CN114138047A - Maximum power point tracking method and system for photovoltaic module and storage medium - Google Patents
Maximum power point tracking method and system for photovoltaic module and storage medium Download PDFInfo
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Abstract
The invention discloses a method and a system for tracking the maximum power point of a photovoltaic module and a storable medium, and relates to the technical field of photovoltaic power generation, wherein the method comprises the following steps: s1: detecting output data of a photovoltaic module to be processed in real time, and adjusting the angle of the photovoltaic module to be processed according to the output data; s2: initializing and setting parameters of a particle population, and setting a threshold; s3: calculating a target function and a fitness value of the particles, wherein the fitness value is the output power of the photovoltaic module to be processed, and searching the individual optimal position and the global optimal solution of the population according to the fitness value of each particle; according to the invention, after the working parameters of the photovoltaic module are considered and environmental factors are considered, the searching of the maximum power point is realized by utilizing a particle swarm algorithm.
Description
Technical Field
The invention relates to the technical field of photovoltaic power generation, in particular to a method and a system for tracking a maximum power point of a photovoltaic module and a storable medium.
Background
At present, with the continuous development of science and technology, the utilization of other energy sources is gradually saturated, solar energy is taken as one of green renewable energy sources, and international energy experts also determine that the solar energy can become the most ideal alternative energy source in the future human life and production.
However, due to the reasons of partial shadow occlusion, difference in output characteristics of photovoltaic cells and the like in a photovoltaic array, an output characteristic curve of the photovoltaic array presents a multi-peak power characteristic, and how to obtain the maximum power becomes an important direction in the field of photovoltaic power generation research.
Therefore, how to provide a method for tracking the maximum power point of a photovoltaic module, which can solve the above problems, is a problem that needs to be solved urgently by those skilled in the art.
Disclosure of Invention
In view of the above, the invention provides a method and a system for tracking a maximum power point of a photovoltaic module, and a storage medium, wherein the method and the system realize the search of the maximum power point by using a particle swarm algorithm after considering the working parameters of the photovoltaic module and considering the environmental factors.
In order to achieve the purpose, the invention adopts the following technical scheme:
a photovoltaic module maximum power point tracking method based on a particle swarm algorithm comprises the following steps:
s1: detecting output data of a photovoltaic module to be processed in real time, and adjusting the angle of the photovoltaic module to be processed according to the output data;
s2: initializing and setting parameters of a particle population, and setting a threshold;
s3: calculating a target function and a fitness value of the particles, wherein the fitness value is the output power of the photovoltaic module to be processed, and searching the individual optimal position and the global optimal solution of the population according to the fitness value of each particle;
s4, updating the positions and the speeds of the particles, recalculating the adaptive values of the particles, and judging whether the individual optimal positions and the global optimal solution need to be updated;
and S5, checking whether the optimizing result meets the termination requirement, if the result meets the convergence precision or the current iteration number is equal to the preset maximum value, ending the iteration, and outputting the optimal solution.
Preferably, the S1 specifically includes:
s11: acquiring the current moment, and detecting the angle peripheral environment parameter information of the solar direct angle in real time;
s12: and adjusting the angle of the photovoltaic module to be processed in real time until the sunlight is perpendicular to the photovoltaic module to be processed and stops adjusting.
Preferably, in S11, the method further includes detecting ambient temperature information and illuminance information of the photovoltaic module to be processed in real time, and re-determining the angle value when the change exceeds a threshold range.
Specifically, because the intensity of solar energy constantly changes along with the change of time, and simultaneously the temperature and the illumination time are also different, and the intensity also constantly changes, through combining temperature, illuminance information, current moment of considering for photovoltaic module is in the optimum state all the time, ensures photovoltaic module's input.
Preferably, the S2 includes: and selecting m points within the range of 0-the maximum value of the output voltage according to the maximum value of the output voltage obtained in the step S13, and using the m points as initial voltages of the particle population to initialize parameters of the particle population, wherein the parameters include the setting ranges of the particle weight, the self-learning factor and the social learning factor.
Preferably, the S5 specifically includes: and judging the difference value between the maximum and minimum fitness values corresponding to all the particles, if the difference value is smaller than a preset difference value, indicating that a convergence condition is reached, and finishing the execution of the particle swarm algorithm, otherwise, not reaching the convergence condition.
Further, the invention also provides a photovoltaic cell multi-peak maximum power tracking system based on particle swarm, comprising:
the angle parameter determining module is used for acquiring the optimal working angle of the photovoltaic module to be processed;
the initialization module is used for initializing parameters of the particle population;
the optimal position calculation module is used for calculating the fitness value of each particle, and the fitness value is equal to the output power of the photovoltaic cell array; searching an individual optimal position and a global optimal position of the population according to the fitness value of each particle;
and the particle population updating module updates the positions and the speeds of the particles, recalculates the adaptive values of the particles, and judges whether the individual optimal positions and the global optimal solution need to be updated.
Further, the present invention also provides a computer readable storage medium having stored thereon instructions which, when executed, implement the steps of any of the methods described above.
According to the technical scheme, compared with the prior art, the invention discloses the maximum power point tracking method and system of the photovoltaic module and the storage medium, simultaneously considers the environmental factors and the structural factors of the photovoltaic module, adjusts the inclination angle of the photovoltaic module in advance, and then searches the maximum power point of the photovoltaic module by initializing the particle swarm parameters so as to determine the maximum power point power.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the provided drawings without creative efforts.
Fig. 1 is a flowchart of a photovoltaic module maximum power point tracking method based on a particle swarm algorithm provided by the invention;
fig. 2 is a structural schematic block diagram of a photovoltaic cell multi-peak maximum power tracking system based on particle swarm provided by the invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Referring to the attached drawing 1, the embodiment of the invention discloses a photovoltaic module maximum power point tracking method based on a particle swarm algorithm, which comprises the following steps:
s1: detecting output data of a photovoltaic module to be processed in real time, and adjusting the angle of the photovoltaic module to be processed according to the output data;
s2: initializing and setting parameters of a particle population, and setting a threshold;
s3: calculating a target function and a fitness value of the particles, wherein the fitness value is the output power of the photovoltaic module to be processed, and searching the individual optimal position and the global optimal solution of the population according to the fitness value of each particle;
s4, updating the positions and the speeds of the particles, recalculating the adaptive values of the particles, and judging whether the individual optimal positions and the global optimal solution need to be updated;
and S5, checking whether the optimizing result meets the termination requirement, if the result meets the convergence precision or the current iteration number is equal to the preset maximum value, ending the iteration, and outputting the optimal solution.
In a specific embodiment, the S1 specifically includes:
s11: acquiring the current moment, and detecting the angle peripheral environment parameter information of the solar direct angle in real time;
s12: and adjusting the angle of the photovoltaic module to be processed in real time until the sunlight is perpendicular to the photovoltaic module to be processed and stops adjusting.
In particular, the inclination angle of the photovoltaic module to be processed and the corresponding output voltage are detected in real time
In a specific embodiment, the step S11 further includes detecting ambient temperature information and illuminance information of the photovoltaic module to be processed in real time, and re-determining the angle value when the change exceeds the threshold range.
In a specific embodiment, the S2 includes: and selecting m points within the range of 0-the maximum value of the output voltage according to the maximum value of the output voltage obtained in the step S13, and using the m points as initial voltages of the particle population to initialize parameters of the particle population, wherein the parameters include the setting ranges of the particle weight, the self-learning factor and the social learning factor.
In a specific embodiment, the S5 specifically includes: and judging the difference value between the maximum and minimum fitness values corresponding to all the particles, if the difference value is smaller than a preset difference value, indicating that a convergence condition is reached, and finishing the execution of the particle swarm algorithm, otherwise, not reaching the convergence condition.
Further, the invention also provides a photovoltaic cell multi-peak maximum power tracking system based on particle swarm, comprising:
the angle parameter determining module is used for acquiring the optimal working angle of the photovoltaic module to be processed;
the initialization module is used for initializing parameters of the particle population;
the optimal position calculation module is used for calculating the fitness value of each particle, and the fitness value is equal to the output power of the photovoltaic cell array; searching an individual optimal position and a global optimal position of the population according to the fitness value of each particle;
and the particle population updating module updates the positions and the speeds of the particles, recalculates the adaptive values of the particles, and judges whether the individual optimal positions and the global optimal solution need to be updated.
In a specific embodiment, the embodiment of the present invention further provides a computer-readable storage medium, on which instructions are stored, and the instructions, when executed, implement the steps of the method of any one of the above-mentioned embodiments.
The embodiments in the present description are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are referred to each other. The device disclosed by the embodiment corresponds to the method disclosed by the embodiment, so that the description is simple, and the relevant points can be referred to the method part for description.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (7)
1. A photovoltaic module maximum power point tracking method based on a particle swarm algorithm is characterized by comprising the following steps:
s1: detecting output data of a photovoltaic module to be processed in real time, and adjusting the angle of the photovoltaic module to be processed according to the output data;
s2: initializing and setting parameters of a particle population, and setting a threshold;
s3: calculating a target function and a fitness value of the particles, wherein the fitness value is the output power of the photovoltaic module to be processed, and searching the individual optimal position and the global optimal solution of the population according to the fitness value of each particle;
s4, updating the positions and the speeds of the particles, recalculating the adaptive values of the particles, and judging whether the individual optimal positions and the global optimal solution need to be updated;
and S5, checking whether the optimizing result meets the termination requirement, if the result meets the convergence precision or the current iteration number is equal to the preset maximum value, ending the iteration, and outputting the optimal solution.
2. The particle swarm algorithm-based photovoltaic module maximum power point tracking method according to claim 1, wherein the S1 specifically comprises:
s11: acquiring the current moment, and detecting the angle peripheral environment parameter information of the solar direct angle in real time;
s12: and adjusting the angle of the photovoltaic module to be processed in real time until the sunlight is perpendicular to the photovoltaic module to be processed and stops adjusting.
3. The particle swarm optimization-based photovoltaic module maximum power point tracking method according to claim 2, wherein in S11, the method further comprises detecting ambient temperature information and illuminance information of the photovoltaic module to be processed in real time, and re-determining the angle value when the change exceeds a threshold range.
4. The particle swarm algorithm-based photovoltaic module maximum power point tracking method according to claim 2, wherein the S2 comprises: and selecting m points within the range of 0-the maximum value of the output voltage according to the maximum value of the output voltage obtained in the step S13, and using the m points as initial voltages of the particle population to initialize parameters of the particle population, wherein the parameters include the setting ranges of the particle weight, the self-learning factor and the social learning factor.
5. The particle swarm algorithm-based photovoltaic module maximum power point tracking method according to claim 2, wherein the S5 specifically comprises: and judging the difference value between the maximum and minimum fitness values corresponding to all the particles, if the difference value is smaller than a preset difference value, indicating that a convergence condition is reached, and finishing the execution of the particle swarm algorithm, otherwise, not reaching the convergence condition.
6. A photovoltaic cell multi-peak maximum power tracking system based on particle swarm is characterized by comprising:
the angle parameter determining module is used for acquiring the optimal working angle of the photovoltaic module to be processed;
the initialization module is used for initializing parameters of the particle population;
the optimal position calculation module is used for calculating the fitness value of each particle, and the fitness value is equal to the output power of the photovoltaic cell array; searching an individual optimal position and a global optimal position of the population according to the fitness value of each particle;
and the particle population updating module updates the positions and the speeds of the particles, recalculates the adaptive values of the particles, and judges whether the individual optimal positions and the global optimal solution need to be updated.
7. A computer-readable storage medium having instructions stored thereon, wherein the instructions, when executed, implement the steps of any of the methods of claims 1-5.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN116126036A (en) * | 2023-02-14 | 2023-05-16 | 国网安徽省电力有限公司营销服务中心 | Method, system, device and storage medium for optimizing solar photovoltaic panel generation power |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106202914A (en) * | 2016-07-07 | 2016-12-07 | 国网青海省电力公司 | Based on the photovoltaic cell parameter identification method improving particle cluster algorithm |
| CN109814651A (en) * | 2019-01-21 | 2019-05-28 | 中国地质大学（武汉） | Multi-peak maximum power tracking method and system for photovoltaic cells based on particle swarm |
| CN111342767A (en) * | 2020-02-24 | 2020-06-26 | 国网浙江嘉善县供电有限公司 | Photovoltaic maximum power point tracking automatic control system and method thereof |
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106202914A (en) * | 2016-07-07 | 2016-12-07 | 国网青海省电力公司 | Based on the photovoltaic cell parameter identification method improving particle cluster algorithm |
| CN109814651A (en) * | 2019-01-21 | 2019-05-28 | 中国地质大学（武汉） | Multi-peak maximum power tracking method and system for photovoltaic cells based on particle swarm |
| CN111342767A (en) * | 2020-02-24 | 2020-06-26 | 国网浙江嘉善县供电有限公司 | Photovoltaic maximum power point tracking automatic control system and method thereof |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116126036A (en) * | 2023-02-14 | 2023-05-16 | 国网安徽省电力有限公司营销服务中心 | Method, system, device and storage medium for optimizing solar photovoltaic panel generation power |
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