CN117639662A - Intelligent monitoring-based photovoltaic power station power prediction method and system - Google Patents
Intelligent monitoring-based photovoltaic power station power prediction method and system Download PDFInfo
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Abstract
The disclosure relates to the field of photovoltaic power station power prediction, in particular to a photovoltaic power station power prediction method and a system thereof based on intelligent monitoring, wherein the method comprises the following steps: acquiring the historical conversion efficiency of the photovoltaic power station, and carrying out running condition inspection of the photovoltaic power station based on the historical conversion efficiency; and predicting the output power of the photovoltaic power station based on the historical conversion efficiency, the running condition of the inspected photovoltaic power station, the collected environmental data of the photovoltaic power station and the predicted meteorological data to obtain predicted power. The photovoltaic power station power prediction method and the photovoltaic power station power prediction system can combine monitoring of photovoltaic power station equipment with meteorological condition prediction to conduct photovoltaic power station power prediction, and are beneficial to stability of power prediction.
Description
Technical Field
The disclosure relates to the field of photovoltaic power station power prediction, in particular to a photovoltaic power station power prediction method and system based on intelligent monitoring.
Background
The power prediction project of the photovoltaic power station is generally applicable to a centralized photovoltaic project, and is not used for a distributed photovoltaic project; the method for using the power prediction project is quite large, and the method can be roughly divided into a direct prediction method and an indirect prediction method, wherein the direct prediction method is used for directly predicting the output power of a photovoltaic power generation system, and the indirect prediction method is used for estimating the power output of the photovoltaic power station by predicting the solar irradiation amount through meteorological data of the place where the photovoltaic power station is purchased.
In general, the existing power prediction adopts various prediction methods to compare and then selects a model with smaller error to predict, in fact, many photovoltaic power station projects consider the meteorological conditions of the place at the beginning of the project, when the meteorological conditions do not change obviously, the whole power capable of being output is left with a margin, but the output power cannot be controlled on a smaller time scale (such as within a week), so that the meteorological data in the next days are needed to be considered, and the power prediction at this time is usually called short-term power prediction; the short-term power prediction establishes a prediction model by acquiring data such as meteorological data and power station inverter operation data in real time, so as to predict the output power within 1 day or even several hours in the future, thereby being convenient for power scheduling, power balance, power transaction and the like of the power system.
The main influencing factors of short-term power prediction are weather conditions, namely solar conditions, but the prediction influencing factors of the weather conditions are complex, the accuracy of power prediction which is completely dependent on weather prediction is required to be improved, and the accuracy is also difficult to be improved, and various prediction fans such as a neural network or an autoregressive model are compensated to solve the problem of instability caused by inaccurate weather data prediction. Technical requirements of photovoltaic power station power prediction systems specify: the error of the root of the predicted month and average of the short-term power prediction of the photovoltaic power generation is less than 20 percent in the next day of 0 to 24 hours, and therefore, the error margin reserved in the power prediction of the photovoltaic power generation is usually larger, in practice, the short-term meteorological data is often more random, the regular prediction of the short-term meteorological data is usually futile, and the photovoltaic power generation is usually reserved with margin to enhance the stability of the power output of the system when in actual operation, so that the prediction of the running stability and the photoelectric conversion efficiency of equipment in the photovoltaic power generation is more significant.
Disclosure of Invention
The photovoltaic power station power prediction method and the system based on intelligent monitoring can combine monitoring of photovoltaic power station equipment with weather condition prediction to conduct photovoltaic power station power prediction, and stability of power prediction is facilitated. In order to solve the technical problems, the present disclosure provides the following technical solutions:
as an aspect of the embodiments of the present disclosure, there is provided a photovoltaic power station power prediction method based on intelligent monitoring, including the steps of:
acquiring the historical conversion efficiency of the photovoltaic power station, and carrying out running condition inspection of the photovoltaic power station based on the historical conversion efficiency;
and predicting the output power of the photovoltaic power station based on the historical conversion efficiency, the running condition of the photovoltaic power station obtained by inspection, the collected environmental data of the photovoltaic power station and the predicted meteorological data to obtain predicted power.
Optionally, obtaining the historical conversion efficiency of the photovoltaic power plant includes:
obtaining average radiation illuminance I in a plurality of time periods in history and output power P in a corresponding time period, wherein the average radiation illuminance I is the radiation illuminance on an illuminated surface in unit area and unit time on the surface of an optical component illuminated by radiation energy, and the calculation formula of the historical irradiance is as follows: i=dΦ/dS, where I represents the illuminance of radiation, Φ represents the luminous flux, and S represents the optical component area;
the historical conversion efficiency eta in a plurality of time periods is obtained according to a plurality of average radiation illuminance I and a plurality of average output powers P.
Optionally, performing the operation condition inspection of the photovoltaic power station based on the historical conversion efficiency includes:
based on the change of the historical conversion efficiency in a plurality of time periods, whether the abnormality exists or not is judged according to whether the set change amount threshold is exceeded or not, and if the abnormality exists, the operation condition of the photovoltaic power station is continuously inspected, wherein the operation condition comprises one or more of the following: shielding condition of the photovoltaic module and power attenuation test results, and operation conditions of the combiner box, the power distribution cabinet, the inverter and the transformer; and if no abnormality exists, acquiring the primary inspection condition of the photovoltaic module, the combiner box, the power distribution cabinet, the inverter and the transformer in the photovoltaic power station.
Optionally, predicting the output power of the photovoltaic power station to obtain the predicted power based on the historical conversion efficiency, the running condition of the inspected photovoltaic power station, the collected environmental data of the photovoltaic power station and the predicted meteorological data includes:
taking the historical conversion efficiency in a plurality of time periods as a dependent variable, and taking the average radiation illuminance and the average output power as independent variables to perform curve fitting to obtain a prediction formula about the predicted power;
obtaining a predicted radiation illuminance value according to the collected environmental data of the photovoltaic power station and the predicted meteorological data;
and calculating by using the prediction formula according to the running condition of the inspected photovoltaic power station and the predicted radiation illuminance value to obtain the predicted power.
Optionally, the predicted power Pcal in the prediction formula is a dependent variable, and the predicted radiation illuminance Ical and the predicted conversion efficiency ηcal are independent variables, specifically:
Pcal＝k1*Ical*k2*ηcal*A，
wherein k1 and k2 are constants obtained by curve fitting for defining the predicted illuminance Ical and the predicted conversion efficiency eta cal, and A is the light receiving area of the photovoltaic module.
Optionally, the historical conversion efficiency η in a plurality of time periods is obtained according to a plurality of average illuminance I and a plurality of average output powers P, specifically:wherein A is the light receiving area of the photovoltaic module.
Optionally, the environmental data includes at least two of the following data: temperature, humidity, wind speed, rainfall and PM value; obtaining an influence factor mu which influences the predicted radiation illuminance value according to at least two of temperature, humidity, wind speed, rainfall or PM value;
the meteorological data includes the following data: total oblique solar radiation, total horizontal solar radiation, direct horizontal solar radiation, or scattered horizontal solar radiation; adding according to the inclined total solar radiation, the horizontal direct solar radiation or the horizontal scattered solar radiation to obtain the total solar radiation quantity, and further obtaining the radiation illuminance IPre on the illuminated surface in unit area and unit time;
and carrying out comprehensive calculation according to the influence factors and the irradiance Ipre to obtain predicted irradiance Ical, ical=mu.ipre.
As another aspect of an embodiment of the present disclosure, there is provided a photovoltaic power station power prediction system based on intelligent monitoring, including:
the historical conversion efficiency acquisition unit is used for acquiring the historical conversion efficiency of the photovoltaic power station and carrying out running condition inspection of the photovoltaic power station based on the historical conversion efficiency;
the power prediction unit predicts the output power of the photovoltaic power station based on the historical conversion efficiency, the running condition of the inspected photovoltaic power station, the collected environmental data of the photovoltaic power station and the predicted meteorological data to obtain predicted power.
As another aspect of the embodiments of the present disclosure, an electronic device is provided, including a memory, a processor, and a computer program stored on the memory and executable on the processor, where the processor implements the intelligent monitoring-based photovoltaic power plant power prediction method when executing the computer program.
As another aspect of the embodiments of the present disclosure, a computer readable storage medium is provided, on which a computer program is stored, which when executed by a processor, implements the intelligent monitoring-based photovoltaic power plant power prediction method.
Compared with the existing photovoltaic power station power prediction system, the intelligent monitoring system disclosed by the invention can intelligently monitor the equipment in the photovoltaic power station according to the running condition of the equipment related to the existing photovoltaic power station and the change condition of the historical conversion efficiency, namely, the inspection mode or the primary inspection or the conventional interval inspection mode is started according to the change threshold value of the historical conversion efficiency; meanwhile, the influence of the surrounding small environment on the conversion efficiency of the photovoltaic power station is fully considered, and the influence of future meteorological data is added, so that relatively more accurate predicted power is obtained.
Drawings
Fig. 1 is a flowchart of a photovoltaic power plant power prediction method based on intelligent monitoring in embodiment 1 of the present disclosure;
FIG. 2 is a detailed flowchart of the step S10 in embodiment 1 of the present disclosure;
FIG. 3 is a detailed flowchart of step S30 in embodiment 1 of the present disclosure;
fig. 4 is a schematic block diagram of a photovoltaic power plant power prediction system based on intelligent monitoring in embodiment 2 of the present disclosure.
Detailed Description
Various exemplary embodiments, features and aspects of the disclosure will be described in detail below with reference to the drawings. In the drawings, like reference numbers indicate identical or functionally similar elements. Although various aspects of the embodiments are illustrated in the accompanying drawings, the drawings are not necessarily drawn to scale unless specifically indicated.
The word "exemplary" is used herein to mean "serving as an example, embodiment, or illustration. Any embodiment described herein as "exemplary" is not necessarily to be construed as preferred or advantageous over other embodiments.
The term "and/or" is herein merely an association relationship describing an associated object, meaning that there may be three relationships, e.g., a and/or B, may represent: a exists alone, A and B exist together, and B exists alone. In addition, the term "at least one" herein means any one of a plurality or any combination of at least two of a plurality, for example, including at least one of A, B, C, and may mean including any one or more elements selected from the group consisting of A, B and C.
Furthermore, numerous specific details are set forth in the following detailed description in order to provide a better understanding of the present disclosure. It will be understood by those skilled in the art that the present disclosure may be practiced without some of these specific details. In some instances, methods, means, elements, and circuits well known to those skilled in the art have not been described in detail in order not to obscure the present disclosure.
It will be appreciated that the above-mentioned method embodiments of the present disclosure may be combined with each other to form a combined embodiment without departing from the principle logic, and are limited to the description of the present disclosure.
In addition, the disclosure further provides a photovoltaic power station power prediction system based on intelligent monitoring, electronic equipment, a computer readable storage medium and a program, and any one of the photovoltaic power station power prediction methods based on intelligent monitoring provided by the disclosure can be realized, and corresponding technical schemes and descriptions and corresponding records of method parts are omitted.
The intelligent monitoring-based photovoltaic power plant power prediction method may be implemented by a computer or other device capable of implementing intelligent monitoring-based photovoltaic power plant power prediction, for example, the method may be implemented by a terminal device or a server or other processing device, and in some possible implementations, the intelligent monitoring-based photovoltaic power plant power prediction method may be implemented by a processor invoking computer readable instructions stored in a memory.
Example 1
As an aspect of the embodiments of the present disclosure, there is provided a photovoltaic power station power prediction method based on intelligent monitoring, as shown in fig. 1, including the following steps:
s10, acquiring historical conversion efficiency of a photovoltaic power station;
s20, carrying out inspection on the operation condition of the photovoltaic power station based on the historical conversion efficiency to obtain the operation condition of the photovoltaic power station;
s30, predicting the output power of the photovoltaic power station based on the historical conversion efficiency, the running condition of the inspected photovoltaic power station, the collected environmental data of the photovoltaic power station and the predicted meteorological data to obtain predicted power.
According to the embodiment of the disclosure, the monitoring of the photovoltaic power station equipment and the weather condition prediction can be combined to predict the power of the photovoltaic power station, so that the stability of the power prediction is facilitated.
The steps of the embodiments of the present disclosure are described in detail below, respectively.
S10, acquiring historical conversion efficiency of a photovoltaic power station;
as shown in fig. 2, the step S10 may be specifically implemented as follows:
s101, obtaining average radiation illuminance I in a plurality of time periods in history and output power P in a corresponding time period, wherein the average radiation illuminance I is the radiation illuminance on a illuminated surface in unit area and unit time on the surface of an optical component illuminated by radiation energy, and the calculation formula of the historical irradiance is as follows: i=dΦ/dS, where I represents the illuminance of radiation, Φ represents the luminous flux, and S represents the optical component area;
s103, obtaining historical conversion efficiency eta in a plurality of time periods according to a plurality of average radiation illuminance I and a plurality of average output powers P, wherein the historical conversion efficiency eta is specifically:wherein A is the light receiving area of the photovoltaic module.
S20, carrying out inspection on the operation condition of the photovoltaic power station based on the historical conversion efficiency to obtain the operation condition of the photovoltaic power station;
whether continuous inspection or conventional inspection is carried out on equipment of the photovoltaic power station is determined according to the historical conversion efficiency condition, and the advantage of the method is that software and hardware cost can be saved according to actual conditions. The inspection comprises the conditions of equipment in a photovoltaic power station such as a photovoltaic module, a combiner box, a power distribution cabinet, an inverter and a transformer, wherein the photovoltaic module is easily shielded by trees, weeds or bird droppings to form a hot spot effect, and the photovoltaic module can be detected through a distributed camera or an unmanned aerial vehicle image inspection mode; in addition, the photovoltaic module is required to be subjected to power attenuation test, namely, the photovoltaic module is tested in a period of time to obtain the output power of the photovoltaic module in the actual working state, so that the power attenuation condition of the photovoltaic module is judged. The power attenuation condition of the photovoltaic module can be tested, so that the predicted output power condition of the photovoltaic power station can be reflected more accurately. Similarly, the conversion efficiency of other devices of the photovoltaic power station, such as the combiner box, the power distribution cabinet, the inverter and the transformer, is also a factor that can affect the output power of the whole photovoltaic power station, and it is also necessary to monitor the conversion efficiency, so that the conversion efficiency can be calculated through the input current/input voltage and the output current/voltage of each device.
S30, predicting the output power of the photovoltaic power station based on the historical conversion efficiency, the running condition of the inspected photovoltaic power station, the collected environmental data of the photovoltaic power station and the predicted meteorological data to obtain predicted power.
As shown in fig. 3, step S30 may include:
s301, performing curve fitting by taking historical conversion efficiency in a plurality of time periods as a dependent variable and taking average radiation illuminance and average output power as independent variables to obtain a prediction formula about predicted power;
s302, obtaining a predicted radiation illuminance value according to the collected environmental data of the photovoltaic power station and the predicted meteorological data;
and S303, calculating by using the prediction formula according to the running condition of the inspected photovoltaic power station and the predicted radiation illuminance value to obtain the predicted power.
In some embodiments, the predicted power Pcal in the prediction formula is a dependent variable, and the predicted radiation illuminance Ical and the predicted conversion efficiency ηcal are independent variables, specifically:
Pcal＝k1*Ical*k2*ηcal*A，
wherein k1 and k2 are constants obtained by curve fitting for defining the predicted illuminance Ical and the predicted conversion efficiency eta cal, and A is the light receiving area of the photovoltaic module.
In some embodiments, the historical conversion efficiency η is obtained for a plurality of time periods according to a plurality of average radiant illuminance I and a plurality of average output powers P, specifically:wherein A is the light receiving area of the photovoltaic module.
In some embodiments, the environmental data includes at least two of the following: temperature, humidity, wind speed, rainfall and PM value; obtaining an influence factor mu which influences the predicted radiation illuminance value according to at least two of temperature, humidity, wind speed, rainfall or PM value; for example, under the condition that the temperature and the humidity in the environmental data are low, it is known that the efficiency of converting the irradiation amount into the electric quantity of the obtained photovoltaic module is lower than the actual prediction condition, so that the conversion efficiency is assigned according to the actual influence of the environmental data, for example, when the wind speed is large and the rainfall is less than the actual predicted weather, the influence factor mu can be assigned to 1.05 according to the historical data condition (namely, the historical condition of the wind speed and the rainfall); and in the case of a large PM value and humidity, 0.96 is assigned based on the history data (i.e., the history of PM value and humidity).
The meteorological data includes the following data: total oblique solar radiation, total horizontal solar radiation, direct horizontal solar radiation, or scattered horizontal solar radiation; adding according to the inclined total solar radiation, the horizontal direct solar radiation or the horizontal scattered solar radiation to obtain the total solar radiation quantity, and further obtaining the radiation illuminance IPre on the illuminated surface in unit area and unit time;
and carrying out comprehensive calculation according to the influence factors and the irradiance Ipre to obtain predicted irradiance Ical, ical=mu.ipre.
Example 2
As another aspect of the embodiments of the present disclosure, there is provided a photovoltaic power plant power prediction system 100 based on intelligent monitoring, as shown in fig. 4, including:
the photovoltaic power station operation condition inspection system comprises a historical conversion efficiency acquisition unit 1, a photovoltaic power station operation condition inspection unit and a photovoltaic power station operation condition inspection unit, wherein the historical conversion efficiency acquisition unit is used for acquiring the historical conversion efficiency of the photovoltaic power station;
the power prediction unit 2 predicts the output power of the photovoltaic power station based on the historical conversion efficiency, the running condition of the inspected photovoltaic power station, the collected environmental data of the photovoltaic power station and the predicted meteorological data to obtain predicted power.
The following describes each module of the embodiments of the present disclosure in detail.
In the history conversion efficiency acquisition unit 1, it can be realized by:
obtaining average radiation illuminance I in a plurality of time periods in history and output power P in a corresponding time period, wherein the average radiation illuminance I is the radiation illuminance on an illuminated surface in unit area and unit time on the surface of an optical component illuminated by radiation energy, and the calculation formula of the historical irradiance is as follows: i=dΦ/dS, where I represents the illuminance of radiation, Φ represents the luminous flux, and S represents the optical component area;
the historical conversion efficiency eta in a plurality of time periods is obtained according to a plurality of average radiation illuminance I and a plurality of average output powers P, and is specifically as follows:wherein A is the light receiving area of the photovoltaic module.
The historical conversion efficiency acquisition unit 1 further comprises a patrol module, and is used for carrying out patrol on the operation condition of the photovoltaic power station based on the historical conversion efficiency to obtain the operation condition of the photovoltaic power station;
the inspection module further comprises: whether continuous inspection or conventional inspection is carried out on equipment of the photovoltaic power station is determined according to the historical conversion efficiency condition, and the advantage of the method is that software and hardware cost can be saved according to actual conditions. The inspection comprises the conditions of equipment in a photovoltaic power station such as a photovoltaic module, a combiner box, a power distribution cabinet, an inverter and a transformer, wherein the photovoltaic module is easily shielded by trees, weeds or bird droppings to form a hot spot effect, and the photovoltaic module can be detected through a distributed camera or an unmanned aerial vehicle image inspection mode; in addition, the photovoltaic module is required to be subjected to power attenuation test, namely, the photovoltaic module is tested in a period of time to obtain the output power of the photovoltaic module in the actual working state, so that the power attenuation condition of the photovoltaic module is judged. The power attenuation condition of the photovoltaic module can be tested, so that the predicted output power condition of the photovoltaic power station can be reflected more accurately. Similarly, the conversion efficiency of other devices of the photovoltaic power station, such as the combiner box, the power distribution cabinet, the inverter and the transformer, is also a factor that can affect the output power of the whole photovoltaic power station, and it is also necessary to monitor the conversion efficiency, so that the conversion efficiency can be calculated through the input current/input voltage and the output current/voltage of each device.
The power prediction unit 2 may further include:
taking the historical conversion efficiency in a plurality of time periods as a dependent variable, and taking the average radiation illuminance and the average output power as independent variables to perform curve fitting to obtain a prediction formula about the predicted power;
obtaining a predicted radiation illuminance value according to the collected environmental data of the photovoltaic power station and the predicted meteorological data;
and calculating by using the prediction formula according to the running condition of the inspected photovoltaic power station and the predicted radiation illuminance value to obtain the predicted power.
In some embodiments, the predicted power Pcal in the prediction formula is a dependent variable, and the predicted radiation illuminance Ical and the predicted conversion efficiency ηcal are independent variables, specifically:
Pcal＝k1*Ical*k2*ηcal*A，
wherein k1 and k2 are constants obtained by curve fitting for defining the predicted illuminance Ical and the predicted conversion efficiency eta cal, and A is the light receiving area of the photovoltaic module.
In some embodiments, the historical conversion efficiency η is obtained for a plurality of time periods according to a plurality of average radiant illuminance I and a plurality of average output powers P, specifically:wherein A is the light receiving area of the photovoltaic module.
In some embodiments, the environmental data includes at least two of the following: temperature, humidity, wind speed, rainfall and PM value; obtaining an influence factor mu which influences the predicted radiation illuminance value according to at least two of temperature, humidity, wind speed, rainfall or PM value; for example, under the condition that the temperature and the humidity in the environmental data are low, it is known that the efficiency of converting the irradiation amount into the electric quantity of the obtained photovoltaic module is lower than the actual prediction condition, so that the conversion efficiency is assigned according to the actual influence of the environmental data, for example, when the wind speed is large and the rainfall is less than the actual predicted weather, the influence factor mu can be assigned to 1.05 according to the historical data condition (namely, the historical condition of the wind speed and the rainfall); and in the case of a large PM value and humidity, 0.96 is assigned based on the history data (i.e., the history of PM value and humidity).
The meteorological data includes the following data: total oblique solar radiation, total horizontal solar radiation, direct horizontal solar radiation, or scattered horizontal solar radiation; adding according to the inclined total solar radiation, the horizontal direct solar radiation or the horizontal scattered solar radiation to obtain the total solar radiation quantity, and further obtaining the radiation illuminance IPre on the illuminated surface in unit area and unit time;
and carrying out comprehensive calculation according to the influence factors and the irradiance Ipre to obtain predicted irradiance Ical, ical=mu.ipre.
Example 3
The embodiment provides an electronic device, which comprises a memory, a processor and a computer program stored on the memory and capable of running on the processor, wherein the processor realizes the photovoltaic power station power prediction method based on intelligent monitoring in the embodiment 1 when executing the computer program.
Embodiment 3 of the present disclosure is merely an example, and should not be construed as limiting the functionality and scope of use of the embodiments of the present disclosure.
The electronic device may be in the form of a general purpose computing device, which may be a server device, for example. Components of an electronic device may include, but are not limited to: at least one processor, at least one memory, a bus connecting different system components, including the memory and the processor.
The buses include a data bus, an address bus, and a control bus.
The memory may include volatile memory such as Random Access Memory (RAM) and/or cache memory, and may further include Read Only Memory (ROM).
The memory may also include program means having a set (at least one) of program modules including, but not limited to: an operating system, one or more application programs, other program modules, and program data, each or some combination of which may include an implementation of a network environment.
The processor executes various functional applications and data processing by running computer programs stored in the memory.
The electronic device may also communicate with one or more external devices (e.g., keyboard, pointing device, etc.). Such communication may be through an input/output (I/O) interface. And, the electronic device may also communicate with one or more networks such as a Local Area Network (LAN), a Wide Area Network (WAN), and/or a public network, such as the Internet, through a network adapter. The network adapter communicates with other modules of the electronic device via a bus. It should be appreciated that although not shown, other hardware and/or software modules may be used in connection with an electronic device, including but not limited to: microcode, device drivers, redundant processors, external disk drive arrays, RAID (disk array) systems, tape drives, data backup storage systems, and the like.
It should be noted that although several units/modules or sub-units/modules of an electronic device are mentioned in the above detailed description, such a division is merely exemplary and not mandatory. Indeed, the features and functionality of two or more units/modules described above may be embodied in one unit/module according to embodiments of the present application. Conversely, the features and functions of one unit/module described above may be further divided into ones that are embodied by a plurality of units/modules.
Example 4
A computer readable storage medium storing a computer program which when executed by a processor implements the steps of the intelligent monitoring-based photovoltaic power plant power prediction method of embodiment 1.
More specifically, among others, readable storage media may be employed including, but not limited to: portable disk, hard disk, random access memory, read only memory, erasable programmable read only memory, optical storage device, magnetic storage device, or any suitable combination of the foregoing.
In a possible embodiment, the disclosure may also be implemented in the form of a program product comprising program code for causing a terminal device to carry out the steps of implementing the intelligent monitoring based photovoltaic power plant power prediction method described in embodiment 1 when said program product is run on the terminal device.
Wherein the program code for carrying out the present disclosure may be written in any combination of one or more programming languages, which program code may execute entirely on the user device, partly on the user device, as a stand-alone software package, partly on the user device, partly on the remote device or entirely on the remote device.
Although embodiments of the present disclosure have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made therein without departing from the principles and spirit of the disclosure, the scope of which is defined in the appended claims and their equivalents.
Claims (10)
1. The photovoltaic power station power prediction method based on intelligent monitoring is characterized by comprising the following steps of:
acquiring the historical conversion efficiency of the photovoltaic power station, and carrying out running condition inspection of the photovoltaic power station based on the historical conversion efficiency;
and predicting the output power of the photovoltaic power station based on the historical conversion efficiency, the running condition of the inspected photovoltaic power station, the collected environmental data of the photovoltaic power station and the predicted meteorological data to obtain predicted power.
2. The intelligent monitoring-based photovoltaic power plant power prediction method according to claim 1, wherein obtaining historical conversion efficiency of the photovoltaic power plant comprises:
obtaining average radiation illuminance I in a plurality of time periods in history and output power P in a corresponding time period, wherein the average radiation illuminance I is the radiation illuminance on an illuminated surface in unit area and unit time on the surface of an optical component illuminated by radiation energy, and the calculation formula of the historical irradiance is as follows: i=dΦ/dS, where I represents the illuminance of radiation, Φ represents the luminous flux, and S represents the optical component area;
the historical conversion efficiency eta in a plurality of time periods is obtained according to a plurality of average radiation illuminance I and a plurality of average output powers P.
3. The intelligent monitoring-based photovoltaic power plant power prediction method according to claim 2, wherein the operation condition inspection of the photovoltaic power plant based on the historical conversion efficiency comprises:
based on the change of the historical conversion efficiency in a plurality of time periods, whether the abnormality exists or not is judged according to whether the set change amount threshold is exceeded or not, and if the abnormality exists, the operation condition of the photovoltaic power station is continuously inspected, wherein the operation condition comprises one or more of the following: shielding condition of the photovoltaic module and power attenuation test results, and operation conditions of the combiner box, the power distribution cabinet, the inverter and the transformer; and if no abnormality exists, acquiring the primary inspection condition of the photovoltaic module, the combiner box, the power distribution cabinet, the inverter and the transformer in the photovoltaic power station.
4. The intelligent monitoring-based photovoltaic power plant power prediction method according to claim 2, wherein predicting the output power of the photovoltaic power plant based on the historical conversion efficiency, the inspected operating condition of the photovoltaic power plant, the collected environmental data of the photovoltaic power plant and the predicted meteorological data to obtain the predicted power comprises:
taking the historical conversion efficiency in a plurality of time periods as a dependent variable, and taking the average radiation illuminance and the average output power as independent variables to perform curve fitting to obtain a prediction formula about the predicted power;
obtaining a predicted radiation illuminance value according to the collected environmental data of the photovoltaic power station and the predicted meteorological data;
and calculating by using the prediction formula according to the running condition of the inspected photovoltaic power station and the predicted radiation illuminance value to obtain the predicted power.
5. The intelligent monitoring-based photovoltaic power station power prediction method according to claim 4, wherein the predicted power Pcal in the prediction formula is a dependent variable, and the predicted radiation illuminance Ical and the predicted conversion efficiency ηcal are independent variables, specifically:
Pcal＝k1*Ical*k2*ηcal*A，
wherein k1 and k2 are constants obtained by curve fitting for defining the predicted illuminance Ical and the predicted conversion efficiency eta cal, and A is the light receiving area of the photovoltaic module.
6. The intelligent monitoring-based photovoltaic power plant power prediction method according to claim 2, wherein the historical conversion efficiency η in a plurality of time periods is obtained according to a plurality of average illuminance I and a plurality of average output powers P, specifically:wherein A is the light receiving area of the photovoltaic module.
7. The intelligent monitoring-based photovoltaic power plant power prediction method of claim 5, wherein the environmental data comprises at least two of the following: temperature, humidity, wind speed, rainfall and PM value; obtaining an influence factor mu which influences the predicted radiation illuminance value according to at least two of temperature, humidity, wind speed, rainfall or PM value;
the meteorological data includes the following data: total oblique solar radiation, total horizontal solar radiation, direct horizontal solar radiation, or scattered horizontal solar radiation; adding according to the inclined total solar radiation, the horizontal direct solar radiation or the horizontal scattered solar radiation to obtain the total solar radiation quantity, and further obtaining the radiation illuminance IPre on the illuminated surface in unit area and unit time;
and (3) carrying out comprehensive calculation according to the influence factor mu and the irradiance Ipre to obtain the predicted irradiance Ical, ical=mu.ipre.
8. Photovoltaic power plant power prediction system based on intelligent monitoring, characterized by comprising:
the historical conversion efficiency acquisition unit is used for acquiring the historical conversion efficiency of the photovoltaic power station and carrying out running condition inspection of the photovoltaic power station based on the historical conversion efficiency;
the power prediction unit predicts the output power of the photovoltaic power station based on the historical conversion efficiency, the running condition of the inspected photovoltaic power station, the collected environmental data of the photovoltaic power station and the predicted meteorological data to obtain predicted power.
9. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements the intelligent monitoring based photovoltaic power plant power prediction method of any of claims 1 to 7 when executing the computer program.
10. A computer readable storage medium having stored thereon a computer program, characterized in that the program when executed by a processor implements the intelligent monitoring based photovoltaic power plant power prediction method of any of claims 1 to 7.
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