CN117873179A - Genetic algorithm-based flat single-axis photovoltaic bracket tracking angle optimization method - Google Patents
Genetic algorithm-based flat single-axis photovoltaic bracket tracking angle optimization method Download PDFInfo
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- CN117873179A CN117873179A CN202410108964.1A CN202410108964A CN117873179A CN 117873179 A CN117873179 A CN 117873179A CN 202410108964 A CN202410108964 A CN 202410108964A CN 117873179 A CN117873179 A CN 117873179A
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- 230000002068 genetic effect Effects 0.000 title claims description 29
- 238000000034 method Methods 0.000 title claims description 25
- 238000005457 optimization Methods 0.000 title claims description 14
- 238000010248 power generation Methods 0.000 claims abstract description 16
- 210000000349 chromosome Anatomy 0.000 claims description 24
- 238000004364 calculation method Methods 0.000 claims description 7
- 238000012163 sequencing technique Methods 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000006243 chemical reaction Methods 0.000 description 1
- 238000009434 installation Methods 0.000 description 1
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
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- G05D3/12—Control of position or direction using feedback
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Abstract
The invention mainly relates to the technical field of photovoltaic power generation, and aims to solve the problem that the tracking angle calculated by the existing tracking algorithm deviates from the angle actually required to be adjusted by a flat single-axis photovoltaic support so as to influence the solar photovoltaic power generation amount.
Description
Technical Field
The invention mainly relates to the technical field of photovoltaic power generation, in particular to a flat single-axis photovoltaic bracket tracking angle optimization method based on a genetic algorithm.
Background
In solar energy utilization, photovoltaic power generation is the conversion of solar radiant energy into electrical energy by a panel assembly. The flat single-shaft photovoltaic support is an advanced solar photovoltaic panel installation system and mainly comprises a support structure, a rotating mechanism and a control system, wherein the angle of a photovoltaic panel is automatically adjusted by a tracking angle algorithm, so that the assembly is ensured to form a right angle with solar rays in the east-west direction, and solar energy is absorbed to the maximum extent. The tracking angle algorithm of the single flat-axis bracket mainly comprises a time control algorithm and an inverse tracking algorithm.
The time control algorithm derives the current solar altitude angle and azimuth angle according to time, so that the two angles are converted into tracking angles of the bracket in the east-west direction, and tracking control of the battery plate assembly is realized; the inverse tracking algorithm calculates tracking angles according to the arrangement condition of the brackets and the theory so as to avoid shadow shielding generated by front and rear row components. Both algorithms have certain problems, which can cause deviation of tracking angles; the time control algorithm only derives the theoretical sun position, sun irradiation information and atmospheric temperature and humidity information are not introduced, and tracking angle deviation caused by the influence of meteorological factors can occur in an actual project; the inverse tracking algorithm can avoid shadow shielding to a certain extent by calculating the width and the distance of the components, but factors such as the height and the fall of the terrain appearing in the actual project still cause tracking angle deviation.
Disclosure of Invention
The invention aims to solve the technical problems
The method for optimizing the tracking angle of the flat single-axis photovoltaic bracket based on the genetic algorithm solves the problem that the tracking angle calculated by the existing flat single-axis photovoltaic bracket tracking algorithm deviates from the angle actually required to be adjusted by the flat single-axis photovoltaic bracket, so that the solar photovoltaic power generation capacity is affected.
The invention solves the technical problems
A genetic algorithm-based flat single-axis photovoltaic bracket tracking angle optimization method comprises the following steps:
the angle of the flat single-shaft photovoltaic bracket is adjusted for the first time;
setting and adjusting deflection angle U k According to the first angle adjusting result of the flat single-axis photovoltaic bracket and the adjusting deflection angle U k Determining the angular range of a second adjustment of a flat uniaxial photovoltaic supportEnclose [ U ] 1 ,U 2 ]The method comprises the steps of carrying out a first treatment on the surface of the In the angle adjustment range [ U ] 1 ,U 2 ]Calculating the angle of the second adjustment of the flat single-axis photovoltaic bracket by adopting a genetic algorithm;
and adjusting the angle of the flat single-axis photovoltaic bracket according to the calculated second adjusting deflection angle.
Further, the angle is within the angle adjusting range [ U ] 1 ,U 2 ]The calculating of the angle of the second adjustment of the flat single-axis photovoltaic bracket by adopting the genetic algorithm comprises the following steps:
step 1, generating a primary population based on the number M of flat single-axis photovoltaic supports in a photovoltaic array;
step 2, adjusting the angle range [ U ] for the second time according to the flat single-axis photovoltaic bracket 1 ,U 2 ]Randomly generating chromosomes for each individual in the primary population and encoding the chromosomes;
step 3, decoding the chromosome, and adjusting the angle of the flat single-axis photovoltaic bracket according to the decoded angle value;
step 4, sorting the flat single-axis photovoltaic brackets subjected to the angle adjustment in the step 3 according to the power generation amount from high to low, and selecting N t A flat single-shaft photovoltaic bracket with high power generation amount;
step 5, for the N t The flat single-axis photovoltaic bracket with high power generation amount performs cross operation, and the cross probability is P c The crossing point is C t ；
Step 6, completing primary iteration, wherein the genetic algebra t=t+1;
and 7, outputting individuals in the optimal population after the iteration termination condition is met, and decoding the chromosome to obtain the angle of the flat single-axis photovoltaic bracket, which is adjusted for the second time.
Further, the U 1 And U 2 The determining method of (1) comprises the following steps: u (U) 1 ＝U 0 -U k ，U 2 ＝U 0 +U k The method comprises the steps of carrying out a first treatment on the surface of the Wherein U is 0 The angle value is an angle value after the angle of the flat single-axis photovoltaic bracket is adjusted for the first time.
Further, the encoding the chromosome in step 2 specifically includes: binary encoding is performed on the chromosome, wherein the length of the chromosome is 16 bits, the first 8 bits of the chromosome represent the first 2 digits of the decimal point of the angle value, wherein the first 4 bits represent ten bits, the last 4 bits represent bits, and the last 8 bits of the chromosome represent the last 2 digits of the decimal point of the angle value, wherein the first 4 bits represent ten bits, and the last 4 bits represent bits.
Further, step 4 is described as N t The calculation method of the optimal individuals comprises the following steps:t is the number of genetics.
Further, the intersection point C in step 5 t The calculation formula is as follows: c (C) t =4t, t is genetic algebra.
Further, the iteration termination condition in step 7 specifically includes: setting the maximum iteration number T, and outputting an iteration result after the genetic algorithm meets the maximum iteration number.
Further, the U k The value of (2) is [1,10 ]]。
Further, in the first adjustment of the angle of the flat single-axis photovoltaic bracket, an inverse tracking algorithm or a time control algorithm is adopted to adjust the angle of the flat single-axis photovoltaic bracket for the first time.
The beneficial effects of the invention are that
According to the invention, the tracking angle is further optimized by utilizing the genetic algorithm on the basis of calculating the tracking angle of a user by using the conventional calculation method, so that the optimal angle is found, the angle deviation caused by the refraction of light rays caused by weather and the actual terrain height difference of the control algorithm can be effectively avoided, and the angle between the component and sunlight can be always kept at the position which enables the photovoltaic power generation capacity of the photovoltaic component to be maximum.
Before each iteration, sequencing the current angle values of all the brackets according to the power generation amount corresponding to the current angle of the brackets, selecting optimal individuals in a cut-off mode after sequencing, reserving partial chromosomes before the intersection of the optimal individuals after selecting the optimal individuals, and performing cross operation on partial chromosomes after the intersection to generate new individuals to finish iteration, wherein the brackets finally converge to the calculated optimal angle.
Drawings
FIG. 1 is a flowchart of a method for performing a second adjustment of the angle of a flat uniaxial photovoltaic bracket based on a genetic algorithm according to the present invention.
Detailed Description
The angle of the flat single-axis photovoltaic bracket is adjusted for the first time by adopting an inverse tracking algorithm or a time control algorithm, and the angle value U of the flat single-axis photovoltaic bracket is preliminarily determined 0 . Setting and adjusting deflection angle U k According to the first angle adjusting result of the flat single-axis photovoltaic bracket and the adjusting deflection angle U k Determining the angular extent [ U ] of the second adjustment of a flat uniaxial photovoltaic support 1 ,U 2 ]The method comprises the steps of carrying out a first treatment on the surface of the In the angle adjustment range [ U ] 1 ,U 2 ]And calculating the angle of the second adjustment of the flat single-axis photovoltaic bracket by adopting a genetic algorithm. The U is 1 And U 2 The determining method of (1) comprises the following steps: u (U) 1 ＝U 0 -U k ，U 2 ＝U 0 +U k The method comprises the steps of carrying out a first treatment on the surface of the Wherein U is 0 The angle value is an angle value after the angle of the flat single-axis photovoltaic bracket is adjusted for the first time.
Preferably, the deflection angle U is adjusted k The value of (2) is [1,10 ]]The best result of the second flat uniaxial photovoltaic bracket adjustment can be ensured.
As shown in FIG. 1, the angle adjustment range [ U ] 1 ,U 2 ]The method for calculating the angle of the second adjustment of the flat single-axis photovoltaic bracket by adopting the genetic algorithm specifically comprises the following steps:
step 1, generating a primary population based on the number M of flat single-axis photovoltaic supports in a photovoltaic array;
step 2, adjusting the range [ U ] by an angle 1 ,U 2 ]Randomly generating chromosomes for each individual in the primary population and encoding the chromosomes;
step 3, decoding the chromosome, and adjusting the angle of the flat single-axis photovoltaic bracket according to the decoded angle value;
step 4, sorting the flat single-axis photovoltaic brackets subjected to the angle adjustment in the step 3 according to the power generation amount from high to low, and selecting N t A flat single-shaft photovoltaic bracket with high power generation amount;
step 5, for the N t The flat single-axis photovoltaic bracket with high power generation amount performs cross operation, and the cross probability is P c The crossing point is C t ；
Step 6, completing primary iteration, wherein population algebra t=t+1;
step 7: judging whether the population algebra T reaches the population iteration times T, if so, completing the evolution, outputting the population optimal individuals and carrying out chromosome treatment on the population optimal individualsDecoding to obtain the angle of the flat single-axis photovoltaic bracket adjusted for the second time; if not, returning to the step 3 to continue iteration until the population iteration times T are met; wherein, the value of T is preferably 4 times, that is, 4 iterations are required for one genetic algorithm to complete the evolution. After the iteration is completed, the angle of the flat single-axis photovoltaic bracket is adjusted according to the obtained second adjustment deflection angle.
As a further optimization, the N in step 4 t The calculation method of the optimal individuals comprises the following steps:wherein N is t For optimal individual number, t is the algebra.
As a further optimization, the intersecting operation in step 5 is a single-point operation, and the intersecting position point calculation formula is: c (C) t =4t, where, C t For crossover points, t is the algebra.
Claims (9)
1. The genetic algorithm-based flat single-axis photovoltaic bracket tracking angle optimization method is characterized by comprising the following steps of:
the angle of the flat single-shaft photovoltaic bracket is adjusted for the first time;
setting and adjusting deflection angle U k According to the first angle adjusting result of the flat single-axis photovoltaic bracket and the adjusting deflection angle U k Determining the angular extent [ U ] of the second adjustment of a flat uniaxial photovoltaic support 1 ,U 2 ]The method comprises the steps of carrying out a first treatment on the surface of the In the angle adjustment range [ U ] 1 ,U 2 ]Calculating the angle of the second adjustment of the flat single-axis photovoltaic bracket by adopting a genetic algorithm;
and adjusting the angle of the flat single-axis photovoltaic bracket according to the calculated second adjusting deflection angle.
2. The genetic algorithm-based flat uniaxial photovoltaic bracket tracking angle optimization method according to claim 1, wherein the angle adjustment range [ U ] 1 ,U 2 ]The calculating of the angle of the second adjustment of the flat single-axis photovoltaic bracket by adopting the genetic algorithm comprises the following steps:
step 1, generating a primary population based on the number M of flat single-axis photovoltaic supports in a photovoltaic array;
step 2, adjusting the angle range [ U ] for the second time according to the flat single-axis photovoltaic bracket 1 ,U 2 ]Randomly generating chromosomes for each individual in the primary population and encoding the chromosomes;
step 3, decoding the chromosome, and adjusting the angle of the flat single-axis photovoltaic bracket according to the decoded angle value;
step 4, sorting the flat single-axis photovoltaic brackets subjected to the angle adjustment in the step 3 according to the power generation amount from high to low, and selecting N t A flat single-shaft photovoltaic bracket with high power generation amount;
step 5, for the N t The flat single-axis photovoltaic bracket with high power generation amount performs cross operation, and the cross probability is P c The crossing point is C t ；
Step 6, completing primary iteration, wherein the genetic algebra t=t+1;
and 7, outputting individuals in the optimal population after the iteration termination condition is met, and decoding the chromosome to obtain the angle of the flat single-axis photovoltaic bracket, which is adjusted for the second time.
3. The genetic algorithm-based flat uniaxial photovoltaic bracket tracking angle optimization method according to claim 1, wherein the U is as follows 1 And U 2 The determining method of (1) comprises the following steps: u (U) 1 ＝U 0 -U k ，U 2 ＝U 0 +U k The method comprises the steps of carrying out a first treatment on the surface of the Wherein U is 0 The angle value is an angle value after the angle of the flat single-axis photovoltaic bracket is adjusted for the first time.
4. The genetic algorithm-based flat uniaxial photovoltaic bracket tracking angle optimization method according to claim 2, wherein the encoding of the chromosome in step 2 specifically comprises: binary encoding is performed on the chromosome, wherein the length of the chromosome is 16 bits, the first 8 bits of the chromosome represent the first 2 digits of the decimal point of the angle value, wherein the first 4 bits represent ten bits, the last 4 bits represent bits, and the last 8 bits of the chromosome represent the last 2 digits of the decimal point of the angle value, wherein the first 4 bits represent ten bits, and the last 4 bits represent bits.
5. The genetic algorithm-based flat uniaxial photovoltaic bracket tracking angle optimization method according to claim 4, wherein the N is as described in the step 4 t The calculation method of the optimal individuals comprises the following steps:t is the number of genetics.
6. The genetic algorithm-based flat uniaxial photovoltaic bracket tracking angle optimization method according to claim 4, wherein the intersecting position point C in the step 5 t The calculation formula is as follows: c (C) t =4t, t is genetic algebra.
7. The method for optimizing tracking angles of flat uniaxial photovoltaic brackets based on genetic algorithm according to any one of claims 2 to 6, wherein the iteration termination condition in step 7 specifically comprises: setting the maximum iteration number T, and outputting an iteration result after the genetic algorithm meets the maximum iteration number.
8. The genetic algorithm-based flat uniaxial photovoltaic bracket tracking angle optimization method according to any one of claims 1 to 6, wherein the deflection angle U is adjusted k The value of (2) is [1,10 ]]。
9. The genetic algorithm-based flat uniaxial photovoltaic bracket tracking angle optimization method according to any one of claims 1 to 6, wherein in the first adjustment of the flat uniaxial photovoltaic bracket angle, an inverse tracking algorithm or a time control algorithm is adopted to perform the first adjustment of the flat uniaxial photovoltaic bracket angle.
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