CN114510110A - Photovoltaic maximum power point tracking method and device - Google Patents
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Abstract
The invention provides a photovoltaic maximum power point tracking method and device. The method comprises the steps of constructing a mixed algorithm combining a hunting deer algorithm and an earthworm algorithm; performing performance test on the hybrid algorithm based on the benchmark function test; constructing a photovoltaic maximum power point tracking algorithm based on a hybrid algorithm; and performing performance verification on the photovoltaic maximum power point tracking algorithm based on different irradiance conditions and shielding conditions of the photovoltaic system. The invention combines the hunting deer algorithm and the earthworm algorithm at the same time, realizes the advantage complementation of the two algorithms, further balances the exploration and development capability of the obtained mixed algorithm in the searching process, and can effectively improve the tracking precision of the global maximum power point of the photovoltaic system; before being applied to a photovoltaic maximum power point tracking controller, performance verification is carried out through a reference function test, and the safety and the effectiveness of an application process are theoretically ensured; different irradiance conditions and shielding conditions of the photovoltaic system are fully considered, and the universality can be effectively ensured.
Description
Technical Field
The invention belongs to the technical field of photovoltaic power generation, and particularly relates to a photovoltaic maximum power point tracking method and device.
Background
Energy occupies a great position in the global economic development, and particularly in the current era where the energy internet is increasingly popular, the energy industry is particularly taken as a pillar industry and is closely related to the development of the world. Each main department of a country depends heavily on energy, and a power failure with a short duration may cause domino effect, and a series of faults from a power grid to communication to national security are brought. However, the current situation that traditional energy sources such as coal, natural gas and fossil fuel are rapidly exhausted and the crisis of the ecological environment caused by the traditional energy sources indicate that the development of renewable alternative energy sources such as solar energy, wind energy, geothermal energy and tidal energy is urgent. Particularly, the cost benefit of renewable energy is equivalent to that of traditional energy such as coal, natural gas and the like, so that the renewable energy is used for replacing the traditional energy, and clean utilization of the energy can be realized on the basis of not increasing the cost.
Among a plurality of renewable energy sources, solar energy is popular due to the characteristics of abundant reserves, cleanness, no noise and the like, and the rapid development and utilization of independent and grid-connected photovoltaic systems in various countries in the world are promoted. However, despite the benefits of solar energy, one of the major problems affecting its efficiency is its complete dependence on weather conditions, such as temperature and irradiance levels, such that the output of the photovoltaic system is non-linear. At a certain temperature and irradiance level, a Maximum Power Point (MPP) exists on the I-V curve and the P-V curve of the photovoltaic system, and the maximum power point is unique only when the photovoltaic array receives a uniform irradiance level. Once the external conditions change, the amplitude and the number of the maximum power points also change. Therefore, how to acquire and accurately track the maximum power point in the photovoltaic system becomes a problem to be solved by those skilled in the art.
Disclosure of Invention
The invention aims to at least solve one of the technical problems in the prior art and provides a photovoltaic maximum power point tracking method and device.
In one aspect of the present invention, a photovoltaic maximum power point tracking method is provided, where the method includes:
constructing a mixed algorithm combining a hunting deer algorithm and an earthworm algorithm;
performing performance test on the hybrid algorithm based on a benchmark function test;
constructing a photovoltaic maximum power point tracking algorithm based on the hybrid algorithm;
and performing performance verification on the photovoltaic maximum power point tracking algorithm based on different irradiance conditions and shielding conditions of the photovoltaic system.
In some embodiments, the hunting deer algorithm comprises:
initializing algorithm parameters and hunter population: hunter population H by PHThe maximum iteration number of the optimization process is MHRandomly initializing the position of hunter population to obtain
Wherein the content of the first and second substances,
respectively representing the positions of all hunters in the hunter population H;
initializing wind direction angle and position angle: the initial settings of the wind direction angle and the position angle are given by the following expression (1) and the following expression (2), respectively, based on the circumference of the circle:
φk＝2π·rand (1)
where k is the current iteration time, φkAnd
respectively, the wind direction angle and the position angle at the iteration time k, and rand is randomly selected from [0,1]]A constant value of (a);
updating the position of the hunter: defining a fitness function according to optimization requirements, calculating the fitness corresponding to the position of each hunter after initialization, taking the position of a candidate solution with the optimal fitness as a position space closest to the optimal solution, and then updating the position of the hunter by the following two ways:
location update based on leader location: assuming that an existing hunter determines the best hunt location, then it is considered as the leader, and the remaining hunters all attempt to reach a more optimal location through their own location updates, the bounding behavior based on hunter location updates is expressed as (3) below:
Hk+1＝Hlead-M·c|R×Hlead-Hk| (3)
in the above formula (3): hk+1And HkRepresenting hunter positions at iteration time k +1 and iteration time k, respectively, HleadIs the position of the leader, and c is taken from (0, 2)]M and R are parameter vectors, wherein:
R＝2rd (5)
rd is a random number in an interval of [0,1], and a is a coefficient vector in the measurement process;
the hunter also updates the position according to the position angle, the hunting process is effective under the preset position angle, and on the basis, a new parameter ds is developed according to the variance between the wind direction angleskUpdating the position angle to give the visual angle vs of the preykIs the following formula (8):
dsk＝φk-vsk (6)
position angle based on post-update iteration time k +1
And (3) correcting the position of the hunter:
location update based on successor location: the hunting process follows a bounding mechanism for modifying the vector R in the exploration phase, employs a random search to broaden the search range during the initial search, and assumes that the value of R is not less than 1, at which time the hunter position is updated based on the position of the successor, resulting in the following equation (10), HsuccessorJudging the value of R for the position of the successor, if the value of R is less than 1, randomly selecting a search agent, otherwise, correcting the position of the search agent by depending on the optimal solution:
Hk+1＝Hsuccessor-M·c|R×Hsuccessor-Hk| (10)；
judging whether the algorithm searching process is terminated: and stopping iteration when the optimal hunting position is determined or the maximum iteration number is reached, and finishing the optimization process.
In some embodiments, the earthworm algorithm comprises:
propagation type 1:
the position of earthworm is marked as Tl,mThe new position of earthworm is marked as Tl1,mThe lower and upper positions of earthworm are denoted as Tmax,mAnd Tmin,mIf the similarity factor determining the distance between the parent object and the child object is denoted as η, the process of breeding earthworms is expressed by the following formula (11):
Tl1,m＝Tmax,m+Tmin,m-ηTl,m (11)
local search occurs at Tl1Approach to TlIn which T isl1Updated earthworm position according to reproduction type 1, TlUpdating the positions of the earthworms before according to the breeding type 1;
if η is 0, then there is the following formula (12):
Tl1,m＝Tmax,m+Tmin,m (12)；
when η is 1, a global search shown by the following equation (13) is performed:
Tl1,m＝Tmax,m+Tmin,m-Tl,m (13)；
propagation type 2:
when N is 1,2 or 3, determining the position T of the earthworms updated according to the propagation type 2 by using N offspringl2Represented by the following formula (14):
wherein, taumThe weight coefficient is referred to, and is found by the following equation (15):
wherein G iszIndicating the fitness of the z-th offspring, the location of the next generation earthworm l is determined according to the following formula (16):
wherein the propagation coefficient
Is expressed as the following equation (17), and λ represents a constant of the cooling coefficient:
in some embodiments, the constructing a hybrid algorithm that combines a hunting deer algorithm and an earthworm algorithm comprises:
updating a solution based on the leader fitness and the leader position by adopting the hunting deer algorithm;
after the hunting deer algorithm updates the solution, performing a next update process using the earthworm algorithm, selecting a leader position from all solutions by fitness evaluation, the update process of the selected leader being completed using the following equation (18) together with the earthworm algorithm;
calculating a fitness function of the updated solution, and adding a calculation model in the earthworm algorithm to obtain the following formula (18) and the following formula (19):
Tl',m＝Tl,m+WFm*x (19)
wherein PN represents the total number of population individuals, WFmRepresenting a weight vector, x representing a random number extracted from the cauchy distribution;
and calculating a fitness function after each iteration, terminating the optimization process when the minimum fitness is reached, evaluating the fitness by aiming at the whole solution set updated in the hunting deer algorithm, selecting a solution with the optimal fitness from all solutions as a leader, and updating the position of the selected leader by using the earthworm algorithm.
In some embodiments, the benchmark function test, which performs a performance check on the hybrid algorithm, includes:
selecting a preset number of functions from a unimodal function, a simple multimodal function, a mixing function and a synthesis function as test functions respectively based on a preset optimization algorithm reference function library;
defining the error-based indicator function as follows (20):
wherein i represents the number of the selected reference function; fi(x) Represents the actual value of the reference function and,
representing the optimal value of the corresponding reference function, and Fit representing the error between the actual value and the optimal value of the reference function in the optimizing process;
and taking the particle swarm algorithm, the wolf algorithm, the hunting deer algorithm and the earthworm algorithm as comparison algorithms, and respectively carrying out statistical analysis on the reference function performance index values under the optimization of each comparison algorithm and the mixing algorithm so as to test the performance of the mixing algorithm.
In some embodiments, the building a photovoltaic maximum power point tracking algorithm based on the hybrid algorithm includes:
introducing the hybrid algorithm into a photovoltaic maximum power point tracking control system, wherein the photovoltaic maximum power point tracking control system comprises a photovoltaic array, a voltage converter, a photovoltaic maximum power point tracking controller and a load, the photovoltaic maximum power point tracking algorithm is packaged in the photovoltaic maximum power point tracking controller, and the output power is adjusted by adjusting the duty ratio d of a pulse width modulation signal in the voltage converter;
taking the maximum output power P as a fitness function, and setting a duty ratio d as a variable to be optimized;
performing iterative optimization according to the steps of the hybrid algorithm until the iteration times reach the upper limit or the maximum output power P and the theoretical maximum power output P*The error of (2) meets the preset precision requirement, and the optimal duty ratio d obtained at the moment is recorded*。
In some embodiments, the performing the performance verification on the photovoltaic maximum power point tracking algorithm based on different irradiance conditions and shading conditions of the photovoltaic system comprises:
dividing the different irradiance conditions into uniform irradiance conditions and variable irradiance conditions in view of the spatiotemporal variation characteristics of the irradiance;
dividing the occlusion condition into a non-occlusion condition and a local occlusion condition;
respectively taking the non-shielding condition and the local shielding condition as large classes, taking the uniform irradiance condition and the variable irradiance condition as first sub-classes of each large class, selecting a preset number of different conditions from each first sub-class as second sub-classes of each first sub-class, and numbering all the second sub-classes;
and sequentially completing the performance test of the photovoltaic maximum power point tracking algorithm according to the number of the second subclass, and obtaining a corresponding maximum power tracking curve and a tracking error curve so as to complete the performance verification of the photovoltaic maximum power point tracking algorithm.
In another aspect of the present invention, there is provided a photovoltaic maximum power point tracking apparatus, the apparatus including:
the first construction module is used for constructing a mixed algorithm combining a hunting deer algorithm and an earthworm algorithm;
the inspection module is used for carrying out performance inspection on the hybrid algorithm based on a benchmark function test;
the second construction module is used for constructing a photovoltaic maximum power point tracking algorithm based on the hybrid algorithm;
and the verification module is used for verifying the performance of the photovoltaic maximum power point tracking algorithm based on different irradiance and shielding conditions of the photovoltaic system.
In another aspect of the present invention, an electronic device is provided, including:
one or more processors;
a storage unit for storing one or more programs which, when executed by the one or more processors, enable the one or more processors to implement the method according to the preceding description.
In another aspect of the invention, a computer-readable storage medium is provided, on which a computer program is stored, which computer program, when being executed by a processor, is adapted to carry out the method according to the above.
The photovoltaic maximum power point tracking method and the device provided by the invention combine the hunting deer algorithm and the earthworm algorithm, realize the advantage complementation of the two algorithms, further balance the exploration and development capability of the obtained mixed algorithm in the searching process, and can effectively improve the tracking precision of the global maximum power point of the photovoltaic system; before being applied to a photovoltaic maximum power point tracking controller, performance verification is carried out through a reference function test, and the safety and the effectiveness of an application process are theoretically ensured; different irradiance conditions and shielding conditions of the photovoltaic system are fully considered, and the universality can be effectively ensured.
Drawings
Fig. 1 is a flowchart of a photovoltaic maximum power point tracking method according to the present invention;
fig. 2 is a flowchart of another photovoltaic maximum power point tracking method proposed by the present invention;
FIG. 3 is a flow chart of a hunting deer algorithm employed in the present invention;
FIG. 4 is a flow chart of a hybrid algorithm incorporating the hunting deer algorithm and the earthworm algorithm employed in the present invention;
fig. 5 is a schematic structural diagram of a photovoltaic maximum power point tracking device according to the present invention.
Detailed Description
In order to make the technical solutions of the present invention better understood, the present invention will be described in further detail with reference to the accompanying drawings and specific embodiments.
One aspect of the present embodiment, as shown in fig. 1 and combined with fig. 2, relates to a method S100, where the method S100 includes:
and S110, constructing a mixed algorithm combining a hunting deer algorithm and an earthworm algorithm.
Specifically, the hunting Deer (DHO) algorithm is first introduced. Hunting deer algorithm is inspired from human hunting behavior on deer. Finding the best position of the hunting deer in the hunting deer algorithm is the key of the search process, since individuals in the deer flock can quickly find the hunter and avoid it by its unique advantages in vision, smell and hearing.
Illustratively, as shown in fig. 3, the optimization process of the hunting deer algorithm mainly consists of the following four stages:
s111, initializing algorithm parameters and hunting population: hunter population H by PHThe maximum iteration number of the optimization process is MHRandomly initializing the position of hunter population to obtain
Wherein the content of the first and second substances,
the positions of the hunters in the hunter population H are indicated separately.
S112, initializing wind direction angle and position angle: the wind direction angle and the position angle are key influence factors for a hunter to select an ideal position of a hunting deer, and the initialization result of the wind direction angle and the position angle has important influence on the efficiency of the hunting deer. Therefore, the initial settings of the wind direction angle and the position angle are given by the following expression (1) and the following expression (2), respectively, on the basis of the circumference of the circle:
φk＝2π·rand (1)
where k is the current iteration time, φkAnd
respectively, the wind direction angle and the position angle at the iteration time k, and rand is randomly selected from [0,1]]Is constant.
S113, hunter position updating: defining a fitness function according to optimization requirements, calculating the fitness corresponding to the position of each hunter after initialization, taking the position of a candidate solution with the optimal fitness as a position space of which the optimal space is closest to the optimal solution, and then updating the position of the hunter by the following two ways:
location update based on leader location: assuming that an existing hunter determines the best hunt location, then it is considered as the leader, and the remaining hunters all attempt to reach a more optimal location through their own location updates, the bounding behavior based on hunter location updates is expressed as (3) below:
Hk+1＝Hlead-M·c|R×Hlead-Hk| (3)
in the above formula (3): hleadTo the location of the leader, Hk+1And HkRepresenting the hunter positions at iteration time k +1 and k, respectively, c being taken from (0, 2)]M and R are parameter vectors, wherein:
R＝2rd (5)
rd is a random number in the interval [0,1], and a is a coefficient vector in the measurement process, and 2 is taken here.
Besides, the hunter can also update the position according to the position angle to achieve the aim of improving the search space. The hunting process is effective under the preset position angle, and on the basis, a new parameter ds is developed according to the variance between the wind direction angleskUpdating the position angle and giving the visual angle vs of the preykIs the following formula (8):
dsk＝φk-vsk (6)
position angle based on post-update iteration time k +1
And (3) correcting the position of the hunter:
location update based on successor location: the hunting process follows a bounding mechanism for modifying the vector R in the exploration phase, employs a random search to broaden the search range during the initial search, and assumes that the value of R is not less than 1, at which time the hunter position is updated based on the position of the successor, resulting in the following equation (10), HsuccessorJudging the value of R for the position of the successor, if the value of R is less than 1, randomly selecting a search agent, otherwise, correcting the position of the search agent by depending on the optimal solution:
Hk+1＝Hsuccessor-M·c|R×Hsuccessor-Hk| (10)；
s114, judging whether the algorithm searching process is terminated: and stopping iteration when the optimal hunting position is determined or the maximum iteration number is reached, and finishing the optimization process.
The Earthworm (EW) algorithm is described below. The earthworm algorithm is inspired on the contribution of earthworms to the nature. There are two main processes for breeding earthworms, which are respectively designated as breeding type 1 and breeding type 2.
Propagation type 1:
since earthworms belong to hermaphrodite and can lay down earthworm larvae by a single parent, the position of earthworm l is marked as Tl,mThe new position of earthworm is marked as Tl1,mThe lower and upper positions of earthworm are denoted as T respectivelymax,mAnd Tmin,mAnd the similarity factor determining the distance between the parent object and the child object is recorded as eta, the breeding process of the earthworms can be expressed by the following formula (11) by using a mathematical expression:
Tl1,m＝Tmax,m+Tmin,m-ηTl,m (11)。
when the similarity factor η is small, the distance between the parent object and the child object is short.
Local search occurs at Tl1Approach to TlIn which T isl1Updated earthworm position according to reproduction type 1, TlThe positions of the earthworms before the updating according to the breeding type 1.
If η is 0, the distance between the parent object and the child object will be large, and there is the following equation (12):
Tl1,m＝Tmax,m+Tmin,m (12)。
when η is 1, a global search shown by the following equation (13) is performed, and this process is also considered as an optimization-based learning method:
Tl1,m＝Tmax,m+Tmin,m-Tl,m (13)；
propagation type 2:
when N is 1,2 or 3, determining the position T of the earthworms updated according to the propagation type 2 by using N offspringl2Represented by the following formula (14):
wherein, taumThe weight coefficient is referred to, and is found by the following equation (15):
wherein G iszIndicating the fitness of the z-th offspring, the location of the next generation earthworm l is determined according to the following formula (16):
as can be seen from the above formula (16), the propagation coefficient was determined by 2 stages of propagation
Can be aligned with Tl1And Tl2Corrected and propagation coefficients are obtained
Is expressed as the following equation (17), where λ represents a constant of the cooling coefficient, which is 0.8:
the hunting deer algorithm is good at solving the complete testing problem and has strong competitiveness and logistical properties compared with other algorithms. The earthworm algorithm is more suitable for parallel computing and allows coordination between reinforcement and diversification. However, the traditional hunting deer algorithm also has the non-negligible defects, such as high probability of trapping in the local optimal solution, gradually slow down search speed and the like. Therefore, to overcome the disadvantages, the earthworm algorithm is combined with the traditional hunting deer algorithm to form the EW-DHO hybrid algorithm shown in fig. 4, i.e. a hybrid algorithm combining the hunting deer algorithm and the earthworm algorithm is constructed. The algorithm not only has high balance but also shows strong area searching capability. In addition, the robustness and reliability of the EW-DHO hybrid algorithm is significant.
Referring to fig. 4, the EW-DHO hybrid algorithm first employs the hunting algorithm to update the solution based on leader fitness and leader position, and after the hunting algorithm updates the solution, the next update process is performed using the earthworm algorithm. A leader position is selected from all solutions through adaptability evaluation, the updating process of the selected leader is completed by using the following formula (18) and a calculation model of an earthworm algorithm, and then a fitness function of the updated solution is calculated. In order to avoid local optimization and enhance the earthworm searching capability, a calculation model is added in the earthworm algorithm and is integrated into the earthworm algorithm to obtain the following formula (18) and the following formula (19). And calculating a fitness function after each iteration, terminating the optimization process when the minimum fitness is reached, evaluating the fitness by aiming at the whole solution set updated in the hunting deer algorithm, selecting a solution with the optimal fitness from all solutions as a leader, and updating the position of the selected leader by using a calculation model of an earthworm algorithm.
T’l,m＝Tl,m+WFm*x (19)
Wherein PN represents the total number of population individuals, and in this example, PN is 30, WFmRepresenting a weight vector and x representing a random number extracted from the cauchy distribution. The obtained new fitness is compared to the leader fitness before the model update is computed. If the new fitness value is less than the leader fitness, the leader fitness and solution will be replaced with the new fitness and solution.
Specifically, as shown in fig. 4, after the EW-DHO hybrid algorithm is started, algorithm parameters and population initialization are first performed, and whether the current iteration time k is less than the maximum iteration number M or not is determinedHIf the current iteration time k is less than the maximum iteration times MHCalculating individual fitness in the population, updating the position angle ds, the visual angle vs, the wind direction angle constant c, the parameter vectors M and R and the coefficient vector a, judging whether c is less than 1 or not, and calculating the fitness of individuals in the population according to the judgment result>1, respectively updating the positions by the above formula (2) and the above formula (9), updating the positions of individuals in the population by the above formula (7), calculating the fitness to obtain the position of a leader, calculating the fitness and performing greedy selection according to the Cauchy variation updating position of the EW, judging whether a termination condition is reached, if the termination condition is reached, finishing the algorithm, and if the termination condition is not reached, returning to the step of judging whether the current iteration time k is less than the maximum iteration time MHThe step (2).
And S120, performing performance test on the hybrid algorithm based on the benchmark function test.
Specifically, since the benchmark function test is an essential part for the performance verification of the bionic algorithm, the benchmark function test is performed on the EW-DHO hybrid algorithm constructed in the above step.
Illustratively, step S120 may include:
CEC14 is a common baseline function library for optimization algorithms, which includes 30 baseline functions, classified into unimodal, simple multimodal, hybrid and synthetic 4. Based on the optimization algorithm reference function library, for testing the universality and excellent performance of the algorithm, a preset number of functions are respectively selected from the 4 types of functions to serve as test functions. In this embodiment, 5 functions are selected from the 4 classes of functions to be tested, and the test functions include 20 functions in total.
In order to visually represent the performance of the algorithm, index quantization is required to be carried out on the algorithm, and an error-based index function is defined as the following formula (20):
wherein, i represents the number of the selected reference function, and since the test function selected in this embodiment includes 20 functions in total, i is 1,2, …, 20; fi(x) Represents the actual value of the reference function and,
representing the optimal value of the corresponding reference function, and Fit representing the error between the actual value and the optimal value of the reference function in the optimizing process;
in order to further show the advantages of the constructed EW-DHO hybrid algorithm, the particle swarm algorithm, the wolf algorithm, the hunting deer algorithm and the earthworm algorithm are used as comparison algorithms, and the reference function performance index values under the optimization of each comparison algorithm and the hybrid algorithm are subjected to statistical analysis respectively to check the performance of the EW-DHO hybrid algorithm and highlight the advantages of the EW-DHO hybrid algorithm in the aspects of search precision and speed.
S130, constructing a photovoltaic maximum power point tracking algorithm based on the hybrid algorithm.
Specifically, in consideration of the strong optimization capability of the hybrid algorithm constructed in step S120, the hybrid algorithm is applied to the design of the photovoltaic maximum power point tracking algorithm of the photovoltaic system in this step.
Illustratively, the step S130 may specifically include the following steps:
firstly, a photovoltaic maximum power point tracking control system in a photovoltaic system is introduced. The photovoltaic maximum power point tracking control system comprises a photovoltaic array, a voltage converter, a photovoltaic maximum power point tracking controller and a load, wherein the photovoltaic maximum power point tracking algorithm is packaged in the photovoltaic maximum power point tracking controller, and the output power is adjusted by adjusting the duty ratio d of a pulse width modulation signal in the voltage converter.
And introducing the hybrid algorithm constructed in the steps into a photovoltaic maximum power point tracking controller of a photovoltaic maximum power point tracking control system, taking the maximum output power P as a fitness function, and setting the duty ratio d as a variable to be optimized. Iterative optimization is carried out by following the steps of a hybrid algorithm until the iteration times reach the upper limit or the maximum output power P and the theoretical maximum power output P are reached*The error of (2) meets the preset precision requirement, and the optimal duty ratio d obtained at the moment is recorded*。
S140, performing performance verification on the photovoltaic maximum power point tracking algorithm based on different irradiance conditions and shielding conditions of the photovoltaic system.
Specifically, considering the influence of weather conditions and external shielding conditions on the photovoltaic array, the global maximum power point tracking effect on the photovoltaic system under different irradiance and shielding conditions needs to be fully considered in the step.
Illustratively, the step S140 may include the steps of:
solar irradiance has the most significant effect on photovoltaic system output power, among many weather factors, and different irradiance conditions are classified into uniform irradiance conditions and variable irradiance conditions in view of the spatiotemporal variation characteristics of irradiance.
A plurality of local maximum power points can appear in the photovoltaic array under local shielding, and the conditions such as dust deposition, leaf fall, building shielding and the like in the nature are difficult to avoid, so that the shielding condition can be divided into a non-shielding condition and a local shielding condition.
Respectively taking the non-shielding condition and the local shielding condition as large classes, taking the uniform irradiance condition and the variable irradiance condition as first sub-classes of each large class, selecting a preset number of different conditions from each first sub-class as second sub-classes of each first sub-class, and numbering all the second sub-classes. The embodiment depends on a photovoltaic array formed by connecting 3 photovoltaic components in series, and the selected irradiance conditions are as shown in the following table 1:
TABLE 1
By fully considering various conditions including a non-shielding condition and a local shielding condition as well as a uniform irradiance condition and a variable irradiance condition, the universality of the photovoltaic maximum power point tracking method provided by the embodiment is improved.
And sequentially completing the performance test of the photovoltaic maximum power point tracking algorithm according to the number of the second subclass, namely the condition number in the table 1, and drawing to obtain a corresponding maximum power tracking curve and a tracking error curve so as to complete the performance verification of the photovoltaic maximum power point tracking algorithm and visually indicate the overall maximum power point tracking precision and rapidity of the algorithm.
The photovoltaic maximum power point tracking method provided by the invention combines the hunting deer algorithm and the earthworm algorithm, realizes the advantage complementation of the two algorithms, further balances the exploration and development capabilities of the obtained hybrid algorithm in the searching process, and can effectively improve the tracking precision of the global maximum power point of the photovoltaic system. Meanwhile, before the photovoltaic maximum power point tracking method is applied to a photovoltaic maximum power point tracking controller, performance verification is carried out through a reference function test, and safety and effectiveness of an application process are guaranteed theoretically. In addition, the photovoltaic maximum power point tracking method provided by the invention fully considers different irradiance conditions and shielding conditions of the photovoltaic system, and can effectively ensure the universality of the provided photovoltaic maximum power point tracking method.
In another aspect of the present invention, as shown in fig. 5, a photovoltaic maximum power point tracking apparatus 100 is provided, and the apparatus 100 is suitable for the method described above. The apparatus 100 comprises:
the first construction module 110 is used for constructing a mixed algorithm combining a hunting deer algorithm and an earthworm algorithm;
a checking module 120, configured to perform performance checking on the hybrid algorithm based on a benchmark function test;
a second constructing module 130, configured to construct a photovoltaic maximum power point tracking algorithm based on the hybrid algorithm;
the verification module 140 is configured to perform performance verification on the photovoltaic maximum power point tracking algorithm based on different irradiance and shielding conditions of the photovoltaic system.
The photovoltaic maximum power point tracking device provided by the invention combines the hunting deer algorithm and the earthworm algorithm, realizes the advantage complementation of the two algorithms, further balances the exploration and development capabilities of the obtained hybrid algorithm in the searching process, and can effectively improve the tracking precision of the global maximum power point of the photovoltaic system. Meanwhile, before the photovoltaic maximum power point tracking device provided by the invention is applied to a photovoltaic maximum power point tracking controller, performance verification is carried out through a reference function test, and the safety and the effectiveness of an application process are theoretically ensured. In addition, the photovoltaic maximum power point tracking device provided by the invention fully considers different irradiance conditions and shielding conditions of a photovoltaic system, and can effectively ensure the universality of the provided photovoltaic maximum power point tracking device.
In another aspect of the present invention, an electronic device is provided, including:
one or more processors;
a storage unit for storing one or more programs which, when executed by the one or more processors, enable the one or more processors to implement the method according to the preceding description.
In another aspect of the invention, a computer-readable storage medium is provided, on which a computer program is stored, which computer program, when being executed by a processor, is adapted to carry out the method according to the above.
The computer readable medium may be included in the apparatus, device, system, or may exist separately.
The computer readable storage medium may be any tangible medium that can contain or store a program, and may be an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, more specific examples of which include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, an optical fiber, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof.
The computer readable storage medium may also include a propagated data signal with computer readable program code embodied therein, for example, in a non-transitory form, such as in a carrier wave or in a carrier wave, wherein the carrier wave is any suitable carrier wave or carrier wave for carrying the program code.
It will be understood that the above embodiments are merely exemplary embodiments taken to illustrate the principles of the present invention, which is not limited thereto. It will be apparent to those skilled in the art that various modifications and improvements can be made without departing from the spirit and substance of the invention, and these modifications and improvements are also considered to be within the scope of the invention.
Claims (10)
1. A photovoltaic maximum power point tracking method, the method comprising:
constructing a mixed algorithm combining a hunting deer algorithm and an earthworm algorithm;
performing performance test on the hybrid algorithm based on a benchmark function test;
constructing a photovoltaic maximum power point tracking algorithm based on the hybrid algorithm;
and performing performance verification on the photovoltaic maximum power point tracking algorithm based on different irradiance conditions and shielding conditions of the photovoltaic system.
2. The method of claim 1, wherein said hunting algorithm comprises:
initializing algorithm parameters and hunter population: hunter population H by PHThe maximum iteration number of the optimization process is MHRandomly initializing the position of hunter population to obtain
Wherein the content of the first and second substances,
respectively representing the positions of all hunters in the hunter population H;
initializing wind direction angle and position angle: the initial settings of the wind direction angle and the position angle are given by the following expression (1) and the following expression (2), respectively, based on the circumference of the circle:
φk＝2π·rand (1)
where k is the current iteration time, φkAnd
respectively, the wind direction angle and the position angle at the iteration time k, and rand is randomly selected from [0,1]]A constant value of (a);
updating the position of the hunter: defining a fitness function according to optimization requirements, calculating the fitness corresponding to the position of each hunter after initialization, taking the position of a candidate solution with the optimal fitness as a position space closest to the optimal solution, and then updating the position of the hunter by the following two ways:
location update based on leader location: assuming that an existing hunter determines the best hunt location, then it is considered as the leader, and the remaining hunters all attempt to reach a more optimal location through their own location updates, the bounding behavior based on hunter location updates is expressed as (3) below:
Hk+1＝Hlead-M·c|R×Hlead-Hk| (3)
in the above formula (3): hk+1And HkRepresenting hunter positions at iteration time k +1 and iteration time k, respectively, HleadIs the position of the leader, and c is taken from (0, 2)]M and R are parameter vectors, wherein:
R＝2rd (5)
rd is a random number in an interval of [0,1], and a is a coefficient vector in the measurement process;
the hunter also updates the position according to the position angle, the hunting process is effective under the preset position angle, and on the basis, a new parameter ds is developed according to the variance between the wind direction angleskUpdating the position angle to give the visual angle vs of the preykIs the following formula (8):
dsk＝φk-vsk (6)
position angle based on post-update iteration time k +1
And (3) correcting the position of the hunter:
location update based on successor location: the hunting process follows a bounding mechanism that is used to correct the vector R of the exploration phase, during the initial search processRandom search is adopted to widen the search range, and assuming that the value of R is not less than 1, at this time, the hunter position is updated based on the position of the successor, resulting in the following expression (10), HsuccessorJudging the value of R for the position of the successor, if the value of R is less than 1, randomly selecting a search agent, otherwise, correcting the position of the search agent by depending on the optimal solution:
Hk+1＝Hsuccessor-M·c|R×Hsuccessor-Hk| (10)；
judging whether the algorithm searching process is terminated: and stopping iteration when the optimal hunting position is determined or the maximum iteration number is reached, and finishing the optimization process.
3. The method of claim 1, wherein the earthworm algorithm comprises:
propagation type 1:
the position of earthworm is marked as Tl,mThe new position of earthworm is marked as Tl1,mThe lower and upper positions of earthworm are denoted as Tmax,mAnd Tmin,mIf the similarity factor determining the distance between the parent object and the child object is denoted as η, the process of breeding earthworms is expressed by the following formula (11):
Tl1,m＝Tmax,m+Tmin,m-ηTl,m (11)
local search occurs at Tl1Approach to TlIn which T isl1Updated earthworm position according to reproduction type 1, TlUpdating the positions of the earthworms before according to the breeding type 1;
if η is 0, then there is the following formula (12):
Tl1,m＝Tmax,m+Tmin,m (12)；
when η is 1, a global search shown by the following equation (13) is performed:
Tl1,m＝Tmax,m+Tmin,m-Tl,m (13)；
propagation type 2:
when N is 1,2 or 3, determining the position T of the earthworms updated according to the propagation type 2 by using N offspringl2Represented by the following formula (14):
wherein, taumThe weight coefficient is referred to, and is found by the following equation (15):
wherein G iszIndicating the fitness of the z-th offspring, the location of the next generation earthworm l is determined according to the following formula (16):
wherein the propagation coefficient
Is expressed as the following equation (17), and λ represents a constant of the cooling coefficient:
4. the method of claim 3, wherein constructing a hybrid algorithm that combines the hunting deer algorithm and the earthworm algorithm comprises:
updating a solution based on the leader fitness and the leader position by adopting the hunting deer algorithm;
after the hunting deer algorithm updates the solution, performing a next update process using the earthworm algorithm, selecting a leader position from all solutions by fitness evaluation, the update process of the selected leader being completed using the following equation (18) together with the earthworm algorithm;
calculating a fitness function of the updated solution, and adding a calculation model in the earthworm algorithm to obtain the following formula (18) and the following formula (19):
Tl',m＝Tl,m+WFm*x (19)
wherein PN represents the total number of population individuals, WFmRepresenting a weight vector, x representing a random number extracted from the cauchy distribution;
and calculating a fitness function after each iteration, terminating the optimization process when the minimum fitness is reached, evaluating the fitness by aiming at the whole solution set updated from the hunting deer algorithm, selecting a solution with the optimal fitness from all solutions as a leader, and updating the position of the selected leader by using the earthworm algorithm.
5. The method of claim 4, wherein the benchmark function test, performing a performance check on the hybrid algorithm, comprises:
selecting a preset number of functions from a unimodal function, a simple multimodal function, a mixing function and a synthesis function as test functions respectively based on a preset optimization algorithm reference function library;
defining the error-based indicator function as follows (20):
Fit＝Fi(x)-Fi * (20)
wherein i represents the number of the selected reference function; fi(x) Representing the actual value of the reference function, Fi *Representing the optimal value of the corresponding reference function, and Fit representing the error between the actual value and the optimal value of the reference function in the optimizing process;
and taking the particle swarm algorithm, the wolf algorithm, the hunting deer algorithm and the earthworm algorithm as comparison algorithms, and respectively carrying out statistical analysis on the reference function performance index values under the optimization of each comparison algorithm and the mixing algorithm so as to test the performance of the mixing algorithm.
6. The method of claim 5, wherein constructing a photovoltaic maximum power point tracking algorithm based on the hybrid algorithm comprises:
introducing the hybrid algorithm into a photovoltaic maximum power point tracking control system, wherein the photovoltaic maximum power point tracking control system comprises a photovoltaic array, a voltage converter, a photovoltaic maximum power point tracking controller and a load, the photovoltaic maximum power point tracking algorithm is packaged in the photovoltaic maximum power point tracking controller, and the output power is adjusted by adjusting the duty ratio d of a pulse width modulation signal in the voltage converter;
taking the maximum output power P as a fitness function, and setting a duty ratio d as a variable to be optimized;
performing iterative optimization according to the steps of the hybrid algorithm until the iteration times reach the upper limit or the maximum output power P and the theoretical maximum power output P*The error of (2) meets the preset precision requirement, and the optimal duty ratio d obtained at the moment is recorded*。
7. The method according to claim 6, wherein the performing the performance verification of the photovoltaic maximum power point tracking algorithm based on different irradiance conditions and shading conditions of the photovoltaic system comprises:
dividing the different irradiance conditions into uniform irradiance conditions and variable irradiance conditions in view of the spatiotemporal variation characteristics of the irradiance;
dividing the occlusion condition into a non-occlusion condition and a local occlusion condition;
respectively taking the non-shielding condition and the local shielding condition as large classes, taking the uniform irradiance condition and the variable irradiance condition as first sub-classes of each large class, selecting a preset number of different conditions from each first sub-class as second sub-classes of each first sub-class, and numbering all the second sub-classes;
and sequentially completing the performance test of the photovoltaic maximum power point tracking algorithm according to the number of the second subclass, and obtaining a corresponding maximum power tracking curve and a tracking error curve so as to complete the performance verification of the photovoltaic maximum power point tracking algorithm.
8. A photovoltaic maximum power point tracking apparatus, the apparatus comprising:
the first construction module is used for constructing a mixed algorithm combining a hunting deer algorithm and an earthworm algorithm;
the inspection module is used for carrying out performance inspection on the hybrid algorithm based on a benchmark function test;
the second construction module is used for constructing a photovoltaic maximum power point tracking algorithm based on the hybrid algorithm;
and the verification module is used for verifying the performance of the photovoltaic maximum power point tracking algorithm based on different irradiance and shielding conditions of the photovoltaic system.
9. An electronic device, comprising:
one or more processors;
a storage unit to store one or more programs that, when executed by the one or more processors, cause the one or more processors to implement the method of any of claims 1 to 7.
10. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, is able to carry out a method according to any one of claims 1 to 7.
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