CN113676135A - Photovoltaic energy efficiency monitoring method and system based on neural network and optical pollution measurement - Google Patents
Photovoltaic energy efficiency monitoring method and system based on neural network and optical pollution measurement Download PDFInfo
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Abstract
The invention provides a photovoltaic energy efficiency monitoring method based on a neural network and optical pollution measurement, which comprises the following steps of intelligent cleaning model analysis: combining SR dust monitoring, adopting a neural network model and a full-wave-band optical pollution measurement technology, measuring and calculating the SR value of dust, and combining the generated energy of a photovoltaic power station to construct model correlation; the method further comprises the following steps of energy efficiency monitoring state perception: the method comprises the steps of collecting power generation and equipment information of a photovoltaic power station, obtaining a linear relation among component conversion efficiency, component attenuation rate, power generation and radiation, further obtaining theoretical electric quantity of the photovoltaic power station, combining actual generated energy of a photovoltaic power station inverter, obtaining power generation efficiency of the photovoltaic power station, considering electric quantity loss factors, and performing trend analysis on lost electric quantity. The invention provides data support for the power generation efficiency and digital cleaning of the photovoltaic power station; and a more refined analysis is provided for the photovoltaic power station in the power production link, and information support and decision basis are provided for the power generation optimization of the photovoltaic power station.
Description
Technical Field
The invention belongs to the field of photovoltaic power generation, and particularly relates to a photovoltaic energy efficiency monitoring method and system based on a neural network and optical pollution measurement.
Background
With the continuous construction and the rapid increase of the installed scale of the photovoltaic power station, a large number of problems gradually occur in the operation of the photovoltaic power station, such as low photoelectric conversion efficiency in photovoltaic power generation, high development and conversion cost, difficult and tedious operation control and maintenance, difficult detection of fault points and difficult metering of efficiency attenuation. For a solar power station which runs for a long time, the power generation efficiency is determined by four main parameters, namely solar irradiance, battery backboard temperature, dust pollutants and power generation capacity. Among them, dust contamination on the photovoltaic module glass is one of the main problems that rapidly affects the performance ratio of the photovoltaic power station, it causes a reduction in the power generation efficiency and Performance Ratio (PR), increases the cleaning cost, and also increases the failure rate of the photovoltaic cell for oxide-containing dust contamination, affects the safety in use, and reduces the life of the solar cell.
At present, due to the limited technical means, the influence of each factor on the generating capacity of a photovoltaic power station is difficult to quantify, the fault maintenance of the power station is generally the after maintenance, the maintenance method and the standard are backward and extensive, and the efficiency attenuation cannot be evaluated, and the main reasons are as follows:
1) besides the generated energy, indexes such as the efficiency of a photovoltaic array system, the mean fault interval time of the photovoltaic array and the like are mostly calculated through manual statistics, and when each index is calculated, the reliability of data is greatly influenced by human factors;
2) the generated energy of the photovoltaic power station is directly related to solar energy resources, but the solar energy has the defects of energy dispersion (low energy density), unstable energy discontinuity and the like, the performance and fault evaluation of the photovoltaic power station is very difficult due to the factors, and the operation management level and the operation maintenance technical level of the photovoltaic power station are difficult to objectively and quantitatively evaluate;
3) because the factors influencing the generating capacity are more and difficult to evaluate, the influence of less ash and coverage thickness on the electric quantity is difficult to quantify, and generally speaking, the influence of the dust on the generating efficiency of the photovoltaic power station mainly has the following points:
a. the dust has the functions of reflecting, scattering and absorbing solar radiation, so that the transmissivity of the photovoltaic cell panel is reduced, and the power generation capacity of the module array is influenced; due to the fact that the photovoltaic module is shielded by dust in a non-uniform mode, the radiation quantity is reduced, the radiation quantity is also caused to be non-uniform, the photovoltaic module is mismatched, and the output power of photovoltaic power generation is reduced;
b. the dust shielding can reduce the heat dissipation of the surface of the photovoltaic module, thereby influencing the photoelectric conversion efficiency of photovoltaic power generation and reducing the generated energy;
c. certain dust containing oxides falls on the surface of the photovoltaic module, and rain dew is added to change the dust into acidic or alkaline substances, so that the solar panel has a certain corrosion effect, rough sugar on the panel surface is uneven after long-time erosion, accumulation of dust is facilitated, diffuse reflection of sunlight is increased, and light transmission is reduced. The failure rate of the photovoltaic cell is improved, the use safety is influenced, and the service life of the solar cell is shortened;
d. temperature is also a main factor affecting the photovoltaic power generation amount, and the power generation amount is smaller as the temperature is higher in the same solar radiation situation. The dust can not only directly reduce the light transmittance of the photovoltaic cell panel and reduce the generated energy, but also influence the heat dissipation due to the shielding of the dust, increase the temperature of the photovoltaic cell panel, and have a certain corrosion effect on the cell panel due to the long-term adhesion of the dust;
4) the support of scientific intelligent operation management systems is lacked, and the rationality of artificial decision needs to be improved.
As mentioned above, the problem that the influence of dust coverage thickness on electric quantity is difficult to quantify is generally judged according to experience of operation and maintenance personnel, random factors are large, a set of photovoltaic energy efficiency monitoring system needs to be adopted, meanwhile, the overall operation performance of the power station is evaluated in real time, and the method has important significance for improving the operation efficiency and the operation management level of the photovoltaic power station.
Disclosure of Invention
The invention provides a photovoltaic energy efficiency monitoring method and system based on a neural network and optical pollution measurement, and provides data support for power generation energy efficiency and cleaning of a photovoltaic power station. And a more refined analysis is provided for the photovoltaic power station in the power production link, and information support and decision basis are provided for the power generation optimization of the photovoltaic power station.
In order to achieve the purpose, the technical scheme of the invention is realized as follows:
a photovoltaic energy efficiency monitoring method based on a neural network and optical pollution measurement comprises two dimensional points:
s1, intelligent cleaning analysis: establishing a neural network analysis model of the photovoltaic power station, wherein the pollutant pollution thickness influences the generating capacity, and carrying out cleaning reminding;
s2, energy efficiency state perception: and (4) combining the analysis model of the step S1, sensing the state of electric quantity caused by attenuation of the power generation unit of the photovoltaic power station, off-site involvement and on-site maintenance, and performing trend analysis on the lost electric quantity.
Further, step S1 specifically includes:
s101, counting the average trend of dust fall in the whole station based on the dust deposition thickness percentage of the solar cell panel measured by the optical pollutant measurement dust monitor;
s102, a neural network is introduced by combining the result of S101 and the power generation condition of the photovoltaic power station, and a relation analysis model of the dust monitoring and the power generation amount of the total station SR is obtained;
s103, establishing a relation comparison of dust fall, generated energy and electric settlement, introducing cleaning cost accounting, and performing cleaning reminding on a time dimension.
Further, the step S103 of introducing the cleaning cost accounting includes: according to the settlement of local power grid to the electricity, the settlement cost of the power grid is converted into the corresponding generated energy of 100 percent in the clean state of the solar photovoltaic power station board; and when the power grid settlement cost corresponding to the generated energy due to dust loss is larger than or equal to the cleaning cost, cleaning reminding is carried out.
Further, step S2 specifically includes:
s201, collecting power generation and equipment information of a photovoltaic power station to obtain linear relations among component conversion efficiency, component attenuation rate, power generation and radiation;
s202, theoretical electric quantity of the photovoltaic power station is obtained, and the power generation efficiency of the photovoltaic power station is obtained by combining the actual power generation quantity of an inverter of the photovoltaic power station;
and S203, performing trend analysis on the lost electric quantity according to the daily theoretical electric quantity and actual deviation and electric quantity loss factors under the conditions of maintenance, electricity limitation and defects.
Further, in step S2, a component health degree model is established for the photovoltaic power plant, and step S201 to step S203 are executed according to the component health degree model.
In another aspect of the present invention, a photovoltaic energy efficiency monitoring system based on a neural network and optical pollution measurement is further provided, including:
the intelligent cleaning analysis module: establishing a neural network analysis model of the photovoltaic power station, wherein the pollutant pollution thickness influences the generating capacity, and carrying out cleaning reminding;
energy efficiency state perception module: the analysis model that combines intelligent cleaning analysis module, from photovoltaic power plant power generation unit decay and the extrafield is tired and the on-the-spot maintenance brings the electric quantity state perception, carries out trend analysis to the loss electric quantity.
Further, the intelligent cleaning model analysis module comprises:
the dust fall average trend unit is used for counting the total station dust fall average trend based on the dust deposition thickness percentage of the solar cell panel measured by the optical pollutant measurement dust monitor;
the modeling unit is used for combining the data of the dust fall average trend unit and the data of the energy efficiency state sensing module and introducing a neural network to obtain a relation model between the total station SR dust monitoring and the generated energy;
and the cleaning reminding unit refers to a neural network, establishes a relation comparison model of dust fall, generated energy and kilowatt-hour settlement, introduces cleaning cost accounting and carries out cleaning reminding on a time dimension.
Furthermore, in the cleaning reminding unit, the introducing of the cleaning cost accounting includes: according to the settlement of local power grid to the electricity, the settlement cost of the power grid is converted into the corresponding generated energy of 100 percent in the clean state of the solar photovoltaic power station board; and when the power grid settlement cost corresponding to the generated energy due to dust loss is larger than or equal to the cleaning cost, cleaning reminding is carried out.
Further, the energy efficiency monitoring state sensing module includes:
the first sensing unit is used for acquiring power generation and equipment information of the photovoltaic power station to obtain the linear relation among the component conversion efficiency, the component attenuation rate, the power generation and the radiation;
the second sensing unit is used for obtaining theoretical electric quantity of the photovoltaic power station and combining actual generated energy of the photovoltaic power station inverter to obtain power generation efficiency of the photovoltaic power station;
and the third sensing unit is used for carrying out trend analysis on the lost electric quantity according to the daily theoretical electric quantity and actual deviation and electric quantity loss factors under the conditions of maintenance, electricity limitation and defects.
Furthermore, the energy efficiency monitoring state sensing module further comprises a component health degree model module, and the first sensing unit, the second sensing unit and the third sensing unit are connected with the component health degree model module.
Compared with the prior art, the invention has the following beneficial effects:
1. the invention collects and models the information of the inverter generating capacity, the environment monitor, the SR dust monitoring equipment and the component nominal power and area of the photovoltaic power station, and provides data support for the power generation efficiency and cleaning of the photovoltaic power station; and a more refined analysis is provided for the photovoltaic power station in the power production link, and information support and decision basis are provided for the power generation optimization of the photovoltaic power station.
2. The photovoltaic energy efficiency monitoring system based on the neural network model and the full-wave band optical pollution measurement calculates the real-time power loss of the photovoltaic module and the photovoltaic power station and the power generation amount lost in a certain time period, calculates the efficiency of the photovoltaic module, and accurately calculates the output loss of the photovoltaic power station caused by floating dust.
Drawings
FIG. 1 is a schematic flow diagram of an embodiment of the present invention;
fig. 2 is a power generation amount graph of the embodiment of the present invention.
Detailed Description
It should be noted that the embodiments and features of the embodiments may be combined with each other without conflict.
In order to make the objects and features of the present invention more comprehensible, embodiments accompanying the present invention are further described below. It is noted that the drawings are in greatly simplified form and employ non-precise ratios for the purpose of facilitating and distinctly aiding in the description of the patented embodiments of the invention.
The invention mainly aims to provide data support for the power generation efficiency and digital cleaning of a photovoltaic power station by acquiring and modeling the generated energy of a sigma inverter of the photovoltaic power station, an environment monitor, SR dust monitoring equipment, and the nominal power and area information of components. And a more refined analysis is provided for the photovoltaic power station in the power production link, and information support and decision basis are provided for the power generation optimization of the photovoltaic power station.
As shown in FIG. 1, the present invention considers two dimensions, including intelligent cleaning analysis and energy efficient state perception.
Firstly, intelligent cleaning analysis:
according to the dimension, the influence of pollutants on the power generation amount of the power station is subjected to osmotic analysis, the linear relation that the pollution thickness influences the power generation amount is found, and a neural network model is introduced to judge the linear relation, so that the power station is guided to carry out cleaning reminding in relative time, and the maximization of the power generation income is promoted; the technology is based on the solar cell panel dust deposition thickness percentage measured by an optical pollutant measurement dust monitor, and combines the corresponding relation of the generated energy of a photovoltaic power station under different pollutant percentages, so that the power station is reminded of cleaning by covering pollutants.
Specifically, SR dust monitoring is combined, a high-stability SCADA is used as a support for platform application, a neural network model and a full-band optical pollution measurement (BOSM) technical principle are adopted, and the cleanliness is reduced to 0% from 100% all the way through measuring and calculating the SR value of the dust. And the construction of model correlation is carried out by combining the generated energy, so that an investor finds balance between the generated energy and the cleaning cost, operation and maintenance personnel of a power station do not need to visually observe through experience any more, and an optimal cleaning scheme can be scientifically and accurately selected, thereby avoiding the loss of the generating efficiency and the waste of the cleaning cost. Effectively improving the income of the power station and the return on investment of investors.
SR dust monitoring equipment is additionally arranged at each period of the power station, the factory default of the SR dust monitoring equipment is 100%, after the SR dust monitoring equipment is additionally arranged, under the normal production condition of the power station, the daily electric quantity of the power station (the daily electric quantity of the power station can be generated by an inverter or/and the electric quantity of an outgoing line measuring meter of a booster station) corresponds to the daily electric quantity of the power station, the 90MW photovoltaic power station is calculated according to the equivalent utilization hour of each day for 4 hours, and then the daily electric quantity is 36 kilo-Wh. Statistics is carried out every day, and corresponding relations are found out, such as: after 1 week of loading, dust monitoring decreased from 100% → corresponding 36 kWh to 98% → corresponding 35.28 kWh.
If the power consumption is calculated according to 1 yuan, 0.72 ten thousand yuan is lost. And (4) calculating according to the cleaning cost of 3000 yuan/MW, and cleaning the total station by about 27 ten thousand yuan. At the moment, the cleaning is not carried out for a while, and only when the accumulated loss reaches or exceeds 27 ten thousand yuan, cleaning prompt is carried out, and comparison of the generated energy before and after the cleaning is carried out. During cleaning, the SR dust monitoring equipment is cleaned synchronously, and the percentage is marked again to be normalized.
Specific statistical analysis of the cleaning model includes:
dust fall tendency of total station
Counting the average trend of the SR dust monitoring of the pollutants by taking a day as a unit, and carrying out monthly counting and quarterly counting; the monthly statistics include: percentage of dust trend daily in 15 minute resolution during the month, query the percentage of dust histogram trend daily during the month of the quarter.
② dust fall electric quantity relation curve
Taking the day as a unit to count the total station SR dust monitoring and generating capacity data, carrying out monthly statistics and quarterly statistics, wherein the statistics content comprises the following steps: the dust percentage and the power generation amount are 15 minutes of resolution ratio every day in the same month; inquiring the all-station dust fall trend and the all-station power generation condition, including the power generation amount of an inverter and the power loss, wherein the power loss can be from the energy efficiency state sensing dimension of a power station; and introducing a neural network according to the data to obtain a relation model between the total-station SR dust monitoring and the generated energy.
③ cleaning and reminding
Establishing a relation comparison of dust fall, generated energy and electric settlement, and introducing cleaning cost accounting; cleaning reminders are performed in the time dimension. The method is characterized in that linear relation of corresponding power generation amount under the dust monitoring proportion is considered, and local power grid settlement is considered at the same time, and the cost is converted into settlement cost of a power grid. And (4) cleaning reminding is carried out when the power grid settlement cost contrasted by the generated energy influenced by the dust is more than or equal to the cleaning cost along with the time.
Fourthly, comparing trend before and after cleaning
And (4) the nominal cleaning date is used for counting the generated energy before and after cleaning. (consider the secondary calibration of SR equipment)
Secondly, energy efficiency state perception:
the technology acquires power generation and equipment information (including component area, total solar radiation on an inclined plane and inverter power generation amount) of the photovoltaic power station, indirectly obtains component conversion efficiency, component attenuation rate and linear relation of power generation and radiation by a formula, further obtains theoretical power of the photovoltaic power station, and combines actual power generation amount of a power station inverter to further obtain power generation efficiency of the photovoltaic power station. According to daily theory and actual deviation, and considering maintenance, the electric quantity loss factor under the conditions of electricity limitation and defects can further perform trend analysis on the lost electric quantity.
The specific analysis comprises the following steps:
average attenuation trend of total station components
And (5) checking the total-station attenuation trend by taking the day as a unit, and carrying out monthly statistics and quarterly statistics.
② total station electric quantity loss statistics
And checking the total-station electric quantity loss trend by taking the day as a unit, and carrying out monthly statistics and quarterly statistics. (consider power-limiting and in-station maintenance power loss)
(iii) Total station component conversion efficiency
Fourthly, generating efficiency trend of total station
PR efficiency statistics were performed on a daily basis, monthly statistics, and quarterly statistics. (actual power generation, theoretical power generation, PR trend curve)
Fifthly, a linear curve of total station radiant quantity and generated energy
And counting the total station radiant quantity and the generated energy by taking the day as a point position, and carrying out monthly counting and quarterly counting.
The method comprises the steps of acquiring power generation and equipment information (including component area, total radiant quantity of solar energy on an inclined plane and power generation quantity of an inverter) of the photovoltaic power station, indirectly obtaining the conversion efficiency of the components, the attenuation rate of the components and the linear relation of power generation and radiation through a formula, further obtaining the theoretical electric quantity of the photovoltaic power station, and further obtaining the power generation efficiency of the photovoltaic power station by combining the actual power generation quantity of the inverter of the power station. According to daily theory and actual deviation, and considering maintenance, the electric quantity loss factor under the conditions of electricity limitation and defects can further perform trend analysis on the lost electric quantity.
Description of model algorithm
Fig. 1 is a schematic flow chart of the method of the present invention, and the algorithm involved in the flow chart is described as follows:
the theoretical electric quantity of the photovoltaic power station is predicted according to the solar energy resource condition of the site, and is calculated and determined after various factors such as photovoltaic power station system design, photovoltaic square matrix arrangement, environmental conditions and the like are considered.
The theoretical electric quantity Ep of the photovoltaic power station is calculated as follows:
ep ═ HA × S × K1 × K2 in the formula:
HA-Total solar dose on inclined plane (kW. h/m)2)；
S-is the sum of the component areas (m)2)
K1-efficiency of conversion of modules;
k2-is the overall efficiency of the system. (generally considered 80%)
The overall efficiency coefficient K2 is a correction coefficient that takes into account the influence of various factors, including:
1) energy reduction of service power, line loss and the like
Alternating current and direct current distribution room and transmission line loss.
2) Inverter reduction
The inverter losses.
The efficiency of a photovoltaic cell varies with the temperature at which it operates. As their temperature increases, photovoltaic modules tend to decrease in power generation efficiency.
Besides the above factors, the influence on the power generation of the photovoltaic power station also includes unavailable solar radiation loss, reduction of the maximum power point tracking precision influence, power grid absorption and other uncertain factors.
Calculating the loss electric quantity of the square matrix by the following calculation formula:
[ INPUT ]: the radiation amount in the period, the total generating capacity of the inverter, the system efficiency, the component area and the component nominal power;
[ output ]: theoretical electric quantity, module conversion efficiency and loss electric quantity.
[ CALCULATION ]:
radiation amount: if the output of the weather station is Mj/m2Then, it needs to be converted into kwh/m2Not (radiation/3.6/1 kW/m in period)2】。
The overall efficiency of the square matrix is calculated in combination with the actual power generation of the inverter:
Wherein the conversion efficiency of the module is 1000W/m of the nominal power of the module/the area of the module2*100％。
Examples are:
by way of example for a 1MW photovoltaic plant, the project uses 250W modules 4000 of size 1640 x 992mm, connected to the grid using a 10kV voltage class.
The module conversion efficiency is 1000000(W)/1.64 0.992(m) 4000 (block) 1000W/m2*100％＝15.36％
The loss deviation is the theoretical electric quantity-the actual electric quantity.
Module attenuation (actual power generation/(total radiation module area) system integrated efficiency)
And (3) final output: sigma daily loss deviation cost is larger than cleaning cost, and cleaning reminding is realized. And the generated energy and the generating efficiency before and after the cleaning are shown in a chart mode.
As shown in fig. 2, the loss power generation amount (theoretical power generation amount/actual power generation amount):
calculated according to the power of the network of 1 yuan/kWh, and the cleaning cost is calculated according to 3 yuan/kW. The cleaning cost is 1MW 3000 Yuan renminty currency.
Theoretical power generation 4543 in 5 months and 5 days, actual power generation 1056 and power loss 3487 kW/h. 3487 Yuan RMB > 3000 Yuan for cleaning.
The electricity loss is 3257kW/h in 5 months and 6 days. That is 3257 Yuan RMB > 3000 Yuan for cleaning.
And the pollution in the area is serious due to continuous reduction.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like that fall within the spirit and principle of the present invention are intended to be included therein.
Claims (10)
1. A photovoltaic energy efficiency monitoring method based on a neural network and optical pollution measurement is characterized by comprising two dimensional points:
s1, intelligent cleaning analysis: establishing a neural network analysis model of the photovoltaic power station, wherein the pollutant pollution thickness influences the generating capacity, and carrying out cleaning reminding;
s2, energy efficiency state perception: and (4) combining the analysis model of the step S1, sensing the state of electric quantity caused by attenuation of the power generation unit of the photovoltaic power station, off-site involvement and on-site maintenance, and performing trend analysis on the lost electric quantity.
2. The method for monitoring photovoltaic energy efficiency based on neural network and optical pollution measurement as claimed in claim 1, wherein step S1 specifically includes:
s101, counting the average trend of dust fall in the whole station based on the dust deposition thickness percentage of the solar cell panel measured by the optical pollutant measurement dust monitor;
s102, a neural network is introduced by combining the result of S101 and the power generation condition of the photovoltaic power station, and a relation analysis model of the dust monitoring and the power generation amount of the total station SR is obtained;
s103, establishing a relation comparison of dust fall, generated energy and electric settlement, introducing cleaning cost accounting, and performing cleaning reminding on a time dimension.
3. The photovoltaic energy efficiency monitoring method based on the neural network and the optical pollution measurement as claimed in claim 2, wherein the step S103 of introducing the cleaning cost accounting includes: according to the settlement of local power grid to the electricity, the settlement cost of the power grid is converted into the corresponding generated energy of 100 percent in the clean state of the solar photovoltaic power station board; and when the power grid settlement cost corresponding to the generated energy due to dust loss is larger than or equal to the cleaning cost, cleaning reminding is carried out.
4. The method for monitoring photovoltaic energy efficiency based on neural network and optical pollution measurement as claimed in claim 1, wherein step S2 specifically includes:
s201, collecting power generation and equipment information of a photovoltaic power station to obtain linear relations among component conversion efficiency, component attenuation rate, power generation and radiation;
s202, theoretical electric quantity of the photovoltaic power station is obtained, and the power generation efficiency of the photovoltaic power station is obtained by combining the actual power generation quantity of an inverter of the photovoltaic power station;
and S203, performing trend analysis on the lost electric quantity according to the daily theoretical electric quantity and actual deviation and electric quantity loss factors under the conditions of maintenance, electricity limitation and defects.
5. The method for monitoring photovoltaic energy efficiency based on neural network and optical pollution measurement as claimed in claim 4, wherein in step S2, the photovoltaic power plant establishes a component health degree model, and executes steps S201 to S203 according to the component health degree model.
6. A photovoltaic energy efficiency monitoring system based on neural network and optical pollution measurement is characterized by comprising:
the intelligent cleaning analysis module: establishing a neural network analysis model of the photovoltaic power station, wherein the pollutant pollution thickness influences the generating capacity, and carrying out cleaning reminding;
energy efficiency state perception module: the analysis model that combines intelligent cleaning analysis module, from photovoltaic power plant power generation unit decay and the extrafield is tired and the on-the-spot maintenance brings the electric quantity state perception, carries out trend analysis to the loss electric quantity.
7. The photovoltaic energy efficiency monitoring system based on the neural network and the optical pollution measurement as claimed in claim 6, wherein the intelligent cleaning model analysis module comprises:
the dust fall average trend unit is used for counting the total station dust fall average trend based on the dust deposition thickness percentage of the solar cell panel measured by the optical pollutant measurement dust monitor;
the modeling unit is used for combining the data of the dust fall average trend unit and the data of the energy efficiency state sensing module and introducing a neural network to obtain a relation model between the total station SR dust monitoring and the generated energy;
and the cleaning reminding unit refers to a neural network, establishes a relation comparison model of dust fall, generated energy and kilowatt-hour settlement, introduces cleaning cost accounting and carries out cleaning reminding on a time dimension.
8. The photovoltaic energy efficiency monitoring system based on the neural network and the optical pollution measurement as claimed in claim 7, wherein the introducing of the cleaning cost accounting in the cleaning reminding unit comprises: according to the settlement of local power grid to the electricity, the settlement cost of the power grid is converted into the corresponding generated energy of 100 percent in the clean state of the solar photovoltaic power station board; and when the power grid settlement cost corresponding to the generated energy due to dust loss is larger than or equal to the cleaning cost, cleaning reminding is carried out.
9. The photovoltaic energy efficiency monitoring system based on the neural network and the optical pollution measurement as claimed in claim 6, wherein the energy efficiency monitoring state perception module comprises:
the first sensing unit is used for acquiring power generation and equipment information of the photovoltaic power station to obtain the linear relation among the component conversion efficiency, the component attenuation rate, the power generation and the radiation;
the second sensing unit is used for obtaining theoretical electric quantity of the photovoltaic power station and combining actual generated energy of the photovoltaic power station inverter to obtain power generation efficiency of the photovoltaic power station;
and the third sensing unit is used for carrying out trend analysis on the lost electric quantity according to the daily theoretical electric quantity and actual deviation and electric quantity loss factors under the conditions of maintenance, electricity limitation and defects.
10. The photovoltaic energy efficiency monitoring system based on neural network and optical pollution measurement according to claim 9, wherein the energy efficiency monitoring state perception module further comprises a component health degree model module, and the first perception unit, the second perception unit and the third perception unit are connected with the component health degree model module.
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114362664A (en) * | 2021-12-29 | 2022-04-15 | 广东南控电力有限公司 | But remote control's photovoltaic module washs matrix device |
| CN114781179A (en) * | 2022-05-12 | 2022-07-22 | 广东华矩检测技术有限公司 | Photovoltaic power station generated energy loss verification method based on optical fiber communication information acquisition |
| CN115146403A (en) * | 2022-06-17 | 2022-10-04 | 中国能源建设集团湖南省电力设计院有限公司 | Photovoltaic module arranged in waveform and calculation method of optimal arrangement angle of photovoltaic module |
| CN117200329A (en) * | 2023-11-06 | 2023-12-08 | 广州菲利斯太阳能科技有限公司 | Off-grid power generation, energy storage and power supply system |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105215034A (en) * | 2015-11-16 | 2016-01-06 | 上海许继电气有限公司 | Realize the system and method for photovoltaic plant solar panel intelligence cleaning |
| CN107092994A (en) * | 2017-06-21 | 2017-08-25 | 苏州协鑫新能源运营科技有限公司 | A kind of method for predicting delivery |
| CN107133713A (en) * | 2017-03-13 | 2017-09-05 | 华电电力科学研究院 | A kind of photovoltaic plant intelligently cleans the method for building up of decision system |
| CN107818410A (en) * | 2017-10-23 | 2018-03-20 | 长沙理工大学 | A kind of photovoltaic module dust stratification degree long-distance intelligent diagnostic method |
| CN212693535U (en) * | 2020-08-11 | 2021-03-12 | 东莞绿光新能源科技有限公司 | Photovoltaic power generation dust measuring device based on full-waveband optical pollution measurement technology |
| CN113224756A (en) * | 2021-05-11 | 2021-08-06 | 湖南中大设计院有限公司 | Method applied to photovoltaic building integrated optimal installed capacity measurement and calculation |
-
2021
- 2021-09-08 CN CN202111048885.9A patent/CN113676135B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105215034A (en) * | 2015-11-16 | 2016-01-06 | 上海许继电气有限公司 | Realize the system and method for photovoltaic plant solar panel intelligence cleaning |
| CN107133713A (en) * | 2017-03-13 | 2017-09-05 | 华电电力科学研究院 | A kind of photovoltaic plant intelligently cleans the method for building up of decision system |
| CN107092994A (en) * | 2017-06-21 | 2017-08-25 | 苏州协鑫新能源运营科技有限公司 | A kind of method for predicting delivery |
| CN107818410A (en) * | 2017-10-23 | 2018-03-20 | 长沙理工大学 | A kind of photovoltaic module dust stratification degree long-distance intelligent diagnostic method |
| CN212693535U (en) * | 2020-08-11 | 2021-03-12 | 东莞绿光新能源科技有限公司 | Photovoltaic power generation dust measuring device based on full-waveband optical pollution measurement technology |
| CN113224756A (en) * | 2021-05-11 | 2021-08-06 | 湖南中大设计院有限公司 | Method applied to photovoltaic building integrated optimal installed capacity measurement and calculation |
Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114362664A (en) * | 2021-12-29 | 2022-04-15 | 广东南控电力有限公司 | But remote control's photovoltaic module washs matrix device |
| CN114362664B (en) * | 2021-12-29 | 2024-03-22 | 广东南控电力有限公司 | Remote-control photovoltaic module cleaning matrix device |
| CN114781179A (en) * | 2022-05-12 | 2022-07-22 | 广东华矩检测技术有限公司 | Photovoltaic power station generated energy loss verification method based on optical fiber communication information acquisition |
| CN115146403A (en) * | 2022-06-17 | 2022-10-04 | 中国能源建设集团湖南省电力设计院有限公司 | Photovoltaic module arranged in waveform and calculation method of optimal arrangement angle of photovoltaic module |
| CN115146403B (en) * | 2022-06-17 | 2024-03-22 | 中国能源建设集团湖南省电力设计院有限公司 | Photovoltaic module with waveform arrangement and calculation method of optimal arrangement angle of photovoltaic module |
| CN117200329A (en) * | 2023-11-06 | 2023-12-08 | 广州菲利斯太阳能科技有限公司 | Off-grid power generation, energy storage and power supply system |
| CN117200329B (en) * | 2023-11-06 | 2024-03-22 | 广州菲利斯太阳能科技有限公司 | Off-grid power generation, energy storage and power supply system |
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