CN109779844B - Method and system for acquiring influence relation of angle measurement errors of fan blade - Google Patents
Method and system for acquiring influence relation of angle measurement errors of fan blade Download PDFInfo
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Abstract
The invention discloses a method and a system for acquiring an influence relation of fan blade angle measurement errors, wherein the method comprises the following steps: classifying the data collected by the anemorumbometer according to wind speed sections to obtain different wind speeds; according to different wind speeds, analyzing the angle measurement error of the fan blade through a pre-established mathematical model to obtain a first error analysis result; according to the angle of the blade, carrying out error analysis on the rotating speed of the fan to obtain a second error analysis result; fitting the first error analysis result and the second error analysis result to obtain an error analysis result; and performing least square fitting on the error analysis result by adopting a least square fitting method to form an angle measurement error function. The method considers the influence of different wind speeds on the angle measurement error, analyzes the wind speed and the blade angle to obtain an angle measurement error function, realizes accurate measurement on the angle measurement error, corrects the error and reduces the power generation amount loss of the fan.
Description
The technical field is as follows:
the invention relates to the field of wind power, in particular to the field of a method and a system for acquiring an influence relation of a fan blade angle measurement error.
Background art:
the blade angle measurement errors mainly come from the aspects of instrument errors, observation errors, errors caused by external condition influences and the like. These errors are carefully analyzed to find out a method for eliminating or reducing the errors, which can improve the observation accuracy.
When the error of the angle measurement is too large, the loss of the generated energy is brought, so that the accurate acquisition of the error of the angle measurement has great influence on the generated energy of the fan. In the prior art, the influence of a single factor is generally considered for the angle measurement error, and the angle measurement is not comprehensively measured from the aspect of the angle measurement error, so that the angle measurement accuracy is influenced, and the angle measurement error is overlarge.
In addition, the power generation efficiency of the single-impeller wind driven generator can only be realized by adjusting the size of the impeller and the shape of the blades, which is often not ideal, and a fan structure with double impellers is urgently needed to be provided so as to select and adjust the impellers according to the wind speed, and meanwhile, the analysis of the angle error of the blades of the fan with the double-impeller structure is also a great problem.
The invention content is as follows:
the invention aims to provide a method and a system for acquiring an angle measurement error influence relation, which consider the influence of different wind speeds on angle measurement errors, analyze the wind speeds and the blade angles to obtain an angle measurement error function, realize accurate measurement on the angle measurement errors, correct errors and reduce the loss of the generated energy of a fan. Meanwhile, the structure of the double-impeller wind driven generator is also provided to improve the generating efficiency, and the structure of the double-impeller wind driven generator is matched to provide an obtaining method of the blade angle error influence relation compatible with a single impeller and a double impeller.
The invention is implemented by the following technical scheme:
in a first aspect, the present invention provides a method for obtaining an angle measurement error influence relationship, including:
step S1, classifying the data collected by the anemorumbometer according to wind speed sections in a learning period to obtain different wind speeds;
step S2, analyzing the angle measurement error of the fan blade through a pre-established mathematical model according to the different wind speeds to obtain a first error analysis result;
step S3, performing error analysis on the rotating speed of the fan according to the angle of the blade to obtain a second error analysis result;
step S4, fitting the first error analysis result and the second error analysis result to obtain an error analysis result;
and step S5, performing least square fitting on the error analysis result by adopting a least square fitting method to form an angle measurement error function of the anemorumbometer as the angle measurement error function.
The invention provides a method for acquiring an angle measurement error influence relation, which has the technical scheme that: in a learning period, classifying data acquired by an anemorumbometer according to wind speed sections to obtain different wind speeds; according to the different wind speeds, analyzing the angle measurement error of the fan blade through a pre-established mathematical model to obtain a first error analysis result; according to the angle of the blade, carrying out error analysis on the rotating speed of the fan to obtain a second error analysis result; fitting the first error analysis result and the second error analysis result to obtain an error analysis result; and performing least square fitting on the error analysis result by adopting a least square fitting method to form an angle measurement error function of the anemorumbometer as the angle measurement error function.
According to the method for acquiring the angle measurement error influence relationship, the influence of different wind speeds on the angle measurement error is considered, the angle measurement error function is obtained by analyzing the wind speed and the blade angle, the angle measurement error is accurately measured, and the loss of the generated energy of the fan is reduced for error correction.
Further, in step S2, the mathematical model established in advance specifically is:
selecting different wind speeds as process parameters, and establishing an experimental model and a calculation model at different fan rotating speeds;
setting parameters including the length of the blades, the maximum chord length of the blades, the minimum chord length of the blades, the maximum torsion angle of the blades, the number of the blades and the diameter of the impeller;
according to the set parameters, combining the experimental model and the calculation model to obtain a geometric model, wherein the geometric model comprises meshes of a rotating flow field and an external flow field;
setting boundary conditions according to the actual operation condition of the fan blade in the flow field;
and obtaining a mathematical model according to the set boundary conditions.
Further, in step S2, specifically, the method includes:
according to the different wind speeds, calculating the mathematical model through an implicit solver based on pressure to obtain a calculated value;
and comparing the calculated value with the measured value to obtain a first error analysis result.
Further, in step S3, specifically, the method includes:
acquiring a fan performance curve when the angle of the maximum blade is obtained;
calculating to obtain an actual fan performance curve according to the blade angle obtained by actual measurement;
taking data of an intersection point of the fan performance curve and the actual fan performance curve as error parameters, wherein the error parameters comprise air volume and air pressure data corresponding to the intersection point;
calculating to obtain the rotating speed of the fan according to the air volume and the air pressure data corresponding to the intersection point;
and comparing and calculating with the actual fan rotating speed according to the fan rotating speed to obtain a fan rotating speed error as a second error analysis result.
Further, in step S5, specifically, the method includes:
s51, setting n as 1;
s52, performing least square fitting on the error analysis result by using a least square fitting method to form an angle measurement error function of the anemorumbometer:
δ＝f(v)≈a0+a1v+a2v2+…+anvn
in the formula, a0、a1、…、anIs constant, v is error analysis result;
and S53, if the total relative error after fitting is better than 0.01, taking the function as an angle measurement error function, and if not, making n equal to n +1, and returning to the step S52.
In another embodiment, the wind turbine is a dual-impeller wind turbine, comprising:
the first-stage impeller, the second-stage impeller and the impeller rotating speed combining mechanism; the impeller rotating speed merging mechanism is provided with a first input shaft, a second input shaft, a first output shaft and a second output shaft, the first-stage impeller is in driving connection with the first input shaft, the second-stage impeller is in driving connection with the second input shaft, the first output shaft is in driving connection with the input shaft of the first generator through a first clutch, and the second output shaft is in driving connection with the input shaft of the second generator through a second clutch;
the first-stage impeller is coaxially connected with the second-stage impeller, the length of blades of the first-stage impeller is greater than that of blades of the second-stage impeller, the rotating directions of the first-stage impeller and the second-stage impeller are opposite during working, and the first-stage impeller is positioned in front of the second-stage impeller;
the impeller rotating speed combining mechanism comprises a sun gear, a gear ring and a planet carrier which are coaxially arranged, a plurality of planet gears are arranged on the planet carrier, the gear ring is provided with inner teeth and outer teeth, the planet gears are meshed between the inner teeth of the gear ring and the sun gear, a driving gear is arranged on the first input shaft and is meshed with the outer teeth of the gear ring, the second input shaft is connected with a rotating shaft of the sun gear, the rotating shaft of the planet carrier is in driving connection with an output shaft through an intermediate shaft, one end of the output shaft forms the first output shaft, and the other end of the output shaft forms the second output shaft;
when the wind speed is less than a first threshold value, blades of the first-stage impeller and blades of the second-stage impeller are subjected to pitch variation, the first-stage impeller stops generating electricity, the second-stage impeller is in a rotating electricity generation state, the first clutch is in a meshing state, and the second clutch is in a separation state;
when the wind speed is not less than the first threshold value and not more than the second threshold value, blades of the first-stage impeller and the second-stage impeller are subjected to pitch variation, so that the first-stage impeller and the second-stage impeller are both in a rotating power generation state, the first clutch is in a meshing state, and the second clutch is in a separating state;
when the wind speed is greater than the second threshold value, the first-stage impeller and the second-stage impeller are both in a rotating power generation state, and the first clutch and the second clutch are both in a meshing state.
In a second aspect, the present invention provides a system for obtaining an angle measurement error influence relationship, including:
the wind speed data acquisition module is used for classifying the data acquired by the anemorumbometer according to wind speed sections in a learning period to obtain different wind speeds;
the first error analysis module is used for analyzing the angle measurement error of the fan blade through a pre-established mathematical model according to the different wind speeds to obtain a first error analysis result;
the second error analysis module is used for carrying out error analysis on the rotating speed of the fan according to the angle of the blade to obtain a second error analysis result;
the error result generating module is used for fitting the first error analysis result and the second error analysis result to obtain an error analysis result;
and the error function generating module is used for performing least square fitting on the error analysis result by adopting a least square fitting method to form an angle measurement error function of the anemorumbometer as the angle measurement error function.
The invention provides a system for acquiring an angle measurement error influence relation, which adopts the technical scheme that: classifying data collected by an anemorumbometer according to wind speed sections in a learning period through a wind speed data acquisition module to obtain different wind speeds; analyzing the angle measurement error of the fan blade through a pre-established mathematical model according to the different wind speeds through a first error analysis module to obtain a first error analysis result; performing error analysis on the rotating speed of the fan according to the angle of the blade through a second error analysis module to obtain a second error analysis result; fitting the first error analysis result and the second error analysis result through an error result generation module to obtain an error analysis result; and performing least square fitting on the error analysis result by adopting a least square fitting method through an error function generation module to form an angle measurement error function of the anemorumbometer as the angle measurement error function.
According to the system for acquiring the angle measurement error influence relationship, the influence of different wind speeds on the angle measurement error is considered, the angle measurement error function is obtained by analyzing the wind speed and the blade angle, the angle measurement error is accurately measured, and the loss of the generated energy of the fan is reduced for error correction.
Further, the first error analysis module is specifically configured to establish a mathematical model:
selecting different wind speeds as process parameters, and establishing an experimental model and a calculation model at different fan rotating speeds;
setting parameters including the length of the blades, the maximum chord length of the blades, the minimum chord length of the blades, the maximum torsion angle of the blades, the number of the blades and the diameter of the impeller;
according to the set parameters, combining the experimental model and the calculation model to obtain a geometric model, wherein the geometric model comprises meshes of a rotating flow field and an external flow field;
setting boundary conditions according to the actual operation condition of the fan blade in the flow field;
and obtaining a mathematical model according to the set boundary conditions.
Further, the first error analysis module is specifically configured to establish a mathematical model:
according to the different wind speeds, calculating the mathematical model through an implicit solver based on pressure to obtain a calculated value;
and comparing the calculated value with the measured value to obtain a first error analysis result.
Further, the second error analysis module is specifically configured to:
acquiring a fan performance curve when the angle of the maximum blade is obtained;
calculating to obtain an actual fan performance curve according to the blade angle obtained by actual measurement;
taking data of an intersection point of the fan performance curve and the actual fan performance curve as error parameters, wherein the error parameters comprise air volume and air pressure data corresponding to the intersection point;
calculating to obtain the rotating speed of the fan according to the air volume and the air pressure data corresponding to the intersection point;
and comparing and calculating with the actual fan rotating speed according to the fan rotating speed to obtain a fan rotating speed error as a second error analysis result.
Further, the error function generating module is specifically configured to generate an error function:
setting n as 1;
and performing least square fitting on the error analysis result by adopting a least square fitting method to form an angle measurement error function of the anemorumbometer:
δ＝f(v)≈a0+a1v+a2v2+…+anvn
in the formula, a0、a1、…、anIs constant, v is error analysis result;
and if the total relative error after fitting is better than 0.01, taking the function as an angle measurement error function, otherwise, enabling n to be n +1, and returning to perform least square fitting on the error analysis result again.
The invention has the advantages that:
the influence of different wind speeds on the angle measurement error is considered, the angle measurement error function is obtained by analyzing the wind speed and the blade angle, the angle measurement error is accurately measured, and the loss of the generated energy of the fan is reduced for error correction. Meanwhile, the structure of the double-impeller wind driven generator is improved, the generating efficiency is increased, and the method for acquiring the angle measurement error influence relation is suitable for the double-impeller wind driven generator with the specific structure.
Description of the drawings:
in order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a flowchart of a method for obtaining an angle measurement error influence relationship according to an embodiment of the present invention;
fig. 2 is a schematic diagram of an acquisition system of an angle measurement error influence relationship according to an embodiment of the present invention;
fig. 3 is a schematic structural diagram of a dual-impeller wind turbine provided in the third embodiment;
fig. 4 is a schematic diagram of an impeller rotation speed combining mechanism provided in the third embodiment.
The specific implementation mode is as follows:
the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Example one
In a first aspect, fig. 1 is a flowchart illustrating a method for obtaining an angle measurement error influence relationship according to an embodiment of the present invention; as shown in fig. 1, an embodiment provides a method for obtaining an angle measurement error influence relationship, including:
step S1, classifying the data collected by the anemorumbometer according to wind speed sections in a learning period to obtain different wind speeds;
step S2, analyzing the angle measurement error of the fan blade through a pre-established mathematical model according to different wind speeds to obtain a first error analysis result;
step S3, performing error analysis on the rotating speed of the fan according to the angle of the blade to obtain a second error analysis result;
step S4, fitting the first error analysis result and the second error analysis result to obtain an error analysis result;
and step S5, performing least square fitting on the error analysis result by using a least square fitting method to form an angle measurement error function of the anemorumbometer as the angle measurement error function.
The invention provides a method for acquiring an angle measurement error influence relation, which has the technical scheme that: in a learning period, classifying data acquired by an anemorumbometer according to wind speed sections to obtain different wind speeds; according to different wind speeds, analyzing the angle measurement error of the fan blade through a pre-established mathematical model to obtain a first error analysis result; according to the angle of the blade, carrying out error analysis on the rotating speed of the fan to obtain a second error analysis result; fitting the first error analysis result and the second error analysis result to obtain an error analysis result; and performing least square fitting on the error analysis result by adopting a least square fitting method to form an angle measurement error function of the anemorumbometer as the angle measurement error function.
According to the method for acquiring the angle measurement error influence relationship, the influence of different wind speeds on the angle measurement error is considered, the angle measurement error function is obtained by analyzing the wind speed and the blade angle, the angle measurement error is accurately measured, and the loss of the generated energy of the fan is reduced for error correction.
As a preferred embodiment of the present invention, in step S2, the pre-established mathematical model specifically includes:
selecting different wind speeds as process parameters, and establishing an experimental model and a calculation model at different fan rotating speeds;
setting parameters including the length of the blades, the maximum chord length of the blades, the minimum chord length of the blades, the maximum torsion angle of the blades, the number of the blades and the diameter of the impeller;
according to the set parameters, combining an experimental model and a calculation model to obtain a geometric model, wherein the geometric model comprises meshes of a rotating flow field and an external flow field;
setting boundary conditions according to the actual operation condition of the fan blade in the flow field;
and obtaining a mathematical model according to the set boundary conditions.
In the process of establishing the mathematical model, the wind speed is selected as a process parameter, and the specific size is shown in table 1. As can be seen from Table 1, the rotation speeds of the experimental model and the calculation model are different, which is to ensure the consistency of the tip speed ratio at the same wind speed. The design parameters are shown in Table 2. The total number of the grids is 2846019, and in addition, boundary layer grids are adopted in the rotating domain, so that the calculation accuracy can be ensured.
TABLE 1 Process parameters
TABLE 2 design parameters of Fan blades
The fluid control equation specifically includes:
continuity equation:
the momentum equation:
in the formula: rho is density; u is a velocity vector; p is static pressure; mu.seffIs the effective viscosity coefficient; f is the volume force.
Setting boundary conditions, specifically:
according to the actual operation condition of the fan blade in the flow field, the boundary conditions are set as follows: (1) and a rotating field, which rotates along with the blades, wherein the rotating speed is the rotating speed of the impeller and is defined as rotor. (2) And a static area, which is a static external flow field and is defined as stat. (3) The inlet, where the outflowing field comes, is defined as velocity-inlet. (4) The outlet, i.e. the outflow field outflow, is defined as the pressure-output. It is known from betz theory that the static pressure far in front of the blades should be equal to the static pressure far behind, and therefore, the static pressure is provided as a pressure outlet. (5) The wall surface, the outer flow field wall surface is defined as wall, the interface of the rotating domain and the static domain is defined as interface, and both adopt the condition of no-slip boundary.
As a preferred embodiment of the present invention, step S2 specifically includes:
according to different wind speeds, calculating the mathematical model through an implicit solver based on pressure to obtain a calculated value;
and comparing the calculated value with the measured value to obtain a first error analysis result.
In the embodiment, through the established three-dimensional mathematical model of the fan blade, the flow field of the fan blade is subjected to simulation analysis by adopting computational fluid dynamics software Fluent, the blade deformation analysis is performed by calling a Static Structural module, and the fluid-solid coupling is realized under a Workbench platform. Based on the method, the flow field distribution condition and the blade deformation condition under different wind speeds are researched, and a first error analysis result is obtained.
The method comprises the following steps of calculating pressure, speed, momentum, turbulent kinetic energy and turbulent energy dissipation rate by an implicit solver based on pressure in consideration of flow field characteristics and incoming flow conditions of a fan; the convergence is better than the separate and explicit.
The method comprises the steps of coupling pressure and speed by a Simple method, interpolating the pressure in a Standard mode, and selecting a second-order discrete windward format for momentum, turbulent kinetic energy and turbulent energy dissipation rate. The calculation precision is improved.
Due to the complexity of the flow condition of a flow field near a fan blade, an SST (shear-stress transport) model is selected as a turbulence model, and compared with a spark-Allamaras single-path model and an RNGk-omega model, the model is more accurate in prediction of cross-sectional pressure distribution and torque and is an improvement of a standard k-omega model. The standard k-epsilon and k-omega models are combined by using a mixing function, transition and shearing options are included, and the boundary layer flow and separation flow are better simulated, so that the method is more suitable for the flow field simulation of the fan blade.
The SSTK-omega turbulence model equation is as follows:
wherein the effective diffusion term equation:
in the formula: gk、GωTurbulent kinetic energy gamma of k and omega respectivelykΓωEffective diffusion terms for k and ω, respectively; mu.stIs the turbulent viscosity coefficient; y isk、YωDivergence terms of k and ω, respectively; dωIs an orthogonal divergence term; sk、SωAre all user-defined items; k is the floc flow pulsation kinetic energy; omega is specific dissipation rate; mu.siIs the i-direction velocity component.
In order to verify the correctness of the mathematical model and the calculation force method, the mathematical model with the same size as the experimental model is adopted for calculation, the calculated value is compared with the measured value, a second error analysis result is obtained, and the maximum error is not more than 5%.
Under different wind speed conditions, the distribution trends of the speed fields along the spanwise direction of the blade are basically consistent, the speed value at the hub is the smallest, and the speed value at the blade tip is the largest, which is consistent with the result calculated by a circumferential speed formula v 2 pi R/T omega R, wherein R is the radius of the wind wheel, T is the period, and omega is the angular speed). The speed values from the blade root to the blade tip are sequentially increased, and the speed value of the surface of the impeller is also increased along with the increase of the incoming flow speed.
The speed field and the pressure distribution of the impeller at corresponding wind speeds are obtained through simulation analysis of the fan blades at different wind speeds. As the wind speed increases, the speed value of the impeller surface increases, and the nonuniformity of the pneumatic pressure distribution is more obvious. When the wind speed is 10m/s, the pneumatic efficiency of the fan is better, and the power generation loss of the fan is the minimum.
As a preferred embodiment of the present invention, step S3 specifically includes:
acquiring a fan performance curve when the angle of the maximum blade is obtained;
calculating to obtain an actual fan performance curve according to the blade angle obtained by actual measurement;
taking data of an intersection point of the fan performance curve and the actual fan performance curve as error parameters, wherein the error parameters comprise air volume and air pressure data corresponding to the intersection point;
calculating to obtain the rotating speed of the fan according to the air volume and the air pressure data corresponding to the intersection point;
and comparing and calculating with the actual fan rotating speed according to the fan rotating speed to obtain a fan rotating speed error as a second error analysis result.
Based on the second analysis result obtained by the method, the variable frequency power, the saved electric power and the saved power rate of the fan can be obtained. Specifically, the rotating speed of the fan during speed regulation is calculated according to air door opening data (namely blade angle data) which can be obtained on site and actual air quantity and air pressure data, and the variable frequency power, the saved electric power and the power saving rate are calculated.
If air volume data exists on site, the ratio of the actual air volume to the rated air volume is used as the rotating speed of a motor (frequency converter) to calculate electric power, but the error of doing so is large. Because any working point of the fan is at the intersection point of the fan performance curve and the system resistance curve, and the resistance curve of the actual working condition is not always intersected with the fan performance curve at the rated working point, the error of the rotating speed, which is the ratio of the actual air volume to the rated air volume, is too large and inaccurate, and the second error analysis result is inaccurate.
The reason is as follows:
1. if the system resistance coefficient is relatively small, and the intersection point of the resistance curve and the fan performance curve is positioned at the lower right side of the rated working point, the ratio of the actual air volume to the air volume at the intersection point is used as the rotating speed, the ratio of the actual air volume to the rated air volume is used as the rotating speed, and the calculated power saving rate is relatively low.
2. If the negative coefficient of the system is relatively large, and the intersection point of the resistance curve and the fan performance curve is positioned at the left lower part of the rated working point, the ratio of the actual air volume to the air volume at the intersection point is used as the rotating speed, the ratio of the actual air volume to the rated air volume is used as the rotating speed, and the calculated power saving rate is relatively high.
The general site can easily obtain the data of the wind pressure, and at this time, the ratio of the actual wind pressure to the rated wind pressure is used to square again as the rotating speed of the motor (frequency converter), and there seems to be no error in principle, but the same error as above is also made:
1. if the system resistance coefficient is relatively small, and the intersection point of the resistance curve and the fan performance curve is positioned at the lower right side of the rated working point, the ratio of the actual wind pressure to the wind pressure at the intersection point is used as the rotating speed after being opened for ten thousands, and the opening square value of the ratio of the actual wind pressure to the rated wind pressure is used as the rotating speed to be smaller, so that the calculated power saving rate is higher.
2. If the system resistance coefficient is relatively large, and the intersection point of the resistance curve and the fan performance curve is positioned at the upper left of the rated working point, the ratio of the actual wind pressure to the wind pressure at the intersection point is squared and then used as the rotating speed, the squared value of the ratio of the actual wind pressure to the rated wind pressure is used as the rotating speed, and the calculated power saving rate is relatively low.
Therefore, the relationship between the air volume and the air pressure and the rotating speed can only be established on the same similar curve (resistance curve), and because the resistance curve of the actual fan system does not pass through the rated working point, the actual working point and the rated working point are not on the same similar curve, so that the calculation cannot be carried out by using the air volume and the air pressure data of the actual working point and the data of the rated working point. A more accurate second error analysis result can be obtained only by calculating data of similar operating points (intersection points of the actual resistance curve and the fan performance curve at the maximum air door opening) of the actual operating points.
As a preferred embodiment of the present invention, step S5 specifically includes:
s51, setting n as 1;
s52, performing least square fitting on the error analysis result by using a least square fitting method to form an angle measurement error function of the anemorumbometer:
δ＝f(v)≈a0+a1v+a2v2+…+anvn
in the formula, a0、a1、…、anIs constant, v is error analysis result;
and S53, if the total relative error after fitting is better than 0.01, taking the function as an angle measurement error function, and if not, making n equal to n +1, and returning to the step S52.
Referring to fig. 2, in a second aspect, the present invention provides an acquisition system 100 for an angle measurement error influence relationship, including:
the wind speed data acquisition module 101 is used for classifying data acquired by the anemorumbometer according to wind speed sections in a learning period to obtain different wind speeds;
the first error analysis module 102 is configured to analyze an angle measurement error of a fan blade through a pre-established mathematical model according to different wind speeds to obtain a first error analysis result;
the second error analysis module 103 is used for performing error analysis on the rotating speed of the fan according to the angle of the blade to obtain a second error analysis result;
an error result generation module 104, configured to perform fitting processing on the first error analysis result and the second error analysis result to obtain an error analysis result;
and the error function generating module 105 is configured to perform least square fitting on the error analysis result by using a least square fitting method to form an angle measurement error function of the anemorumbometer, where the angle measurement error function is used as the angle measurement error function.
The technical scheme of the system 10 for acquiring the angle measurement error influence relationship provided by the invention is as follows: classifying the data collected by the anemorumbometer according to the wind speed section in a learning period through a wind speed data acquisition module 101 to obtain different wind speeds; analyzing the angle measurement error of the fan blade through a pre-established mathematical model according to different wind speeds by a first error analysis module 102 to obtain a first error analysis result; performing error analysis on the rotating speed of the fan according to the angle of the blade through a second error analysis module 103 to obtain a second error analysis result; fitting the first error analysis result and the second error analysis result through an error result generation module 104 to obtain an error analysis result; and performing least square fitting on the error analysis result by using a least square fitting method through the error function generating module 105 to form an angle measurement error function of the anemorumbometer as the angle measurement error function.
According to the system 10 for acquiring the angle measurement error influence relationship, the influence of different wind speeds on the angle measurement error is considered, the angle measurement error function is obtained by analyzing the wind speed and the blade angle, the angle measurement error is accurately measured, and the loss of the generated energy of the fan is reduced for error correction.
As a preferred embodiment of the present invention, the first error analysis module 102 is specifically configured to establish a mathematical model:
selecting different wind speeds as process parameters, and establishing an experimental model and a calculation model at different fan rotating speeds;
setting parameters including the length of the blades, the maximum chord length of the blades, the minimum chord length of the blades, the maximum torsion angle of the blades, the number of the blades and the diameter of the impeller;
according to the set parameters, combining an experimental model and a calculation model to obtain a geometric model, wherein the geometric model comprises meshes of a rotating flow field and an external flow field;
setting boundary conditions according to the actual operation condition of the fan blade in the flow field;
and obtaining a mathematical model according to the set boundary conditions.
As a preferred embodiment of the present invention, the first error analysis module 102 is specifically configured to establish a mathematical model:
according to different wind speeds, calculating the mathematical model through an implicit solver based on pressure to obtain a calculated value;
and comparing the calculated value with the measured value to obtain a first error analysis result.
As a preferred embodiment of the present invention, the second error analysis module 103 is specifically configured to:
acquiring a fan performance curve when the angle of the maximum blade is obtained;
calculating to obtain an actual fan performance curve according to the blade angle obtained by actual measurement;
taking data of an intersection point of the fan performance curve and the actual fan performance curve as error parameters, wherein the error parameters comprise air volume and air pressure data corresponding to the intersection point;
calculating to obtain the rotating speed of the fan according to the air volume and the air pressure data corresponding to the intersection point;
and comparing and calculating with the actual fan rotating speed according to the fan rotating speed to obtain a fan rotating speed error as a second error analysis result.
As a preferred embodiment of the present invention, the error function generating module 105 is specifically configured to generate an error function:
setting n as 1;
and (3) performing least square fitting on the error analysis result by adopting a least square fitting method to form an angle measurement error function of the anemorumbometer:
δ＝f(v)≈a0+a1v+a2v2+…+anvn
in the formula, a0、a1、…、anIs constant, v is error analysis result;
and if the total relative error after fitting is better than 0.01, taking the function as an angle measurement error function, otherwise, enabling n to be n +1, and returning to perform least square fitting on the error analysis result again.
Compared with the prior art, the invention has the beneficial effects that:
the influence of different wind speeds on the angle measurement error is considered, the angle measurement error function is obtained by analyzing the wind speed and the blade angle, the angle measurement error is accurately measured, and the loss of the generated energy of the fan is reduced for error correction.
Example two
Based on the method and the system for acquiring the angle measurement error influence relationship in the first embodiment, the predicted wind power curve is monitored, and the abnormal condition of the curve can be monitored in real time through monitoring the curve, so that the wind turbine generator can be maintained and diagnosed in time. Based on this, the embodiment provides an abnormality diagnosis method for predicting a wind power curve, and the specific scheme is as follows:
collecting wind power data at different wind speeds;
sequencing the collected data according to the wind speed, and calculating the power drift area under unit accuracy, wherein the calculation formula is as follows:
wherein, Δ w is the sorted adjacent wind speed difference, and Δ w is wi+1-wi，
Δ p is the power difference of the sequenced adjacent wind speed points, and Δ p is pi+1-pi，
i is the data index number of the wind speed and the corresponding power after the sorting, i is 1, 2 and 3 … … n;
establishing a diagnosis model, determining diagnosis parameters, and determining a positive warning line, a negative warning line and a normal interval according to the diagnosis parameters;
and diagnosing, namely comparing the calculation result with a positive warning line and a negative warning line, wherein the more positive warning line, the more negative warning line, the more wrong wind speed measurement is diagnosed, and the more negative warning line, the more abnormal pneumatic performance of the blade is diagnosed.
In this embodiment, there is a certain requirement on the data quality related to the predicted wind power curve, and the data mainly relates to information such as a wind farm number (numerical quantity or character), a unit number (character), a date and time (time quantum), a unit site wind speed (numerical quantity), a unit power (numerical quantity), a real-time environment temperature (numerical quantity), and the like.
Preferably, in the collecting step, the total number of the data collecting samples of the single wind turbine generator is not less than 200.
Generally, the data acquisition conditions are set to be that the number of wind speed points is not less than 2, the data volume is not less than 200, the number of the wind speed points in the data acquisition conditions is not less than 2, namely, the wind speed is accurate to an integer part, the number of corresponding wind speed value points related to an acquired sample is more than two, and the number of the data volume is not less than 200, namely, the total number of the data acquisition samples of a single wind turbine is more than 200.
Preferably, in the power drift amount calculating step, because the wind speed fluctuates randomly, the wind power characteristic of the wind turbine generator is calculated, that is, the power fluctuation range of the wind turbine generator is calculated at different wind speed points, so that the wind speed data and the power at the corresponding wind speed are sorted according to the wind speed from low to high before calculation, so as to meet the requirement of power fluctuation, namely difference calculation, of adjacent wind speed points. The specific method comprises the following steps:
firstly, wind speed and power under corresponding wind speed are arranged in ascending order according to wind speed, and then a power drift area delta Sp under unit accuracy is calculated by utilizing a formula of a product of wind speed difference and power difference, wherein delta w is adjacent wind speed difference, and delta w is equal to wi+1-wiΔ p is the power difference between adjacent wind speed points, and Δ p is pi+1-piAnd i is the index number of the data in ascending order of wind speed, and i is 1, 2 and 3 … … n.
When the wind speed point is missing or the distribution difference of the wind speed points is large in calculating the power curve drift amount, the power difference value integration result is influenced to cause misjudgment, and therefore when the data amount is insufficient, the accuracy compensation needs to be performed on the power difference value. The accuracy compensation method is to accurately position the wind speed point to two decimal points through curve interpolation operation, then carry out integral area division to obtain the power drift amount under the unit wind speed of every 0.01m/s, specifically to increase the denominator part (1+ | delta w |. 100) in the above formula to obtain the average power drift amount under the unit wind speed of every 0.01 m/s. The reason for adding (1+ | Δ w |. 100) is that different differential values exist between the actual adjacent wind speed points, the reason for multiplying by 100 is the number of wind speed points accurate to percentile, and adding 1 is the case of considering the wind speed rounding point and the denominator to be 0. Because the actual data is matrix data, the integral can be realized by multiplying the wind power matrix array, and during actual calculation, the difference component is a single array, and the array variable is directly substituted into a formula for calculation.
In the step of establishing the diagnosis model, R language is adopted for calculation and modeling, diagnosis parameters are determined, positive and negative warning lines and normal intervals are determined according to the diagnosis parameters, and then the model is introduced into a wind power plant early warning system platform and applied to a wind power plant monitoring system.
The formula for calculating the diagnostic parameters is:
[b*(1+a％)-b*(1-a％)]*c
wherein a is an allowable power deviation proportion, b is normal operation power of the wind turbine generator, and c is accuracy.
Wherein the value range of a is 5-15, and the value range of c is 0.1-0.0001. Preferably, a is 10 and c is 0.01.
And when the diagnosis model is a static diagnosis model, b is the rated power or full power of the wind turbine generator.
And b is a power value corresponding to each wind speed point on a standard power curve of the wind turbine generator.
The diagnosis parameter can be a static diagnosis parameter or a dynamic diagnosis parameter, when the system works, the power drift amount is compared with the diagnosis parameter, the abnormity of the power curve of the wind turbine generator can be judged when the power drift amount is larger than or equal to the set diagnosis parameter, the coordinate position corresponding to the abnormal wind speed interval is [ (the minimum alarm wind speed point, the maximum power fluctuation value corresponding to the minimum alarm wind speed point), (the maximum alarm wind speed point, the maximum power fluctuation value corresponding to the maximum alarm wind speed point) ], and the rectangular area formed by the coordinate is the area where the abnormal drift of the power curve of the wind turbine generator occurs. The diagnosis model can adopt two schemes of a static diagnosis model and a dynamic diagnosis model, a normal interval and positive and negative warning lines are defined according to corresponding diagnosis parameters, the two schemes can judge the condition that the power curve of the wind turbine generator abnormally drifts, and any scheme can be selected according to requirements.
① static diagnosis parameter model only calculates maximum power fluctuation value under rated power static diagnosis parameter is set according to rated capacity or full power grade of wind turbine generator, normally setting fluctuation range within 10% to be normal, taking 1500kw wind turbine generator as an example, within the accuracy range of 0.01m/s, calculating (1500 x 1.1-1500 x 0.9) x 0.01 to obtain rated fluctuation value of 3, wherein 3kw is the static diagnosis parameter, when the absolute value of power drift amount calculated by the static diagnosis parameter model is not less than 3, the wind turbine generator has serious abnormal drift problem of power curve.
② the dynamic diagnosis parameter model adds different dynamic diagnosis parameters set at different wind speed points according to the distribution characteristic of the standard power curve in the static diagnosis parameter model, the dynamic diagnosis parameters are the maximum power fluctuation range of each wind speed point calculated one by one according to each wind speed point of the standard power curve.
In addition, when the diagnosis parameters are adopted to judge the abnormity, alarm ranges with different proportions can be added so as to report the deviation proportion in an alarm system. The calculation offset proportion adopted at this time is calculated according to the power drift amount of 10%, and in practical application, the calculation offset proportion can be set to different severity levels of 5%, 8%, 10%, 15% and the like to alarm the power drift amount according to requirements, and taking a 1500kw model as an example, the corresponding static diagnostic parameters are 1.5, 2.4, 3 and 5 respectively.
In this embodiment, a data differential integration principle is introduced into a wind farm or a large-scale wind farm cluster monitoring and early warning system, power curve data is calculated in real time, the distribution region boundary of power differences of adjacent wind speed points is calculated, and then the degree of the power curve crossing is judged, not only can static diagnosis parameters for abnormal warning of the power curve be set, but also power curve diagnosis can be performed by using dynamic diagnosis parameters of a wind turbine design standard power curve, power output problems such as wind power matching and stability problems of the wind turbine, wind speed measurement problems, wind turbine control problems, wind turbine blade icing, wind vane wind and crosswind abnormality and the like can be warned according to power curve drift diagnosis, and the wind turbine reporting output power drift is timely controlled and optimized or operation and maintenance strategies are adjusted, so that the safety, the safety and the side wind performance of the wind turbine are guaranteed, Healthy, stable and reliable operation.
EXAMPLE III
In this embodiment, the fan to which the method and the system for obtaining an angle measurement error influence relationship described in the first embodiment are applied may be a dual-impeller wind turbine with a specific structure, and when analyzing an influence caused by an angle measurement error, the method steps in the first embodiment are performed for each impeller in the dual-impeller wind turbine, that is, an analysis result of an angle measurement error influence relationship of a blade of each impeller may be obtained.
Referring to fig. 3 and 4, fig. 3 is a schematic structural diagram of a dual-impeller wind turbine generator provided in this embodiment, and fig. 4 is a schematic diagram of an impeller rotation speed combining mechanism provided in this embodiment.
In order to effectively utilize wind energy and increase the generated power, the present embodiment provides the following wind power generator, including: the first-stage impeller 1, the second-stage impeller 2 and the impeller rotating speed combining mechanism; the impeller rotating speed combining mechanism is provided with a first input shaft 31, a second input shaft 32, a first output shaft 41 and a second output shaft 42, the primary impeller 1 is in driving connection with the first input shaft 31, the secondary impeller 2 is in driving connection with the second input shaft 32, the first output shaft 41 is in driving connection with an input shaft of a first generator through a first clutch, and the second output shaft 42 is in driving connection with an input shaft of a second generator through a second clutch.
The first-stage impeller 1 is coaxially connected with the second-stage impeller 2, the length of the blade of the first-stage impeller 1 is larger than that of the blade of the second-stage impeller 2, the rotating directions of the first-stage impeller 1 during working are opposite, and the first-stage impeller 1 is located in front of the second-stage impeller 2.
When the wind turbine works, airflow firstly passes through the first-stage impeller 1 and then passes through the second-stage impeller 2, and the diameter of the second-stage impeller 2 is smaller than that of the first-stage impeller 1, so that the lowest wind speed required by the work of the second-stage impeller 2 is also smaller than that of the first-stage impeller 1. In order to increase the stability of the machine head during operation, the primary impeller 1 and the secondary impeller 2 rotate in opposite directions, so that the torque is offset.
The impeller rotating speed combining mechanism can combine the rotating speeds of the first-stage impeller 1 and the second-stage impeller 2, so that a larger output rotating speed is obtained, the generator is driven to work, the residual wind energy is effectively utilized, and the generating efficiency is improved.
Impeller rotational speed merges mechanism includes sun gear 51, ring gear 52 and the planet carrier 53 of coaxial setting, be equipped with a plurality of planet wheels 54 on the planet carrier 53, ring gear 52 is equipped with internal tooth and external tooth, planet wheel 54 meshes the internal tooth of ring gear 52 with between the sun gear 51, first input shaft 31 is equipped with drive gear 55, drive gear 55 with the external tooth meshing of ring gear 52, second input shaft 32 with sun gear 51's pivot is connected, the pivot of planet carrier 53 is passed through jackshaft 6 and is connected with the output shaft drive, the one end of output shaft forms first output shaft 41, the other end forms second output shaft 42.
For example, the rotation speed of the sun gear 51 is n1, the rotation speed of the ring gear 52 is n2, the rotation speed of the carrier 53 is n3, the tooth number ratio of the internal teeth of the ring gear 52 to the sun gear 51 is a, and n3 is (n1+ a × n2)/(1+ a). Thereby realizing the superposition of the rotating speed and the moment.
In one example, the blade length of the primary impeller is 75m and the blade length of the secondary impeller is 35 m. Under the condition that one generator works, when only one primary impeller works, the starting wind speed of the fan is 4m/s, the rated wind speed is 15m/s, the safe wind speed is 25m/s and the rated power is 3MW, and when only one secondary impeller works, the starting wind speed of the fan is 3m/s, the rated wind speed is 10m/s, the safe wind speed is 25m/s and the rated power is 1.5 MW.
Since the energy loss is large and the power is low in the operation of the first impeller in the low wind speed operation, two thresholds are involved in the control of the wind turbine, the first threshold being 6m/s and the second threshold being 10m/s, in order to enable the wind turbine to adapt to wind speeds in a wide range and to effectively utilize wind resources.
The specific control method comprises the following steps: and obtaining the wind speed, and when the wind speed is less than a first threshold value, changing the pitch of the blades of the first-stage impeller 1 and the second-stage impeller 2 to enable the first-stage impeller 1 to stop generating power, enabling the second-stage impeller 2 to be in a rotating power generation state, enabling the first clutch to be in a meshing state and enabling the second clutch to be in a separating state. Therefore, the fan is started to generate power at low wind speed, the internal consumption of the fan in the power generation process is reduced, and the power generation efficiency is improved.
When the wind speed is not less than the first threshold value and not more than the second threshold value, blades of the first-stage impeller 1 and the second-stage impeller 2 are changed into the pitch, so that the first-stage impeller 1 and the second-stage impeller 2 are both in a rotating power generation state, the first clutch is in a meshing state, and the second clutch is in a separating state. Thereby, high power generation is performed through the first impeller, and the surplus wind energy is effectively utilized through the second impeller. In this mode, the maximum power of the fan can reach 4 MW.
When the wind speed is greater than the second threshold value, the first-stage impeller 1 and the second-stage impeller 2 are both in a rotating power generation state, and the first clutch and the second clutch are both in an engaged state. When the wind speed greatly exceeds the rated wind speed 10m/s required by a single generator, the two generators are used for generating power simultaneously, the maximum generating power can reach 8MW, wind energy can be effectively utilized, the generating power is improved, the diameter of an impeller cannot be increased, the blades are prevented from being too long, and the manufacturing, transporting and installing and maintaining costs are increased.
Because the diameter of the primary impeller 1 is large, when the wind power is smaller than a first threshold value, the primary impeller 1 cannot be driven to rotate, the blades of the primary impeller 1 are adjusted, the windward area is reduced, airflow passes through the primary impeller 1, the secondary impeller 2 is directly driven to rotate, and in order to reduce the starting wind speed, the second clutch is in a separation state, and only the first generator works.
When the wind power is increased to a first threshold value and a second threshold value, the first impeller also starts to rotate, the second impeller effectively utilizes the residual wind energy, and the rotating speeds of the first impeller and the second impeller are superposed by the impeller rotating speed combining mechanism to drive the first generator to work.
When the wind power is continuously increased to be larger than the second threshold value, the rotating speed of the impeller cannot be infinitely increased, but the driving force is increased, the second clutch is engaged, and the first generator and the second generator are driven to generate electricity at the same time, so that the generating efficiency is improved.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like that fall within the spirit and principle of the present invention are intended to be included therein.
Claims (4)
1. A method for acquiring influence relation of fan blade angle measurement errors is characterized by comprising the following steps:
step S1, classifying the data collected by the anemorumbometer according to wind speed sections in a learning period to obtain different wind speeds;
s2, selecting different wind speeds as process parameters, and establishing an experimental model and a calculation model at different fan rotating speeds; setting parameters including the length of the blades, the maximum chord length of the blades, the minimum chord length of the blades, the maximum torsion angle of the blades, the number of the blades and the diameter of the impeller; according to the set parameters, combining the experimental model and the calculation model to obtain a geometric model, wherein the geometric model comprises meshes of a rotating flow field and an external flow field; setting boundary conditions according to the actual operation condition of the fan blade in the flow field; obtaining a mathematical model according to the set boundary conditions; according to the different wind speeds, calculating the mathematical model through an implicit solver based on pressure to obtain a calculated value; comparing the calculated value with the measured value to obtain a first error analysis result;
step S3, acquiring a fan performance curve when the angle of the maximum blade is obtained; calculating to obtain an actual fan performance curve according to the blade angle obtained by actual measurement; taking data of an intersection point of the fan performance curve and the actual fan performance curve as error parameters, wherein the error parameters comprise air volume and air pressure data corresponding to the intersection point; calculating to obtain the rotating speed of the fan according to the air volume and the air pressure data corresponding to the intersection point; comparing and calculating the actual fan rotating speed according to the fan rotating speed to obtain a fan rotating speed error as a second error analysis result;
step S4, fitting the first error analysis result and the second error analysis result to obtain an error analysis result;
and step S5, performing least square fitting on the error analysis result by adopting a least square fitting method to form an angle measurement error function of the anemorumbometer as the angle measurement error function.
2. The method for obtaining the influence relationship of the fan blade angle measurement error according to claim 1,
the step S5 specifically includes:
s51, setting n as 1;
s52, performing least square fitting on the error analysis result by using a least square fitting method to obtain the following function:
δ＝f(v)≈a0+a1v+a2v2+…+anvn
in the formula, a0、a1、…、anIs constant, v is error analysis result;
s53, if the total relative error after fitting is better than 0.01, the function is used as the angle measurement error function, otherwise, n is added by 1, and the step S52 is returned.
3. The method for acquiring the influence relation of the angle measurement errors of the fan blades according to claim 1, wherein the method comprises the following steps:
the fan is bilobed wheel aerogenerator, includes:
the first-stage impeller, the second-stage impeller and the impeller rotating speed combining mechanism; the impeller rotating speed merging mechanism is provided with a first input shaft, a second input shaft, a first output shaft and a second output shaft, the first-stage impeller is in driving connection with the first input shaft, the second-stage impeller is in driving connection with the second input shaft, the first output shaft is in driving connection with the input shaft of the first generator through a first clutch, and the second output shaft is in driving connection with the input shaft of the second generator through a second clutch;
the first input shaft is parallel to the second input shaft, the first-stage impeller is connected with the second-stage impeller in a non-coaxial mode, the length of blades of the first-stage impeller is larger than that of the blades of the second-stage impeller, the rotating directions of the first-stage impeller and the second-stage impeller are opposite during working, and the first-stage impeller is located in front of the second-stage impeller;
the impeller rotating speed combining mechanism comprises a sun gear, a gear ring and a planet carrier which are coaxially arranged, a plurality of planet gears are arranged on the planet carrier, the gear ring is provided with inner teeth and outer teeth, the planet gears are meshed between the inner teeth of the gear ring and the sun gear, a driving gear is arranged on the first input shaft and is meshed with the outer teeth of the gear ring, the second input shaft is connected with a rotating shaft of the sun gear, the rotating shaft of the planet carrier is in driving connection with an output shaft through an intermediate shaft, one end of the output shaft forms the first output shaft, and the other end of the output shaft forms the second output shaft;
when the wind speed is less than a first threshold value, blades of the first-stage impeller and blades of the second-stage impeller are subjected to pitch variation, the first-stage impeller stops generating electricity, the second-stage impeller is in a rotating electricity generation state, the first clutch is in a meshing state, and the second clutch is in a separation state;
when the wind speed is not less than the first threshold value and not more than the second threshold value, blades of the first-stage impeller and the second-stage impeller are subjected to pitch variation, so that the first-stage impeller and the second-stage impeller are both in a rotating power generation state, the first clutch is in a meshing state, and the second clutch is in a separating state;
when the wind speed is greater than the second threshold value, the first-stage impeller and the second-stage impeller are both in a rotating power generation state, and the first clutch and the second clutch are both in a meshing state.
4. The utility model provides a system for obtaining of fan blade angle measurement error influence relation which characterized in that includes:
the wind speed data acquisition module is used for classifying the data acquired by the anemorumbometer according to wind speed sections in a learning period to obtain different wind speeds;
the first error analysis module is used for selecting different wind speeds as process parameters and establishing an experimental model and a calculation model at different fan rotating speeds; setting parameters including the length of the blades, the maximum chord length of the blades, the minimum chord length of the blades, the maximum torsion angle of the blades, the number of the blades and the diameter of the impeller; according to the set parameters, combining the experimental model and the calculation model to obtain a geometric model, wherein the geometric model comprises meshes of a rotating flow field and an external flow field; setting boundary conditions according to the actual operation condition of the fan blade in the flow field; obtaining a mathematical model according to the set boundary conditions; according to the different wind speeds, calculating the mathematical model through an implicit solver based on pressure to obtain a calculated value; comparing the calculated value with the measured value to obtain a first error analysis result;
the second error analysis module is used for acquiring a fan performance curve when the angle of the maximum blade is obtained; calculating to obtain an actual fan performance curve according to the blade angle obtained by actual measurement; taking data of an intersection point of the fan performance curve and the actual fan performance curve as error parameters, wherein the error parameters comprise air volume and air pressure data corresponding to the intersection point; calculating to obtain the rotating speed of the fan according to the air volume and the air pressure data corresponding to the intersection point; comparing and calculating the actual fan rotating speed according to the fan rotating speed to obtain a fan rotating speed error as a second error analysis result;
the error result generating module is used for fitting the first error analysis result and the second error analysis result to obtain an error analysis result;
and the error function generating module is used for performing least square fitting on the error analysis result by adopting a least square fitting method to form an angle measurement error function of the anemorumbometer as the angle measurement error function.
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| CN114687951B (en) * | 2020-12-28 | 2024-09-20 | 北京金风科创风电设备有限公司 | Wind speed measuring equipment, method and device, storage medium and wind generating set |
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