Pitch control method based on hierarchical fuzzy and load control and wind turbine generator set

Abstract

The present application discloses a pitch control method and a wind turbine based on hierarchical fuzzy and load control. The method is used for a wind turbine and includes: generating a unified pitch command for each blade according to the obtained deviation of the generator speed; generating an additional pitch command for each blade based on hierarchical fuzzy and load control according to the obtained load at the blade root of each blade; generating an independent pitch command for each blade according to the unified pitch command of each blade and the additional pitch command of each blade; controlling each pitch drive system correspondingly arranged for each blade to execute the independent pitch command of each blade, so that the loads at the blade roots of each blade tend to be balanced. According to the loads at the blade roots of each blade, the method generates an additional pitch command for separately adjusting the pitch angles of each blade based on hierarchical fuzzy and load control, reduces the degree of load imbalance of the impeller, and is beneficial to reducing the load levels of each component during the operation of the unit.

Description

Technical Field

[0001] This application relates to the field of variable pitch control technology for large wind turbines, and specifically relates to a variable pitch control method and a wind turbine based on hierarchical fuzzy and load control. Background Art

[0002] The wind conditions where large wind turbines are located vary widely. The existence of various factors such as wind turbulence, wind shear, and yaw deviation causes unbalanced loads on the impellers of wind turbines. Moreover, the larger the impeller diameter and the longer the blades, the higher the degree of uneven force on the entire impeller surface in the unbalanced wind field, and the more obvious the unbalanced load.

[0003] For large-capacity, large-scale, and long-blade wind turbines, with the increase in blade diameter and tower height, the problem of uneven load distribution on the impeller, nacelle, and tower is particularly prominent, which is not conducive to the stable and safe operation of the unit.

[0004] The traditional independent drive and unified variable pitch technology used in wind turbines can no longer meet the load control requirements, and a variable pitch control method that is conducive to further reducing the degree of load imbalance needs to be proposed. Summary of the Invention

[0005] In view of the above problems in the prior art, this application provides a variable pitch control method, device, and wind turbine based on hierarchical fuzzy and load control to solve the problem of high load imbalance in wind turbines in the prior art.

[0006] In a first aspect, this application provides a variable pitch control method based on hierarchical fuzzy and load control for a wind turbine, including:

[0007] Generate a unified variable pitch command for each blade according to the obtained deviation of the generator speed;

[0008] Generate an additional variable pitch command for each blade based on hierarchical fuzzy and load control according to the obtained loads at the blade roots of each blade;

[0009] Generate an independent variable pitch command for each blade according to the unified variable pitch command of each blade and the additional variable pitch command of each blade;

[0010] Control each variable pitch drive system correspondingly set for each blade to execute the independent variable pitch command of each blade, so that the loads at the blade roots of each blade tend to be balanced.

[0011] Further, the generating an additional variable pitch command for each blade based on hierarchical fuzzy and load control according to the obtained loads at the blade roots of each blade includes:

[0012] According to the loads of each blade at the blade root and the azimuth angle of the impeller obtained, the pitch moment and the yaw moment are obtained through Park transformation;

[0013] Based on the deviation of the pitch moment, a desired d-axis pitch angle is generated based on load control;

[0014] Based on the deviation of the yaw moment, a desired q-axis pitch angle is generated based on load control;

[0015] Based on hierarchical fuzzy, multi-level pitch angle limits for limiting the desired d-axis pitch angle and the desired q-axis pitch angle are generated;

[0016] According to the multi-level pitch angle limits, the desired d-axis pitch angle and the desired q-axis pitch angle are limited;

[0017] According to the limited desired d-axis pitch angle and the desired q-axis pitch angle, an additional pitch command for each blade is generated through inverse Park transformation.

[0018] Further, the generating, based on hierarchical fuzzy, multi-level pitch angle limits for limiting the desired d-axis pitch angle and the desired q-axis pitch angle includes:

[0019] According to the deviation of the pitch moment and the change rate of the deviation, a first pitch angle limit is generated based on hierarchical fuzzy;

[0020] According to the deviation of the yaw moment and the change rate of the deviation, a second pitch angle limit is generated based on hierarchical fuzzy;

[0021] The larger value of the first pitch angle limit and the second pitch angle limit is determined for limiting the desired d-axis pitch angle and the desired q-axis pitch angle.

[0022] Further, the generating, according to the deviation of the pitch moment and the change rate of the deviation, a first pitch angle limit based on hierarchical fuzzy includes:

[0023] The deviation of the pitch moment and the change rate of the deviation are respectively subjected to hierarchical fuzzy;

[0024] Using the preset fuzzy inference rules, the fuzzy exact value of the multi-level pitch angle limit corresponding to the deviation of the pitch moment and the change rate of the deviation is determined;

[0025] Using the preset defuzzification strategy, the actual exact value of the multi-level pitch angle limit corresponding to the fuzzy exact value of the multi-level pitch angle limit is determined, and the actual exact value of the multi-level pitch angle limit is the first pitch angle limit.

[0026] Further, generating a second pitch angle limit value based on hierarchical fuzzy according to the deviation of the yaw moment and the change rate of the deviation includes:

[0027] Performing hierarchical fuzzy on the deviation of the yaw moment and the change rate of the deviation respectively;

[0028] Using a preset fuzzy inference rule to determine the fuzzy exact value of the multi-level pitch angle limit value corresponding to the deviation of the yaw moment and the change rate of the deviation;

[0029] Using a preset defuzzification strategy to determine the actual exact value of the multi-level pitch angle limit value corresponding to the fuzzy exact value of the multi-level pitch angle limit value, and the actual exact value of the multi-level pitch angle limit value is the second pitch angle limit value.

[0030] Further, the load at the blade root is obtained by an optical fiber strain sensor installed at the blade root;

[0031] The azimuth angle of the impeller is obtained by an absolute encoder installed on the low-speed shaft of the wind turbine generator set;

[0032] The generator speed is obtained by an incremental encoder installed on the generator.

[0033] In a second aspect, the present application provides a pitch control device based on hierarchical fuzzy and load control for a wind turbine generator set, including:

[0034] A unified pitch command generation module, configured to generate a unified pitch command for each blade according to the obtained deviation of the generator speed;

[0035] An additional pitch command generation module, configured to generate an additional pitch command for each blade based on hierarchical fuzzy and load control according to the obtained load at the blade root of each blade;

[0036] An independent pitch command generation module, configured to generate an independent pitch command for each blade according to the unified pitch command of each blade and the additional pitch command of each blade;

[0037] A pitch device control module, configured to control each pitch drive system correspondingly arranged for each blade to execute the independent pitch command of each blade, so that the loads at the blade roots of each blade tend to be balanced.

[0038] Further, the additional pitch command generation module is specifically configured to:

[0039] According to the obtained load at the blade root of each blade and the azimuth angle of the impeller, obtaining the pitch moment and the yaw moment through Park transformation;

[0040] According to the deviation of the pitch moment, generating a d-axis desired pitch angle based on load control;

[0041] Generate a desired pitch angle of the q-axis based on load control according to the deviation of the yaw moment.

[0042] Generate multi-level pitch angle limits for limiting the desired pitch angle of the d-axis and the desired pitch angle of the q-axis based on hierarchical fuzzy.

[0043] Limit the desired pitch angle of the d-axis and the desired pitch angle of the q-axis according to the multi-level pitch angle limits.

[0044] Generate additional pitch control commands for each blade through Park inverse transformation according to the limited desired pitch angle of the d-axis and the desired pitch angle of the q-axis.

[0045] Furthermore, the additional pitch control command generation module is specifically configured to:

[0046] Generate a first pitch angle limit based on hierarchical fuzzy according to the deviation of the pitch moment and the change rate of the deviation.

[0047] Generate a second pitch angle limit based on hierarchical fuzzy according to the deviation of the yaw moment and the change rate of the deviation.

[0048] Determine the larger value of the first pitch angle limit and the second pitch angle limit for limiting the desired pitch angle of the d-axis and the desired pitch angle of the q-axis.

[0049] In a third aspect, the present application provides a wind turbine generator set provided with a pitch control device as described in the second aspect.

[0050] These and other aspects of the present application will become more clearly understandable in the following description of (one or more) embodiments. Description of the Drawings

[0051] The following further describes each feature of the present application and the relationship between each feature with reference to the drawings. The drawings are all exemplary. Some features are not shown in actual proportion, and some conventional features in the field related to the present application that are not necessary for the present application may be omitted in some drawings, or some features that are not necessary for the present application may be shown additionally. The combination of the features shown in the drawings does not limit the present application. Additionally, throughout the present specification, the content referred to by the same reference numerals is also the same. The specific description of the drawings is as follows:

[0052] Figure 1 It is a schematic flowchart of a pitch control method based on hierarchical fuzzy and load control according to an embodiment of the present application;

[0053] Figure 2 It is a schematic composition diagram of a pitch control device based on hierarchical fuzzy and load control according to an embodiment of the present application;

[0054] Figure 3 It is a schematic diagram of the composition of the pitch control system of the wind turbine in the embodiment of the present application;

[0055] Figure 4 It is a schematic diagram of the independent pitch fuzzy control loop based on hierarchical fuzzy and load control in the embodiment of the present application;

[0056] Figure 5A It is a schematic diagram of the input variables, fuzzy sets and membership functions based on hierarchical fuzzy in the embodiment of the present application;

[0057] Figure 5B It is a schematic diagram of the output variables, fuzzy sets and membership functions based on hierarchical fuzzy in the embodiment of the present application;

[0058] Figure 6 It is a schematic diagram of obtaining the fuzzy exact value of the output variable by using the centroid method in the hierarchical fuzzy of the embodiment of the present application. Detailed implementation manners

[0059] Unless otherwise defined, all technical and scientific terms used in the present application have the same meanings as those commonly understood by those skilled in the technical field to which the present application belongs. In case of inconsistency, the meanings described in this specification or the meanings obtained according to the content recorded in this specification shall prevail. In addition, the terms used in the present application are only for the purpose of describing the embodiments of the present application and are not intended to limit the present application.

[0060] In order to accurately describe the technical content in the present application and to accurately understand the present application, the following explanations or definitions are given to the terms used in this specification before describing the detailed implementation manners.

[0061] Proportional Integral (PI) control.

[0062] Proportional Integral Derivative (PID) control.

[0063] An industrial controller or industrial personal computer (IPC) is a ruggedized enhanced personal computer that can operate reliably for a long time in an industrial environment.

[0064] Generally, a large wind turbine generator unit (hereinafter referred to as wind turbine, unit or turbine) includes a tower barrel, a nacelle, a main frame, a hub, blades, a main shaft, a gearbox and a generator. The low-speed shaft of the gearbox is connected to the main shaft, and the high-speed shaft of the gearbox is connected to the generator. The blades are connected to the hub at the blade roots. The blades are affected by the wind, driving the hub to rotate. The blades and the hub are also called an impeller.

[0065] The blades being affected by the wind is the driving force for the impeller to rotate, the source of the blade root load, and also the source of the loads borne by components such as the blades, hub, main shaft, nacelle, main frame and tower barrel. If the loads borne at each blade root are equivalent to the average value of the loads in the fixed hub coordinate system being 0, it is considered that load balance is achieved.

[0066] Generally, a rotating hub coordinate system is used to describe the load conditions borne by the impeller, hub and main shaft respectively. In the rotating hub coordinate system, the direction where the main shaft is located is the x-axis, and the other two coordinate axes perpendicular to the main shaft are the y-axis direction and the z-axis direction respectively.

[0067] Generally, fiber optic strain sensors are installed at the blade roots of each blade to measure the blade root load M in the rotating hub coordinate system y . An absolute encoder is installed on the low-speed shaft to measure the azimuth angle of the impeller. An incremental encoder is installed on the generator to measure the rotational speed of the generator.

[0068] Large wind turbine generator units generally adopt independent drive and unified pitch control. When the wind speed is lower than the rated wind speed, by controlling each blade to always be near the optimal pitch angle, maximum energy capture is achieved. When the wind speed is higher than the rated wind speed, by adjusting the pitch angle, the output power of the generator is kept stable. Here, the rated wind speed, the rated output power of the generator, and the rated rotational speed of the generator correspond one by one.

[0069] When adopting unified pitch control, each pitch drive system respectively set for each blade receives the same pitch angle command. Each pitch drive system independently executes this pitch angle command and independently drives the inner ring of the pitch bearing to rotate to adjust the pitch angle of the blade fixedly connected to the inner ring of the pitch bearing. When adopting independent drive and unified pitch control, for the same pitch angle command, the pitch angles of each blade are the same.

[0070] The pitch control method for a wind turbine generator unit based on hierarchical fuzzy and load control proposed in this application is an asymmetric, hierarchical fuzzy independent drive and independent pitch control method, which is beneficial to reducing the unevenness degree of the loads at the blade roots of each blade.

[0071] As Figure 1 shown, the pitch control method based on hierarchical fuzzy and load control of this application includes:

[0072] S11: Generate a unified pitch command for each blade according to the deviation of the generator speed obtained.

[0073] S12: Generate an additional pitch command for each blade based on hierarchical fuzzy and load control according to the load at the root of each blade obtained.

[0074] S13: Generate an independent pitch command for each blade according to the unified pitch command and the additional pitch command of each blade.

[0075] S14: Control each pitch drive system set corresponding to each blade to execute the independent pitch command of each blade, so that the loads at the roots of each blade tend to be balanced.

[0076] In step S11, the deviation of the generator speed is the difference between the generator speed and the rated generator speed. In some embodiments, the generator speed is a speed filtering value determined by filtering multiple speed values of the generator collected in consecutive sampling periods and control periods, such as low-pass filtering and band-stop filtering.

[0077] In step S11, a unified pitch command is generated for the impeller according to the deviation of the generator speed, and this unified pitch command is shared by each blade. Generally, the unified pitch control is realized by a speed pitch control loop, and the controller in the speed pitch control loop is mostly a PID controller. The generated unified pitch command corresponds to the basic pitch angle shared by each blade. As Figure 3 shown, the deviation of the generator speed is obtained by subtracting the generator speed obtained in real time by the speed sensor from the preset rated generator speed.

[0078] In step S12, load sensors provided at the roots of each blade obtain the loads at the roots of each blade in real time. Figure 3 shows the loads at the roots of 3 blades, such as the root 1M of blade 1 y , the root 2M of blade 2 y , the root 3M of blade 3 y .

[0079] In some embodiments, as Figure 3 shown, an additional pitch command for each blade is generated by an independent pitch fuzzy control loop based on hierarchical fuzzy and load control, such as the additional pitch command 1 for blade 1, the additional pitch command 2 for blade 2, and the additional pitch command 3 for blade 3.

[0080] Corresponding to the load imbalance degree of the impeller, different additional pitch commands are generated for each blade based on hierarchical fuzzy and load control. For example, an additional pitch angle corresponds to each blade, so as to reduce the load imbalance degree of the impeller as a whole through independent pitching.

[0081] In step S13, the independent pitch instructions for each blade include a unified pitch instruction and an additional pitch instruction. Referring to the foregoing description, the unified pitch instructions for each blade are the same, and the additional pitch instructions for each blade are different. Therefore, the independent pitch instructions for each blade are different.

[0082] In some embodiments, as Figure 3 shown, the pitch angle coupler generates independent pitch instructions for each blade according to the foregoing unified pitch instruction and the additional pitch instruction for each blade, such as independent pitch instruction 1 for blade 1, independent pitch instruction 2 for blade 2, and independent pitch instruction 3 for blade 3. For example, the pitch angle coupler includes a plurality of independent adders, and the adder couples the unified pitch instruction and the additional pitch instruction for each blade to obtain the independent pitch instruction for each blade, such as adding the basic pitch angle corresponding to the foregoing unified pitch instruction and the additional pitch angle corresponding to the additional pitch instruction for each blade.

[0083] In step S14, after each pitch drive system correspondingly set for each blade executes the independent pitch instruction of each blade, the pitch angles of each blade may be different. At this time, the load at the blade root of each blade is less unbalanced and tends to be balanced with respect to the overall impeller.

[0084] In some embodiments, as Figure 3 , Figure 4 shown, the independent pitch fuzzy control loop generates additional pitch instructions for each blade based on hierarchical fuzzy and load control according to the loads at the blade roots of each blade obtained, including:

[0085] Obtaining the pitch moment and yaw moment through Park transformation according to the loads at the blade roots of each blade obtained and the azimuth angle of the impeller;

[0086] Generating a d-axis desired pitch angle based on load control according to the deviation of the pitch moment;

[0087] Generating a q-axis desired pitch angle based on load control according to the deviation of the yaw moment;

[0088] Generating multi-level pitch angle limits for limiting the d-axis desired pitch angle and the q-axis desired pitch angle based on hierarchical fuzzy;

[0089] Limiting the d-axis desired pitch angle and the q-axis desired pitch angle according to the multi-level pitch angle limits;

[0090] Generating additional pitch instructions for each blade through Park inverse transformation according to the limited d-axis desired pitch angle and the q-axis desired pitch angle.

[0091] In some embodiments, as Figure 4 shown, in combination with the azimuth angle of the impeller, the loads M at each blade root obtained in the rotating hub coordinate system y are transformed by Park transformation to obtain the pitch moment M and the yaw moment M y in the fixed hub coordinate system. Subsequently, based on the pitch moment M z and the yaw moment M y generated in real time, a pitch moment controller and a yaw moment controller with PID or PI control are designed respectively based on load control, so that the pitch moment and the yaw moment tend to 0 independently. For example, the set value M z of the pitch moment controller is designed to be 0, and the set value M y * of the yaw moment controller is designed to be 0. When the pitch moment and the yaw moment of the impeller in the fixed hub coordinate system tend to 0 respectively, the loads borne by components such as the blade root, the hub, the main shaft, and the tower barrel will also decrease accordingly. z * Specifically, as

[0092] shown, when the set value M Figure 4 of the pitch moment is 0, the pitch moment controller generates the d-axis desired pitch angle based on load control according to the deviation between the pitch moment generated in real time and the set value M y * , that is, the pitch moment M y * generated in real time. y Specifically, as

[0093] shown, when the set value M Figure 4 of the yaw moment is 0, the yaw moment controller generates the q-axis desired pitch angle based on load control according to the deviation between the yaw moment generated in real time and the set value M z * , that is, the yaw moment M z * generated in real time. z Based on the above pitch moment PID control and yaw moment PID control based on load control, further, it further includes: generating multi-level pitch angle limit values for limiting the aforementioned d-axis desired pitch angle and the aforementioned q-axis desired pitch angle based on hierarchical fuzzy.

[0094] In some embodiments, as

[0095] shown, Figure 4As shown, based on the fuzzy control theory, the fuzzy inference unit A generates a first pitch angle limit value, i.e., output limit value 1, based on hierarchical fuzzy according to the deviation of the pitch moment and the change rate of the deviation; the fuzzy inference unit B generates a second pitch angle limit value, i.e., output limit value 2, based on hierarchical fuzzy according to the deviation of the yaw moment and the change rate of the deviation; the maximum value module Max determines the larger value between the first pitch angle limit value and the second pitch angle limit value as the aforementioned multi-level pitch angle limit value for limiting the d-axis desired pitch angle and the q-axis desired pitch angle. Subsequently, the d-axis desired pitch angle and the q-axis desired pitch angle are limited according to the multi-level pitch angle limit value. Subsequently, according to the limited d-axis desired pitch angle and the q-axis desired pitch angle, an additional pitch command for each blade is generated through the inverse Park transformation. For example, Figure 3 the additional command 1 for blade 1 in Figure 4 corresponds to the additional pitch angle 1 in Figure 3 the additional command 2 for blade 2 in Figure 4 corresponds to the additional pitch angle 2 in Figure 3 the additional command 3 for blade 1 in Figure 4 corresponds to the additional pitch angle 3 in, where the additional pitch angle 1, the additional pitch angle 2, and the additional pitch angle 3 are the aforementioned additional pitch angles.

[0096] As mentioned above, when limiting the d-axis desired pitch angle and the q-axis desired pitch angle according to the multi-level pitch angle limit value, the pitch angle limit value is the maximum value of positive numbers or the minimum value of negative numbers. Referring to the foregoing description, the deviation of the pitch moment is the pitch moment M y generated in real time as described above. As Figure 4 shown, the change rate of the deviation of the pitch moment can be obtained according to the differential dM y / dt of the deviation of the pitch moment or by difference. Referring to the foregoing description, the deviation of the yaw moment is the yaw moment M z generated in real time as described above. As Figure 4 shown, the change rate of the deviation of the yaw moment can be obtained according to the differential dM z / dt of the deviation of the yaw moment or by difference.

[0097] Specifically, the multi-level pitch angle limit value for limiting the d-axis desired pitch angle and the q-axis desired pitch angle has multiple values, and the steps for determining the multiple values of the hierarchical pitch angle limit value are referred to the following description.

[0098] In some embodiments, the fuzzy inference unit A, such as the pitch moment fuzzy inference unit, generates a first pitch angle limit value based on hierarchical fuzzy according to the deviation of the pitch moment and the change rate of the deviation, including:

[0099] Perform hierarchical fuzzification on the deviation of the pitch moment and the rate of change of its deviation respectively;

[0100] Using pre-set fuzzy inference rules, determine the fuzzy exact values of the multi-level pitch angle limits corresponding to the deviation of the pitch moment and the rate of change of its deviation;

[0101] Using a pre-set defuzzification strategy, determine the actual exact values of the multi-level pitch angle limits corresponding to the fuzzy exact values of the multi-level pitch angle limits. The actual exact values of the multi-level pitch angle limits are the first pitch angle limits.

[0102] In some embodiments, the fuzzy inference unit B, such as the yaw moment fuzzy inference unit, generates the second pitch angle limit based on the deviation of the yaw moment and the rate of change of its deviation through hierarchical fuzzification, including:

[0103] Perform hierarchical fuzzification on the deviation of the yaw moment and the rate of change of its deviation respectively;

[0104] Using pre-set fuzzy inference rules, determine the fuzzy exact values of the multi-level pitch angle limits corresponding to the deviation of the yaw moment and the rate of change of its deviation;

[0105] Using a pre-set defuzzification strategy, determine the actual exact values of the multi-level pitch angle limits corresponding to the fuzzy exact values of the multi-level pitch angle limits. The actual exact values of the multi-level pitch angle limits are the second pitch angle limits.

[0106] In this way, when the deviation of the yaw moment and the rate of change of its deviation are respectively approximately the same as the deviation of the pitch moment and the rate of change of its deviation, the values of the first pitch angle limit or the second pitch angle limit are also approximately the same. Although the principles of the fuzzy inference unit A and the fuzzy inference unit B are the same, their functions are similar, and even the values of the generated variables are quite equivalent and can be implemented with reference to each other, as Figure 4 shown, the fuzzy inference unit A and the fuzzy inference unit B are executed independently of each other without affecting each other.

[0107] As above, limit the d-axis desired pitch angle and the q-axis desired pitch angle respectively according to the hierarchical pitch angle limits to achieve load control based on hierarchical fuzzification. In this way, by applying fuzzy control, the outputs of the pitch moment PID control and the yaw moment PID control based on load control are corrected respectively through limiting, so as to adaptively adjust the additional pitch angles of each blade hierarchically according to the load imbalance degree of the impeller, which is beneficial to enhancing the dynamic response ability of the independent pitch fuzzy control loop, shortening the pitch adjustment time, reducing the pitch angle, improving the sensitivity of the root load adjustment, reducing the loads at the roots of each blade, achieving better load control effects, effectively reducing the fatigue loads of components, reducing the component overload problems caused by load imbalance, and extending the service life of each pitch device.

[0108] Thus, for the pitch control method of the wind turbine based on hierarchical fuzzy and load control, by determining the adjustment of the pitch angles of each blade separately based on the loads at the blade roots, the degree of load imbalance of the impeller is reduced, which is beneficial to reducing the load levels of various components during the operation of the unit.

[0109] The following combines the design process of the fuzzy inference engine to illustrate the steps for determining multiple values of the hierarchical pitch angle limit.

[0110] As Figure 5A 、 Figure 5B respectively show the fuzzy states and membership functions of the designed pitch moment fuzzy inference engine and yaw moment fuzzy inference engine as follows. Referring to the foregoing description, the pitch moment fuzzy inference engine and the yaw moment fuzzy inference engine have the same principle and similar functions. The following takes the pitch moment fuzzy inference engine as an example for illustration, and the yaw moment fuzzy inference engine can be implemented by reference and will not be elaborated.

[0111] One input variable of the pitch moment fuzzy inference engine is the deviation e of the pitch moment. In a certain type of wind turbine, the actual variation range of the deviation e of the pitch moment of the overall impeller is [-5000000, 5000000]. Considering the hierarchical accuracy and operation efficiency comprehensively, the fuzzy domain variation range of the deviation e is determined to be [-4, 4]. Another input variable of the pitch moment fuzzy inference engine is the change rate de of the deviation. In this type of wind turbine, the actual variation range of the change rate de of the deviation of the pitch moment of the overall impeller is [-2000000, 2000000]. Considering the hierarchical accuracy and operation efficiency comprehensively, the fuzzy domain variation range of the change rate de of the deviation is determined to be [-4, 4].

[0112] The pitch moment fuzzy inference engine has only one output variable, which is the first pitch angle limit described above or Figure 4 the output limit 1 in . In this type of wind turbine, the actual variation range of the output limits of each pitch drive system is [-3, 3]. Considering the hierarchical accuracy and operation efficiency comprehensively, the fuzzy domain variation range of the pitch output limit is determined to be [-6, 6].

[0113] Above, the actual variation ranges of the input variables or output variables of the determined fuzzy inference engine are respectively used as the actual domains of the variables. And the membership functions of the input variables or output variables are respectively used to describe the mapping from the actual domain to the fuzzy domain. For any type of long blade wind turbine, the actual variation ranges of the input variables or output variables of the fuzzy inference engine are determined according to the actual loads of the unit and combined with the on-site operation records.

[0114] For the two input variables of the fuzzy inference engine, such as Figure 5AAs shown in the figure, the five-level fuzzy set is used for fuzzification, such as {negative large, negative small, zero, positive small, positive large}. At this time, the five fuzzy states can be represented by symbols as {negative large, negative small, zero, positive small, positive large} = {NB, NS, ZO, PS, PB}. The membership function of each fuzzy state selects a symmetric triangular membership function. This is because, in the reasoning process, the value of the symmetric triangular membership function can be conveniently determined through simple calculations, which is beneficial to improving the real-time performance of the pitch control method based on hierarchical fuzzy in this embodiment.

[0115] As above, the selection of the five-level fuzzy set is determined according to the requirements of fuzzy control performance and taking into account the complexity of fuzzy reasoning. The more levels, the greater the complexity of fuzzy reasoning. For occasions with high requirements for control performance, 7 levels can be selected; for occasions with low requirements for control performance, 3 levels can be selected; for occasions with general requirements for control performance, 5 levels can be selected.

[0116] Regarding the output variable of the fuzzy inference engine, such as Figure 5B As shown in the figure, the seven-level fuzzy set is used for fuzzification, such as {negative large, negative medium, negative small, zero, positive small, positive medium, positive large}. At this time, the seven fuzzy states can be represented by symbols as {negative large, negative medium, negative small, zero, positive small, positive medium, positive large} = {NB, NM, NS, ZO, PS, PM, PB}. Similarly, the membership function of each fuzzy state selects a symmetric triangular membership function. This is because, in the reasoning process, the value of the symmetric triangular membership function can be conveniently determined through simple calculations, which is beneficial to improving the real-time performance of the pitch control method based on hierarchical fuzzy in this embodiment.

[0117] The fuzzy inference rules determined according to the on-site operation records are as follows: The two input variables of the fuzzy inference engine: the deviation e of the pitch moment and the change rate de of the pitch moment deviation and the output variable, that is, the first pitch angle limit value y satisfy the following control law:

[0118] 1) When |e| is relatively large, if |de| is also relatively large, then the value of y takes the maximum; otherwise, the value of y takes the medium.

[0119] 2) When |e| is zero, if |de| is zero, then y is zero; if |de| is relatively small, then y takes a relatively small value; if |de| is relatively large, then y takes a medium value.

[0120] 3) When |e| is relatively small, if de is zero or in the same direction and relatively small, then the value of y takes a relatively small value; if de is in the same direction and relatively large, then the value of y takes a medium value; if de is in the opposite direction and relatively small, then the value of y takes zero; if de is in the opposite direction and relatively large, then the value of y takes a relatively small value.

[0121] The fuzzy inference rules corresponding to the above control law are shown in Table 1.

[0122] Table 1 Fuzzy Inference Rules

[0123]

[0124] After determining the fuzzy values of the output variables, for the defuzzification of the output variables of the fuzzy inference engine, methods such as the maximum membership degree method, the centroid method, and the weighted average method can be used to obtain the fuzzy exact values of the fuzzy universe of discourse of the output variables of the fuzzy controller, and using the previously defined fuzzy universe of discourse of the output variables, in the reverse process of "converting the actual exact value to the fuzzy exact value" of the input variables, the fuzzy exact value of the output variables is converted into the corresponding actual exact value, and the absolute value is taken, which is the first pitch angle limit value.

[0125] As above, fuzzy control, as area control, can perform hierarchical control on y, that is, the pitch angle limit value, according to the load change interval, that is, the deviation e of the pitch moment and the change rate de of the deviation. When the input variables are in different interval ranges, different interval ranges of the output variables can be obtained.

[0126] In this way, when the deviation or the change rate of the deviation of the pitch moment or the yaw moment is within the allowable load change interval, based on hierarchical fuzzy, hierarchical pitch angle limit values are realized, and thus amplitude limiting and load control are realized, which is beneficial to avoiding frequent actions of each pitch drive system and is beneficial to extending the service life of each pitch device.

[0127] The following combines Figure 5A 、 Figure 5B to specifically illustrate the working process of the pitch fuzzy inference engine.

[0128] If the deviation e = 1.5 of the pitch moment M y and the change rate de of the deviation = 2.2 are obtained, then according to Figure 5A , the process of obtaining the membership degree values of the fuzzy sets within the fuzzy universe of discourse is as follows.

[0129] For the deviation e, there is μ ZO (1.5) = 0.25, μ PS (1.5) = 0.75; for the change rate de of the deviation, there is μ PS (2.2) = 0.9, μ PB (2.2) = 0.1. According to the fuzzy inference rules in Table 1, the following valid rules can be obtained:

[0130] Rule 1: If e is ZO and de is PS, then the output variable y is NS;

[0131] Rule 2: If e is ZO and de is PB, then the output variable y is NM;

[0132] Rule 3: If e is PS and de is PS, then the output variable y is NS;

[0133] Rule 4: If e is PS and de is PB, then the output variable y is NM.

[0134] By the minimum and maximum reasoning method, the membership degree of the output variable y under each fuzzy set can be obtained.

[0135] Rule 1: μ NS (1.5,2.2)=min(0.25,0.9)=0.25

[0136] Rule 2: μ NM (1.5,2.2)=min(0.25,0.1)=0.1

[0137] Rule 3: μ NS (1.5,2.2)=min(0.75,0.9)=0.75

[0138] Rule 4: μ NM (1.5,2.2)=min(0.75,0.1)=0.1

[0139] Based on the above four inference results, for the values in the same fuzzy set, the larger one is taken, and the membership degree of the output variable y under the fuzzy set is obtained as follows:

[0140] μ NS =max(0.25,0.75)=0.75

[0141] μ NM =max(0.1,0.1)=0.1

[0142] If the maximum membership method is used, the maximum membership value in the above results is taken as the membership of the output variable y, and the exact value y* of the output variable y is obtained based on this.

[0143] μ NS =max(0.25,0.75)=0.75

[0144] y*=-2. In this way, the calculation of the fuzzy inference device is simple, but the control performance is not high.

[0145] Again, the center of gravity of the area enclosed by the fuzzy membership function curve and the horizontal coordinate is taken as the final output value of the fuzzy reasoning. The calculation process is as follows: Figure 6 shown. Figure 6 In the example, the inflection points of the membership function are (-6, 0), (-5.8, 0.1), (-3.8, 0.1), (-2.5, 0.75), (-1.5, 0.75), and (0, 0). The exact value of the output variable y* is:

[0146]

[0147] In this way, compared with the maximum membership degree method, the centroid method can obtain a smoother output. Further, the fuzzy exact value of the output variable y is mapped to the actual domain [-3, 3] according to the variation range [-6, 6] of the fuzzy domain defined above, and the output value of the actual domain can be obtained, that is, based on the pitching moment M y The exact value y* of the output variable y obtained through fuzzy reasoning is: -1.14, that is, the output limit of the controller obtained through fuzzy reasoning based on the pitching moment My is 1.14.

[0148] As Figure 2 shown, the pitch control device based on hierarchical fuzzy and load control according to the embodiment of the present invention is used for a wind turbine generator and includes:

[0149] A unified pitch command generation module 21, configured to generate a unified pitch command for each blade according to the deviation of the obtained generator speed;

[0150] An additional pitch command generation module 22, configured to generate an additional pitch command for each blade based on hierarchical fuzzy and load control according to the load of each blade at the blade root;

[0151] An independent pitch command generation module 23, configured to generate an independent pitch command for each blade according to the unified pitch command of each blade and the additional pitch command of each blade;

[0152] A pitch device control module 24, configured to control each pitch drive system correspondingly arranged for each blade to execute the independent pitch command of each blade, so that the loads of each blade at the blade root tend to be balanced.

[0153] In some embodiments, the additional pitch command generation module 22 is specifically configured to:

[0154] Obtain the pitching moment and yaw moment through Park transformation according to the load of each blade at the blade root and the azimuth angle of the impeller;

[0155] Generate a desired pitch angle for the d-axis based on load control according to the deviation of the pitching moment;

[0156] Generate a desired pitch angle for the q-axis based on load control according to the deviation of the yaw moment;

[0157] Generate multi-level pitch angle limits for limiting the desired pitch angle of the d-axis and the desired pitch angle of the q-axis based on hierarchical fuzzy;

[0158] Limit the desired pitch angle of the d-axis and the desired pitch angle of the q-axis according to the multi-level pitch angle limits;

[0159] Based on the limited d-axis desired pitch angle and the q-axis desired pitch angle, an additional pitch command for each blade is generated through the inverse Park transformation.

[0160] In some embodiments, the additional pitch command generation module 22 is further specifically configured to:

[0161] Based on hierarchical fuzzy control, a first pitch angle limit value is generated according to the deviation of the pitching moment and the change rate of the deviation.

[0162] Based on hierarchical fuzzy control, a second pitch angle limit value is generated according to the deviation of the yawing moment and the change rate of the deviation.

[0163] Determine the larger value between the first pitch angle limit value and the second pitch angle limit value for limiting the d-axis desired pitch angle and the q-axis desired pitch angle.

[0164] The wind turbine generator set provided by the embodiment of the present invention is provided with the pitch control device described in any one of the above embodiments.

[0165] As Figure 3 、 Figure 4 shown, when the pitch control device based on hierarchical fuzzy and load control in an embodiment of the present invention is applied to a three-blade unit, the rotational speed pitch control loop obtains a unified pitch command for each blade of the wind turbine through PID calculation based on the deviation of the generator speed. The independent pitch fuzzy control loop calculates the additional angle commands for each blade according to the loads M y at the blade roots of the 3 blades and the azimuth angle of the impeller. The pitch angle coupler is used to obtain the independent pitch commands 1, 2, and 3 for each of the three blades after compensation by using the additional commands of each blade to compensate the aforementioned unified pitch command.

[0166] In the independent pitch fuzzy control loop, the Park transformation, as a coordinate transformation device, is used to convert the 3 blade root loads in the rotating hub coordinate system into the pitching moment M y and the yawing moment M z in the fixed hub coordinate system; the pitching moment controller obtains the d-axis desired pitch angle through PID calculation based on the deviation of the pitching moment M y ; the yawing moment controller obtains the q-axis desired pitch angle through PID calculation based on the deviation of the yawing moment M y .

[0167] The fuzzy inference device A, such as the pitching moment fuzzy inference device, takes the deviation of the pitching moment M y and the change rate of the deviation as input variables to obtain the first output limit value 1. The fuzzy inference device B, such as the yawing moment fuzzy inference device, takes the deviation of the yawing moment M z and the change rate of the deviation as input variables to obtain the second output limit value 2.

[0168] After the larger value module Max operates on the first output limit value 1 and the second output limit value 2, the result is used as the pitch angle limit for amplitude limiting, and the outputs of the pitch moment controller and the yaw moment controller are respectively amplitude-limited and corrected.

[0169] The Park inverse transformation, as another type of coordinate transformation, is used to convert the amplitude-limited q-axis desired pitch angle and the amplitude-limited d-axis desired pitch angle into the respective additional angles 1, 2, and 3 of the three blades.

[0170] In this way, the pitch control method and device for a wind turbine based on hierarchical fuzzy and load control determine to adjust the pitch angles of the respective blades separately based on the loads at the blade roots, reducing the load imbalance degree of the impeller and being beneficial to reducing the load levels of various components during the operation of the unit.

[0171] An embodiment of the present invention also provides a computer-readable storage medium, in which instructions are stored. When it runs on a computer, it causes the computer to execute the pitch control method based on hierarchical fuzzy and load control described in any one of the above embodiments.

[0172] An embodiment provided by the present invention also provides a computer program product containing instructions. When it runs on a computer, it causes the computer to execute the pitch control method based on hierarchical fuzzy and load control described in any one of the above embodiments.

[0173] The terms "first, second, third, etc." or similar terms such as module A, module B, module C, etc. in the description and claims are only used to distinguish similar objects and do not represent a specific order for the objects. It can be understood that, where permitted, the specific order or sequence can be interchanged so that the embodiments of the present application described here can be implemented in an order other than that illustrated or described here.

[0174] The term "comprising" used in the description and claims should not be construed as being limited to the content listed thereafter; it does not exclude other elements or steps. Therefore, it should be interpreted as specifying the existence of the mentioned features, wholes, steps, or components, but does not exclude the existence or addition of one or more other features, wholes, steps, or components and their groups. Therefore, the expression "a device including device A and B" should not be limited to a device consisting only of components A and B.

[0175] As used herein, the term "one embodiment" or "an embodiment" means that the specific features, structures, or characteristics described in connection with that embodiment are included in at least one embodiment of the present application. Thus, the phrases "in one embodiment" or "in an embodiment" that appear throughout this specification do not necessarily all refer to the same embodiment, but may. Additionally, in one or more embodiments, the various specific features, structures, or characteristics can be combined in any suitable manner, as will be apparent to those of ordinary skill in the art from this disclosure.

[0176] Those skilled in the art will appreciate that the embodiments in the embodiments of the present application can be provided as a method, system, or computer program product. Accordingly, the embodiments in the embodiments of the present application can take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Moreover, the embodiments in the embodiments of the present application can take the form of a computer program product implemented on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0177] The embodiments in the embodiments of the present application are described with reference to the flowcharts and / or block diagrams of methods, apparatus (systems), and computer program products according to the embodiments of the present application. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, and combinations of flows and / or blocks in the flowcharts and / or block diagrams, can be realized by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, such that the instructions executed by the processor of the computer or other programmable data processing device generate means for implementing the functions specified in Figure 1 one or more of the flows Figure 1 or blocks or combinations of blocks.

[0178] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to operate in a particular manner, such that the instructions stored in the computer-readable memory produce a manufacture including instruction means that implement the functions specified in Figure 1 one or more of the flows Figure 1 or blocks or combinations of blocks.

[0179] These computer program instructions can also be loaded onto a computer or other programmable data processing device, such that a series of operation steps are performed on the computer or other programmable device to produce a computer-implemented process, and thus the instructions executed on the computer or other programmable device provide means for implementing the functions specified in Figure 1 one or more of the flows Figure 1Steps of the functions specified in one or more boxes.

[0180] Although the preferred embodiments in the embodiments of the present application have been described, those skilled in the art can make additional changes and modifications to these embodiments once they learn the basic creative concept. Therefore, the appended claims are intended to be construed to include the preferred embodiments as well as all changes and modifications that fall within the scope of the embodiments of the present application.

[0181] Obviously, those skilled in the art can make various changes and modifications to the embodiments in the embodiments of the present application without departing from the spirit and scope of the embodiments in the embodiments of the present application. Thus, if these modifications and variations of the embodiments in the embodiments of the present application fall within the scope of the claims of the embodiments of the present application and their equivalent technologies, the embodiments of the present application are also intended to include these changes and modifications.

Claims

1. A pitch control method based on hierarchical fuzzy and load control, characterized in that For a wind turbine generator set, including: Generating a unified pitch command for each blade according to the deviation of the obtained generator speed; Generating an additional pitch command for each blade based on hierarchical fuzzy and load control according to the loads of each blade at the blade root; Generating an independent pitch command for each blade according to the unified pitch command of each blade and the additional pitch command of each blade; Controlling each pitch drive system correspondingly arranged for each blade to execute the independent pitch command of each blade so that the loads of each blade at the blade root tend to be balanced; The generating an additional pitch command for each blade based on hierarchical fuzzy and load control according to the loads of each blade at the blade root includes: Obtaining the pitch moment and yaw moment through Park transformation according to the loads of each blade at the blade root and the azimuth angle of the impeller; Generating a d-axis desired pitch angle based on load control according to the deviation of the pitch moment; Generating a q-axis desired pitch angle based on load control according to the deviation of the yaw moment; Generating multi-level pitch angle limits for limiting the d-axis desired pitch angle and the q-axis desired pitch angle based on hierarchical fuzzy; Limiting the d-axis desired pitch angle and the q-axis desired pitch angle according to the multi-level pitch angle limits; Generating an additional pitch command for each blade through Park inverse transformation according to the limited d-axis desired pitch angle and the q-axis desired pitch angle; The generating multi-level pitch angle limits for limiting the d-axis desired pitch angle and the q-axis desired pitch angle based on hierarchical fuzzy includes: Generating a first pitch angle limit based on hierarchical fuzzy according to the deviation of the pitch moment and the change rate of the deviation; Generating a second pitch angle limit based on hierarchical fuzzy according to the deviation of the yaw moment and the change rate of the deviation; Determining the larger value of the first pitch angle limit and the second pitch angle limit for limiting the d-axis desired pitch angle and the q-axis desired pitch angle.

2. The control method according to claim 1, characterized in that The generating a first pitch angle limit based on hierarchical fuzzy according to the deviation of the pitch moment and the change rate of the deviation includes: Performing hierarchical fuzzy on the deviation of the pitch moment and the change rate of the deviation respectively; Using a preset fuzzy inference rule to determine the fuzzy exact value of the multi-level pitch angle limit corresponding to the deviation of the pitch moment and the change rate of the deviation; Using a preset defuzzification strategy to determine the actual exact value of the multi-level pitch angle limit corresponding to the fuzzy exact value of the multi-level pitch angle limit, and the actual exact value of the multi-level pitch angle limit is the first pitch angle limit.

3. The control method according to claim 1, characterized in that The generating a second pitch angle limit based on hierarchical fuzzy according to the deviation of the yaw moment and the change rate of the deviation includes: Performing hierarchical fuzzy on the deviation of the yaw moment and the change rate of the deviation respectively; Using a preset fuzzy inference rule to determine the fuzzy exact value of the multi-level pitch angle limit corresponding to the deviation of the yaw moment and the change rate of the deviation; Using a preset anti-fuzziness strategy, determine the actual exact value of the multi-level pitch angle limit corresponding to the fuzzy exact value of the multi-level pitch angle limit, and the actual exact value of the multi-level pitch angle limit is the second pitch angle limit.

4. The control method according to claim 1, wherein the load at the blade root of the blade is obtained by an optical fiber strain sensor installed at the blade root; the azimuth angle of the impeller is obtained by an absolute encoder installed on the low-speed shaft of the wind turbine generator set; the generator speed is obtained by an incremental encoder installed on the generator.

5. A variable pitch control device based on hierarchical fuzzy and load control, characterized in that, For a wind turbine generator set, including: A unified pitch command generation module, configured to generate a unified pitch command for each blade according to the deviation of the obtained generator speed; An additional pitch command generation module, configured to generate an additional pitch command for each blade based on hierarchical fuzziness and load control according to the load at the blade root of each obtained blade; An independent pitch command generation module, configured to generate an independent pitch command for each blade according to the unified pitch command of each blade and the additional pitch command of each blade; A pitch device control module, configured to control each pitch drive system correspondingly arranged for each blade to execute the independent pitch command of each blade, so that the loads at the blade roots of each blade tend to be balanced; The additional pitch command generation module is specifically configured to: Obtain the pitch moment and yaw moment through Park transformation according to the load at the blade root of each obtained blade and the azimuth angle of the impeller; Generate a d-axis desired pitch angle based on load control according to the deviation of the pitch moment; Generate a q-axis desired pitch angle based on load control according to the deviation of the yaw moment; Generate a multi-level pitch angle limit for limiting the d-axis desired pitch angle and the q-axis desired pitch angle based on hierarchical fuzziness; Limit the d-axis desired pitch angle and the q-axis desired pitch angle according to the multi-level pitch angle limit; Generate an additional pitch command for each blade through Park inverse transformation according to the limited d-axis desired pitch angle and the q-axis desired pitch angle; The additional pitch command generation module is specifically configured to: Generate a first pitch angle limit based on hierarchical fuzziness according to the deviation of the pitch moment and the change rate of the deviation; Generate a second pitch angle limit based on hierarchical fuzziness according to the deviation of the yaw moment and the change rate of the deviation; Determine the larger value of the first pitch angle limit and the second pitch angle limit for limiting the d-axis desired pitch angle and the q-axis desired pitch angle.

6. A wind power generating set, characterized in that, A pitch control device as set forth in claim 5 is provided.

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