CN113064937B - Dust cleaning early warning method and system for photovoltaic power station - Google Patents
Dust cleaning early warning method and system for photovoltaic power station Download PDFInfo
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- 239000000428 dust Substances 0.000 title claims abstract description 80
- 238000004140 cleaning Methods 0.000 title claims abstract description 64
- 238000000034 method Methods 0.000 title claims abstract description 54
- 238000012544 monitoring process Methods 0.000 claims abstract description 58
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- 238000012545 processing Methods 0.000 claims abstract description 23
- 238000012216 screening Methods 0.000 claims abstract description 10
- 238000004364 calculation method Methods 0.000 claims description 19
- 238000010248 power generation Methods 0.000 claims description 16
- 230000005611 electricity Effects 0.000 claims description 13
- 238000004590 computer program Methods 0.000 claims description 6
- QKSKPIVNLNLAAV-UHFFFAOYSA-N bis(2-chloroethyl) sulfide Chemical compound ClCCSCCCl QKSKPIVNLNLAAV-UHFFFAOYSA-N 0.000 claims description 4
- 238000001228 spectrum Methods 0.000 claims description 3
- 238000012360 testing method Methods 0.000 claims description 3
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- 238000013178 mathematical model Methods 0.000 abstract description 4
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/24—Querying
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- G06F16/2458—Special types of queries, e.g. statistical queries, fuzzy queries or distributed queries
- G06F16/2477—Temporal data queries
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- G08B21/182—Level alarms, e.g. alarms responsive to variables exceeding a threshold
Abstract
The invention provides a dust cleaning and early warning method and system for a photovoltaic power station, wherein the method comprises the following steps: acquiring historical monitoring data of a first preset time period from a power station monitoring system; calculating the photovoltaic attenuation rate according to the historical monitoring data; collecting real-time monitoring data from a data collector, wherein the data interval is a preset period; screening the real-time monitoring data; carrying out standardized processing on the real-time monitoring data; calculating the current dust rate of the photovoltaic module according to the real-time monitoring data after the standardized treatment; calculating the dust accumulation loss electric charge of the photovoltaic module according to the current dust accumulation rate of the photovoltaic module; calculating the manual cleaning cost; and determining the early warning level according to the accumulated dust loss electric charge and the manual cleaning charge of the photovoltaic module. The method is based on historical data and real-time data. And establishing a mathematical model, and establishing three grades of early warning dates according to different requirements.
Description
Technical Field
The invention belongs to the field of photovoltaic power generation, and particularly relates to a dust cleaning and early warning method and system for a photovoltaic power station.
Background
The generated energy of the photovoltaic power station is an important index for evaluating the power generation capacity of the photovoltaic power station, and the influence on the power generation capacity is caused by a plurality of factors, wherein the surface dust of the photovoltaic module can generate certain reflection, scattering and absorption to solar radiation, and the photoelectric conversion efficiency of the photovoltaic module can be rapidly reduced along with the increase of the dust accumulation amount, so that the power generation capacity is greatly influenced. Currently, the treatment of dust accumulation is generally classified into natural cleaning and manual cleaning.
The natural cleaning means that the photovoltaic panel is naturally washed by falling rain so as to achieve the cleaning purpose. The method is greatly influenced by weather and has great fluctuation, and a great amount of electricity loss can be caused annually in areas with little precipitation. The manual cleaning is to manually clean the photovoltaic panel to achieve the effect of removing dust. At present, a regular dust removing cleaning method is generally adopted, the period of the method is not accurate enough, the manual cleaning period is too long, the power generation capacity of the assembly can be reduced, and the white loss of the generated energy is caused; when the manual cleaning period is shorter, the manual cleaning cost is increased, and economic loss and waste are caused.
Disclosure of Invention
The embodiment of the application provides a dust cleaning and early warning method and system for a photovoltaic power station, which are based on historical data and real-time data, a mathematical model is built, and three levels of early warning dates are built according to different requirements.
In a first aspect, an embodiment of the present application provides a dust cleaning and early warning method for a photovoltaic power station, including:
acquiring historical monitoring data of a first preset time period from a power station monitoring system;
calculating the photovoltaic attenuation rate according to the historical monitoring data;
collecting real-time monitoring data from a data collector, wherein the data interval is a preset period; screening the real-time monitoring data; carrying out standardized processing on the real-time monitoring data;
calculating the current dust rate of the photovoltaic module according to the real-time monitoring data after the standardized treatment;
calculating the dust accumulation loss electric charge of the photovoltaic module according to the current dust accumulation rate of the photovoltaic module;
calculating the manual cleaning cost;
and determining the early warning level according to the accumulated dust loss electric charge and the manual cleaning charge of the photovoltaic module.
Wherein, calculate the photovoltaic decay rate according to historical monitoring data includes:
the calculation formula of the attenuation rate α is:
actual fill factor FF real The method comprises the following steps:
P pv-max in historical dataThe maximum direct current input power at a certain moment in a first preset time period;
U real-oc the open-circuit voltage is obtained at the temperature corresponding to the maximum direct current input power;
the fill factor FF is:
P max is the maximum output power of the system.
The standardized processing of the real-time monitoring data comprises the following steps:
the filtered instantaneous direct current power P pv Instantaneous power P converted to standard test conditions STC of photovoltaic modules STC And (3) irradiating: 1000w/m 2 Atmospheric spectrum: AM1.5, temperature: t25 ℃, can be obtained:
G now irradiation at the instantaneous power;
G STC taking 1000 watts per square meter for irradiation under STC standard;
delta is the power temperature coefficient of the component;
T now is the temperature at the instant power;
T STC taking 25 ℃ for the temperature under STC standard;
so that the instantaneous irradiation of the day is more than 300w/m 2 When the average direct current power corresponding to STC standard is:
the method for calculating the current dust rate of the photovoltaic module according to the real-time monitoring data after the standardized processing comprises the following steps:
from average DC power P of the same day STC-AV The dust accumulation rate eta of the day day ：
P STC-max Maximum power under STC standard; g real-max Corresponding irradiation under the maximum power; t (T) real-max Is the corresponding temperature at maximum power.
The method for calculating the dust accumulation loss electric charge of the photovoltaic module according to the current daily dust accumulation rate of the photovoltaic module comprises the following steps: lost electric quantity W on the same day day Can be obtained from daily dust accumulation rate eta day The approximate calculation results in:
W day ＝M*H day *η day
H day equivalent hours per day;
E day daily power generation for the current day;
m is the installed capacity of the power station;
therefore, the accumulated loss electricity quantity in D days is as follows:
W all ＝D*W day
if the electricity charge is R per kilowatt hour e The total electric charge S lost e-all The method comprises the following steps:
S e-all ＝W all *R e 。
wherein, calculate the manual cleaning expense, include:
if each kilowatt of manual cleaning is R c Total cost of cleaning S c-all The method comprises the following steps:
S c-all ＝M*R c 。
the early warning level is determined according to the accumulated dust loss electricity fee and the manual cleaning fee of the photovoltaic module, and the method comprises the following steps: if there is no future weather forecast for thirty days,
when the total electric charge S is lost e-all Reaching the total cost S c-all Half of (i.e.)Therefore(s)>Three-level early warning is performed;
when the total electric charge S is lost e-all Reaching the total cost S c-all A kind of electronic deviceI.e. < ->Therefore(s)>The second-level early warning is performed;
when the total electric charge S is lost e-all Reaching the total cost S c-all When, S e-all ≥S c-all Therefore, it isThe early warning is first-level.
In a second aspect, the present application provides a dust cleaning and early warning system for a photovoltaic power station, comprising:
the acquisition unit is used for acquiring historical monitoring data of a first preset time period from the power station monitoring system;
the first calculation unit is used for calculating the photovoltaic attenuation rate according to the historical monitoring data;
the acquisition unit is used for acquiring real-time monitoring data from the data acquisition unit, and the data interval is a preset period; screening the real-time monitoring data; carrying out standardized processing on the real-time monitoring data;
the second calculation unit is used for calculating the current dust rate of the photovoltaic module according to the real-time monitoring data after the standardized processing;
the third calculation unit is used for calculating the dust accumulation loss electric charge of the photovoltaic module according to the current daily dust accumulation rate of the photovoltaic module;
a fourth calculation unit for calculating a manual cleaning cost;
and the determining unit is used for determining the early warning level according to the accumulated dust loss electric charge and the manual cleaning charge of the photovoltaic module.
In a third aspect, embodiments of the present application provide a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, performs the steps of any of the methods described above.
In a fourth aspect, embodiments of the present application provide a computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the steps of any one of the methods described above when the program is executed.
The dust cleaning and early warning method and system for the photovoltaic power station have the following beneficial effects:
the method is based on historical data and real-time data. And establishing a mathematical model, and establishing three grades of early warning dates according to different requirements. In operation, the early warning date can be updated continuously along with rainwater and manual cleaning. Meanwhile, the attenuation rate of the assembly is considered, so that the precision is more accurate. Meanwhile, the time for cleaning can be more reasonably estimated by adding the future weather forecast, and different grades are selected, so that the cost reduction and synergy result is achieved.
Drawings
Fig. 1 is a schematic flow chart of a dust cleaning and early warning method of a photovoltaic power station according to an embodiment of the present application;
fig. 2 is a schematic flow chart of another dust cleaning and early warning method of the photovoltaic power station according to the embodiment of the application;
FIG. 3 is a schematic diagram of an equivalent circuit of a photovoltaic cell;
fig. 4 is a schematic structural diagram of a dust cleaning and early warning system of a photovoltaic power station according to an embodiment of the present application;
fig. 5 is a schematic structural diagram of a computer device according to an embodiment of the present application.
Detailed Description
The present application is further described below with reference to the drawings and examples.
In the following description, the terms "first," "second," and "first," are used for descriptive purposes only and are not to be construed as indicating or implying relative importance. The following description provides various embodiments of the invention that may be substituted or combined between different embodiments, and thus this application is also intended to encompass all possible combinations of the same and/or different embodiments described. Thus, if one embodiment includes feature A, B, C and another embodiment includes feature B, D, then the present application should also be considered to include embodiments that include one or more of all other possible combinations including A, B, C, D, although such an embodiment may not be explicitly recited in the following.
The following description provides examples and does not limit the scope, applicability, or examples set forth in the claims. Changes may be made in the function and arrangement of elements described without departing from the scope of the application. Various examples may omit, replace, or add various procedures or components as appropriate. For example, the described methods may be performed in a different order than described, and various steps may be added, omitted, or combined. Furthermore, features described with respect to some examples may be combined into other examples.
According to the method, real-time data such as instantaneous irradiation, instantaneous direct current power, daily power generation, daily irradiation, assembly temperature, ambient temperature, wind speed, humidity, total power generation, total radiation are collected, a fitting model between a dust accumulation quantization index of a photovoltaic power station and monitored data is established by combining historical big data analysis, an estimated value of dust accumulation of the photovoltaic assembly is calculated in a natural environment, future meteorological data and manual cleaning cost are combined, and the method is divided into three different levels, so that dust cleaning dates are given.
As shown in fig. 1-3, the dust cleaning and early warning method for the photovoltaic power station in the embodiment of the application includes: step S101, historical monitoring data of a first preset time period is obtained from a power station monitoring system; s103, calculating the photovoltaic attenuation rate according to the historical monitoring data; s105, collecting real-time monitoring data from a data collector, wherein the data interval is a preset period; screening the real-time monitoring data; carrying out standardized processing on the real-time monitoring data; s107, calculating the current dust rate of the photovoltaic module according to the real-time monitoring data after the standardized processing; s109, calculating the dust accumulation loss electric charge of the photovoltaic module according to the current daily dust accumulation rate of the photovoltaic module; s111, calculating the manual cleaning cost; s113, determining an early warning level according to the accumulated dust loss electricity fee and the manual cleaning fee of the photovoltaic module. The following is a detailed description.
And (3) a step of: historical data processing
1.1: historical data entry
Historical monitoring data is obtained from the power station monitoring system for at least 30 days for later data processing, and the data indexes comprise: instantaneous irradiation, instantaneous direct current power, daily power generation, daily irradiation, component temperature, ambient temperature, wind speed, humidity, total power generation, total radiation, and the like.
1.2: photovoltaic decay rate approximation calculation
The basic principle of photovoltaic cells is the photoelectric effect. Light is irradiated onto the component panel material, photons are converted into electrons, and the basic equivalent circuit is shown in fig. 3:
I sh is a photo-generated current;
I d diode junction current;
C i is junction capacitance;
R h is a parallel resistor (the resistance value is larger and is in the order of 103 omega);
R s is a series resistor (the resistance value is smaller than 1 omega).
In general series resistance R s The voltage-current output of the solar cell can be expressed as:
U D is junction voltage;
i is output current;
I 0 an inverted saturation current for the diode;
n is an ideal coefficient;
q is an electron charge;
K B is the Boltzmann constant;
t is the temperature.
Respectively, from the above, the short-circuit current I of the system sh And open circuit voltage U oc The expression:
I sc ＝I sh
i.e. the short circuit is a photo-generated current proportional to the received light intensity. When the photovoltaic panel carries a load, the maximum output power P of the system can be obtained in theory according to different output powers max The method comprises the following steps:
P max ＝I m *U m
U m is the optimal working voltage;
I m is the optimal operating current.
The fill factor FF is:
the fill factor FF represents the characteristic of the maximum power output by the solar cell at the optimum load.
FF depends on the incident light intensity, the influence of the material, etc., and is also influenced by dust deposition, etc. In practice, however, the component often does not reach the theoretical fill factor FF value,
actual fill factor FF real The method comprises the following steps:
P pv-max the maximum direct current input power is obtained at a certain moment in 30 days in the historical data;
U real-oc the open-circuit voltage is obtained at the temperature corresponding to the maximum direct current input power;
in this way, the decay rate α can be approximately replaced by the decay rate of the fill factor:
and II: real-time data processing
2.1: real-time data input
The data collector collects real-time monitoring data in a MODBUS485 mode, the data interval is taken once for 5 minutes, the data are used for subsequent data processing, and the data indexes comprise: instantaneous irradiation, instantaneous direct current power, daily power generation, daily irradiation, component temperature, ambient temperature, wind speed, humidity, total power generation, total radiation, and the like.
2.2: screening of real-time data
Deleting the whole data sample with zero or negative value and the sample with abnormally large or small value; duplicate data samples are stripped; measuring the maximum value of the day for the daily power generation amount and the daily irradiation;
finally, screening, wherein the daily instantaneous irradiation is more than 300w/m 2 The retention of the temperature greater than 25 ℃ records each corresponding instantaneous dc power.
2.3: real-time data normalization
The instantaneous direct current power P obtained by screening pv Transient power P converted to Standard Test Conditions (STC) for photovoltaic modules STC (irradiation: 1000 w/m) 2 Atmospheric spectrum: AM1.5, temperature: t25 ℃). The method comprises the following steps:
G now irradiation at the instantaneous power;
G STC for irradiation under STC standard, 1000w/m was taken 2 ；
Delta is the power temperature coefficient of the component;
T now is the temperature at the instant power;
T STC taking 25 ℃ for the temperature under STC standard;
so that the instantaneous irradiation of the day is more than 300w/m 2 The average direct current power corresponding to STC standard is:
thirdly,: component dust rate calculation
From average DC power P of the same day STC-AV The dust accumulation rate eta of the day day ：
P STC-max Maximum power under STC standard;
G real-max corresponding irradiation under the maximum power;
T real-max is the corresponding temperature under the maximum power;
fourth, the method comprises the following steps: component dust accumulation and electricity loss fee
Lost electric quantity W on the same day day Can be obtained from daily dust accumulation rate eta day The approximate calculation results in:
W day ＝M*H day *η day
H day equivalent hours per day;
E day daily power generation for the current day;
m is the installed capacity of the power station;
therefore, the accumulated loss electricity quantity in D days is as follows:
W all ＝D*W day
if the electricity charge is R per kilowatt hour e The total electric charge S lost e-all The method comprises the following steps:
S e-all ＝W all *R e
fifth step: cost of manual cleaning
If each kilowatt of manual cleaning is R c Total cost of cleaning S c-all The method comprises the following steps:
S c-all ＝M*R c
sixth,: dust early warning time
6.1: if there is no weather forecast for thirty days in future
When the total electric charge S is lost e-all Reaching the total cost S c-all Half of (i.e.)Therefore(s)>Three-level early warning is performed;
when the total electric charge S is lost e-all Reaching the total cost S c-all A kind of electronic deviceI.e. < ->Therefore(s)>The second-level early warning is performed;
when the total electric charge S is lost e-all Reach the totalCost S c-all When, S e-all ≥S c-all Therefore, it isThe early warning is first-level.
6.2 if there is a future weather forecast for thirty days
Days D of future rainy days yu Greater than D 1 、D 2 、D 3 . The first and second stage of early warning is D 1 、D 2 、D 3 。
Days D of future rainy days yu Less than D 1 、D 2 、D 3 . The first and second stage of early warning is D 2 、D 3 、D yu 。
Days D of future rainy days yu Less than D 1 、D 2 Greater than D 3 . The first and second stage of early warning is D 2 、D yu 、D 3 。
Days D of future rainy days yu Less than D 1 Greater than D 2 、D 3 . The first and second stage of early warning is D yu 、D 2 、D 3 。
Seventh,: principle of system operation
When the system algorithm is first run, it is the first day. And analyzing and obtaining three-level early warning dates based on the data of the same day and the data of the first thirty days. The first day after rain, the dust accumulation rate of the assembly is reduced. The first day after rain is taken as the first day, and data of thirty days before the first day is taken as the basis, so that three-level early warning dates are obtained through analysis. When the manual cleaning is performed, the first day after the cleaning is taken as the first day, and the data of the previous thirty days are taken as the basis, so that three-level early warning dates are obtained through analysis.
The method is based on historical data and real-time data. And establishing a mathematical model, and establishing three grades of early warning dates according to different requirements. In operation, the early warning date can be updated continuously along with rainwater and manual cleaning. Meanwhile, the attenuation rate of the assembly is considered, so that the precision is more accurate. Meanwhile, the time for cleaning can be more reasonably estimated by adding future weather forecast, and different grades are selected, so that the aims of reducing cost and enhancing efficiency are fulfilled.
As shown in fig. 4, the present application further provides a dust cleaning and early warning system of a photovoltaic power station, including:
an obtaining unit 201, configured to obtain historical monitoring data of a first preset period from a power station monitoring system;
a first calculation unit 202 for calculating a photovoltaic attenuation rate according to the historical monitoring data;
the acquisition unit 203 is configured to acquire real-time monitoring data from the data acquisition unit, where a data interval is a preset period; screening the real-time monitoring data; carrying out standardized processing on the real-time monitoring data;
the second calculating unit 204 is configured to calculate a current dust rate of the photovoltaic module according to the real-time monitoring data after the standardized processing;
the third calculation unit 205 is configured to calculate a dust accumulation loss electric charge of the photovoltaic module according to the current daily dust accumulation rate of the photovoltaic module;
a fourth calculation unit 206 for calculating a manual cleaning cost;
the determining unit 207 is configured to determine an early warning level according to the accumulated dust loss electricity fee and the manual cleaning fee of the photovoltaic module.
In the application, the embodiment of the dust cleaning and early warning system of the photovoltaic power station is basically similar to the embodiment of the dust cleaning and early warning method of the photovoltaic power station, and for relevant points, please refer to the description of the embodiment of the dust cleaning and early warning method of the photovoltaic power station.
It will be clear to those skilled in the art that the technical solutions of the embodiments of the present invention may be implemented by means of software and/or hardware. "Unit" and "module" in this specification refer to software and/or hardware capable of performing a specific function, either alone or in combination with other components, such as an FPGA (Field-Programmable Gate Array, field programmable gate array), an IC (Integrated Circuit ), etc.
The processing units and/or modules of the embodiments of the present invention may be implemented by an analog circuit that implements the functions described in the embodiments of the present invention, or may be implemented by software that executes the functions described in the embodiments of the present invention.
The embodiment of the invention also provides a computer readable storage medium, on which a computer program is stored, which when being executed by a processor, realizes the steps of the dust cleaning and early warning method of the photovoltaic power station. The computer readable storage medium may include, among other things, any type of disk including floppy disks, optical disks, DVDs, CD-ROMs, micro-drives, and magneto-optical disks, ROM, RAM, EPROM, EEPROM, DRAM, VRAM, flash memory devices, magnetic or optical cards, nanosystems (including molecular memory ICs), or any type of media or device suitable for storing instructions and/or data.
Fig. 5 is a schematic structural diagram of a computer device according to an embodiment of the present application, such as a laptop computer, a desktop computer, a workstation, a personal digital assistant, a server, a blade server, a mainframe computer, and other suitable computers, as shown in fig. 5. The computer device may also represent various forms of mobile apparatuses, such as personal digital processing, cellular telephones, smartphones, wearable devices, and other similar computing apparatuses. The computer device of the present application comprises a processor 401, a memory 402, input means 403 and output means 404. The processor 401, memory 402, input device 403, and output device 404 may be connected by a bus 405 or otherwise. The memory 402 stores a computer program, which can be run on the processor 401, and the processor 401 executes the program to implement the steps of the dust cleaning and early warning method of the photovoltaic power station.
The input device 403 may receive input numeric or character information and generate key signal inputs related to user settings and function control of the data processing computer device, such as a touch screen, keypad, mouse, trackpad, touchpad, pointer stick, one or more mouse buttons, trackball, joystick, and like input devices. The output device 404 may include a display apparatus, auxiliary lighting devices (e.g., LEDs), and haptic feedback devices (e.g., vibration motors), among others. Display devices may include, but are not limited to, liquid Crystal Displays (LCDs), light Emitting Diode (LED) displays, plasma displays, and touch screens.
In the several embodiments provided in this application, it should be understood that the disclosed apparatus and method may be implemented in other ways. The above-described embodiment of the apparatus is merely illustrative, and for example, the division of the units is merely a logic function division, and there may be other division manners 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 performed. In addition, the various components shown or discussed may be coupled or directly coupled or communicatively coupled to each other via some interface, whether indirectly coupled or communicatively coupled to devices or units, whether electrically, mechanically, or otherwise.
The functional units in the embodiments of the present invention may be all integrated in one processing unit, or each unit may be separately used as one unit, or two or more units may be integrated in one unit; the integrated units may be implemented in hardware or in hardware plus software functional units.
The above description is only of the preferred embodiments of the present invention and is not intended to limit the present invention, but various modifications and variations can be made to the present invention by those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (7)
1. The dust cleaning and early warning method for the photovoltaic power station is characterized by comprising the following steps of:
acquiring historical monitoring data of a first preset time period from a power station monitoring system;
calculating the photovoltaic attenuation rate according to the historical monitoring data;
collecting real-time monitoring data from a data collector, wherein the data interval is a preset period; screening the real-time monitoring data; carrying out standardized processing on the real-time monitoring data;
calculating the current dust rate of the photovoltaic module according to the real-time monitoring data after the standardized treatment;
calculating the dust accumulation loss electric charge of the photovoltaic module according to the current dust accumulation rate of the photovoltaic module;
calculating the manual cleaning cost;
determining an early warning level according to the accumulated dust loss electric charge and the manual cleaning charge of the photovoltaic module;
the method for calculating the current-day dust accumulation rate of the photovoltaic module according to the real-time monitoring data after standardized processing comprises the following steps:
from average DC power P of the same day STC-AV The dust accumulation rate eta of the day day ：
P STC-max Maximum power under STC standard; g real-max Corresponding irradiation under the maximum power; t (T) real-max Is the corresponding temperature under the maximum power;
the method for calculating the dust accumulation loss electric charge of the photovoltaic module according to the current dust accumulation rate of the photovoltaic module comprises the following steps:
lost electric quantity W on the same day day Can be obtained from daily dust accumulation rate eta day The approximate calculation results in:
W day ＝M*H day *η day
H day equivalent hours per day;
E day daily power generation for the current day;
m is the installed capacity of the power station;
therefore, the accumulated loss electricity quantity in D days is as follows:
W all ＝D*W day
if the electricity charge is R per kilowatt hour e The total electric charge S lost e-all The method comprises the following steps:
S e-all ＝W all *R e ；
the early warning level is determined according to the accumulated dust loss electric charge and the manual cleaning charge of the photovoltaic module, and the method comprises the following steps: if there is no future weather forecast for thirty days,
when the total electric charge S is lost e-all Reaching the total cost S c-all Half of (i.e.)Therefore(s)>Three-level early warning is performed;
when the total electric charge S is lost e-all Reaching the total cost S c-all A kind of electronic deviceI.e. < ->Therefore(s)>The second-level early warning is performed;
when the total electric charge S is lost e-all Reaching the total cost S c-all When, S e-all ≥S c-all Therefore, it isThe early warning is first-level.
2. The dust cleaning and early warning method of a photovoltaic power plant according to claim 1, wherein the calculating the photovoltaic decay rate according to the historical monitoring data comprises:
the calculation formula of the attenuation rate α is:
actual fill factor FF real The method comprises the following steps:
P pv-max the method comprises the steps of obtaining the maximum direct current input power at a certain moment in a first preset time period in historical data;
U real-oc the open-circuit voltage is obtained at the temperature corresponding to the maximum direct current input power;
the fill factor FF is:
P max is the maximum output power of the system.
3. The dust cleaning and early warning method of a photovoltaic power station according to claim 2, wherein the standardized processing of the real-time monitoring data comprises:
the filtered instantaneous direct current power P pv Instantaneous power P converted to standard test conditions STC of photovoltaic modules STC And (3) irradiating: 1000w/m 2 Atmospheric spectrum: AM1.5, temperature: t25 ℃, can be obtained:
G now irradiation at the instantaneous power;
G STC taking 1000 watts per square meter for irradiation under STC standard;
delta is the power temperature coefficient of the component;
T now is the temperature at the instant power;
T STC taking 25 ℃ for the temperature under STC standard;
so that the instantaneous irradiation of the day is more than 300w/m 2 When the average direct current power corresponding to STC standard is:
4. a dust cleaning pre-warning method for a photovoltaic power plant according to any one of claims 1 to 3, wherein said calculating the manual cleaning cost comprises:
if each kilowatt of manual cleaning is R c Total cost of cleaning S c-all The method comprises the following steps:
S c-all ＝M*R c 。
5. a dust washs early warning system of photovoltaic power plant, characterized by, include:
the acquisition unit is used for acquiring historical monitoring data of a first preset time period from the power station monitoring system;
the first calculation unit is used for calculating the photovoltaic attenuation rate according to the historical monitoring data;
the acquisition unit is used for acquiring real-time monitoring data from the data acquisition unit, and the data interval is a preset period; screening the real-time monitoring data; carrying out standardized processing on the real-time monitoring data;
the second calculation unit is used for calculating the current dust rate of the photovoltaic module according to the real-time monitoring data after the standardized processing;
from average DC power P of the same day STC-AV The dust accumulation rate eta of the day day ：
P STC-max Maximum power under STC standard; g real-max Corresponding irradiation under the maximum power; t (T) real-max Is the corresponding temperature under the maximum power;
the third calculation unit is used for calculating the dust accumulation loss electric charge of the photovoltaic module according to the current daily dust accumulation rate of the photovoltaic module;
lost electric quantity W on the same day day Can be obtained from daily dust accumulation rate eta day The approximate calculation results in:
W day ＝M*H day *η day
H day equivalent hours per day;
E day daily power generation for the current day;
m is the installed capacity of the power station;
therefore, the accumulated loss electricity quantity in D days is as follows:
W all ＝D*W day
if the electricity charge is R per kilowatt hour e The total electric charge S lost e-all The method comprises the following steps:
S e-all ＝W all *R e ；
a fourth calculation unit for calculating a manual cleaning cost;
the determining unit is used for determining an early warning level according to the accumulated dust loss electric charge and the manual cleaning charge of the photovoltaic module;
if there is no future weather forecast for thirty days,
when the total electric charge S is lost e-all Reaching the total cost S c-all Half of (i.e.)Therefore(s)>Three-level early warning is performed;
when the total electric charge S is lost e-all Reaching the total cost S c-all A kind of electronic deviceI.e. < ->Therefore(s)>The second-level early warning is performed;
when the total electric charge S is lost e-all Reaching the total cost S c-all When, S e-all ≥S c-all Therefore, it isThe early warning is first-level.
6. A computer readable storage medium, on which a computer program is stored, characterized in that the program, when being executed by a processor, implements the steps of the method according to any of the claims 1-4.
7. A computer 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 steps of the method of any of claims 1-4 when the program is executed.
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