CN115310839A - Photovoltaic power station dust deposition early warning assessment method and system - Google Patents
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Abstract
A method and a system for early warning and evaluating the dust deposition of a photovoltaic power station are disclosed, wherein the method comprises the following steps: acquiring historical operating data of a photovoltaic power station; acquiring equipment information of a photovoltaic power station; performing data cleaning on historical operation data; establishing a theoretical power generation model in a clean state; calculating a daily cleanliness index; calculating accumulated ash loss electric quantity; calculating and cleaning the lifting power generation rate; acquiring future weather forecast data; comprehensively judging according to the obtained related information and giving a power station cleaning prompt; the system comprises an acquisition and storage module and a processing module, wherein the signal output end of the acquisition and storage module is connected with the signal input end of the processing module; the method establishes a theoretical power generation model of the photovoltaic power station in a historical clean state, provides a cleaning early warning prompt of the power station by combining with future weather forecast data, can automatically calculate the daily cleaning index of the power station in real time, evaluates the operation level of the power station from the aspect of cleaning degree, provides theoretical support for operation and maintenance personnel to make a power station cleaning decision, and has the characteristics of simplicity in operation and high accuracy.
Description
Technical Field
The invention belongs to the technical field of early warning of photovoltaic power stations, and particularly relates to a method and a system for early warning and evaluation of dust deposition of a photovoltaic power station.
Background
Dust is one of key factors influencing the power generation efficiency of the photovoltaic power station, and the dust accumulated on the cell panel generates a shielding phenomenon, so that the solar energy absorbed by the surface of the photovoltaic module is seriously reduced; in addition, the ash deposition can cause the surface temperature of the component to rise, the heat dissipation of the component is influenced, and the power generation capacity of the power station is further reduced. The research shows that: under the same conditions, the output power of the clean battery plate assembly is at least 5% higher than that of the dust-deposited assembly. The longer the ash deposition time of the power station is, the larger the ash deposition amount is, and the worse the output performance of the component is. Therefore, the assessment of the dust deposition degree of the power station component is timely carried out, the power station cleaning early warning is carried out at the correct time, the operation and maintenance work of the power station operation and maintenance personnel is assisted, and the method has important significance for improving the power generation level of the power station.
Chinese patent CN110046329A discloses a construction method of a multiple regression model for calculating the deposition loss of a photovoltaic module, which comprises the following steps: 1) Establishing cleaning models of different rainfall for the accumulated dust of the components; 2) Assuming that the dust deposition on the surface of the photovoltaic module is stacked of multiple layers of dust, designing the number of dust deposition layers related to the daily average PM10 index, the daily average wind speed and the daily average humidity, and constructing a multiple regression model of the dust deposition loss of the photovoltaic module. According to the invention, the influence of accumulated dust on the photovoltaic module in different environments can be obtained, so that a foundation is laid for the formulation of a photovoltaic module cleaning strategy. However, as more meteorological data such as rainfall depth and PM10 are introduced in the process of establishing the model, inaccurate collection of the indexes can generate a large precision influence on establishment of the dust deposition model.
Chinese patent CN108960453A discloses a photovoltaic power station dust deposit economic cleaning calculation method, which selects a part of components in a photovoltaic power station as components to be cleaned, and comprises the following steps: s1, obtaining a daily accumulated power generation theoretical value Qn and a daily accumulated power generation actual value Qc of a component to be cleaned; S7A, determining rainfall information x according to weather forecast, and predicting a power loss value K after rain according to the rainfall information x; S7B, calculating the pre-estimated generated energy loss amount ME before rain according to the weather forecast, the generated energy loss rate R on the same day and the average generated energy in sunny days and cloudy days in the near Y days; S7A and S7B are carried out simultaneously or sequentially, if the power loss value K after rain is larger than M% or the estimated generating capacity loss amount ME before rain is larger than S% of the cleaning cost D, the step S8 is directly skipped, otherwise, the step S1 is returned to for continuous monitoring; and S8, cleaning the assembly of the photovoltaic power station. The method can determine the most economical photovoltaic module cleaning cycle by combining weather forecast information. However, in the patent, the module is not cleaned at the beginning of modeling, and a certain error is introduced when the electric quantity loss is calculated by directly using the difference between the theoretical electric quantity and the actual electric quantity of the module. Furthermore, this method only tests if several components need to be cleaned, and cannot assess the cleanliness status of all equipment of the total station.
Disclosure of Invention
In order to overcome the defects of the prior art, the invention aims to provide a photovoltaic power station dust deposition early warning and evaluation method and system, based on historical cleaning data of a power station, the system establishes a theoretical power generation model of the photovoltaic power station in a historical cleaning state, introduces indexes of cleaning indexes, can evaluate the power generation capacity of the power station in real time, carries out early warning on the cleaning date of the power station, provides more objective basis for power station cleaning decision, enhances the accuracy of power station dust deposition loss electric quantity evaluation, and has the characteristics of simple operation and high accuracy.
In order to achieve the purpose, the technical scheme adopted by the invention is as follows:
a photovoltaic power station dust deposition early warning assessment method comprises the following steps:
s1, acquiring historical operating data of a photovoltaic power station;
s2, acquiring equipment information of the photovoltaic power station;
s3, performing data cleaning on historical operation data;
s4, establishing a theoretical power generation model of the photovoltaic power station in a clean state;
s5, calculating a daily cleanliness index of the photovoltaic power station;
s6, calculating the accumulated dust loss electric quantity of the photovoltaic power station;
s7, calculating the power generation rate which can be improved by cleaning the photovoltaic power station;
s8, acquiring future weather forecast data;
and S9, comprehensively judging according to the relevant information obtained in the steps S5, S7 and S8, and giving a power station cleaning prompt.
The historical operation data in the step S1 comprise weather, daily radiant quantity, daily actual power generation quantity, system efficiency, power limit, fault conditions, daily power generation quantity of the inverter and historical cleaning records of the photovoltaic power station.
And the photovoltaic power station equipment information in the step S2 comprises total installed capacity of the whole station and installed capacity approved by each inverter.
The step S3 of performing data cleaning on the historical operation data refers to performing deep cleaning on abnormal data of the inverter output power, the radiation, the component temperature, and the power station grid-connected power of the photovoltaic power station up to now in the last cleaning date, and specifically includes: and eliminating the influence of weather, and selecting the system efficiency as an index for measuring the operation level of the photovoltaic power station.
The theoretical power generation model of the photovoltaic power station in the clean state in the step S4 is mainly established according to the data of the historical cleaning cycle of the photovoltaic power station, and the established theoretical power generation model of the photovoltaic power station is as follows:
P theory of the invention ＝a*R 2 +b*R+c*T
In the formula, R is the instantaneous irradiance collected by the environmental monitor, T is the component temperature, P is the inverter output power, and a, b and c are correlation coefficients.
The daily cleanliness index calculation formula in step S5 is:
in the formula, n is the total number of data acquisition in any day,
using theoretical power generation model calculations for the ith time,
and the actual generated power of the photovoltaic power station at the ith moment.
The calculation formula of the accumulated dust loss electric quantity in the step S6 is as follows:
in the formula, n is the number of data acquisition in any day,
using the clean theoretical power generation model calculation for the ith time,
actual generated power, Q, of the photovoltaic power station at the ith moment In fact Is the actual power generation capacity, delta Q, of the photovoltaic power station In fact The generated energy can be improved after the cleaning in the same day.
The calculation formula for the photovoltaic power station cleaning in the step S7 capable of improving the power generation rate is as follows:
wherein M is the Mth day after cleaning,
the power loss for the ash deposition on the j day,
the actual power generation amount of the photovoltaic power station on the j th day.
The cleaning early warning decision in the step S9 is as follows: firstly, judging the current calculated cleaning power generation rate of the photovoltaic power station, and if the current value is greater than a set threshold value, further judging the future weather forecast condition; if the photovoltaic power station has no rain in the future L days, a cleaning early warning prompt is sent out; l in said step is 3 to 7 days.
A photovoltaic power station dust deposition early warning and evaluation system comprises an acquisition and storage module and a processing module, wherein the signal output end of the acquisition and storage module is connected with the signal input end of the processing module; the acquisition and storage module is used for realizing the steps S1 and S2 of the method of the photovoltaic power station dust deposition early warning and evaluation system; the processing module is used for realizing steps S3 to S9 of the method of the photovoltaic power station dust deposition early warning and evaluation system.
Compared with the prior art, the invention has the following beneficial effects:
the utility model provides a photovoltaic power plant deposition early warning aassessment method and system, utilize collection storage module to obtain photovoltaic power plant historical operation data and photovoltaic power plant equipment information, data that collection storage module gathered are analyzed through processing module, thereby establish the ideal power generation model under the clean state of power plant, and this model is adapted to single dc-to-ac converter, also be adapted to photovoltaic power plant wholly, can calculate power plant day cleanness index and clean promotion power generation rate in real time simultaneously, when clean promotion power generation rate is greater than the threshold value, combine future weather forecast data, give the washing early warning suggestion of photovoltaic power plant, processing module can also calculate the day cleanness index of power plant in real time automatically, from the operation level of clean degree angle aassessment power plant, for fortune dimension personnel carry out the power plant and wash the decision and provide theoretical support, have easy operation, the high characteristics of accuracy.
Drawings
FIG. 1 is a flow chart of a photovoltaic power station dust deposition early warning and evaluation method of the invention.
Fig. 2 is a block diagram of the present invention.
Fig. 3 is a statistical table of a theoretical power generation model of the tianjin certain photovoltaic power station inverter in embodiment 1.
Fig. 4 is a trend graph of the cleaning index and the cleaning boost power generation rate of the 1# inverter in the embodiment 1 from 12/3/2021 to 1/5/2022.
Detailed Description
In order to make the technical solutions of the present invention better understood, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The present invention is described in further detail below with reference to the attached drawings.
Referring to fig. 1, a photovoltaic power station dust deposition early warning and evaluation method includes the following steps:
s1, acquiring historical operating data of a photovoltaic power station; the historical operation data comprises weather, daily radiant quantity, daily actual power generation quantity, system efficiency, power limit, fault conditions, daily power generation quantity of the inverter and historical cleaning records of the photovoltaic power station.
S2, acquiring equipment information of the photovoltaic power station; the photovoltaic power station equipment information comprises total installed capacity of the whole station and approved installed capacity of each inverter.
S3, performing data cleaning on historical operation data; the step of performing data cleaning on historical operation data refers to performing deep cleaning on abnormal data of the inverter output power, the radiation illumination, the component temperature and the power station grid-connected power of a photovoltaic power station at the latest cleaning date and till now. The calculation result of the photovoltaic power station cleaning index is greatly influenced by the radiation quantity and the power generation quantity level of the power station. If the historical days of the photovoltaic power station are rainy and snowy days, the radiation quantity received by the photovoltaic power station is low, the power generation quantity of the photovoltaic power station is poor, and the data under the weather cannot be selected to establish a photovoltaic power station model, so that the influence of the weather needs to be eliminated when the theoretical power generation model of the photovoltaic power station is established.
In addition, in order to eliminate the situation that the generated energy of the photovoltaic power station is low due to power limitation and faults, the system efficiency is selected as an index for measuring the operation level of the photovoltaic power station. The quadridentate range theory is introduced to measure the data dispersion of system efficiency. The days with system efficiency greater than Q1-1.5 × r1 were selected for plant modeling. When the system efficiency of the day is < Q1-1.5 × r1, the system efficiency of the day is considered to be abnormal. Q1 and R1 are calculated as follows:
the first quantile Q1= quitile (data list, 1);
third quantile Q3= quatline (data list, 3);
range R1= Q3-Q1;
and the data list takes data of A days ahead of the current date, and A is at least more than 20 days in the step.
After a photovoltaic power station is cleaned, historical data of a plurality of days after system efficiency is filtered by four-position range difference is processed, irradiance in the time period is segmented, and the irradiance is divided into (0, m), [ m,2m ], [ 2m,3m ], \ 823030and N sections at intervals of m, wherein average irradiance, average component temperature and average inverter output total power in each section are respectively counted; wherein m is 15 to 50 w/square meter, and N is calculated by the processing module according to the maximum irradiance and m in the current period.
S4, establishing a theoretical power generation model of the photovoltaic power station in a clean state; the theoretical power generation model is mainly established according to data of a historical cleaning cycle of the photovoltaic power station, the output power of the photovoltaic power station is greatly influenced by irradiance and temperature, the influence degrees of different independent variables on power are different, and the theoretical power generation model after the photovoltaic power station is cleaned is established through multiple tests as follows:
P theory of the invention ＝a*R 2 +b*R+c*T
In the formula, R is the instantaneous irradiance collected by the environmental monitor, T is the component temperature, P is the inverter output power, and a, b and c are correlation coefficients.
And substituting the N groups of average irradiance, average assembly temperature and average inverter output total power subjected to data cleaning into the power generation model, and solving corresponding coefficients a, b and c by using a regression method. And therefore, the theoretical power generation model of the photovoltaic power station after being cleaned is built.
The model is suitable for a single inverter and is also suitable for the whole photovoltaic power station. When the method is used for overall analysis of the photovoltaic power station, only P is needed Theory of the invention And taking the sum of the output power of the inverters in the whole station.
S5, calculating a daily cleanliness index of the photovoltaic power station; and calculating the power to be generated of the photovoltaic power station at each acquisition time on the rest dates by using a theoretical power generation model, and comparing and analyzing the power to the actual power of the photovoltaic power station on the same day to determine the cleanliness index of the photovoltaic power station under the historical operating conditions. The daily cleanliness index calculation formula is as follows:
in the formula, n is the total number of data acquisition in any day,
using the theoretical power generation model calculation value for the ith time,
and the actual generated power of the photovoltaic power station at the ith moment.
S6, calculating the accumulated dust loss electric quantity of the photovoltaic power station; the calculation formula of the accumulated dust loss electric quantity is as follows:
in the formula, n is the number of data acquisition in any day,
using the clean theory power generation model calculation value for the ith time,
for the actual generated power of the photovoltaic power station at the ith moment, Q Practice of Is the actual power generation capacity, delta Q, of the photovoltaic power station In fact The generated energy can be improved after the cleaning in the same day.
S7, calculating the power generation rate which can be improved by cleaning the photovoltaic power station; the calculation formula for improving the power generation rate of photovoltaic power station cleaning is as follows:
wherein M is the Mth day after cleaning,
the power loss caused by the ash deposition on the j day,
the actual power generation amount of the photovoltaic power station on the j th day.
And S8, acquiring future weather forecast data.
And S9, comprehensively judging according to the relevant information obtained in the steps S5, S7 and S8, and giving a power station cleaning prompt.
The cleaning early warning decision in the step S9 is as follows: firstly, judging the current calculated cleaning power generation rate of the photovoltaic power station, and if the current value is greater than a set threshold value, further judging the future weather forecast condition; if the photovoltaic power station is rainless in the future L days, sending a cleaning early warning prompt; l in said step is 3 to 7 days.
Referring to fig. 2, the photovoltaic power station dust deposition early warning and evaluating system includes an acquisition and storage module and a processing module, wherein a signal output end of the acquisition and storage module is connected with a signal input end of the processing module; the acquisition and storage module is used for realizing the steps S1 and S2 of the method of the photovoltaic power station dust deposition early warning and evaluation system; the processing module is used for realizing steps S3 to S9 of the method of the photovoltaic power station dust deposition early warning and evaluation system.
Example 1
Referring to fig. 3, the method of the invention is already in trial operation in a certain Tianjin photovoltaic power station, the power station completes cleaning work of the power station in 2021 year in 11-24 months, historical operating data after cleaning date is cleaned, operating data of 11-25-12-2 months is selected to establish a theoretical power generation model of the power station under the cleaning state of all inverters
P Theory of the invention ＝a*R 2 +b*R+c*T
The theoretical power generation model parameters of each inverter are shown in fig. 3.
By using the theoretical power generation model of the inverter in fig. 3, the daily cleaning index of the inverter after 12 months and 2 days can be evaluated, and the daily accumulated dust loss electric quantity and the cleaning improved power generation rate can be calculated. The cleaning index and the cleaning-up power generation rate trend of the 1# inverter from 12/3/2021 to 1/5/2022 are shown in fig. 4, and can be seen from the graph: the cleaning index of the power station is reduced from the cleaning. The cleaning can improve the power generation rate to be continuously increased, the daily cleaning index of a power station is 0.86 after 1 month and 5 days in 2022, and the cleaning can improve the power generation rate to be 11.26 percent.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solutions of the present invention and not for limiting the same, and although the present invention is described in detail with reference to the above embodiments, those of ordinary skill in the art should understand that: modifications and equivalents may be made to the embodiments of the invention without departing from the spirit and scope of the invention, which is to be covered by the claims.
Claims (10)
1. The method for early warning and evaluating the dust deposition of the photovoltaic power station is characterized by comprising the following steps of:
s1, acquiring historical operating data of a photovoltaic power station;
s2, acquiring equipment information of the photovoltaic power station;
s3, performing data cleaning on historical operation data;
s4, establishing a theoretical power generation model of the photovoltaic power station in a clean state;
s5, calculating a daily cleanliness index of the photovoltaic power station;
s6, calculating the accumulated dust loss electric quantity of the photovoltaic power station;
s7, calculating the power generation rate which can be improved by cleaning the photovoltaic power station;
s8, acquiring future weather forecast data;
and S9, comprehensively judging according to the relevant information obtained in the steps S5, S7 and S8, and giving a power station cleaning prompt.
2. The method for early warning and evaluating the dust deposition of the photovoltaic power station as claimed in claim 1, wherein the historical operation data in the step S1 comprises weather, daily radiant quantity, daily actual power generation quantity, system efficiency, power limit, fault condition, daily power generation quantity of the inverter and historical cleaning records of the photovoltaic power station.
3. The method and system for early warning and evaluating ash deposition of a photovoltaic power plant of claim 1, wherein the photovoltaic power plant equipment information in step S2 includes total installed capacity of the whole plant and installed capacity approved by each inverter.
4. The method and system for early warning and evaluating the ash deposition of the photovoltaic power station as claimed in claim 1, wherein the step S3 of performing data cleaning on the historical operation data refers to performing deep cleaning on abnormal data of the inverter output power, the radiation, the component temperature and the power station grid-connected power of the photovoltaic power station up to the latest cleaning date, and specifically comprises: and eliminating the influence of weather, and selecting the system efficiency as an index for measuring the operation level of the photovoltaic power station.
5. The method and system for early warning and evaluating the dust deposition of the photovoltaic power station as claimed in claim 1, wherein the theoretical power generation model of the photovoltaic power station in the clean state in the step S4 is mainly established according to the data of the historical cleaning cycle of the photovoltaic power station, and the theoretical power generation model of the photovoltaic power station is established by:
P theory of the invention ＝a*R 2 +b*R+c*T
In the formula, R is the instantaneous irradiance collected by the environmental monitor, T is the component temperature, P is the inverter output power, and a, b and c are correlation coefficients.
6. The method and the system for early warning and evaluating the dust deposition of the photovoltaic power station as claimed in claim 1, wherein the daily cleanliness index in the step S5 is calculated by the following formula:
in the formula, n is the total number of data acquisition in any day,
using theoretical power generation model calculations for the ith time,
and the actual generated power of the photovoltaic power station at the ith moment.
7. The method and the system for early warning and evaluating the ash deposition of the photovoltaic power station as claimed in claim 1, wherein the calculation formula of the ash deposition loss electric quantity in the step S6 is as follows:
in the formula, n is the number of data acquisition in any day,
using the clean theoretical power generation model calculation for the ith time,
for the actual generated power of the photovoltaic power station at the ith moment, Q In fact Is the actual power generation capacity, delta Q, of the photovoltaic power station Practice of Is that whenThe generated energy can be improved after the cleaning in the day.
8. The method and system for early warning and evaluating the ash deposition of the photovoltaic power station as claimed in claim 1, wherein the calculation formula of the power generation rate which can be improved by cleaning the photovoltaic power station in the step S7 is as follows:
wherein M is the Mth day after cleaning,
the power loss for the ash deposition on the j day,
the actual power generation amount of the photovoltaic power station on the j th day.
9. The method and system for early warning and evaluating the dust deposition of the photovoltaic power station as claimed in claim 1, wherein the cleaning early warning decision in the step S9 is as follows: firstly, judging the current calculated cleaning power generation rate of the photovoltaic power station, and if the current value is greater than a set threshold value, further judging the future weather forecast condition; if the photovoltaic power station has no rain in the future L days, a cleaning early warning prompt is sent out; l in said step is 3 to 7 days.
10. The photovoltaic power station dust deposition early warning and evaluating system for realizing the method of any one of claims 1 to 9 is characterized by comprising a collecting and storing module and a processing module, wherein the signal output end of the collecting and storing module is connected with the signal input end of the processing module; the acquisition and storage module is used for realizing the steps S1 and S2 of the method of the photovoltaic power station dust deposition early warning and evaluation system; the processing module is used for realizing steps S3 to S9 of the method of the photovoltaic power station dust deposition early warning and evaluation system.
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| CN118095810A (en) * | 2024-04-28 | 2024-05-28 | 成都秦川物联网科技股份有限公司 | Intelligent gas pipe network auxiliary facility maintenance method and Internet of things system |
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN118095810A (en) * | 2024-04-28 | 2024-05-28 | 成都秦川物联网科技股份有限公司 | Intelligent gas pipe network auxiliary facility maintenance method and Internet of things system |
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