CN111985732B - Photovoltaic module pollution degree prediction method and system - Google Patents
Photovoltaic module pollution degree prediction method and system Download PDFInfo
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Abstract
The application discloses a pollution degree prediction method of a photovoltaic module, which comprises the steps of obtaining meteorological data information of a place where the photovoltaic module is located when a pollution prediction instruction is received; and preliminarily determining the pollution degree of the photovoltaic module according to the relation between the meteorological data information and a preset pollution condition threshold value. The pollution degree of the photovoltaic module is mainly from the environment, and the pollution degree prediction method of the photovoltaic module in the application determines the pollution degree according to the relation between the meteorological data information and the preset pollution condition threshold value by obtaining the meteorological data information of the place where the photovoltaic module is located, so that the pollution degree is prevented from being determined according to the change of the photovoltaic module, the early prediction of the pollution degree of the photovoltaic module is realized, and the hysteresis of prediction is prevented. In addition, the application also provides a system with the advantages.
Description
Technical Field
The application relates to the technical field of photovoltaic modules, in particular to a method and a system for predicting the dirt degree of a photovoltaic module.
Background
The photovoltaic module is inevitably covered with dust, bird droppings, fallen leaves and other shielding objects in use, the shielding objects can form shadows on the photovoltaic module, the shadows can cause the current and the voltage of certain battery pieces of the photovoltaic module to change, and then the photovoltaic module is caused to locally heat, so that the phenomenon is called a hot spot effect. The hot spot effect can severely damage the photovoltaic module, and the battery piece where the shadow is located can consume the energy generated by the battery piece which is not shielded.
In order to avoid irreversible damage to the photovoltaic module caused by the hot spot effect, the dust accumulation degree of the photovoltaic module needs to be detected. At present, the methods commonly adopted are as follows: (1) The IV curve change rule of the photovoltaic module is researched, so that the ash deposition rule of the surface of the photovoltaic module is predicted and judged, and when the photovoltaic module deposits ash, the current and the voltage of the photovoltaic module can be reduced, namely, when the IV curve changes, the surface of the photovoltaic module deposits ash, and hysteresis exists in the prediction mode; (2) The gray level of the surface of the photovoltaic module is measured by using the photoresistor, and when the photovoltaic module is sensed to be covered by dust, the alarm is given, and although the gray level of the surface of the photovoltaic module can be measured, the solar module has certain hysteresis.
Therefore, how to solve the above technical problems should be of great interest to those skilled in the art.
Disclosure of Invention
The application aims to provide a method and a system for predicting the pollution degree of a photovoltaic module, so as to realize the advanced prediction of the pollution degree of the photovoltaic module.
In order to solve the technical problems, the application provides a method for predicting the dirt degree of a photovoltaic module, which comprises the following steps:
when a pollution prediction instruction is received, weather data information of the place where the photovoltaic module is located is obtained;
and preliminarily determining the pollution degree of the photovoltaic module according to the relation between the meteorological data information and a preset pollution condition threshold value.
Optionally, when the weather data information includes a particulate matter concentration and a first rainfall, the preliminarily determining, according to a relationship between the weather data information and a preset fouling condition threshold, a fouling degree of the photovoltaic module includes:
judging whether the first accumulation time of the particulate matter concentration exceeding a preset concentration threshold value within a preset period exceeds a first preset orange early warning time or not and a first preset red early warning time;
judging whether the first number of times that the first rainfall is smaller than a first preset rainfall threshold value in the preset period exceeds a first preset orange early warning frequency and a first preset red early warning frequency;
when the first accumulated time exceeds the first preset orange early warning time and the first number exceeds the first preset orange early warning times, preliminarily determining that the dirt degree is the orange early warning degree;
and when the first accumulated time exceeds the first preset red early warning time and the first time number exceeds the first preset red early warning times, preliminarily determining that the dirt degree is red early warning degree.
Optionally, when the weather data information further includes air humidity, the prediction method further includes:
judging whether the second times of the air humidity exceeding a preset humidity threshold value in the preset period exceeds the second preset orange early warning times or not, and the second preset red early warning times;
when the first accumulated time exceeds the first preset orange early warning time and the second time exceeds the second preset orange early warning time, preliminarily determining that the dirt degree is the orange early warning degree;
and when the first accumulated time exceeds the first preset red early warning time and the second time exceeds the second preset red early warning time, preliminarily determining that the dirt degree is red early warning degree.
Optionally, when the weather data information further includes a day-night temperature difference in an early morning period, the prediction method further includes:
determining a third time when the diurnal temperature difference exceeds a preset temperature difference threshold;
when the first accumulated time exceeds the first preset orange pre-warning time, the third times exceed third preset orange pre-warning times, and the air humidity exceeds the preset humidity threshold value in the early morning period, the dirt degree is preliminarily determined to be the orange pre-warning degree;
when the first accumulated time exceeds the first preset red early warning time, the third times exceed the third preset red early warning times, and the air humidity exceeds the preset humidity threshold in the early morning period, the dirt degree is preliminarily determined to be red early warning degree.
Optionally, when the meteorological data information further includes frost and snow amount, the prediction method further includes:
judging whether the fourth time number of which the frost and snow quantity is smaller than the first preset rainfall threshold value in the preset time period exceeds fourth preset orange early warning times and fourth preset red early warning times or not;
when the first accumulated time exceeds the first preset orange early warning time and the fourth number exceeds the fourth preset orange early warning times, preliminarily determining that the dirt degree is the orange early warning degree;
and when the first accumulated time exceeds the first preset red early warning time and the fourth number exceeds the fourth preset red early warning times, preliminarily determining that the dirt degree is red early warning degree.
Optionally, after the preliminary determining that the dirt degree is the red early warning degree, the method further includes:
acquiring the wind speed in a future preset period;
judging whether the second accumulated time of the wind speed exceeding a preset wind speed threshold value exceeds the first preset time within the future preset time period;
if the first preset time is exceeded, modifying the red early warning degree to be orange early warning degree;
if the first preset time is not exceeded, further determining that the dirt degree is red early warning degree.
Optionally, after the preliminary determining that the dirt degree is the red early warning degree, the method further includes:
acquiring a second rainfall in the future preset period;
judging whether the second rainfall exceeds a second preset rainfall threshold value in the future preset period; the second preset rainfall threshold is greater than the first preset rainfall threshold;
if the second preset rainfall threshold value is exceeded, modifying the red early warning degree to be orange early warning degree;
if the second preset rainfall threshold is not exceeded, further determining that the dirt degree is red early warning degree.
Optionally, after the preliminary determining that the dirt degree is the orange pre-warning degree, the method further includes:
sending a first instruction to an on-duty operation and maintenance terminal so that the on-duty operation and maintenance terminal sends first notification information to inform operation and maintenance personnel to confirm whether the pollution degree of the photovoltaic module is real or not on site;
if true, receiving a confirmation instruction sent by the on-duty operation and maintenance terminal, and forwarding the confirmation instruction to a headquarter terminal so that the headquarter terminal sends out second notification information to inform a cleaning personnel to clean the photovoltaic module;
if not, receiving an error instruction sent by the on-duty operation and maintenance terminal, and setting the analysis result to zero.
Optionally, after the further determining that the dirt level is the red early warning level, the method further includes:
and sending a second instruction to the headquarter terminal so that the headquarter terminal sends the second notification information to inform a cleaning personnel to clean the photovoltaic module, and sending a matched cleaning instruction to the on-duty operation and maintenance terminal so that the on-duty operation and maintenance terminal sends the first notification information to inform the operation and maintenance personnel to match with the on-site cleaning of the photovoltaic module.
The application further provides a pollution degree prediction system of the photovoltaic module, which comprises pollution degree prediction equipment of the photovoltaic module, an anemometer, a rain gauge, a snow frost meter, a temperature and humidity measuring instrument and a dust meter, wherein the pollution degree prediction equipment of the photovoltaic module is used for realizing the steps of any pollution degree prediction method of the photovoltaic module.
The application further provides a pollution degree prediction system of the photovoltaic module, which comprises pollution degree prediction equipment of the photovoltaic module, an anemometer, a rain gauge, a snow frost meter, a temperature and humidity measuring instrument and a dust meter, wherein the pollution degree prediction equipment of the photovoltaic module is used for realizing the steps of any pollution degree prediction method of the photovoltaic module.
The application provides a method for predicting the dirt degree of a photovoltaic module, which comprises the following steps: when a pollution prediction instruction is received, weather data information of the place where the photovoltaic module is located is obtained; and preliminarily determining the pollution degree of the photovoltaic module according to the relation between the meteorological data information and a preset pollution condition threshold value.
The pollution degree of the photovoltaic module is mainly from the environment, and the pollution degree prediction method of the photovoltaic module in the application determines the pollution degree according to the relation between the meteorological data information and the preset pollution condition threshold value by obtaining the meteorological data information of the place where the photovoltaic module is located, so that the pollution degree is prevented from being determined according to the change of the photovoltaic module, the early prediction of the pollution degree of the photovoltaic module is realized, and the hysteresis of prediction is prevented.
In addition, the application also provides a system with the advantages.
Drawings
For a clearer description of embodiments of the application or of the prior art, the drawings that are used in the description of the embodiments or of the prior art will be briefly described, it being apparent that the drawings in the description below are only some embodiments of the application, and that other drawings can be obtained from them without inventive effort for a person skilled in the art.
Fig. 1 is a flowchart of a method for predicting a contamination level of a photovoltaic module according to an embodiment of the present application;
FIG. 2 is a flow chart of determining a fouling level according to a particulate matter concentration and a first rainfall amount provided by an embodiment of the present application;
FIG. 3 is a flowchart of another method for predicting the contamination level of a photovoltaic module according to an embodiment of the present application;
FIG. 4 is a flowchart of another method for predicting the contamination level of a photovoltaic module according to an embodiment of the present application;
fig. 5 is a schematic structural diagram of a pollution level prediction system of a photovoltaic module according to an embodiment of the present application.
Detailed Description
In order to better understand the aspects of the present application, the present application will be described in further detail with reference to the accompanying drawings and detailed description. It will be apparent that the described embodiments are only some, but not all, embodiments of the application. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present application, but the present application may be practiced in other ways other than those described herein, and persons skilled in the art will readily appreciate that the present application is not limited to the specific embodiments disclosed below.
As in the background art, at present, the dirt degree of the photovoltaic module is often judged by adopting an IV curve change rule or a photoresistor, and the two modes can calculate the dust accumulation degree on the surface of the photovoltaic module, but have certain hysteresis.
In view of the above, the present application provides a method for predicting the contamination level of a photovoltaic module, please refer to fig. 1, fig. 1 is a flowchart of the method for predicting the contamination level of a photovoltaic module, which includes:
step S101: and when a pollution prediction instruction is received, acquiring meteorological data information of the place where the photovoltaic module is located.
Step S102: and preliminarily determining the pollution degree of the photovoltaic module according to the relation between the meteorological data information and a preset pollution condition threshold value.
Referring to fig. 2, when the weather data information includes a particulate matter concentration and a first rainfall, determining the contamination level of the photovoltaic module preliminarily includes
Step S201: and judging whether the first accumulation time when the concentration of the particulate matters exceeds a preset concentration threshold value in a preset period exceeds a first preset orange early warning time and a first preset red early warning time.
In the present application, the concentration of the particulate matter is not particularly limited, and as the case may be, PM10 is generally taken, the total dust content in the air is "total suspended particulate matter", the particulate matter of 10 μm or more is removed, and the remaining particulate matter is "inhalable particulate matter", which is called PM10.
In the embodiment, the preset time period is not particularly limited, and the uninterrupted acquisition of the meteorological data information of the place where the photovoltaic module is located is generally implemented for 24 hours,the preset period may be one week, or 10 days, etc. Similarly, the preset concentration threshold is not limited in this embodiment, and may be set by itself. For example, the preset concentration threshold may be 100mg/m 3 Or any other concentration value.
It should be further noted that, the first preset orange warning time is not limited in the present application, and may be set by itself, for example, 24 hours, 36 hours, and so on. Similarly, the first preset red warning time is not limited in the present application, and may be set by itself, for example, 24 hours, 36 hours, etc. It can be appreciated that the first preset red warning time is greater than the first preset orange warning time.
The first accumulated time is the sum of the times of exceeding the preset concentration threshold value in the week, and is compared and judged with the first preset orange early warning time and the first preset red early warning time respectively.
Step S202: judging whether the first times of the first rainfall, which is smaller than a first preset rainfall threshold value in a preset period, exceed first preset orange early warning times and first preset red early warning times or not.
Note that, in this embodiment, the first preset rainfall threshold is not specifically limited, and generally does not exceed the rainfall value of light rain, for example, may be 10 mm, or 8 mm, or the like. And comparing and judging the first rainfall with the first preset orange early warning times and the first preset red early warning times respectively.
The first preset orange early warning times are not particularly limited, and can be set by oneself. For example, it may be 4 times, or 6 times, etc. Similarly, the first preset red warning times are not limited in the present application, and may be, for example, 7 times, 10 times, or the like.
It can be understood that the first rainfall smaller than the first preset rainfall threshold value drops once, so that the surface of the photovoltaic module is wet, a layer of particles are adsorbed on the surface of the photovoltaic module, and the larger the first time number is, the more the number of layers of particles are adsorbed on the surface of the photovoltaic module, and the more dirt is.
Step S203: when the first accumulation time exceeds the first preset orange early warning time and the first number exceeds the first preset orange early warning times, the pollution degree is preliminarily determined to be the orange early warning degree.
It can be appreciated that when the first accumulated time does not exceed the first preset orange warning time and/or the first number of times does not exceed the first preset orange warning time, it cannot be initially determined that the dirt degree is the orange warning degree.
Step S204: when the first accumulation time exceeds the first preset red early warning time and the first time number exceeds the first preset red early warning times, the pollution degree is preliminarily determined to be the red early warning degree.
It can be appreciated that when the first accumulated time does not exceed the first preset red warning time and/or the first number of times does not exceed the first preset red warning time, the dirt level cannot be initially determined to be the red warning level.
The pollution degree of the photovoltaic module is mainly from the environment, and the pollution degree prediction method of the photovoltaic module in the application determines the pollution degree according to the relation between the meteorological data information and the preset pollution condition threshold value by obtaining the meteorological data information of the place where the photovoltaic module is located, so that the pollution degree is prevented from being determined according to the change of the photovoltaic module, the early prediction of the pollution degree of the photovoltaic module is realized, and the hysteresis of prediction is prevented.
On the basis of the above embodiment, in one embodiment of the present application, when the weather data information further includes air humidity, that is, the weather data information includes particulate matter concentration, first rainfall amount, and air humidity, the prediction method further includes:
judging whether the second times of the air humidity exceeding the preset humidity threshold value in the preset period exceed the second preset orange early warning times and the second preset red early warning times;
when the first accumulation time exceeds the first preset orange early warning time and the second time exceeds the second preset orange early warning time, preliminarily determining that the dirt degree is the orange early warning degree;
when the first accumulated time exceeds the first preset red early warning time and the second time exceeds the second preset red early warning time, the pollution degree is preliminarily determined to be the red early warning degree.
Alternatively, the preset humidity threshold may be 70% or 80%, but the present application is not limited thereto, and in other embodiments of the present application, the preset humidity threshold may be 85%. When the air humidity is great, dew can be condensed on the photovoltaic module surface, and then makes the particulate matter adsorb on the photovoltaic module surface, increases the dirty degree.
It should be noted that, the second preset orange warning frequency is not limited in particular in the present application, and may be set by itself, for example, 20 times, 40 times, and the like. Similarly, the second preset red warning times are not limited in the present application, and may be set by itself, for example, 30 times, 50 times, and so on. It can be appreciated that the second preset red warning times are greater than the second preset orange warning times.
It can be appreciated that when the first accumulated time does not exceed the first preset orange pre-warning time and/or the second times do not exceed the second preset orange pre-warning times, the pollution degree cannot be preliminarily determined to be the orange pre-warning degree; when the first accumulated time does not exceed the first preset red early warning time and/or the second time does not exceed the second preset red early warning time, the dirt degree cannot be preliminarily determined to be the red early warning degree.
Particulate matters are main factors influencing the dirt degree of the photovoltaic module, the dirt degree of the photovoltaic module is predicted according to two meteorological data information of the concentration of the particulate matters and the air humidity in the embodiment, and the longer the first accumulation time is, the more the second times are, the more serious the dirt degree is.
Further, when the weather data information includes a particulate matter concentration, a first rainfall, and an air humidity, the prediction method further includes:
judging whether the second times of the air humidity exceeding the preset humidity threshold value in the preset period exceed the second preset orange early warning times and the second preset red early warning times;
when the first accumulation time exceeds the first preset orange early warning time, the first time number exceeds the first preset orange early warning times, and the second time number exceeds the second preset orange early warning times, primarily determining that the dirt degree is the orange early warning degree;
when the first accumulated time exceeds the first preset red early warning time, the first time number exceeds the first preset red early warning times, and the second time number exceeds the second preset red early warning times, the dirt degree is preliminarily determined to be the red early warning degree.
It can be understood that when the first accumulation time, the first times and the second times cannot simultaneously meet that the first accumulation time exceeds the first preset orange early warning time, the first times exceed the first preset orange early warning times, and the second times exceed the second preset orange early warning times, the pollution degree cannot be preliminarily determined to be the orange early warning degree; when the first accumulation time, the first times and the second times cannot meet the requirement that the first accumulation time exceeds the first preset red early warning time, the first times exceed the first preset red early warning times, and the second times exceed the second preset red early warning times, the pollution degree cannot be initially determined to be the red early warning degree.
In this embodiment, the pollution degree of the photovoltaic module is predicted according to three kinds of meteorological data information including the concentration of particulate matters, the first rainfall and the air humidity, so that the accuracy of primarily determining the pollution degree is improved.
On the basis of the above embodiment, in one embodiment of the present application, when the weather data information further includes a diurnal temperature difference in an early morning period, that is, the weather data information includes a particulate matter concentration, a first rainfall, an air humidity, and a diurnal temperature difference, the prediction method further includes:
determining a third time when the day-night temperature difference exceeds a preset temperature difference threshold value;
when the first accumulated time exceeds the first preset orange early warning time, the third times exceed the third preset orange early warning times, and the air humidity exceeds a preset humidity threshold value in the early morning period, the pollution degree is preliminarily determined to be the orange early warning degree;
when the first accumulated time exceeds the first preset red early warning time, the third times exceed the third preset red early warning times, and the air humidity exceeds the preset humidity threshold value in the early morning period, the dirt degree is preliminarily determined to be the red early warning degree.
It should be noted that, in this embodiment, the preset temperature difference threshold is not specifically limited, and is determined according to circumstances. For example, the preset temperature difference threshold is 15 degrees celsius, 17 degrees celsius, and so on. The early morning period refers to a period of 0:00 to 9:00 or a period of 1:00 to 10:00.
When the temperature difference between day and night is great, and humidity is great, dew or frost is easy to be formed, and because photovoltaic module surface temperature is higher than air temperature, frost can melt to make the particulate matter adsorb on photovoltaic module surface, increase photovoltaic module's dirty degree.
It can be understood that when the first accumulation time, the third times and the values of the day and night temperature difference cannot meet the requirement that the first accumulation time exceeds the first preset orange early warning time, the third times exceed the third preset orange early warning times, and the air humidity exceeds the preset humidity threshold value in the early morning period, the dirt becomes orange early warning degree cannot be judged; when the first accumulation time, the third times and the values of the day and night temperature difference cannot meet the requirement that the first accumulation time exceeds the first preset red early warning time, the third times exceed the third preset red early warning times, and the air humidity in the early morning period exceeds the preset humidity threshold value, the pollution can not be judged to be the red early warning degree.
In this embodiment, the pollution degree of the photovoltaic module is predicted according to three kinds of weather data information including the concentration of particulate matters, the air humidity and the day-night temperature difference, the longer the first accumulation time is, the more the third time is, and the day-night temperature difference exceeds the preset temperature difference threshold value, so that the surface humidity of the photovoltaic module is increased, the more serious the pollution is, and the higher the accuracy is when the three kinds of weather data information are combined for prediction.
On the basis of the above embodiment, in one embodiment of the present application, when the weather data information further includes a frost amount, the prediction method further includes:
judging whether the fourth time number of which the frost and snow quantity is smaller than the first preset rainfall threshold value in a preset period exceeds the fourth preset orange early warning times or not and the fourth preset red early warning times or not;
when the first accumulation time exceeds the first preset orange early warning time and the fourth time number exceeds the fourth preset orange early warning times, preliminarily determining that the dirt degree is the orange early warning degree;
when the first accumulated time exceeds the first preset red early warning time and the fourth time exceeds the fourth preset red early warning times, the pollution degree is preliminarily determined to be the red early warning degree.
The surface temperature of the photovoltaic module is higher than the air temperature, and frost and snow can be melted into rainwater, so that particles are adsorbed on the surface of the photovoltaic module, and the pollution degree is increased.
It can be understood that each time frost and snow melt can make the photovoltaic module surface moist, and the more the fourth time number is greater, the more the particulate matter number of adsorption on the photovoltaic module surface, the more dirty the photovoltaic module surface.
It should be noted that, the fourth preset orange warning frequency is not particularly limited in the application, and can be set by oneself. For example, it may be 10 times, 20 times, or the like. Similarly, the number of the fourth red warning is not particularly limited in the present application, and may be, for example, 15 times, 20 times, or the like.
It can be understood that when the first duration and the fourth number cannot simultaneously meet that the first accumulation time exceeds the first preset orange early warning time and the fourth number exceeds the fourth preset orange early warning time, the dirt degree cannot be judged to be the orange early warning degree; when the first duration and the fourth number cannot simultaneously meet that the first accumulation time exceeds the first preset red early warning time and the fourth number exceeds the fourth preset red early warning time, the dirt degree cannot be judged to be the red early warning degree.
In this embodiment, the pollution degree of the photovoltaic module is predicted according to two weather data information including the concentration of particulate matters and the amount of frost and snow, and the longer the first duration, the more the fourth times, the more serious the particulate matters are accumulated on the surface of the photovoltaic module, and the more serious the pollution is.
Referring to fig. 3, fig. 3 is a flowchart of another method for predicting a contamination level of a photovoltaic module according to an embodiment of the present application, where the method includes:
step S301: and when a dirt prediction instruction is received, acquiring the particle concentration and the first rainfall of the place where the photovoltaic module is located.
Step S302: and judging whether the first accumulation time when the concentration of the particulate matters exceeds a preset concentration threshold value in a preset period exceeds a first preset orange early warning time and a first preset red early warning time.
Step S303: judging whether the first times of the first rainfall, which is smaller than a first preset rainfall threshold value in a preset period, exceed first preset orange early warning times and first preset red early warning times or not.
Step S304: when the first accumulation time exceeds the first preset orange early warning time and the first number exceeds the first preset orange early warning times, the pollution degree is preliminarily determined to be the orange early warning degree.
Step S305: when the first accumulation time exceeds the first preset red early warning time and the first time number exceeds the first preset red early warning times, the pollution degree is preliminarily determined to be the red early warning degree.
Step S306: the wind speed at a preset time period in the future is acquired.
The future preset period means that after the preset period, the time can be 48 hours in the future, or one week in the future, etc., and can be set by itself.
Step S307: whether the second accumulated time when the wind speed exceeds the preset wind speed threshold value exceeds the first preset time in a future preset time period is judged.
The preset wind speed threshold value is not particularly limited in the present application, and may be 8m/s, or 12m/s, for example. Similarly, the first preset time is not limited in the present application, and may be set by itself, for example, 5 hours, 7 hours, etc.
Step S308: if the first preset time is exceeded, the red early warning degree is modified to be orange early warning degree.
Step S309: if the first preset time is not exceeded, the pollution degree is further determined to be red early warning degree.
It can be understood that when the second accumulation time exceeds the first preset time, the wind speed is very high, the time exceeding the wind speed threshold is long, the particles on the surface of the photovoltaic module can be blown away, and the pollution degree is reduced, so that the red early warning degree is modified to be orange early warning degree, when the second accumulation time does not exceed the first preset time, the wind cannot blow away the particles on the surface of the photovoltaic module, and at the moment, the pollution degree is further determined to be red early warning degree.
On the basis of the foregoing embodiment, in one embodiment of the present application, after the preliminary determination that the contamination level is the red warning level, the method further includes:
acquiring a second rainfall in a future preset period;
judging whether the second rainfall exceeds a second preset rainfall threshold value in a future preset period; the second preset rainfall threshold is greater than the first preset rainfall threshold;
if the second preset rainfall threshold is exceeded, modifying the red early warning degree to be orange early warning degree;
if the second preset rainfall threshold value is not exceeded, further determining that the dirt degree is red early warning degree.
The second preset rainfall threshold is not particularly limited, the second preset rainfall threshold is larger than the first preset rainfall threshold, and the rainfall value of heavy rain is generally exceeded, for example, 50 mm or 60 mm or the like, the second rainfall exceeds the second preset rainfall threshold, which indicates that the rainfall is large, the particles adsorbed on the surface can be cleaned, the red early warning degree is modified to be orange early warning degree, the second rainfall does not exceed the second preset rainfall threshold, the particles on the surface of the photovoltaic module cannot be cleaned, and the pollution degree is determined to be red early warning degree at the moment.
Referring to fig. 4, fig. 4 is a flowchart of another method for predicting the contamination level of a photovoltaic module according to an embodiment of the present application.
Step S401: and when a pollution prediction instruction is received, acquiring the concentration of particles and the air humidity of the place where the photovoltaic module is located.
Step S402: and judging whether the first accumulation time when the concentration of the particulate matters exceeds a preset concentration threshold value in a preset period exceeds a first preset orange early warning time and a first preset red early warning time.
Step S403: judging whether the second times of the air humidity exceeding the preset humidity threshold value in the preset period exceeds the second preset orange early warning times and the second preset red early warning times.
Step S404: when the first accumulation time exceeds the first preset orange early warning time and the second times exceed the second preset orange early warning times, preliminarily determining that the dirt degree is the orange early warning degree.
Step S405: the method comprises the steps of sending a first instruction to an on-duty operation and maintenance terminal so that the on-duty operation and maintenance terminal sends first notification information to inform operation and maintenance personnel to confirm whether the dirt degree of the photovoltaic module is true or not on site;
step S406: if true, receiving a confirmation instruction sent by the on-duty operation and maintenance terminal, and forwarding the confirmation instruction to the headquarter terminal so that the headquarter terminal sends out second notification information to inform a cleaning personnel to clean the photovoltaic module;
step S407: if not, receiving an error instruction sent by the on-duty operation and maintenance terminal, and setting the analysis result to zero.
Step S408: when the first accumulated time exceeds the first preset red early warning time and the second time exceeds the second preset red early warning time, the pollution degree is preliminarily determined to be the red early warning degree.
In the embodiment, when the primary prediction is the orange early warning degree, the operation and maintenance personnel are informed to confirm whether the operation and maintenance personnel are real or not on site, misjudgment is avoided, and when the operation and maintenance personnel are truly the orange early warning degree, the cleaning personnel and the operation and maintenance personnel are informed to clean the photovoltaic module together, so that the hot spot effect is avoided.
On the basis of the foregoing embodiment, in one embodiment of the present application, after further determining that the contamination level is the red early warning level, the method further includes:
and sending a second instruction to the headquarter terminal so that the headquarter terminal sends second notification information to inform a cleaning person to clean the photovoltaic module, and sending a matched cleaning instruction to the on-duty operation and maintenance terminal so that the on-duty operation and maintenance terminal sends first notification information to inform the operation and maintenance person to match with the on-site cleaning photovoltaic module.
In the embodiment, after the pollution degree is further determined to be red early warning degree, the particles deposited on the photovoltaic module cannot be removed by means of strong wind or large rainfall, cleaning personnel are timely informed to carry out cleaning treatment, the hot spot effect of the photovoltaic module is avoided,
the pollution degree prediction system of the photovoltaic module provided by the embodiment of the application is introduced below, and the pollution degree prediction system of the photovoltaic module and the pollution degree prediction method of the photovoltaic module described below can be correspondingly referred to each other.
Referring to fig. 5, fig. 5 is a schematic structural diagram of a system for predicting a contamination level of a photovoltaic module according to an embodiment of the present application.
The utility model provides a dirty degree prediction system of photovoltaic module, includes dirty degree prediction equipment 6 of photovoltaic module, anemograph 1, rain gauge 2, frost and snow appearance 3, temperature and humidity measuring instrument 4, dust appearance 5, dirty degree prediction equipment 6 of photovoltaic module is used for realizing the step of the dirty degree prediction method of arbitrary photovoltaic module of above-mentioned.
The pollution degree prediction system of the photovoltaic module can also comprise a bracket 8 for supporting and installing an anemometer 1, a rain gauge 2, a snow frost 3, a temperature and humidity measuring instrument 4 and a dust meter 5; the pollution degree prediction system of the photovoltaic module further comprises a signal electrical cabinet 7, and the signal electrical cabinet is used for collecting various meteorological data information collected by the anemograph 1, the rain gauge 2, the snow and frost gauge 3, the temperature and humidity measuring instrument 4 and the dust gauge 5 and sending the meteorological data information to the pollution degree prediction device 6 of the photovoltaic module.
In this specification, each embodiment is described in a progressive manner, and each embodiment is mainly described in a different point from other embodiments, so that the same or similar parts between the embodiments are referred to each other. For the device disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant points refer to the description of the method section.
The method, the device and the system for predicting the pollution degree of the photovoltaic module provided by the application are described in detail. The principles and embodiments of the present application have been described herein with reference to specific examples, the description of which is intended only to facilitate an understanding of the method of the present application and its core ideas. It should be noted that it will be apparent to those skilled in the art that various modifications and adaptations of the application can be made without departing from the principles of the application and these modifications and adaptations are intended to be within the scope of the application as defined in the following claims.
Claims (9)
1. The method for predicting the dirt degree of the photovoltaic module is characterized by comprising the following steps of:
when a pollution prediction instruction is received, weather data information of the place where the photovoltaic module is located is obtained;
preliminarily determining the pollution degree of the photovoltaic module according to the relation between the meteorological data information and a preset pollution condition threshold value;
when the weather data information includes a particulate matter concentration and a first rainfall, the preliminarily determining the pollution degree of the photovoltaic module according to the relationship between the weather data information and a preset pollution condition threshold value includes:
judging whether the first accumulation time of the particulate matter concentration exceeding a preset concentration threshold value within a preset period exceeds a first preset orange early warning time or not and a first preset red early warning time;
judging whether the first number of times that the first rainfall is smaller than a first preset rainfall threshold value in the preset period exceeds a first preset orange early warning frequency and a first preset red early warning frequency;
when the first accumulated time exceeds the first preset orange early warning time and the first number exceeds the first preset orange early warning times, preliminarily determining that the dirt degree is the orange early warning degree;
and when the first accumulated time exceeds the first preset red early warning time and the first time number exceeds the first preset red early warning times, preliminarily determining that the dirt degree is red early warning degree.
2. The method of claim 1, wherein when the weather data information further includes air humidity, the method further comprises:
judging whether the second times of the air humidity exceeding a preset humidity threshold value in the preset period exceeds the second preset orange early warning times or not, and the second preset red early warning times;
when the first accumulated time exceeds the first preset orange early warning time and the second time exceeds the second preset orange early warning time, preliminarily determining that the dirt degree is the orange early warning degree;
and when the first accumulated time exceeds the first preset red early warning time and the second time exceeds the second preset red early warning time, preliminarily determining that the dirt degree is red early warning degree.
3. The method of claim 2, wherein when the weather data information further includes a diurnal temperature difference in an early morning period, the method further comprises:
determining a third time when the diurnal temperature difference exceeds a preset temperature difference threshold;
when the first accumulated time exceeds the first preset orange pre-warning time, the third times exceed third preset orange pre-warning times, and the air humidity exceeds the preset humidity threshold value in the early morning period, the dirt degree is preliminarily determined to be the orange pre-warning degree;
when the first accumulated time exceeds the first preset red early warning time, the third times exceed the third preset red early warning times, and the air humidity exceeds the preset humidity threshold in the early morning period, the dirt degree is preliminarily determined to be red early warning degree.
4. The method of claim 1, wherein when the weather data information further includes a frost amount, the method further comprises:
judging whether the fourth time number of which the frost and snow quantity is smaller than the first preset rainfall threshold value in the preset time period exceeds fourth preset orange early warning times and fourth preset red early warning times or not;
when the first accumulated time exceeds the first preset orange early warning time and the fourth number exceeds the fourth preset orange early warning times, preliminarily determining that the dirt degree is the orange early warning degree;
and when the first accumulated time exceeds the first preset red early warning time and the fourth number exceeds the fourth preset red early warning times, preliminarily determining that the dirt degree is red early warning degree.
5. The method for predicting a fouling level of a photovoltaic module according to any one of claims 1 to 4, further comprising, after the preliminary determination that the fouling level is a red pre-warning level:
acquiring the wind speed in a future preset period;
judging whether the second accumulated time of the wind speed exceeding a preset wind speed threshold value exceeds the first preset time within the future preset time period;
if the first preset time is exceeded, modifying the red early warning degree to be orange early warning degree;
if the first preset time is not exceeded, further determining that the dirt degree is red early warning degree.
6. The method for predicting the contamination level of a photovoltaic module according to claim 5, further comprising, after the preliminary determination that the contamination level is a red pre-warning level:
acquiring a second rainfall in the future preset period;
judging whether the second rainfall exceeds a second preset rainfall threshold value in the future preset period; the second preset rainfall threshold is greater than the first preset rainfall threshold;
if the second preset rainfall threshold value is exceeded, modifying the red early warning degree to be orange early warning degree;
if the second preset rainfall threshold is not exceeded, further determining that the dirt degree is red early warning degree.
7. The method for predicting the contamination level of a photovoltaic module according to claim 6, wherein after the preliminary determination that the contamination level is an orange pre-warning level, further comprises:
sending a first instruction to an on-duty operation and maintenance terminal so that the on-duty operation and maintenance terminal sends first notification information to inform operation and maintenance personnel to confirm whether the pollution degree of the photovoltaic module is real or not on site;
if true, receiving a confirmation instruction sent by the on-duty operation and maintenance terminal, and forwarding the confirmation instruction to a headquarter terminal so that the headquarter terminal sends out second notification information to inform a cleaning personnel to clean the photovoltaic module;
if not, receiving an error instruction sent by the on-duty operation and maintenance terminal, and setting the analysis result to zero.
8. The method for predicting the fouling level of a photovoltaic module according to claim 7, wherein after further determining that the fouling level is a red pre-warning level, further comprising:
and sending a second instruction to the headquarter terminal so that the headquarter terminal sends the second notification information to inform a cleaning personnel to clean the photovoltaic module, and sending a matched cleaning instruction to the on-duty operation and maintenance terminal so that the on-duty operation and maintenance terminal sends the first notification information to inform the operation and maintenance personnel to match with the on-site cleaning of the photovoltaic module.
9. A system for predicting the fouling degree of a photovoltaic module, which is characterized by comprising a photovoltaic module fouling degree predicting device, an anemometer, a rain gauge, a snow and frost gauge, a temperature and humidity gauge and a dust gauge, wherein the photovoltaic module fouling degree predicting device is used for realizing the steps of the photovoltaic module fouling degree predicting method according to any one of claims 1 to 8.
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