CN115541915A - Multi-sensor fusion anemometer based on Kalman filter and anemometry method - Google Patents

Multi-sensor fusion anemometer based on Kalman filter and anemometry method Download PDF

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CN115541915A
CN115541915A CN202210960163.9A CN202210960163A CN115541915A CN 115541915 A CN115541915 A CN 115541915A CN 202210960163 A CN202210960163 A CN 202210960163A CN 115541915 A CN115541915 A CN 115541915A
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wind
temperature
wind speed
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value
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刘洋
侯天浩
王�琦
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Nanjing College of Information Technology
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01P—MEASURING LINEAR OR ANGULAR SPEED, ACCELERATION, DECELERATION, OR SHOCK; INDICATING PRESENCE, ABSENCE, OR DIRECTION, OF MOVEMENT
    • G01P1/00—Details of instruments
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01P—MEASURING LINEAR OR ANGULAR SPEED, ACCELERATION, DECELERATION, OR SHOCK; INDICATING PRESENCE, ABSENCE, OR DIRECTION, OF MOVEMENT
    • G01P5/00—Measuring speed of fluids, e.g. of air stream; Measuring speed of bodies relative to fluids, e.g. of ship, of aircraft
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/10—Complex mathematical operations
    • G06F17/15—Correlation function computation including computation of convolution operations
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/10—Complex mathematical operations
    • G06F17/16—Matrix or vector computation, e.g. matrix-matrix or matrix-vector multiplication, matrix factorization
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
    • Y02A90/00—Technologies having an indirect contribution to adaptation to climate change
    • Y02A90/10—Information and communication technologies [ICT] supporting adaptation to climate change, e.g. for weather forecasting or climate simulation

Abstract

The invention discloses a multi-sensor fusion anemometer based on a Kalman filter and a anemometry method, wherein the anemometry method comprises the following steps: the main control circuit acquires data of the heating rods and the 36 temperature sensors, and measures wind speed and wind direction according to a thermal wind measurement principle to obtain initial values of the wind speed and the wind direction; the main control circuit acquires data of the air guide pipe and the micro-pressure sensor, and measures the wind speed and the wind direction according to a wind pressure anemometry principle to obtain observed values of the wind speed and the wind direction; and the main control circuit processes the initial value and the observed value by adopting a preset Kalman filtering algorithm to obtain the wind speed and the wind direction. The invention provides a wind meter which can be suitable for a field severe scene, and provides a wind measuring method based on the wind meter, which can accurately measure wind speed and wind direction.

Description

Multi-sensor fusion anemometer based on Kalman filter and anemometry method
Technical Field
The invention relates to a Kalman filter-based multi-sensor fusion anemometer and a anemometer method, and belongs to the technical field of meteorological detection.
Background
The solar radiation heat enables the earth to absorb heat anywhere, the air is different in cooling and heating degree due to uneven heating of all places, warm air expands and rises, cold air cools and falls, and air flows to form wind.
The measurement of wind speed and wind direction has great significance to our life, and plays an important role in the fields of aviation, agriculture, industry, meteorological monitoring and the like. The aviation field can influence flight safety, and high-altitude turbulent flow can cause the aircraft to jolt, out of control, even stall and crash. The low altitude wind shear is called airplane killer, and safety accidents are possibly caused in the airplane landing stage; in the agricultural field, wind has important influence on the aspects of growth, development, reproduction, shape, behavior and the like of crops; the industrial field, such as coal mining industry, belongs to the high risk industry of China, the body and mind health of operating personnel can be influenced when the wind speed is too high or too low, explosion statistics can be triggered when the wind speed is serious, nearly 50% of coal mines in China belong to high gas mines, and the gas outburst mine is as high as 17.6%.
At present, all the fields of society are refined forward, intensive and fast developed, and accurate monitoring of meteorological elements under small-scale conditions has great significance for human production and life. Therefore, by integrating the above points, accurate measurement of wind speed and direction is still a problem that needs to be paid close attention in China in order to reduce the risk of coal dust explosion in the industrial field and guide aspects of aerospace, industrial and agricultural production, environmental monitoring, meteorological disaster warning and the like.
At present, the common anemometer mainly has: mechanical anemometers, hot-wire anemometers, ultrasonic anemometers, and the like. However, they all have certain technical drawbacks, such as: the mechanical anemometer has a rotating mechanical structure, so that the rotating shaft is abraded in a long-time use process to influence the anemometry precision. Meanwhile, the mechanical anemometer is influenced by the static friction force of the contact surface of the rotating shaft, so that a starting wind speed exists, and the measurement accuracy is poor in the environment with low wind speed; the hot-wire anemometer has poor reliability due to the fact that a fragile heating wire is arranged inside the hot-wire anemometer, and cannot be used in a large scale in the field. The cost of the ultrasonic wind measuring instrument is high, the surface of the ultrasonic transducer is fragile, scratches and collisions can cause sound wave scattering to cause errors, meanwhile, the ultrasonic wind measuring instrument has a shadow effect, and the wind measuring error is high at a specific angle.
Disclosure of Invention
The invention aims to overcome the defects in the prior art, and provides a multi-sensor fusion anemoscope and a method for measuring wind based on a Kalman filter, which can be suitable for severe outdoor scenes. In order to achieve the purpose, the invention is realized by adopting the following technical scheme:
in a first aspect, the present invention provides a kalman filter-based multi-sensor fusion anemometer, including:
the wind meter comprises a wind meter main body, a thermal wind meter panel arranged on the upper part of the wind meter main body and a wind meter top cover arranged on the upper part of the thermal wind meter panel;
a heating rod and 36 temperature sensors are fixed on the thermal type wind measuring panel through holes, and the 36 temperature sensors are uniformly distributed around the heating rod;
the upper surface of the anemoscope top cover is provided with four air guide pipes, the lower surface of the anemoscope top cover is provided with a micro-pressure sensor, and the air guide pipes are connected with the micro-pressure sensor;
a main control circuit is arranged in the anemoscope main body, is connected with the heating rod and the 36 temperature sensors, and measures the wind speed and the wind direction according to a thermal anemometry principle to obtain initial values of the wind speed and the wind direction; the main control circuit is connected with the air guide pipe and the micro-pressure sensor, and is used for measuring the wind speed and the wind direction according to a wind pressure anemometry principle to obtain observed values of the wind speed and the wind direction; and the main control circuit processes the initial value and the observed value to obtain the wind speed and the wind direction.
With reference to the first aspect, further, 32 temperature sensors of the 36 temperature sensors are uniformly distributed in a 4 × 8 annular array, an included angle between adjacent sensors in each ring is 45 °, and each ring is a first ring, a second ring, a third ring and a fourth ring from inside to outside; the 4 temperature sensors are positioned on the outer side of the fourth ring to form a fifth ring, and the included angle between every two adjacent sensors in the fifth ring is 90 degrees.
In combination with the first aspect, further, the air guide pipes are distributed in a cross shape, wherein two air guide pipes which are 180 degrees from each other are connected with the same micro-pressure sensor.
In a second aspect, the invention provides a multi-sensor fusion anemometry method based on the kalman filter, which includes:
the main control circuit acquires data of the heating rods and the 36 temperature sensors, and measures wind speed and wind direction according to a thermal wind measurement principle to obtain initial values of the wind speed and the wind direction;
the main control circuit acquires data of the air guide pipe and the micro-pressure sensor, and measures the wind speed and the wind direction according to a wind pressure anemometry principle to obtain observed values of the wind speed and the wind direction;
and the main control circuit processes the initial value and the observed value by adopting a preset Kalman filtering algorithm to obtain the wind speed and the wind direction.
With reference to the second aspect, further, the thermal anemometry principle includes:
when the sensors are uniformly distributed around the heat source, wind blows to the annular array from any direction, partial heat of the heat source is taken away through the central constant-temperature heat source, the temperature value of each annular temperature sensor changes, temperature difference is formed, the difference value of the first annular temperature change is obvious, and the temperature difference presents obvious Gaussian distribution, so that for calculation of a wind direction angle, only 8 temperature sensors at the innermost circle are needed to be used for evaluation, but for calculation of wind speed, the average value of 32 temperature sensors on the array is needed to be used for evaluating the flow speed:
Figure BDA0003792693520000031
in the formula (1), T n N =1,2,3, …,32 for the temperature values of the respective temperature sensors;
setting the environmental temperature to be-10 ℃, 0 ℃, 10 ℃, 20 ℃, 30 ℃ and 40 ℃, and setting the wind speed to be 0.5m/s, 1m/s, 1.5m/s, 2m/s, 2.5m/s, 3m/s, 3.5m/s, 4m/s, 4.5m/s, 5m/s, 5.5m/s and 6m/s, respectively, and fitting the average temperature values of the 32 temperature sensors measured in multiple tests into a curve; setting the environment temperature as x, the average temperature value of the temperature sensor as y, the wind speed as z, and the fitting function as follows:
Figure BDA0003792693520000041
in the formula (2), p 1 ＝10.8945，p 2 ＝0.6475，p 3 ＝-1.019，p 4 ＝0.0055，p 5 ＝-0.2575，p 6 ＝0.0012，p 7 ＝0.227，p 8 ＝0.0002，p 9 ＝-2.5708e -5 ；
When the sensors are uniformly distributed around the heat source, wind blows to the annular array from any direction, partial heat of the heat source is taken away through the central constant-temperature heat source, the temperature value of each annular temperature sensor is changed, a temperature difference is formed, the difference value of the first annular temperature change is particularly obvious, the temperature difference presents obvious Gaussian distribution, the wind temperature in the wind direction is higher, and the peak value of a Gaussian curve is detected to determine the flowing direction of the wind;
when wind in any direction blows over the anemometer, the relationship between the temperature sensor at different angles in the innermost circle and the value of the temperature sensor can be expressed by a Gaussian function as follows:
Figure BDA0003792693520000042
in the formula (3), (theta) i ,y i ) (i =1,2,3, … …, 8) represents temperature sensors θ of different angles i Corresponding temperature index value y i ，y max The peak value of the Gaussian curve represents the index of the temperature sensor with the highest index; theta max The peak position of the Gaussian curve represents the angle position of the temperature sensor with the highest index; s is half-width information of a Gaussian curve;
taking natural logarithms on both sides of formula (3) yields:
Figure BDA0003792693520000043
order:
Figure BDA0003792693520000051
equation (5) is expressed in matrix form as:
Figure BDA0003792693520000052
formula (6) is denoted as Z = XB, and according to the least-squares principle, the generalized least-squares solution of the constructed matrix B is:
B＝(X T X) -1 X T Z (7)
finally, the parameter y is obtained from the equation (7) max And theta max And determining the flowing direction of the wind.
With reference to the second aspect, further, the wind pressure anemometry principle includes:
four air guide pipes are sequentially numbered as a pipe A, a pipe C, a pipe B and a pipe D in the clockwise direction when viewed from above, an angular bisector of an included angle between the pipe A and the pipe C is taken as an axis Y to establish a coordinate system, and the flow velocity V of airflow in the directions of the pipe A and the pipe B AB Velocity V of air flow in the direction of pipe C and pipe D CD And a wind speed V, represented by the formula:
Figure BDA0003792693520000053
Figure BDA0003792693520000054
Figure BDA0003792693520000055
in the equations (8) and (9), K is a correction coefficient for correcting the actual wind speed, ρ is the fluid density, and P is A For internal wind pressure, P, at the opening of the tube A B For internal wind pressure, P, at the opening of the tube B C For internal wind pressure, P, at the opening of the pipe C D Is the internal air pressure at the opening of the tube D;
the wind direction angle α is an included angle between the wind direction and the Y axis, and is represented by the following formula:
Figure BDA0003792693520000061
with reference to the second aspect, further, the preset kalman filtering algorithm includes:
step 1: the prediction process is represented by the following formula:
Figure BDA0003792693520000062
P k∣k-1 ＝FP k-1∣k-1 F T +Q k (13)
wind speed deviation amount based on last-moment wind speed v and thermal wind measurement principle
Figure BDA0003792693520000063
Describe the state, i.e.
Figure BDA0003792693520000064
Establishing a time series model of the states, represented by the following formula:
Figure BDA0003792693520000065
since there is no control process, formula (14) is substituted for formula (12) to obtain:
Figure BDA0003792693520000066
in the formula (I), the compound is shown in the specification,
Figure BDA0003792693520000067
namely a state transition matrix;
error co-ordination represented by a 2 x 2 matrixVariance matrix P k∣k-1 ：
Figure BDA0003792693520000068
Estimating covariance Q from state representing wind speed v Covariance of deviation from estimated speed
Figure BDA0003792693520000069
Representing process noise covariance Q k ：
Figure BDA0003792693520000071
The error covariance matrix P at time k k∣k-1 Represented by the following formula:
Figure BDA0003792693520000072
step 2: the calibration procedure is represented by the following formula:
S k ＝HP - k∣k-1 H T +R (17)
Figure BDA0003792693520000073
Figure BDA0003792693520000074
P k∣k ＝(I-K k H)P k∣k-1 (20)
observed value z k If the wind speed value is measured by the wind pressure anemometry principle, H = [ 10 ]]From z k And
Figure BDA0003792693520000075
together, the residual values are obtained:
Figure BDA0003792693520000076
error covariance matrix P at time k of equation (16) k∣k-1 Residual covariance can be obtained by substituting equation (16):
Figure BDA0003792693520000077
observed value z k Is the wind speed value measured by the wind pressure anemometry principle, so R in the formula (17) is equal to the variance of the observed value;
mixing H, P k∣k-1 And S k Substituting equation (18) to obtain a kalman coefficient:
Figure BDA0003792693520000081
the joint formula (19), (21) and the formula (23) obtain the system state at the time k after Kalman filtering:
Figure BDA0003792693520000082
the general formula (23), H, P k∣k-1 Substituting equation (20) to obtain an updated error covariance matrix:
Figure BDA0003792693520000083
the wind speed and the wind direction are obtained by fusing the equations (22) to (25).
Compared with the prior art, the multi-sensor fusion anemoscope based on the Kalman filter and the anemometry method provided by the embodiment of the invention have the following beneficial effects:
the invention provides a anemometer main body, a thermal anemometer panel arranged on the upper part of the anemometer main body and an anemometer top cover arranged on the upper part of the thermal anemometer panel; a heating rod and 36 temperature sensors are fixed on the thermal type wind measuring panel through holes, and the 36 temperature sensors are uniformly distributed around the heating rod; a main control circuit is arranged in the anemoscope main body, is connected with the heating rod and the 36 temperature sensors, and measures the wind speed and the wind direction according to a thermal anemometry principle to obtain initial values of the wind speed and the wind direction; the upper surface of the anemoscope top cover is provided with four air guide pipes, the lower surface of the anemoscope top cover is provided with a micro-pressure sensor, the air guide pipes are connected with the micro-pressure sensor, the main control circuit is connected with the air guide pipes and the micro-pressure sensor, and the wind speed and the wind direction are measured according to a wind pressure anemometry principle to obtain observed values of the wind speed and the wind direction;
the main control circuit of the invention adopts a preset Kalman filtering algorithm to process the initial value and the observed value, and obtains the wind speed and the wind direction. The invention integrates the result of the thermal type wind measuring principle and the result of the wind pressure wind measuring principle, has the advantage of accurate measurement of wind speed and wind direction, and has stable structure and accurate wind measuring method. The defects that a common mechanical anemoscope is easy to damage and influences the anemometry precision, the defects that a hot-wire anemoscope is easy to damage and cannot be used in the field on a large scale and the defect that an ultrasonic anemoscope has high error are overcome, and the anemoscope and the anemometry method which are high in precision, easy to maintain and capable of being used in severe outdoor scenes are provided.
Drawings
Fig. 1 is a structural diagram of a multi-sensor fusion anemometer based on a kalman filter according to embodiment 1 of the present invention;
fig. 2 is a top view of a roof-removing structure of a kalman filter-based multi-sensor fusion anemometer according to embodiment 1 of the present invention;
fig. 3 is a temperature of an innermost sensor probe at different wind speeds and at different ambient temperatures in the multi-sensor fusion anemometry method based on the kalman filter according to embodiment 2 of the present invention;
fig. 4 is a distribution diagram of the temperatures of the heating rods on the thermal wind measuring panel under different wind directions in the multi-sensor fusion wind measuring method based on the kalman filter according to embodiment 2 of the present invention;
fig. 5 is a peripheral temperature distribution diagram around a heating source in a kalman filter-based multi-sensor fusion anemometry method according to embodiment 2 of the present invention;
fig. 6 is a schematic diagram of establishing a coordinate system in the multi-sensor fusion anemometry method based on the kalman filter according to embodiment 2 of the present invention.
In the figure: 1. a anemometer body; 2. a thermal wind measuring panel; 3. a anemometer top cover; 4. a temperature sensor; 5. a heating rod; 6. an air guide pipe; 7. a micro-pressure sensor.
Detailed Description
The invention is further described below with reference to the accompanying drawings. The following examples are only for illustrating the technical solutions of the present invention more clearly, and the protection scope of the present invention is not limited thereby.
Example 1:
as shown in fig. 1, the present embodiment provides a multi-sensor fusion anemometer based on a kalman filter, which includes a anemometer main body, a thermal anemometer panel disposed on an upper portion of the anemometer main body, and an anemometer top cover disposed on an upper portion of the thermal anemometer panel.
The thermal wind measuring panel is a non-heat-conducting nylon structure with the diameter of 150mm and the thickness of 4mm, and holes are formed in the thermal wind measuring panel for mounting the temperature sensor and the heating plate.
The hot type wind measurement panel is fixed with heating rod and 36 temperature sensors through the hole, and 36 temperature sensors surround the heating rod evenly distributed.
The heating rod is a ceramic heating rod with power of 150w and diameter of 3mm, is arranged in the center of the thermal wind measuring panel through a central hole of the thermal wind measuring panel, and the top of the heating rod is 50mm higher than the thermal wind measuring panel.
The 36 temperature sensors are PT100A grade temperature sensors, the diameter of the temperature sensors is 3mm, and the surfaces of the temperature sensors are covered by pure copper. Holes formed in the thermal wind measuring panel are uniformly arranged on the thermal wind measuring panel, and the distance between the top of the holes and the thermal wind measuring panel is 15mm.
As shown in fig. 2, 32 temperature sensors out of 36 temperature sensors are uniformly distributed in a 4 × 8 annular array, an included angle between adjacent sensors in each ring is 45 °, and each ring is a first ring, a second ring, a third ring and a fourth ring from inside to outside. The distance between 8 temperature sensor of innermost ring and central heating source is 3mm, and the distance between first ring and second ring, second ring and third ring, third ring and the fourth ring is 5mm, and the distance of fourth ring and base edge is 8mm. The remaining 4 temperature sensor are used for measuring ambient temperature, lie in the fourth ring outside and form the fifth ring, and the contained angle between the adjacent sensor in the fifth ring is 90, and the distance between fifth ring and the fourth ring is 5mm.
Four air guide pipes are arranged on the upper surface of the wind meter top cover, a micro-pressure sensor is arranged on the lower surface of the wind meter top cover, and the air guide pipes are connected with the micro-pressure sensor.
The air guide pipes are distributed in a cross shape, wherein two air guide pipes which are 180 degrees from each other are connected with the same micro-pressure sensor. The pipe internal diameter of the air guide pipe is 2mm and the wall thickness is 1mm. The micro-pressure sensor adopts an sm9541 micro-pressure difference sensor.
A main control circuit is arranged in the anemoscope main body, is connected with the heating rod and the 36 temperature sensors, and measures the wind speed and the wind direction according to a thermal wind measurement principle to obtain initial values of the wind speed and the wind direction. The main control circuit is connected with the air guide pipe and the micro-pressure sensor, and measures the wind speed and the wind direction according to the wind pressure anemometry principle to obtain the observed values of the wind speed and the wind direction. And the main control circuit processes the initial value and the observed value to obtain the wind speed and the wind direction.
Example 2:
the embodiment provides a multi-sensor fusion anemometry method based on a Kalman filter, which comprises the following steps:
the main control circuit acquires data of the heating rods and the 36 temperature sensors, and measures wind speed and wind direction according to a thermal wind measurement principle to obtain initial values of the wind speed and the wind direction;
the main control circuit acquires data of the air guide pipe and the micro-pressure sensor, and measures the wind speed and the wind direction according to a wind pressure anemometry principle to obtain observed values of the wind speed and the wind direction;
and the main control circuit processes the initial value and the observed value by adopting a preset Kalman filtering algorithm to obtain the wind speed and the wind direction.
The thermal wind measuring principle is as follows: the thermal wind meter works under the direct action of a thermal field and a wind field, and the working principle of the thermal wind meter is a comprehensive theory relating to various subjects such as thermotics, hydromechanics and the like. Before designing a thermal anemometer, the direct heat transfer condition of the fluid needs to be analyzed. When the fluid is in direct contact with and moves relative to the solid wall which generates heat, this heat transfer process is known as convective heat transfer.
When the sensors are uniformly distributed around the heat source, wind blows to the annular array from any direction, partial heat of the heat source is taken away through the central constant-temperature heat source, the temperature value of each annular temperature sensor changes, temperature difference is formed, the difference value of the first annular temperature change is obvious, and the temperature difference presents obvious Gaussian distribution, so that for calculation of a wind direction angle, only 8 temperature sensors at the innermost circle are needed to be used for evaluation, but for calculation of wind speed, the average value of 32 temperature sensors on the array is needed to be used for evaluating the flow speed:
Figure BDA0003792693520000121
in the formula (1), T n N =1,2,3, …,32 for the temperature values of the respective temperature sensors;
setting the environmental temperature as-10 deg.C, 0 deg.C, 10 deg.C, 20 deg.C, 30 deg.C, 40 deg.C, the wind speed as 0.5m/s, 1m/s, 1.5m/s, 2m/s, 2.5m/s, 3m/s, 3.5m/s, 4m/s, 4.5m/s, 5m/s, 5.5m/s, 6m/s, fitting the average temperature values of 32 temperature sensors measured in multiple tests into a curve, the fitting curve is shown in FIG. 3. Setting the environment temperature as x, the average temperature value of the temperature sensor as y, the wind speed as z, and the fitting function as:
Figure BDA0003792693520000122
in the formula (2), p 1 ＝10.8945，p 2 ＝0.6475，p 3 ＝-1.019，p 4 ＝0.0055，p 5 ＝-0.2575，p 6 ＝0.0012，p 7 ＝0.227，p 8 ＝0.0002，p 9 ＝-2.5708e -5 。
The distribution of the heating rod temperature on the thermal wind measuring panel at different wind directions is shown in fig. 4. It can be known that the peripheral temperature distribution around the central heating rod of the circular array anemometer is similar to a gaussian distribution function from inside to outside, and the wind temperature is higher as it goes toward the wind direction. Therefore, the wind flow direction can be determined by detecting the peak of the gaussian curve as shown in fig. 5.
When wind in any direction blows over the anemometer, the relationship between the temperature sensors at different angles of the innermost circle and the values of the temperature sensors can be expressed by a Gaussian function as follows:
Figure BDA0003792693520000131
in the formula (3), (theta) i ,y i ) (i =1,2,3, … …, 8) represents temperature sensors θ of different angles i Corresponding temperature index value y i ，y max The peak value of the Gaussian curve represents the index of the temperature sensor with the highest index; theta max The peak position of the Gaussian curve represents the angle position of the temperature sensor with the highest index; and S is half-width information of the Gaussian curve.
Taking natural logarithms on both sides of formula (3) yields:
Figure BDA0003792693520000132
order:
Figure BDA0003792693520000133
equation (5) is expressed in matrix form as:
Figure BDA0003792693520000134
formula (6) is denoted as Z = XB, and according to the least-squares principle, the generalized least-squares solution of the constructed matrix B is:
B＝(X T X) -1 X T Z (7)
finally, the parameter y is obtained from the equation (7) max And theta max And determining the flowing direction of the wind.
Wind pressure anemometry principle: assuming that air is an incompressible gas, the following bernoulli equation for an ideal incompressible gas is used: in an ideal flow field, the sum of the kinetic energy, the gravitational potential energy and the pressure potential energy of any two points of fluid on the same streamline is a constant, as shown in formula (8):
Figure BDA0003792693520000141
in the formula (8), ρ is the fluid density, v is the fluid velocity, g is the gravitational acceleration, h is the plumb height, P is the pressure potential energy, and C is a constant.
If in the flow path, there are two thin tubes with parallel flow direction and opposite opening direction: the tube A and the tube B are represented by the formula (8):
Figure BDA0003792693520000142
the influence of air stagnation at the opening of tube A can be considered as being stationary, i.e. v A =0m/s, internal wind pressure P A Full pressure of air at that height; air velocity v at opening of tube B B Can be approximately equal to the incoming flow velocity v and the internal wind pressure P B Only the static pressure of the air there. And the height of the opening of the tube A is consistent with that of the opening of the tube B A ＝h B . Therefore, the incoming flow velocity v is solved by the formula (9):
Figure BDA0003792693520000143
however, in actual measurement, it is usually influenced by the viscosity of the fluid
Figure BDA0003792693520000144
Correcting the actual wind speed by introducing a correction coefficient K, as shown in the formula (11)) Shown in the figure:
Figure BDA0003792693520000145
based on the principle, 4 thin tubes are orthogonally arranged in a plane, so that the speed and the angle of incoming flow parallel to the two-dimensional plane in any direction can be measured.
Establishing a coordinate system as shown in FIG. 6, numbering the four air guide pipes as pipe A, pipe C, pipe B and pipe D in turn clockwise when overlooking, establishing the coordinate system by taking the angular bisector of the included angle between pipe A and pipe C as the Y axis, and establishing the flow velocity V of the airflow in the directions of pipe A and pipe B AB Velocity V of air flow in the direction of pipe C and pipe D CD And a wind speed V, represented by the formula:
Figure BDA0003792693520000151
Figure BDA0003792693520000152
Figure BDA0003792693520000153
in the equations (12) and (13), K is a correction coefficient for correcting the actual wind speed, ρ is the fluid density, and P is A For internal wind pressure, P, at the opening of the tube A B For internal wind pressure, P, at the opening of the tube B C Internal wind pressure, P, at the opening of the pipe C D Is the internal air pressure at the opening of the tube D;
the wind direction angle α is an included angle between the wind direction and the Y axis, and is represented by the following formula:
Figure BDA0003792693520000154
the preset Kalman filtering algorithm comprises the following steps:
step 1: the prediction process is represented by the following formula:
Figure BDA0003792693520000155
P k∣k-1 ＝FP k-1∣k-1 F T +Q k (17)
wind speed deviation based on last-moment wind speed v and thermal wind measurement principle
Figure BDA0003792693520000156
Describe the state, i.e.
Figure BDA0003792693520000157
Establishing a time series model of the states, represented by the following formula:
Figure BDA0003792693520000161
since there is no control process, substituting equation (18) into equation (16) yields:
Figure BDA0003792693520000162
in the formula (I), the compound is shown in the specification,
Figure BDA00037926935200001610
namely a state transition matrix;
error covariance matrix represented by a 2 x 2 matrix
Figure BDA0003792693520000163
Estimating covariance Q from state representing wind speed v Covariance of deviation from estimated speed
Figure BDA0003792693520000164
Representing process noise covariance Q k ：
Figure BDA0003792693520000165
The error covariance matrix P at time k k∣k-1 Represented by the following formula:
Figure BDA0003792693520000166
step 2: the calibration procedure is represented by the following formula:
S k ＝HP - k∣k-1 H T +R (21)
Figure BDA00037926935200001611
Figure BDA0003792693520000167
P k∣k ＝(I-K k H)P k∣k-1 (24)
observed value z k If the wind speed value is measured by the wind pressure anemometry principle, H = [ 10 ]]From z k And
Figure BDA0003792693520000168
together, the residual values are obtained:
Figure BDA0003792693520000169
error covariance matrix P at time k of equation (20) k∣k-1 Residual covariance can be obtained by substituting equation (21):
Figure BDA0003792693520000171
observed value z k Is the wind speed value measured by the wind pressure anemometry principle, so R in the formula (21) is equal to the variance of the observed value;
mixing H, P k∣k-1 And S k Substituting equation (22) to obtain a kalman coefficient:
Figure BDA0003792693520000172
the joint formula (23), (25) and the formula (27) obtain the system state at the time k after Kalman filtering:
Figure BDA0003792693520000173
the general formula (28), H, P k∣k-1 Substituting equation (24) to obtain an updated error covariance matrix:
Figure BDA0003792693520000174
the wind speed and the wind direction are obtained by fusing equations (26) to (29).
The above description is only a preferred embodiment of the present invention, and it should be noted that, for those skilled in the art, several modifications and variations can be made without departing from the technical principle of the present invention, and these modifications and variations should also be regarded as the protection scope of the present invention.

Claims (7)

1. A multi-sensor fusion anemometer based on Kalman filter, characterized in that, include:
the wind meter comprises a wind meter main body, a thermal wind meter panel arranged on the upper part of the wind meter main body and a wind meter top cover arranged on the upper part of the thermal wind meter panel;
a heating rod and 36 temperature sensors are fixed on the thermal wind measuring panel through holes, and the 36 temperature sensors are uniformly distributed around the heating rod;
the upper surface of the anemometer top cover is provided with four air guide pipes, the lower surface of the anemometer top cover is provided with a micro-pressure sensor, and the air guide pipes are connected with the micro-pressure sensor;
a main control circuit is arranged in the anemoscope main body, is connected with the heating rod and the 36 temperature sensors, and measures the wind speed and the wind direction according to a thermal anemometry principle to obtain initial values of the wind speed and the wind direction; the main control circuit is connected with the air guide pipe and the micro-pressure sensor, and is used for measuring the wind speed and the wind direction according to a wind pressure anemometry principle to obtain observed values of the wind speed and the wind direction; and the main control circuit processes the initial value and the observed value to obtain the wind speed and the wind direction.
2. The Kalman filter-based multi-sensor fusion anemometer of claim 1, wherein 32 temperature sensors among the 36 temperature sensors are uniformly distributed in a 4 x 8 annular array form, an included angle between adjacent sensors in each ring is 45 °, and each ring from inside to outside is a first ring, a second ring, a third ring and a fourth ring respectively; the 4 temperature sensors are positioned on the outer side of the fourth ring to form a fifth ring, and the included angle between every two adjacent sensors in the fifth ring is 90 degrees.
3. The Kalman filter-based multi-sensor fusion anemometer of claim 1, wherein the air ducts are distributed in a cross shape, wherein two air ducts which are 180 ° from each other are connected to the same micro-pressure sensor.
4. The Kalman filter-based multi-sensor fusion anemometry method is characterized by comprising the following steps of:
the main control circuit acquires data of the heating rods and the 36 temperature sensors, and measures wind speed and wind direction according to a thermal wind measurement principle to obtain initial values of the wind speed and the wind direction;
the main control circuit acquires data of the air guide pipe and the micro-pressure sensor, and measures the wind speed and the wind direction according to a wind pressure anemometry principle to obtain observed values of the wind speed and the wind direction;
and the main control circuit processes the initial value and the observed value by adopting a preset Kalman filtering algorithm to obtain the wind speed and the wind direction.
5. The Kalman filter based multi-sensor fusion anemometry method according to claim 4, characterized in that the thermal anemometry principle comprises:
when the sensors are uniformly distributed around the heat source, wind blows to the annular array from any direction, partial heat of the heat source is taken away through the central constant-temperature heat source, the temperature value of each annular temperature sensor changes, temperature difference is formed, the difference value of the first annular temperature change is obvious, and the temperature difference presents obvious Gaussian distribution, so that for calculation of a wind direction angle, only 8 temperature sensors at the innermost circle are needed to be used for evaluation, but for calculation of wind speed, the average value of 32 temperature sensors on the array is needed to be used for evaluating the flow speed:
Figure FDA0003792693510000021
in the formula (1), T n N =1,2,3, …,32 for the temperature values of the respective temperature sensors;
setting the environmental temperature as-10 ℃, 0 ℃, 10 ℃, 20 ℃, 30 ℃ and 40 ℃ respectively, setting the wind speed as 0.5m/s, 1m/s, 1.5m/s, 2m/s, 2.5m/s, 3m/s, 3.5m/s, 4m/s, 4.5m/s, 5m/s, 5.5m/s and 6m/s respectively, and fitting the average temperature values of the 32 temperature sensors measured by multiple tests into a curve; setting the environment temperature as x, the average temperature value of the temperature sensor as y, the wind speed as z, and the fitting function as:
Figure FDA0003792693510000022
in the formula (2), p 1 ＝10.8945，p 2 ＝0.6475，p 3 ＝-1.019，p 4 ＝0.0055，p 5 ＝-0.2575，p 6 ＝0.0012，p 7 ＝0.227，p 8 ＝0.0002，p 9 ＝-2.5708e -5 ；
When the sensors are uniformly distributed around the heat source, wind blows to the annular array from any direction, partial heat of the heat source is taken away through the central constant-temperature heat source, the temperature value of each annular temperature sensor is changed, a temperature difference is formed, the difference value of the first annular temperature change is particularly obvious, the temperature difference presents obvious Gaussian distribution, the wind temperature in the wind direction is higher, and the peak value of a Gaussian curve is detected to determine the flowing direction of the wind;
when wind in any direction blows over the anemometer, the relationship between the temperature sensors at different angles of the innermost circle and the values of the temperature sensors can be expressed by a Gaussian function as follows:
Figure FDA0003792693510000031
in the formula (3), (theta) i ,y i ) (i =1,2,3, … …, 8) represents temperature sensors θ of different angles i Corresponding temperature index value y i ，y max The peak value of the Gaussian curve represents the index of the temperature sensor with the highest index; theta max The peak position of the Gaussian curve represents the angle position of the temperature sensor with the highest index; s is half-width information of a Gaussian curve;
taking natural logarithms on both sides of formula (3) yields:
Figure FDA0003792693510000032
order:
Figure FDA0003792693510000033
equation (5) is expressed in matrix form as:
Figure FDA0003792693510000034
formula (6) is denoted as Z = XB, and according to the least-squares principle, the generalized least-squares solution of the constructed matrix B is:
B＝(X T X) -1 X T Z (7)
finally, the parameter y is obtained from the equation (7) max And theta max And determining the flowing direction of the wind.
6. The Kalman filter-based multi-sensor fusion anemometry method according to claim 4, wherein the wind pressure anemometry principle comprises:
four air guide pipes are sequentially numbered as a pipe A, a pipe C, a pipe B and a pipe D in the clockwise direction when viewed from above, an angular bisector of an included angle between the pipe A and the pipe C is taken as an axis Y to establish a coordinate system, and the flow velocity V of airflow in the directions of the pipe A and the pipe B AB Velocity V of air flow in the direction of pipe C and pipe D CD And a wind speed V, represented by the formula:
Figure FDA0003792693510000041
Figure FDA0003792693510000042
Figure FDA0003792693510000043
in the equations (8) and (9), K is a correction coefficient for correcting the actual wind speed, ρ is the fluid density, and P is A For internal wind pressure, P, at the opening of the tube A B For internal wind pressure, P, at the opening of the tube B C For internal wind pressure, P, at the opening of the pipe C D Is the internal air pressure at the opening of the tube D;
the wind direction angle α is an included angle between the wind direction and the Y axis, and is represented by the following formula:
Figure FDA0003792693510000044
7. the Kalman filter based multi-sensor fusion anemometry method of claim 4, wherein the preset Kalman filtering algorithm comprises:
step 1: the prediction process is represented by the following formula:
Figure FDA0003792693510000051
P k∣k-1 ＝FP k-1∣k-1 F T +Q k (13)
wind speed deviation amount based on last-moment wind speed v and thermal wind measurement principle
Figure FDA0003792693510000052
Describe the state, i.e.
Figure FDA0003792693510000053
Establishing a time series model of the states, represented by the following formula:
Figure FDA0003792693510000054
since there is no control process, formula (14) is substituted for formula (12) to obtain:
Figure FDA0003792693510000055
in the formula (I), the compound is shown in the specification,
Figure FDA0003792693510000056
namely a state transition matrix;
the error covariance matrix P is represented by a 2 x 2 matrix k∣k-1 ：
Figure FDA0003792693510000057
Estimating covariance Q from state representing wind speed v Co-operating with the deviation of the estimated speedVariance (variance)
Figure FDA0003792693510000058
Representing process noise covariance Q k ：
Figure FDA0003792693510000059
The error covariance matrix P at time k k∣k-1 Represented by the following formula:
Figure FDA00037926935100000510
step 2: the calibration procedure is represented by the following formula:
S k ＝HP - k∣k-1 H T +R (17)
Figure FDA0003792693510000061
Figure FDA0003792693510000062
P k∣k ＝(I-K k H)P k∣k-1 (20)
observed value z k If the wind speed value is measured by the wind pressure anemometry principle, H = [ 10 ]]From z k And
Figure FDA0003792693510000063
together, the residual values are obtained:
Figure FDA0003792693510000064
error covariance matrix P at time k of equation (16) k∣k-1 Substitution intoEquation (17) can give the residual covariance:
Figure FDA0003792693510000065
observed value z k Is the wind speed value measured by the wind pressure anemometry principle, so R in the formula (17) is equal to the variance of the observed value;
mixing H, P k∣k-1 And S k Substituting equation (18) to obtain kalman coefficient:
Figure FDA0003792693510000066
the joint formula (19), (21) and the formula (23) obtain the system state at the time k after Kalman filtering:
Figure FDA0003792693510000067
the general formula (23) H, P k∣k-1 Substituting equation (20) to obtain an updated error covariance matrix:
Figure FDA0003792693510000071
the wind speed and the wind direction are obtained by fusing the equations (22) to (25).
CN202210960163.9A 2022-08-11 2022-08-11 Multi-sensor fusion anemometer based on Kalman filter and anemometry method Pending CN115541915A (en)

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