CN113114105B - Dynamic measurement method for output characteristics of photovoltaic cell assembly - Google Patents
Dynamic measurement method for output characteristics of photovoltaic cell assembly Download PDFInfo
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02S—GENERATION OF ELECTRIC POWER BY CONVERSION OF INFRARED RADIATION, VISIBLE LIGHT OR ULTRAVIOLET LIGHT, e.g. USING PHOTOVOLTAIC [PV] MODULES
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Abstract
The invention relates to a dynamic measurement method for output characteristics of a photovoltaic cell module, which is used for acquiring the output voltage or current value of a photovoltaic cell when a carrier fixedly provided with a solar cell panel moves, and comprises the following steps: 1) respectively calibrating a geographic coordinate system and a carrier coordinate system; 2) acquiring acceleration, angular velocity and inclination angle data in real time; 3) converting the acceleration and angular velocity data from a carrier coordinate system to a geographic coordinate system; 4) fusing acceleration, angular velocity and inclination angle data based on a Kalman filtering fusion algorithm to obtain more accurate carrier motion attitude angle data; 5) and acquiring the illumination intensity received by the solar cell panel according to the carrier motion attitude angle data, and dynamically acquiring the output characteristic of the photovoltaic cell by combining a photovoltaic cell mathematical model and the relation between the environment and the model parameter. Compared with the prior art, the method has the advantages of considering the dynamic environment of the carrier, accurately estimating and the like.
Description
Technical Field
The invention relates to the technical field of photovoltaic power generation, in particular to a dynamic measurement method for output characteristics of a photovoltaic cell module under the condition of carrier motion.
Background
Solar energy is popular in the world as one of the most potential clean energy sources in the world. The photovoltaic cell is widely applied to various occasions, such as spaceflight, unmanned aerial vehicles, ships, rail vehicles and other motion carriers. However, in recent years, the output characteristics of photovoltaic cells are mostly studied domestically and abroad based on that the photovoltaic module is in a static state, the static output characteristics of the photovoltaic cells are studied by changing external environmental conditions, and the situation in a carrier motion state needs to be considered in order to improve the conversion efficiency of the photovoltaic module to the maximum extent to realize high-power generation.
From the aspect of a measurement method, the existing measurement method for the output characteristic of the photovoltaic module under the motion condition is less researched, the output characteristic of the photovoltaic module is difficult to obtain by adopting a traditional direct measurement method under the action of complex mechanical vibration, and the measurement method relying on a single sensor is easily influenced by low-frequency accumulated errors, so that the measurement precision is low. How to accurately measure the dynamic output characteristics of the photovoltaic module under the condition that the photovoltaic module is subjected to multi-frequency nonlinear vibration becomes a problem of priority in photovoltaic cell characteristic analysis.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provide a method for dynamically measuring the output characteristic of a photovoltaic cell module.
The purpose of the invention can be realized by the following technical scheme:
a dynamic measurement method for output characteristics of a photovoltaic cell module is used for accurately acquiring output voltage or current values of a photovoltaic cell when a carrier fixedly provided with a solar cell panel moves, and comprises the following steps:
1) respectively calibrating a geographic coordinate system and a carrier coordinate system;
2) acquiring acceleration, angular velocity and inclination angle data in real time;
3) converting the acceleration and angular speed data from a carrier coordinate system to a geographic coordinate system;
4) under a unified geographic coordinate system, fusing acceleration, angular velocity and inclination angle data based on a Kalman filtering fusion algorithm to obtain more accurate carrier motion attitude angle data, namely motion attitude angle data of the solar cell panel;
5) and acquiring the illumination intensity received by the solar cell panel according to the carrier motion attitude angle data, and dynamically acquiring the output characteristic of the photovoltaic cell by combining a photovoltaic cell mathematical model and the relation between the environment and the model parameter.
The step 1) specifically comprises the following steps:
11) establishing a geographical coordinate system with its origin at the center of mass of the carrier, Z t Axis pointing from origin to sky along local geographical vertical line, X t Axis perpendicular to Z t In the plane of the axis and pointing in the direction of the north pole along the local meridian, Y t The shaft always points east along the local latitude line;
12) establishing a carrier coordinate system, wherein the origin of the carrier coordinate system is the same as the origin of the geographic coordinate system and is the carrier centroid, X b The axial direction being forward along the longitudinal axis of the carrier, Y b The axial direction is along the transverse axis of the carrier to the right, Z b Axis orthogonal to X b And Y b A shaft.
And 2) acquiring triaxial acceleration data, triaxial angular velocity data and inclination angle data under a carrier coordinate system in real time through an inertia measurement unit arranged on the motion carrier, wherein the inertia measurement unit comprises an accelerometer, a gyroscope and an inclinometer.
The step 3) specifically comprises the following steps:
31) obtaining a rotation matrix based on inertial information and European space rotation theory
32) And (4) converting the coordinate system of the triaxial acceleration and the angular velocity data under the carrier coordinate system by adopting an Euler angle method according to the rotation matrix to obtain the triaxial acceleration and the angular velocity data under the geographic coordinate system.
In the step 32), the coordinate conversion formula of the triaxial acceleration and angular velocity data in the geographic coordinate system is:
wherein the content of the first and second substances,
respectively, three-axis acceleration under a geographic coordinate system,
respectively, three-axis acceleration under a carrier coordinate system,
respectively, the three-axis angular velocity under the geographic coordinate system,
three-axis angular velocity in a carrier coordinate system, theta, and theta, respectively,
ψ is an angle of rotation about the x, y, z axes, respectively.
The step 4) specifically comprises the following steps:
41) according to the three-axis angular velocity data under the geographic coordinate system
Establishing a system state prediction equation;
42) according to triaxial acceleration data under a geographic coordinate system
And inclination data
Establishing a system observation equation;
43) motion attitude angle is recurrently solved by utilizing five core formulas of Kalman filtering algorithm
In the step 41), the expression of the system state prediction equation is as follows:
where X (k) is the system state vector at time k,
the motion attitude angle of the carrier at the k moment under the geographic coordinate system,
three-axis angular velocity data at the moment k-1 in a geographic coordinate system, W (k) is dynamic noise at the moment k in the system, and dk is sampling time.
In the step 42), the expression of the system observation equation is:
wherein, z (k) is an observation vector at the time k, and v (k) is observation noise at the time k of the system.
The step 5) specifically comprises the following steps:
51) obtaining the illumination intensity S received by the solar cell panel at the current moment according to the carrier motion attitude angle data, and then:
wherein (theta) et ,ψ et ,Ψ et ) T Is the included angle between the illumination intensity and the photovoltaic cell panel，
Is the initial angle of illumination intensity with respect to the three axes of the geographic coordinate system,
for carrier motion attitude angle data, S 0 The illumination intensity irradiated on the plane of the cell panel;
52) obtaining the current I of the photovoltaic cell at the current moment according to the illumination intensity L Then, there are:
wherein, I Lr For the photoproduction current, k, of a battery under standard test conditions i Is the temperature coefficient of current, T r Is a temperature value under standard test conditions, T is a battery temperature, S r The illumination intensity under the standard condition;
53) and calculating according to the photovoltaic cell five-parameter model to obtain a photovoltaic cell output characteristic curve.
In the step 53), the expression of the photovoltaic cell five-parameter model is as follows:
wherein, I L For photovoltaic cell monomer current, i.e. photo-generated current, I O For reverse saturation current of the battery, U S Is the output voltage of the photovoltaic cell, q is the charge constant, K is the Prziman constant, n is the ideal factor of the diode, R S For connecting equivalent resistance, R, in series with the battery P The equivalent resistance is connected in parallel with the battery, and I is the output current of the photovoltaic battery.
Compared with the prior art, the invention has the following advantages:
the invention considers that under the condition of nonlinear vibration of a carrier, acceleration, angular velocity and inclination angle data of the carrier under the motion condition are acquired in real time through an inertial measurement unit arranged on the motion carrier, and data acquired by three different sensors are fused based on a Kalman filtering fusion algorithm to obtain a more accurate and wider carrier motion attitude angle.
And secondly, on the basis of the existing static photovoltaic cell power generation output model, establishing a dynamic photovoltaic cell output model, and accurately acquiring the output power, the output voltage and the output current of the dynamic photovoltaic cell.
Drawings
FIG. 1 is a flow chart of the method of the present invention.
Fig. 2 is a schematic view of a motion carrier.
Fig. 3 is a diagram of the vibration angle transformation of the photovoltaic cell.
Fig. 4 is a graph of the relationship between the vibration of the photovoltaic cell and the illumination intensity.
Fig. 5 is a train vehicle/track coupling dynamics model.
Fig. 6 is a dynamic output voltage-current (V-I) curve of a photovoltaic cell in motion.
Fig. 7 is a dynamic output voltage-power (V-P) curve of a photovoltaic cell under motion.
Fig. 8 is a dynamic output curve of the maximum power point voltage of the photovoltaic cell under the continuous motion of the carrier.
Fig. 9 is a dynamic output curve of the maximum power point current of the photovoltaic cell under the continuous motion of the carrier.
Fig. 10 is a dynamic output curve of the maximum power point current of the photovoltaic cell under the continuous motion of the carrier.
Detailed Description
Examples
The invention provides a dynamic measurement method for output characteristics of a photovoltaic cell module, which is characterized in that an inertia measurement unit (an accelerometer, a gyroscope and an inclinometer) arranged on a motion carrier (a vehicle body) is utilized to collect acceleration, angular velocity and inclination angle data of the motion carrier in real time, the three data are fused based on a Kalman filtering fusion algorithm to obtain more accurate and wider motion attitude angles of the carrier, and a dynamic photovoltaic cell model is established based on a photovoltaic cell mathematical model to accurately and quickly obtain the output voltage or current value of a dynamic photovoltaic cell.
As shown in fig. 1, the method specifically includes the following steps:
step 1: respectively calibrating a geographic coordinate system and a carrier coordinate system;
step 2: acquiring acceleration, angular velocity and inclination angle data in real time by using an inertial measurement unit (an accelerometer, a gyroscope and an inclinometer) arranged on a motion carrier;
and step 3: performing conversion calculation of the acceleration and angular speed data from a carrier coordinate system to a geographic coordinate system;
and 4, step 4: under a unified geographic coordinate system, fusing acceleration, angular velocity and inclination angle data based on a Kalman filtering fusion algorithm to obtain a more accurate carrier motion attitude angle;
and 5: and establishing a dynamic photovoltaic cell model based on the photovoltaic cell mathematical model to obtain the output voltage or current value of the dynamic photovoltaic cell.
The detailed description of each step is as follows:
step 1: calibrating a coordinate system, as shown in fig. 2, establishing a geographic coordinate system and a carrier coordinate system according to the geographic position of the carrier at the initial moment, wherein the geographic coordinate system (i.e. a t system) has an origin O as the center of mass of the carrier and a Z t Axis pointing from origin to sky along local geographical vertical, X t Axis perpendicular to Z t In the plane of the axis, pointing in the direction of the north pole along the local meridian, Y t The axis always points to the east along the local latitude lines, and the direction of the coordinate axis of the geographic coordinate system does not change along with the movement of the carrier.
A carrier coordinate system (namely a b system), the origin is defined as the same as the geographic coordinate system and is the center of mass, X, of the carrier b The axial direction being forward along the longitudinal axis of the carrier, Y b The axial direction is along the transverse axis of the carrier to the right, Z b Axis orthogonal to X b And Y b And the coordinate axis direction of the carrier coordinate system can change along with the movement of the carrier at any moment.
because the data collected by the accelerometer and the gyroscope installed on the carrier are based on the carrier coordinate system, the acceleration and the angular velocity data need to be converted into the initial geographic coordinate system through attitude conversion, and the following steps are included:
and 4, information fusion: and the Kalman filtering carries out real-time state accurate estimation on the target data through a system state prediction equation and an observation equation.
Setting a state prediction equation and an observation equation of a dynamic system as follows:
X(k)＝A(k-1|k)X(k-1)+B(k-1|k)U(k-1)+W(k-1)
Z(k)＝H(k)X(k)+V(k)
wherein, X (k) is a system state vector at the time k, A (k-1| k) is a state transition matrix from the time k-1 to the time k, B (k-1| k) is an input system control matrix from the time k-1 to the time k, U (k-1) is a control quantity of the system at the time k-1, Z (k) is an observation vector at the time k, H (k) is a measurement matrix at the time k, W (k) is dynamic noise at the time k, and V (k) is observation noise at the time k.
For convenience of processing, it is assumed that there is no control quantity input in the system, and the dynamic noise w (k) and the observation noise v (k) are both zero, and they are independent from each other, and the specific flow of kalman filtering is:
pre-estimating:
calculating a pre-estimation covariance matrix: p (kk-1) ═ a (k-1k) P (k-1| k-1) a T (k-1|k)+Q
Calculating a Kalman gain matrix: k (k) ═ p (k) H T (k)[H(k)P(k)H T (k)+R] -1
Updating estimation:
and (3) covariance updating: p (k) ═ I-k (k) h (k) ] P (k | k-1)
(1) The state prediction equation of the system is as follows:
wherein θ (k),
Psi (k) is the motion attitude angle of the carrier at the k moment under the geographic coordinate system,
w (k) is the dynamic noise of the system at the time k, which is the angular velocity data at the time k-1.
(2) The system's observation equation is:
where V (k) is the observed noise at time k of the system.
After a state prediction equation and an observation equation of the system are constructed, the fused carrier motion attitude angle can be recursively solved by five core formulas of Kalman filtering.
Step 5, photovoltaic cell dynamic modeling, which specifically comprises the following steps:
(1) obtaining the illumination intensity S received by the solar panel at the current moment according to the carrier motion attitude angle data, as shown in fig. 3 and 4, there are:
wherein (theta) et ,ψ et ,Ψ et ) T Is the included angle between the illumination intensity and the photovoltaic cell panel,
is the initial angle of illumination intensity with respect to the three axes of the geographic coordinate system,
for carrier motion attitude angle data, S 0 The illumination intensity irradiated on the plane of the cell panel;
(2) in the embodiment, the photovoltaic cell dynamic modeling adopts a photovoltaic cell five-parameter model, and the expression is as follows:
in the formula I L For solar energy electricityThe cell monomer current, also known as photogenerated current, a; i is O Is the reverse saturation current of the battery, A; u shape S Is the output voltage of the solar cell, V; q is a charge constant, 1.6 × 10 -19 C; k is Primuman constant, K is 1.38 × 10 -23 J/K; n is the ideal factor of the diode; t is the battery temperature, K; r S The equivalent resistance, omega, is the series connection of the battery; r p The battery is connected with an equivalent resistor omega in parallel; i is the photovoltaic cell output current, A.
The relationship among the five parameters of the photovoltaic cell, the illumination intensity and the temperature is as follows:
n＝nr
in the formula I Lr The cell generates a current under standard test conditions (T25 ℃, S1000W/m 2), a; ki-temperature coefficient of current; t-cell temperature, deg.C; tr-temperature value under standard test conditions, 25 ℃; s-illumination intensity, W/m 2; sr-light intensity in standard case, 1000W/m 2; ior-diode reverse saturation current under standard test conditions, A; EG-semiconductor forbidden band broadband in photovoltaic cells, typical value of EG under standard test conditions is 1.12 eV; b is equal to about 0.217.
Simulation example
According to the invention, a photovoltaic cell dynamic simulation model is built under a Simulink platform, and the output voltage or current value of the dynamic photovoltaic cell is obtained.
(1) SIMPACK vehicle body modeling
In order to obtain an acceleration value and an angular velocity value under the condition of train vibration, a train vehicle/track coupling dynamic model is established by using a multi-body dynamic software SIMPACK, as shown in figure 5, the model totally comprises 1 train body, 2 frameworks, 4 wheel pairs and 7 rigid bodies, and the rigid bodies are considered to be symmetrical from the front to the back and from the left to the right of the center of mass in the model. The vehicle body is connected with the framework through a secondary suspension spring damping device, and the framework is connected with the wheel pair through a primary suspension spring damping device.
Adding a German orbit spectrum into the model as model excitation, and carrying out simulation to obtain the acceleration g x 、g y 、g z Angular velocity w x 、w y 、w z 。
(2) Matlab photovoltaic cell dynamic model simulation
A photovoltaic cell dynamic simulation model is built on a Matlab/Simulink platform, a photovoltaic cell module with the model of MSX-60 is selected as a simulation object, and data obtained by simulation of a SIMPACK model is input into the photovoltaic cell dynamic simulation model after complementary filtering processing.
(3) Analysis of results
Fig. 6 is a dynamic output voltage-current (V-I) curve of the photovoltaic cell in a motion state, and fig. 7 is a dynamic output voltage-power (V-P) curve of the photovoltaic cell in a motion state. It can be seen from fig. 6 and 7 that both graph output curves conform to the photovoltaic cell output characteristics, and that over time, the V-I, V-P curve is changing, i.e. it is illustrated that carrier motion has an effect on the photovoltaic cell output characteristics.
Fig. 8 shows the photovoltaic cell maximum power point voltage dynamic output curve under the continuous movement of the carrier. Fig. 8 shows that the output voltage of the maximum power output point of the photovoltaic cell is continuously changed under the condition that the carrier is continuously changed, and the change is obvious.
Fig. 9 shows the maximum power point current dynamic output curve of the photovoltaic cell under the continuous motion of the carrier. Fig. 9 shows that the output current of the maximum power output point of the photovoltaic cell is continuously changed under the condition that the carrier is continuously changed, and the change is obvious.
Fig. 10 shows the maximum power point current dynamic output curve of the photovoltaic cell under the continuous motion of the carrier. Fig. 10 shows that the output current of the maximum power output point of the photovoltaic cell is continuously changed under the condition that the carrier is continuously changed, and the change is obvious.
It is finally necessary to point out here: the above description is only a preferred embodiment of the present invention and should not be taken as limiting the invention, and any modifications, equivalents and improvements made within the spirit and principle of the present invention should be included in the scope of the present invention.
Claims (10)
1. A dynamic measurement method for output characteristics of a photovoltaic cell module is used for accurately acquiring output voltage or current values of a photovoltaic cell of a carrier fixedly provided with a solar cell panel under the condition of motion, and is characterized by comprising the following steps:
1) respectively calibrating a geographic coordinate system and a carrier coordinate system;
2) acquiring acceleration, angular velocity and inclination angle data in real time;
3) converting the acceleration and angular velocity data from a carrier coordinate system to a geographic coordinate system;
4) under a unified geographic coordinate system, fusing acceleration, angular velocity and inclination angle data based on a Kalman filtering fusion algorithm to obtain carrier motion attitude angle data, namely motion attitude angle data of the solar cell panel;
5) and acquiring the illumination intensity received by the solar cell panel according to the carrier motion attitude angle data, and dynamically acquiring the output characteristic of the photovoltaic cell by combining a photovoltaic cell mathematical model and the relation between the environment and the model parameter.
2. The method according to claim 1, wherein the step 1) specifically comprises the following steps:
11) establishing a geographical coordinate system with its origin at the center of mass of the carrier, Z t Axis pointing from origin to sky along local geographical vertical line, X t Axis perpendicular to Z t In the plane of the axis and pointing in the direction of the north pole along the local meridian, Y t The shaft always points east along the local latitude line;
12) establishing a carrier coordinate system, wherein the origin of the carrier coordinate system is the same as the origin of the geographic coordinate system and is the carrier centroid, X b The axial direction being forward along the longitudinal axis of the carrier, Y b The axial direction is along the transverse axis of the carrier to the right, Z b Axis orthogonal to X b And Y b A shaft.
3. The method as claimed in claim 1, wherein in step 2), the three-axis acceleration data, the three-axis angular velocity data and the tilt angle data in the carrier coordinate system are collected in real time by an inertial measurement unit mounted on the moving carrier, wherein the inertial measurement unit comprises an accelerometer, a gyroscope and a tilt angle meter.
4. The method according to claim 1, wherein the step 3) specifically comprises the following steps:
31) obtaining a rotation matrix based on inertial information and European space rotation theory
32) And (4) converting the coordinate system of the triaxial acceleration and the angular velocity data under the carrier coordinate system by adopting an Euler angle method according to the rotation matrix to obtain the triaxial acceleration and the angular velocity data under the geographic coordinate system.
5. The method as claimed in claim 4, wherein in the step 32), the coordinate transformation formula of the triaxial acceleration and angular velocity data in the geographic coordinate system is:
wherein the content of the first and second substances,
respectively, three-axis acceleration under a geographic coordinate system,
respectively, three-axis acceleration under a carrier coordinate system,
respectively, three-axis angular velocity under a geographic coordinate system,
three-axis angular velocity in a carrier coordinate system, theta, and theta, respectively,
ψ is an angle of rotation about the x, y, z axes, respectively.
6. The method for dynamically measuring the output characteristic of the photovoltaic cell module according to claim 1, wherein the step 4) specifically comprises the following steps:
41) according to the three-axis angular velocity data under the geographic coordinate system
Establishing a system state prediction equation;
42) according to triaxial acceleration data under a geographic coordinate system
And inclination data
Establishing a system observation equation;
43) motion attitude angle is recurrently solved by utilizing five core formulas of Kalman filtering algorithm
7. The method according to claim 6, wherein in step 41), the expression of the system state prediction equation is:
wherein X (k) is the system state vector at time k,
the motion attitude angle of the carrier at the k moment under the geographic coordinate system,
three-axis angular velocity data at the moment k-1 in a geographic coordinate system, W (k) is dynamic noise at the moment k in the system, and dk is sampling time.
8. The method according to claim 7, wherein in step 42), the expression of the system observation equation is:
wherein, z (k) is an observation vector at the time k, and v (k) is observation noise at the time k of the system.
9. The method according to claim 1, wherein the step 5) specifically comprises the following steps:
51) obtaining the illumination intensity S received by the solar cell panel at the current moment according to the carrier motion attitude angle data, and then:
wherein (theta) et ,ψ et ,Ψ et ) T Is the included angle between the illumination intensity and the photovoltaic cell panel,
is the initial angle of illumination intensity with respect to the three axes of the geographic coordinate system,
for carrier motion attitude angle data, S 0 The illumination intensity irradiated on the plane of the cell panel;
52) obtaining the current I of the photovoltaic cell at the current moment according to the illumination intensity L Then, there are:
wherein, I Lr For the photo-generated current, k, of the cell under standard test conditions i Is the temperature coefficient of current, T r Is a temperature value under standard test conditions, and T is a batteryTemperature, S r The illumination intensity under the standard condition;
53) and calculating according to the photovoltaic cell five-parameter model to obtain a photovoltaic cell output characteristic curve.
10. The method according to claim 9, wherein in step 53), the expression of the photovoltaic cell five-parameter model is:
wherein, I L For photovoltaic cell monomer current, i.e. photo-generated current, I O For reverse saturation current of the battery, U S Is the output voltage of the photovoltaic cell, q is the charge constant, K is the Prziman constant, n is the ideal factor of the diode, R S For connecting equivalent resistance, R, in series with the battery P The equivalent resistance is connected in parallel with the battery, and I is the output current of the photovoltaic battery.
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