CN112884200A - Photovoltaic power station assembly cleaning operation and maintenance time prediction method and device - Google Patents
Photovoltaic power station assembly cleaning operation and maintenance time prediction method and device Download PDFInfo
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- 238000004140 cleaning Methods 0.000 title claims abstract description 152
- 238000012423 maintenance Methods 0.000 title claims abstract description 95
- 238000000034 method Methods 0.000 title claims abstract description 57
- 238000010248 power generation Methods 0.000 claims abstract description 126
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- Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
- Y02E40/00—Technologies for an efficient electrical power generation, transmission or distribution
- Y02E40/70—Smart grids as climate change mitigation technology in the energy generation sector
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
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Abstract
The invention discloses a method and a device for predicting cleaning, operation and maintenance time of a photovoltaic power station component, which comprises the steps of determining a sample component and a comparison component in a photovoltaic power station, wherein the sample component is a component which is cleaned periodically; respectively acquiring power generation data of the comparison assembly and the cleaned sample assembly; predicting the lifting amplitude of the generated energy of the photovoltaic power station after cleaning based on the power generation data of the comparison assembly and the cleaned sample assembly; determining variation trend data of the lifting amplitude of the power generation amount of the cleaned photovoltaic power station according to the lifting amplitude of the power generation amount of the cleaned photovoltaic power station; and determining the target time for cleaning, operation and maintenance of the photovoltaic power station based on the variation trend data. According to the method, the time for cleaning, operating and maintaining the photovoltaic power station component is effectively predicted through actual data, and the power generation efficiency of the photovoltaic power station is improved.
Description
Technical Field
The invention relates to the technical field of photovoltaics, in particular to a method and a device for predicting cleaning, operation and maintenance time of a photovoltaic power station assembly.
Background
The generated power of a photovoltaic power station is directly related to the amount of radiation received by the surface of the photovoltaic module. Because the photovoltaic module is installed outdoors, the radiation quantity on the surface of the module can be reduced by deposited dust, accumulated snow and the like on the surface of the photovoltaic module, the generating capacity of the module is reduced, the generating benefit of a power station is influenced, and the surface of the module needs to be cleaned.
In the operation and maintenance of the existing photovoltaic power station, the fixed cleaning period of the photovoltaic power station component is usually determined according to the operation and maintenance experience of operation and maintenance personnel, and the component is cleaned regularly. However, the operation and maintenance time for cleaning the photovoltaic power station component is determined through the operation and maintenance experience, and the actual situation of the photovoltaic power station component is difficult to meet, so that the power generation efficiency of the photovoltaic power station component is reduced.
Disclosure of Invention
In order to solve the problems, the invention provides a method and a device for predicting the cleaning operation and maintenance time of a photovoltaic power station component, and the purpose of improving the power generation efficiency of the photovoltaic power station component is achieved.
In order to achieve the purpose, the invention provides the following technical scheme:
a photovoltaic power station component cleaning operation and maintenance time prediction method comprises the following steps:
determining a sample assembly and a comparison assembly in a photovoltaic power station, wherein the sample assembly is an assembly which is cleaned periodically;
respectively acquiring power generation data of the comparison assembly and the cleaned sample assembly;
predicting the lifting amplitude of the generated energy of the photovoltaic power station after cleaning based on the power generation data of the comparison assembly and the cleaned sample assembly;
determining variation trend data of the lifting amplitude of the generated energy of the cleaned photovoltaic power station according to the lifting amplitude of the generated energy of the cleaned photovoltaic power station;
and determining the target time for cleaning, operation and maintenance of the photovoltaic power station based on the variation trend data.
Optionally, the comparison module is a module which is in the same group string with the sample module and is not affected by the cleaning of the sample module; or the comparison component is a component in other groups of strings meeting the preset proximity condition with the sample component.
Optionally, the predicting the lifting amplitude of the power generation amount of the photovoltaic power station after cleaning based on the power generation data of the comparison module and the cleaned sample module includes:
acquiring a first power generation amount of the sample assembly in a target time period after operation and maintenance cleaning is carried out last time;
acquiring a second power generation amount of the comparison component in the target time period;
respectively obtaining a third power generation amount and a fourth power generation amount of the sample assembly and the comparison assembly in the same time period after the sample assembly is cleaned;
and calculating to obtain the lifting amplitude of the power generation of the photovoltaic power station after cleaning based on the first power generation amount, the second power generation amount, the third power generation amount and the fourth power generation amount.
Optionally, the determining the target time for the photovoltaic power station to perform the cleaning operation and maintenance includes:
determining a threshold value of the generated energy lifting amplitude;
and determining the target time for cleaning, operation and maintenance of the photovoltaic power station based on the variation trend data and the threshold value.
Optionally, the method further comprises:
and adjusting the threshold value based on the environmental parameters and the operation and maintenance requirement parameters to obtain the adjusted threshold value.
Optionally, the determining the target time for the photovoltaic power station to perform the cleaning operation and maintenance includes:
inputting the change trend data into a time prediction model to obtain target time for cleaning, operation and maintenance of the photovoltaic power station; the time prediction model is a neural network model obtained by training based on the change trend data of the power generation capacity increasing amplitude of the historically cleaned photovoltaic power station.
Optionally, the method further comprises:
and after cleaning operation and maintenance, acquiring the power generation data of the comparison assembly and the sample assembly so as to predict the next time of cleaning operation and maintenance by the power generation data.
A photovoltaic power plant component cleaning operation and maintenance time prediction device, the device comprising:
the device comprises a first determining unit, a comparison unit and a control unit, wherein the first determining unit is used for determining a sample assembly and a comparison assembly in the photovoltaic power station, and the sample assembly is an assembly which is cleaned periodically;
the data acquisition unit is used for respectively acquiring the power generation data of the comparison assembly and the cleaned sample assembly;
the prediction unit is used for predicting the lifting amplitude of the generated energy of the photovoltaic power station after cleaning based on the power generation data of the comparison assembly and the cleaned sample assembly;
the second determining unit is used for determining the variation trend data of the lifting amplitude of the power generation of the cleaned photovoltaic power station according to the lifting amplitude of the power generation of the cleaned photovoltaic power station;
and the third determining unit is used for determining the target time for cleaning, operation and maintenance of the photovoltaic power station based on the change trend data.
A storage medium storing executable instructions that, when executed by a processor, implement a photovoltaic power plant component cleaning operation and maintenance time prediction method as described in any one of the above.
An electronic device, comprising:
a memory for storing a program;
a processor configured to execute the program, where the program is specifically configured to implement the photovoltaic power plant component cleaning operation and maintenance time prediction method as described in any one of the above.
Compared with the prior art, the invention provides a method and a device for predicting the cleaning, operation and maintenance time of a photovoltaic power station assembly, which comprises the steps of determining a sample assembly and a comparison assembly in the photovoltaic power station, wherein the sample assembly is an assembly for cleaning periodically; respectively acquiring power generation data of the comparison assembly and the cleaned sample assembly; predicting the lifting amplitude of the generated energy of the photovoltaic power station after cleaning based on the power generation data of the comparison assembly and the cleaned sample assembly; determining variation trend data of the lifting amplitude of the power generation amount of the cleaned photovoltaic power station according to the lifting amplitude of the power generation amount of the cleaned photovoltaic power station; and determining the target time for cleaning, operation and maintenance of the photovoltaic power station based on the variation trend data. According to the method, the time for cleaning, operating and maintaining the photovoltaic power station component is effectively predicted through actual data, and the power generation efficiency of the photovoltaic power station is improved.
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 schematic flow chart of a method for predicting cleaning operation and maintenance time of a photovoltaic power station component according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of the time for cleaning, operation and maintenance of a photovoltaic power plant component according to an embodiment of the present invention;
fig. 3 is a schematic structural diagram of a photovoltaic power plant module cleaning operation and maintenance time prediction apparatus according to an embodiment of the present 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.
The terms "first" and "second," and the like in the description and claims of the present invention and the above-described drawings are used for distinguishing between different objects and not for describing a particular order. Furthermore, the terms "comprising" and "having," as well as any variations thereof, are intended to cover non-exclusive inclusions. For example, a process, method, system, article, or apparatus that comprises a list of steps or elements is not set forth for a listed step or element but may include steps or elements not listed.
The embodiment of the invention provides a method for predicting the cleaning operation and maintenance time of a photovoltaic power station component, which realizes dynamic calculation according to actual conditions after cleaning is carried out each time, predicts the time for cleaning operation and maintenance to be carried out next time, and dynamically adjusts the prediction result along with the change of the surface dust deposition condition of the component, thereby reducing errors.
Referring to fig. 1, a method for predicting the cleaning operation and maintenance time of a photovoltaic power plant component provided by the embodiment of the invention may include the following steps:
s101, determining a sample assembly and a comparison assembly in the photovoltaic power station.
The device comprises a sample assembly, a comparison assembly, a control assembly and a control assembly, wherein the sample assembly is an assembly which is cleaned periodically, and the comparison assembly is an assembly which is in the same group with the sample assembly and is not influenced by cleaning of the sample assembly; or the comparison component is a component in other groups of strings meeting the preset proximity condition with the sample component. Specifically, the sample assembly can only select a single photovoltaic assembly, so that the cost of cleaning and data acquisition is reduced, and a plurality of assemblies can be selected, so that the cleaning prediction accuracy is improved. And selecting a component which works normally and is not shielded by shadow from the sample component. The cleaning mode of the sample assembly can adopt various modes such as spraying water washing, rolling brush waterless dry cleaning and the like, and can be manually cleaned or automatically cleaned by applying a cleaning device.
The comparison component can select components which are in the same cluster as the sample component and are not affected by cleaning of the sample component, and can also select components in other clusters adjacent to the sample component. And selecting a component which works normally and is not shielded by shadow from the comparison component.
The comparison component can only select a single photovoltaic component, so that the cost of cleaning and data acquisition is reduced, and a plurality of components can be selected, so that the cleaning prediction accuracy is improved. The power generation data can be the power generation data of the comparison component and the cleaned sample component in the same time period, and can also be other data which can show the power generation performance difference, such as the power generation power of the comparison component and the cleaned sample component at the same time point.
The proximity condition refers to a position close to the sample assembly, for example, the comparison assembly may select an assembly in the same cluster as the sample assembly, or may select an assembly in another cluster close to the cluster where the sample assembly is located. The environmental conditions (factors influencing the generating capacity such as irradiation and temperature) and the operating conditions of the comparison assembly and the sample assembly are the same, and only the difference of the cleanliness is ensured.
And S102, respectively acquiring power generation data of the comparison assembly and the cleaned sample assembly.
S103, predicting the lifting amplitude of the generated energy of the photovoltaic power station after cleaning based on the power generation data of the comparison assembly and the cleaned sample assembly.
It should be noted that, when the cleaning operation and maintenance are carried out, the total station assembly is cleaned, the cleaning degree of the comparison assembly after cleaning is consistent with that of the sample assembly, the comparison assembly always operates under the natural dust accumulation condition in the subsequent operation process without cleaning, and the generated energy can be increased after cleaning by comparing the comparison assembly with the cleaned sample assembly. Until the next cleaning operation is carried out, the comparison component is cleaned together with the total station component again.
And predicting the lifting amplitude of the generated energy of the photovoltaic power station module after cleaning based on the generated energy of the sample module before and after cleaning and the generated energy of the comparison module in the same time period with the sample module.
And S104, determining the variation trend data of the photovoltaic power station generated energy lifting amplitude after cleaning according to the lifting amplitude of the photovoltaic power station generated energy after cleaning.
And S105, determining the target time for cleaning, operation and maintenance of the photovoltaic power station based on the change trend data.
The prediction method needs to preset a threshold value of the power generation amount increase amplitude after cleaning, the threshold value is set according to the cleaning cost of the photovoltaic module of the full power station and the expectation of a user on the profit, when the power generation amount increase amplitude after cleaning is predicted to be lower than the preset threshold value, the profit of operation and maintenance implementation is lower than the expectation of the user, and cleaning operation and maintenance are not needed; and when the predicted lifting amplitude of the generated energy after cleaning reaches or is higher than the preset threshold value, judging that cleaning operation and maintenance are required to be carried out.
The operation and maintenance cleaning is to implement operation and maintenance of the power station and clean the photovoltaic modules of the full power station. The sample assembly cleaning is to clean only the sample assembly, and other assemblies are not cleaned. In the embodiment of the invention, the 'operation and maintenance cleaning data' used for the prediction calculation at each time is dynamically adjusted according to the actual condition of the power station instead of a fixed preset value.
The invention provides a method for predicting the cleaning, operation and maintenance time of a photovoltaic power station component, which comprises the steps of determining a sample component and a comparison component in a photovoltaic power station, wherein the sample component is a component which is cleaned periodically; respectively acquiring power generation data of the comparison assembly and the cleaned sample assembly; predicting the lifting amplitude of the generated energy of the photovoltaic power station after cleaning based on the power generation data of the comparison assembly and the cleaned sample assembly; determining variation trend data of the lifting amplitude of the power generation amount of the cleaned photovoltaic power station according to the lifting amplitude of the power generation amount of the cleaned photovoltaic power station; and determining the target time for cleaning, operation and maintenance of the photovoltaic power station based on the variation trend data. According to the method, the time for cleaning, operating and maintaining the photovoltaic power station component is effectively predicted through actual data, and the power generation efficiency of the photovoltaic power station is improved.
The following is a description of possible implementations of various steps in the examples of the present invention.
The method for predicting the lifting amplitude of the generated energy of the photovoltaic power station after cleaning based on the power generation data of the comparison assembly and the cleaned sample assembly comprises the following steps: acquiring a first power generation amount of the sample assembly in a target time period after operation and maintenance cleaning is carried out last time; acquiring a second power generation amount of the comparison component in the target time period; respectively obtaining a third power generation amount and a fourth power generation amount of the sample assembly and the comparison assembly in the same time period after the sample assembly is cleaned; and calculating to obtain the lifting amplitude of the power generation of the photovoltaic power station after cleaning based on the first power generation amount, the second power generation amount, the third power generation amount and the fourth power generation amount.
For example, the first power generation amount, the second power generation amount, the third power generation amount and the fourth power generation amount are represented by specific parameters, and the prediction process is as shown in formula (1):
F＝Ed 0·Es1/Ed 1·Es0 (1)
in the above formula (1), F represents the predicted power generation amount increase amplitude of the power station after cleaning, and Es0Showing the sample assembly at a time t after the last maintenance cleaning0First amount of power generation in Ed 0Indicating the comparison component during the same time period t0Second power generation amount, Es1Showing the time t after the cleaning of the sample assembly1Internal third power generation amount, Ed 1Indicating an unwashed comparative assembly for the same time period t1The fourth power generation amount.
Taking a certain 200kW photovoltaic power station as an example, the power station is provided with 36 photovoltaic group strings, each group string is provided with 22 photovoltaic components of 250kWp, 1 component in the group string 1 is selected as a sample component, and the other adjacent 1 component in the same group string is selected as a comparison component. After operation and maintenance cleaning is carried out for a certain time, the generated energy Es of the sample assembly within 24 hours01.125kWh, the power generation E of the comparison module in the same time periodd 0It was 1.175 kwh. After 10 days of operation, the sample assembly is cleaned, and the generated energy Es of the cleaned sample assembly within 24h11.210kWh, the power generation E of the comparison module in the same time periodd 1It was 1.235 kwh. Calculating according to the expression (1) to obtain the predicted power generation capacity increase amplitude of the power station if the cleaning is carried out at the moment
F＝(1.175×1.210)/(1.235×1.125)＝1.023
The generated energy of the power station after the cleaning can be predicted to be improved by 2.3 percent.
Correspondingly, the determining the target time for cleaning, operating and maintaining the photovoltaic power station includes: determining a threshold value of the generated energy lifting amplitude; and determining the target time for cleaning, operation and maintenance of the photovoltaic power station based on the variation trend data and the threshold value.
The threshold of the power generation amount increase amplitude can be adjusted, for example, the threshold can be adjusted based on environmental parameters and operation and maintenance demand parameters, so as to obtain an adjusted threshold. Namely, the setting of the threshold value is matched with the environment and the actual operation and maintenance requirements of the photovoltaic power station in real time.
The prediction method needs to preset a threshold value of the power generation amount increase amplitude after cleaning, the threshold value is set according to the cleaning cost of the photovoltaic module of the full power station and the expectation of a user on the profit, when the power generation amount increase amplitude after cleaning is predicted to be lower than the preset threshold value, the profit of operation and maintenance implementation is lower than the expectation of the user, and cleaning operation and maintenance are not needed; and when the predicted lifting amplitude of the generated energy after cleaning reaches or is higher than the preset threshold value, judging that cleaning operation and maintenance are required to be carried out. For example, the predetermined threshold range is 1.05 to 1.15.
It should be noted that the prediction method may adopt an extrapolation method, and extrapolation is performed to a preset threshold according to the generated energy lifting amplitude data obtained after the sample assembly is washed for many times, and the corresponding time is the time for predicting the washing operation and maintenance needs to be performed. The threshold value can be adjusted regularly according to the theoretical generating capacity change condition of the power station, for example, the generating capacity of the power station in autumn is high, the threshold value is set to be 1.1, and the expectation of a user on the income can be met by increasing the generating capacity by 10%; however, the power generation amount of the power station in winter is low, and the expectation of the user on input and output can be met only by increasing the power generation amount by 15% by a threshold value of 1.15. Further, the method can be refined to the month, and different thresholds are set according to the difference of the theoretical power generation amount of each month. And further, the method can be further refined to the date, and the threshold value is dynamically adjusted according to the generated energy in a period of time in the future when the weather prediction and the power generation prediction are combined and the cleaning operation and maintenance time is predicted each time. Similarly, when the operation and maintenance cleaning cost and the user expectation change, the threshold value is adjusted accordingly.
In an implementation manner of the embodiment of the present invention, the determining a target time for the cleaning operation and maintenance of the photovoltaic power station includes: inputting the change trend data into a time prediction model to obtain target time for cleaning, operation and maintenance of the photovoltaic power station; the time prediction model is a neural network model obtained by training based on the change trend data of the power generation capacity increasing amplitude of the historically cleaned photovoltaic power station.
Namely, the prediction method can also adopt an artificial intelligence algorithm such as machine learning and the like, and the time for predicting the cleaning operation and maintenance needs to be carried out can be obtained through the analysis of historical cleaning data. The input to the time prediction model is generated energy lifting amplitude data obtained after the sample assembly is washed for multiple times, and the output is predicted washing operation and maintenance time. The method uses an artificial intelligence algorithm to predict the trend, replaces simple curve fitting and improves the accuracy.
The extrapolation method is still described by taking the above specific application example, i.e. a certain 200KW photovoltaic power station as an example. The threshold value of the power generation capacity increase amplitude of the power station is set to be 1.1, after operation and maintenance cleaning is carried out for a certain time, cleaning of the sample assembly is carried out every 10 days, and the power generation capacity increase amplitude of the power station after prediction cleaning is obtained and is shown in the following table 1.
TABLE 1
| Last time of operation after cleaning | Power station generated energy lifting amplitude after prediction cleaning |
| 10 days | 1.023 |
| 20 days | 1.045 |
| 30 days | 1.061 |
| 40 days | 1.085 |
According to the predicted generated energy increasing amplitude of the cleaned photovoltaic power station in the table 1, the time reaching the preset threshold value, namely the target time for cleaning operation and maintenance can be determined.
Referring to fig. 2, a schematic diagram of the time for cleaning operation and maintenance of a photovoltaic power plant component according to an embodiment of the present invention is shown.
In fig. 2, the abscissa is the number of days from the last time of performing operation and maintenance cleaning, the ordinate is the power generation amount increase amplitude of the cleaned power station, and the dot is the predicted power generation amount increase amplitude of the cleaned power station obtained each time. And fitting and extrapolating the power generation capacity lifting amplitude data of the power station after each predicted cleaning to a position of 1.1 on the ordinate, wherein the abscissa corresponds to 44 days. That is, the embodiment of the application can predict that the preset threshold value will be reached and the operation and maintenance cleaning needs to be performed on the 44 th day after the operation and maintenance cleaning is performed last time on the 40 th day, and the preset threshold value is found to be reached when the sample assembly cleaning is not required to be performed again on the 50 th day.
In the embodiment of the invention, after the cleaning operation and maintenance are carried out, the power generation data of the comparison component and the sample component are obtained, so that the power generation data can predict the time of the next cleaning operation and maintenance.
Namely, the embodiment of the invention is implementedAfter the photovoltaic module of the power station is cleaned, operated and maintained, the generated energy data of the comparison module and the sample module in the same time period is obtained and can be used as E in the formula (1)d 0、Es0And the method is used for calculating the power generation capacity increase amplitude of the power station after subsequent prediction and cleaning.
According to the photovoltaic power station module cleaning operation and maintenance time prediction method provided by the embodiment of the invention, the actual cleaning effect of the photovoltaic module can be monitored, so that a user can arrange the cleaning operation and maintenance of the photovoltaic module of the full power station according to the lifting range of the power generation amount of the cleaned power station, and the problem that the power generation amount is lost after too late cleaning or the operation and maintenance resources are wasted after too early cleaning is effectively avoided. Through analyzing the variation trend of the lifting amplitude of the generated energy after cleaning, the time for cleaning, operating and maintaining the photovoltaic module of the full power station is predicted, so that a user can arrange operation and maintenance resources in advance and clean the power station in time.
Referring to fig. 3, it shows a photovoltaic power plant component cleaning operation and maintenance time prediction apparatus provided in an embodiment of the present invention, the apparatus includes:
the device comprises a first determining unit 10, a comparison unit and a control unit, wherein the first determining unit is used for determining a sample assembly and a comparison assembly in the photovoltaic power station, and the sample assembly is cleaned periodically;
the data acquisition unit 20 is used for respectively acquiring the power generation data of the comparison assembly and the cleaned sample assembly;
the prediction unit 30 is used for predicting the lifting amplitude of the generated energy of the photovoltaic power station after cleaning based on the power generation data of the comparison assembly and the cleaned sample assembly;
the second determining unit 40 is configured to determine, according to the increase amplitude of the power generation amount of the cleaned photovoltaic power station, change trend data of the increase amplitude of the power generation amount of the cleaned photovoltaic power station;
and the third determining unit 50 is configured to determine, based on the change trend data, a target time for the photovoltaic power station to perform cleaning operation and maintenance.
On the basis of the embodiment of the device, the comparison assembly is an assembly which is in the same group string with the sample assembly and is not influenced by the cleaning of the sample assembly; or the comparison component is a component in other groups of strings meeting the preset proximity condition with the sample component.
Optionally, the prediction unit is specifically configured to:
acquiring a first power generation amount of the sample assembly in a target time period after operation and maintenance cleaning is carried out last time;
acquiring a second power generation amount of the comparison component in the target time period;
respectively obtaining a third power generation amount and a fourth power generation amount of the sample assembly and the comparison assembly in the same time period after the sample assembly is cleaned;
and calculating to obtain the lifting amplitude of the power generation of the photovoltaic power station after cleaning based on the first power generation amount, the second power generation amount, the third power generation amount and the fourth power generation amount.
Optionally, the third determining unit is specifically configured to:
determining a threshold value of the generated energy lifting amplitude;
and determining the target time for cleaning, operation and maintenance of the photovoltaic power station based on the variation trend data and the threshold value.
And adjusting the threshold value based on the environmental parameters and the operation and maintenance demand parameters to obtain the adjusted threshold value.
Optionally, the third determining unit is further specifically configured to:
inputting the change trend data into a time prediction model to obtain target time for cleaning, operation and maintenance of the photovoltaic power station; the time prediction model is a neural network model obtained by training based on the change trend data of the power generation capacity increasing amplitude of the historically cleaned photovoltaic power station.
Optionally, the data obtaining unit is further configured to:
and after cleaning operation and maintenance, acquiring the power generation data of the comparison assembly and the sample assembly so as to predict the next time of cleaning operation and maintenance by the power generation data.
The invention provides a photovoltaic power station assembly cleaning operation and maintenance time prediction device, which comprises: the method comprises the following steps that a first determining unit determines a sample assembly and a comparison assembly in a photovoltaic power station, wherein the sample assembly is an assembly which is cleaned periodically; the data acquisition unit respectively acquires the power generation data of the comparison assembly and the cleaned sample assembly; the prediction unit predicts the lifting amplitude of the generated energy of the photovoltaic power station after cleaning based on the power generation data of the comparison assembly and the cleaned sample assembly; the second determining unit determines variation trend data of the lifting amplitude of the power generation amount of the cleaned photovoltaic power station according to the lifting amplitude of the power generation amount of the cleaned photovoltaic power station; and the third determining unit determines the target time for cleaning, operation and maintenance of the photovoltaic power station based on the variation trend data. According to the method, the time for cleaning, operating and maintaining the photovoltaic power station component is effectively predicted through actual data, and the power generation efficiency of the photovoltaic power station is improved.
Based on the foregoing embodiments, embodiments of the present application provide a storage medium storing executable instructions that, when executed by a processor, implement a photovoltaic power plant component cleaning operation and maintenance time prediction method as described in any one of the above.
The embodiment of the invention also provides the electronic equipment, which comprises a memory, a storage device and a control device, wherein the memory is used for storing the program; a processor configured to execute the program, where the program is specifically configured to implement the photovoltaic power plant component cleaning operation and maintenance time prediction method as described in any one of the above.
The Processor or the CPU may be at least one of an Application Specific Integrated Circuit (ASIC), a Digital Signal Processor (DSP), a Digital Signal Processing Device (DSPD), a Programmable Logic Device (PLD), a Field Programmable Gate Array (FPGA), a Central Processing Unit (CPU), a controller, a microcontroller, and a microprocessor. It is understood that the electronic device implementing the above-mentioned processor function may be other electronic devices, and the embodiments of the present application are not particularly limited.
The computer storage medium/Memory may be a Read Only Memory (ROM), a Programmable Read Only Memory (PROM), an Erasable Programmable Read Only Memory (EPROM), an Electrically Erasable Programmable Read Only Memory (EEPROM), a magnetic Random Access Memory (FRAM), a Flash Memory (Flash Memory), a magnetic surface Memory, an optical Disc, or a Compact Disc Read-Only Memory (CD-ROM); but may also be various terminals such as mobile phones, computers, tablet devices, personal digital assistants, etc., that include one or any combination of the above-mentioned memories.
In the several embodiments provided in the present application, it should be understood that the disclosed apparatus and method may be implemented in other ways. The above-described device embodiments are merely illustrative, for example, the division of the unit is only a logical functional division, and there may be other division ways in actual implementation, such as: multiple units or components may be combined, or may be integrated into another system, or some features may be omitted, or not implemented. In addition, the coupling, direct coupling or communication connection between the components shown or discussed may be through some interfaces, and the indirect coupling or communication connection between the devices or units may be electrical, mechanical or other forms.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, that is, may be located in one place, or may be distributed on a plurality of network units; some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, all functional units in the embodiments of the present application may be integrated into one processing module, or each unit may be separately regarded as one unit, or two or more units may be integrated into one unit; the integrated unit can be realized in a form of hardware, or in a form of hardware plus a software functional unit. Those of ordinary skill in the art will understand that: all or part of the steps for implementing the method embodiments may be implemented by hardware related to program instructions, and the program may be stored in a computer readable storage medium, and when executed, the program performs the steps including the method embodiments; and the aforementioned storage medium includes: various media capable of storing program codes, such as a removable Memory device, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk, and an optical disk.
The methods disclosed in the several method embodiments provided in the present application may be combined arbitrarily without conflict to obtain new method embodiments.
Features disclosed in several of the product embodiments provided in the present application may be combined in any combination to yield new product embodiments without conflict.
The features disclosed in the several method or apparatus embodiments provided in the present application may be combined arbitrarily, without conflict, to arrive at new method embodiments or apparatus embodiments.
The above description is only for the specific embodiments of the present application, but the scope of the present application is not limited thereto, and any person skilled in the art can easily conceive of the changes or substitutions within the technical scope of the present application, and shall be covered by the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
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 (10)
1. A photovoltaic power station component cleaning operation and maintenance time prediction method is characterized by comprising the following steps:
determining a sample assembly and a comparison assembly in a photovoltaic power station, wherein the sample assembly is an assembly which is cleaned periodically;
respectively acquiring power generation data of the comparison assembly and the cleaned sample assembly;
predicting the lifting amplitude of the generated energy of the photovoltaic power station after cleaning based on the power generation data of the comparison assembly and the cleaned sample assembly;
determining variation trend data of the lifting amplitude of the generated energy of the cleaned photovoltaic power station according to the lifting amplitude of the generated energy of the cleaned photovoltaic power station;
and determining the target time for cleaning, operation and maintenance of the photovoltaic power station based on the variation trend data.
2. The method of claim 1, wherein the contrast assembly is an assembly in the same cluster as the sample assembly and unaffected by the cleaning of the sample assembly; or the comparison component is a component in other groups of strings meeting the preset proximity condition with the sample component.
3. The method of claim 1, wherein predicting the magnitude of the increase in the power generation of the photovoltaic power plant after cleaning based on the power generation data of the comparison module and the cleaned sample module comprises:
acquiring a first power generation amount of the sample assembly in a target time period after operation and maintenance cleaning is carried out last time;
acquiring a second power generation amount of the comparison component in the target time period;
respectively obtaining a third power generation amount and a fourth power generation amount of the sample assembly and the comparison assembly in the same time period after the sample assembly is cleaned;
and calculating to obtain the lifting amplitude of the power generation of the photovoltaic power station after cleaning based on the first power generation amount, the second power generation amount, the third power generation amount and the fourth power generation amount.
4. The method of claim 1, wherein the determining the target time for the photovoltaic power plant to perform the cleaning operation comprises:
determining a threshold value of the generated energy lifting amplitude;
and determining the target time for cleaning, operation and maintenance of the photovoltaic power station based on the variation trend data and the threshold value.
5. The method of claim 4, further comprising:
and adjusting the threshold value based on the environmental parameters and the operation and maintenance requirement parameters to obtain the adjusted threshold value.
6. The method of claim 1, wherein the determining the target time for the photovoltaic power plant to perform the cleaning operation comprises:
inputting the change trend data into a time prediction model to obtain target time for cleaning, operation and maintenance of the photovoltaic power station; the time prediction model is a neural network model obtained by training based on the change trend data of the power generation capacity increasing amplitude of the historically cleaned photovoltaic power station.
7. The method of claim 1, further comprising:
and after cleaning operation and maintenance, acquiring the power generation data of the comparison assembly and the sample assembly so as to predict the next time of cleaning operation and maintenance by the power generation data.
8. A photovoltaic power plant component cleaning operation and maintenance time prediction device is characterized by comprising:
the device comprises a first determining unit, a comparison unit and a control unit, wherein the first determining unit is used for determining a sample assembly and a comparison assembly in the photovoltaic power station, and the sample assembly is an assembly which is cleaned periodically;
the data acquisition unit is used for respectively acquiring the power generation data of the comparison assembly and the cleaned sample assembly;
the prediction unit is used for predicting the lifting amplitude of the generated energy of the photovoltaic power station after cleaning based on the power generation data of the comparison assembly and the cleaned sample assembly;
the second determining unit is used for determining the variation trend data of the lifting amplitude of the power generation of the cleaned photovoltaic power station according to the lifting amplitude of the power generation of the cleaned photovoltaic power station;
and the third determining unit is used for determining the target time for cleaning, operation and maintenance of the photovoltaic power station based on the change trend data.
9. A storage medium storing executable instructions which, when executed by a processor, implement the photovoltaic power plant component cleaning operation and maintenance time prediction method according to any one of claims 1 to 7.
10. An electronic device, comprising:
a memory for storing a program;
a processor for executing the program, the program being particularly adapted to implement the photovoltaic power plant component cleaning operation and maintenance time prediction method according to any of claims 1-7.
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116029703A (en) * | 2023-03-28 | 2023-04-28 | 山东科技大学 | Distributed photovoltaic cleaning period determining method, system, medium and equipment |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2012124188A (en) * | 2010-12-06 | 2012-06-28 | Hitachi Ltd | Photovoltaic power generation prediction device, photovoltaic power generation prediction method, system voltage controller, system voltage control method |
| US20130066569A1 (en) * | 2011-09-13 | 2013-03-14 | Kabushiki Kaisha Toshiba | Power generation predicting apparatus and method thereof |
| CN107133713A (en) * | 2017-03-13 | 2017-09-05 | 华电电力科学研究院 | A kind of photovoltaic plant intelligently cleans the method for building up of decision system |
| CN109858188A (en) * | 2019-03-12 | 2019-06-07 | 河海大学常州校区 | A method of calculating photovoltaic module gene-ration revenue under different cleaning nodes |
| US10599747B1 (en) * | 2011-07-25 | 2020-03-24 | Clean Power Research, L.L.C. | System and method for forecasting photovoltaic power generation system degradation |
| CN111219942A (en) * | 2018-11-26 | 2020-06-02 | 珠海格力电器股份有限公司 | Refrigerator fault prediction method and device |
| CN112149861A (en) * | 2019-06-28 | 2020-12-29 | 北京天诚同创电气有限公司 | Operation and maintenance task scheduling method and device for photovoltaic power station group |
-
2021
- 2021-01-15 CN CN202110055902.5A patent/CN112884200B/en active Active
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2012124188A (en) * | 2010-12-06 | 2012-06-28 | Hitachi Ltd | Photovoltaic power generation prediction device, photovoltaic power generation prediction method, system voltage controller, system voltage control method |
| US10599747B1 (en) * | 2011-07-25 | 2020-03-24 | Clean Power Research, L.L.C. | System and method for forecasting photovoltaic power generation system degradation |
| US20130066569A1 (en) * | 2011-09-13 | 2013-03-14 | Kabushiki Kaisha Toshiba | Power generation predicting apparatus and method thereof |
| CN107133713A (en) * | 2017-03-13 | 2017-09-05 | 华电电力科学研究院 | A kind of photovoltaic plant intelligently cleans the method for building up of decision system |
| CN111219942A (en) * | 2018-11-26 | 2020-06-02 | 珠海格力电器股份有限公司 | Refrigerator fault prediction method and device |
| CN109858188A (en) * | 2019-03-12 | 2019-06-07 | 河海大学常州校区 | A method of calculating photovoltaic module gene-ration revenue under different cleaning nodes |
| CN112149861A (en) * | 2019-06-28 | 2020-12-29 | 北京天诚同创电气有限公司 | Operation and maintenance task scheduling method and device for photovoltaic power station group |
Non-Patent Citations (3)
| Title |
|---|
| MOHAMMAD HAMMOUD ET AL: "Effect of dust cleaning on the enhancement of the power generation of a coastal PV-power plant at Zahrani Lebanon", SOLAR ENERGY, vol. 184, pages 195 - 201 * |
| 唐治平;周璐;魏超;: "光伏电站智能清洗模型及实例分析", 发电与空调, no. 01, pages 25 - 29 * |
| 赵波 等: "基于状态监测的电池板积灰清洗周期确定与费用评估", 中国电机工程学报, vol. 39, no. 14, pages 4205 - 4213 * |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116029703A (en) * | 2023-03-28 | 2023-04-28 | 山东科技大学 | Distributed photovoltaic cleaning period determining method, system, medium and equipment |
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