electromagnetic power under different control strategies. As shown in the data in Table 1, the average value of the output electromagnetic power is 3.431 MW using the wind turbine rotor kinetic energy control power optimization method based on variable parameter Kalman filtering, which is only second to the MPPT control, and the standard deviation of the output electromagnetic power is 0.662 MW, which is the lowest among all control strategies. It can be seen that using the application, the output electromagnetic power is suppressed while not losing too much electric energy, ensuring economic benefits.

[0107] Table 1 compares the relevant data of the electromagnetic power output of the method of the present invention with those of MPPT control, traditional first-order low-pass filter LPF, and fixed-parameter Kalman filter:

[0108]

[0109] like Figure 5 As shown, the output electromagnetic power variation rate (RoCoP) of the wind turbine rotor kinetic energy control power optimization method based on variable parameter Kalman filtering is compared with that of the traditional MPPT control strategy. It can be seen that the strategy of the present invention effectively reduces the output electromagnetic power fluctuation and has a good smoothing effect.

[0110] like Figure 6 The figure shows a comparison of wind turbine rotor speeds 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 filtering, the rotor kinetic energy state variables of the wind turbine can be controlled. The kinetic energy demand of wind turbine rotor Adjusting Kalman gain using fuzzy control K k This effectively reduces the operating load caused by the rotor kinetic power smoothing method, and lowers the probability of speed exceeding the limit and the risk of shutdown.

[0111] like Figure 7 The figure shown is a comparison of the rotor kinetic energy of wind turbines under different control strategies. This invention introduces state variables... The speed boundary is quantized into a continuous state variable and used as the fuzzy control input value. Figure 7 It is evident that the wind turbine rotor kinetic energy control power optimization method based on variable parameter Kalman filtering fully utilizes rotor kinetic energy while avoiding overcharging / over-discharging of rotor energy storage, ensuring the smoothness of the wind turbine unit and preventing wind turbine unit instability.

[0112] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A variable parameter Kalman filter based power optimization method for fan rotor kinetic energy control, characterized in that, Comprise the following steps: S1: derive the 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 the Kalman filter equation for the wind turbine, and smooth 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 controller that takes into account the rotor kinetic energy demand of the wind turbine and the rotor kinetic energy state variable of the wind turbine of the Kalman gain K k of the power smoothing strategy that results in fatigue loads on the rotor of the wind turbine Facing the turbulent wind speed, the rotor kinetic energy state variable of wind turbine and the rotor kinetic energy demand of wind turbine As an input, the covariance of the measurement noise in the Kalman filter R As an output, construct a double-input single-output fuzzy control algorithm to adjust and 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 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 does not act until the release or storage power is restored to normal; S4: obtain the reference smoothing power through the variable parameter Kalman filtering, obtain the reference speed through the wind turbine rotor kinetic energy control, and realize the 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 moment of inertia of the wind turbine; is the rotational speed of the wind turbine rotor; is the input aerodynamic power of the wind turbine; is the output electromagnetic power of the wind turbine; Converting the wind turbine output electromagnetic power Substituting the smoothed electromagnetic 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 represent the rotor speed at the time and respectively, then is expressed as: , is the rotor speed change amount in the period 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） calculated according to equation (7) is the reference rotational speed output under the smoothness of the wind turbine rotor kinetic energy control ; 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 input quantity 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 of the wind turbine at the previous moment after Kalman filtering; is the wind turbine k is the smoothed power reference signal at the moment; is the wind turbine k is the covariance of the prior estimation at the moment, is the covariance of the prior estimation at the previous moment; is the gain of the Kalman filter, in a 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 of wind turbine rotor storage kinetic energy; represents the maximum kinetic energy storage of the wind turbine rotor; represents the current moment of 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, In the step S4, the reference smoothing power is obtained through the variable parameter Kalman filtering, the reference speed is obtained through the 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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