CN111461407A - Photovoltaic power station cleaning frequency prediction method and storage medium - Google Patents
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Abstract
The invention provides a method for predicting the cleaning frequency of a photovoltaic power station and a storage medium. According to the invention, the panel power data of the preset historical time period is subjected to modeling learning, the air data is added into the sample data, the accuracy of the predicted power data can be improved, a cost calculation model is built, and the optimal cleaning frequency is obtained by adjusting the threshold power. The invention can also estimate the performance of the panel according to the predicted power, and adjust the panel at the next moment, so that the performance of the panel is improved.
Description
Technical Field
The invention belongs to the technical field of photovoltaic power generation, and particularly relates to a method for predicting cleaning frequency of a photovoltaic power station and a storage medium.
Background
Over time, there is dust accumulation in photovoltaic power plants, which can lead to a reduction in the power generation efficiency of the photovoltaic panels, but cleaning the photovoltaic panels can also bring about a certain cost. Considering the difference of dust accumulation rate of different regions; and different frequencies of rainfall in different seasons; the optimal cleaning frequency for different regions cannot be replaced by a uniform cleaning frequency.
The existing cleaning scheme does not adjust according to the conditions of different regions when the problem of cleaning frequency is mentioned. The higher the frequency of washing, the higher the power generation of the photovoltaic power plant, but the greater the washing costs. However, because the dust accumulation speed is different in different areas and the cleaning cost is different in different areas, a certain waste of productivity is caused by replacing the optimal cleaning frequency with a single cleaning frequency.
Therefore, it is necessary to provide a method and a storage medium for predicting the cleaning frequency of the photovoltaic power station, which are based on big data.
Disclosure of Invention
The invention provides a method for predicting the cleaning frequency of a photovoltaic power station and a storage medium, which are used for predicting the cleaning frequency of the photovoltaic power station and calculating the cleaning cost with excellent adjustment according to a cost formula.
The invention provides a method for predicting cleaning frequency of a photovoltaic power station, which comprises the following steps: the method comprises the steps of obtaining the generated power of a plurality of photovoltaic panels in a historical time period as data samples; a model construction step, namely learning the data sample by using machine learning to obtain a prediction model; a prediction step, inputting partial continuous data samples into the prediction model to obtain the predicted power generation power of the next time sequence; a judging step, namely comparing the predicted power generation power of each photovoltaic panel with a power threshold, and if the actual power generation power is lower than the predicted power threshold, judging that the photovoltaic panel needs to be cleaned; if the actual power generation power is higher than the predicted cleaning power threshold value, judging that the photovoltaic panel does not need to be cleaned; and a summarizing step of determining the panel which needs to be cleaned in the photovoltaic power station every day.
Further, still include: and a cost calculation step, namely calculating the profit cost of the photovoltaic power station according to the predicted power generation power and the panel cleaning cost of the photovoltaic power station.
Further, the cost calculating step specifically includes: a first calculation step of calculating the generation yield of the photovoltaic power station according to the predicted generation power; a second calculation step, multiplying the number of panels to be cleaned by the single cleaning cost to obtain the cleaning cost; and a third calculation step, wherein the net benefit of the photovoltaic power station is obtained by subtracting the cleaning cost from the power generation benefit.
Further, after the cost calculating step, the method further includes: and adjusting, namely adjusting the power threshold value and increasing the net yield of the photovoltaic power station to the highest value.
Further, the model building step specifically includes: a sample classification step, wherein the data samples are randomly classified into two types, namely training samples and testing samples; a primary model construction step of training and constructing a primary model by using the training sample; a verification step, inputting the test sample into the primary model for verification; and a primary model optimization step, namely optimizing the primary model according to the verification result of the verification step to obtain the prediction model.
Further, the verifying step specifically includes: inputting test samples, namely inputting X test samples to the primary model to obtain X prediction results; comparing the X prediction results with X generated power of the X test samples, and counting the number Y of the prediction results with the difference of the generated power exceeding a certain threshold; and a calculating step, calculating the predicted error rate and evaluating the model, wherein the error rate is not the ratio of Y to the sample number X of the test sample.
Further, the time length of the preset historical time period is more than or equal to 3 months.
Further, the data samples also include daily air quality data for the photovoltaic power plant.
Further, the method for predicting the cleaning frequency of the photovoltaic power station further comprises the following steps: and monitoring, namely monitoring the dust accumulation condition of each photovoltaic panel according to the curve of the predicted power and the air quality data.
The present invention also provides a storage medium storing computer-readable instructions, which are executable by at least one processor to perform the method steps.
The invention has the beneficial effects that: the method comprises the steps of carrying out modeling learning on panel power data of a preset historical time period, adding air data into a sample, improving the accuracy of power data obtained at the next moment by prediction, building a cost calculation model, and adjusting threshold power to obtain the optimal cleaning frequency. The invention can also pre-estimate the performance of the panel according to the predicted power, and adjust the parameters of the panel at the next moment, so that the performance of the panel is improved.
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The technical solution and other advantages of the present invention will become apparent from the following detailed description of specific embodiments of the present invention, which is to be read in connection with the accompanying drawings.
Fig. 1 is a flowchart of a method for predicting a photovoltaic power plant cleaning frequency provided by the present invention.
FIG. 2 is a flow chart of the model building steps provided by the present invention.
FIG. 3 is a flow chart of the verification steps provided by the present invention.
FIG. 4 is a flow chart of the cost calculation steps provided by the present invention.
Detailed Description
The technical solution in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. It is to be understood that the described embodiments are merely exemplary of the invention, and not restrictive of the full scope of the invention. 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.
In the description of the present invention, it is to be understood that the terms "first", "second" and the like are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implying any number of technical features indicated. Thus, features defined as "first", "second", may explicitly or implicitly include one or more of the described features. In the description of the present invention, "a plurality" means two or more unless specifically defined otherwise.
The following disclosure provides many different embodiments or examples for implementing different features of the invention. To simplify the disclosure of the present invention, the components and arrangements of specific examples are described below. Of course, they are merely examples and are not intended to limit the present invention. Furthermore, the present invention may repeat reference numerals and/or letters in the various examples, such repetition is for the purpose of simplicity and clarity and does not in itself dictate a relationship between the various embodiments and/or configurations discussed. In addition, the present invention provides examples of various specific processes and materials, but one of ordinary skill in the art may recognize applications of other processes and/or uses of other materials.
As shown in FIG. 1, the invention provides a method for predicting the cleaning frequency of a photovoltaic power station, which comprises the following steps S1-S8.
And S1, an obtaining step, namely obtaining the generated power of the photovoltaic panels in the historical time period as data samples. The time length of the preset historical time period is more than or equal to 3 months, and the data sample further comprises daily air quality data of the photovoltaic power station.
In order to predict the obtained data more accurately, data with large time span is needed, air data is added into a data sample, and different air environments have influence on the power generation power of the panel; for example, the generated power may be reduced during cloudy days.
And S2, a model construction step, namely learning the data sample by using machine learning to obtain a prediction model.
The machine learning model comprises naive Bayes, SVM or a deep learning network, which belong to supervision models, wherein deep learning is the preferred embodiment.
As shown in fig. 2, the model building step specifically includes S201 to S203.
S201, a sample classification step, namely randomly classifying the data samples into a training sample and a testing sample.
S202, a primary model building step, namely training and building a primary model by using the training samples.
S203, a verification step, namely inputting the test sample into the primary model for verification.
As shown in fig. 3, the verifying step specifically includes S2031 to S2033.
S2031, inputting test samples, namely inputting X test samples into the primary model to obtain X prediction results.
S2032, a comparison step, namely comparing the X prediction results with the X generated power of the X test samples, and counting the number Y of the prediction results with the difference of the generated power exceeding a certain threshold value.
S2033, calculating the prediction error rate, wherein the error rate formula is the ratio of Y to the sample number X of the test samples.
S204, a primary model optimizing step, wherein the primary model is optimized according to the verification result of the verifying step to obtain the prediction model.
And S3, a prediction step, namely inputting partial continuous data samples into the prediction model to obtain the predicted generated power of the next time sequence.
If the data samples collected are the daily generated power, the predicted power is in units of days.
If the collected data sample is instantaneous power generation power, the predicted power is hour unit
S4, a judging step, namely comparing the predicted generated power of each photovoltaic panel with a power threshold, and if the actual generated power is lower than the predicted power threshold, judging that the photovoltaic panel needs to be cleaned; and if the actual generating power is higher than the predicted cleaning power threshold value, judging that the photovoltaic panel does not need to be cleaned.
The power threshold and the minimum power value of the photovoltaic panel generally do not affect the normal operation of the photovoltaic panel. If the predicted instantaneous power is below the power threshold in continuous time, troubleshooting or cleaning should be performed immediately, which gives the photovoltaic plant sufficient work preparation.
If the air environment is considered, when the predicted power is lower than the power threshold value but is in a cloudy day, the cleaning is generally not considered, the reference is made according to the power at the next moment, and the cleaning is performed if the power at the next moment is lower than the power threshold value.
And S5, summarizing, namely determining the panel needing to be cleaned of the photovoltaic power station every day, wherein the statistical time unit can be one day or every hour.
And S6, a cost calculation step, namely calculating the income cost of the photovoltaic power station according to the predicted power generation power and the panel cleaning cost of the photovoltaic power station.
As shown in fig. 4, the cost calculation step specifically includes S601 to S603.
S601, a first calculation step, namely calculating the power generation income of the photovoltaic power station according to the predicted power generation power. The generation profit is calculated based on the charging standard for each region.
S602, a second calculation step, namely multiplying the number of panels to be cleaned by the single cleaning cost to obtain the cleaning cost. The cleaning cost is generally robot cleaning.
The working scene of the invention is limited in that the photovoltaic power station is put into operation, and only the cleaning cost and the maintenance cost are generally considered.
S603, a third calculation step, namely subtracting the cleaning cost from the power generation benefit to obtain the benefit of the photovoltaic power station.
And S7, adjusting the power threshold value, and increasing the income cost of the photovoltaic power station to the highest value. The power threshold is generally not lower than the lowest power of the panel, and if the power threshold is lower than the lowest power, the panel needs to be cleaned or detected, so that the normal work is prevented from being influenced.
Because the income cost and the cleaning cost can be different in different areas, each area adjusts the power threshold value according to the cost formula of the area, and the income of the whole photovoltaic power station is maximized.
And S8, monitoring the aging condition of each photovoltaic panel according to the predicted power curve and the air quality data.
Specifically, the power curve represents dynamic changes of the power value, an aging degree standard is defined, and if the predicted power is lower than the power threshold, the difference between the power threshold and the predicted power is divided by the power threshold multiplied by 100% to obtain an aging coefficient.
The panel is scientifically managed by building the panel cloud management platform, the future aging condition is predicted, and the panel can be maintained or maintained at the next moment, so that the panel is prevented from being broken down at the next moment.
The invention can also pre-estimate the performance of the panel according to the predicted power, and adjust or clean the parameters of the panel at the next moment, so that the performance of the panel is improved.
The invention provides a method for predicting the cleaning frequency of a photovoltaic power station, which is characterized in that the accuracy of power data at the next moment obtained by prediction is improved by carrying out modeling learning on panel power data in a preset historical time period and referring to the air data condition, a simple cost calculation model is built, and the optimal cleaning frequency is obtained by adjusting threshold power. The invention can also predict the performance of the panel according to the predicted power, predict the aging condition of the panel, replace the panel which is seriously aged or damaged (such as hot spots) in time and prevent the influence on the power generation of the whole string.
The present invention also provides a storage medium storing computer readable instructions which, when executed by one or more processors, cause the one or more processors to perform the method steps of predicting the cleaning frequency of a photovoltaic power plant.
In the foregoing embodiments, the descriptions of the respective embodiments have respective emphasis, and for parts that are not described in detail in a certain embodiment, reference may be made to related descriptions of other embodiments.
The principle and the implementation of the present invention are explained in the present text by applying specific examples, and the above description of the examples is only used to help understanding the technical solution and the core idea of the present invention; those of ordinary skill in the art will understand that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.
Claims (10)
1. A method for predicting cleaning frequency of a photovoltaic power station is characterized by comprising the following steps:
the method comprises the steps of obtaining the generated power of a plurality of photovoltaic panels in a historical time period as data samples;
a model construction step, namely learning the data sample by using machine learning to obtain a prediction model;
a prediction step, inputting partial continuous data samples into the prediction model to obtain the predicted power generation power of the next time sequence;
a judging step, namely comparing the predicted power generation power of each photovoltaic panel with a power threshold, and if the actual power generation power is lower than the predicted power threshold, judging that the photovoltaic panel needs to be cleaned; if the actual power generation power is higher than the predicted cleaning power threshold value, judging that the photovoltaic panel does not need to be cleaned; and
and a summarizing step of determining the panel which needs to be cleaned in the photovoltaic power station every day.
2. The method of predicting photovoltaic power plant cleaning frequency of claim 1, further comprising:
and a cost calculation step, namely calculating the profit cost of the photovoltaic power station according to the predicted power generation power and the panel cleaning cost of the photovoltaic power station.
3. The method of predicting photovoltaic power plant cleaning frequency of claim 2,
the cost calculating step specifically comprises:
a first calculation step of calculating the generation yield of the photovoltaic power station according to the predicted generation power;
a second calculation step, multiplying the number of panels to be cleaned by the single cleaning cost to obtain the cleaning cost; and
and a third calculation step, wherein the net benefit of the photovoltaic power station is obtained by subtracting the cleaning cost from the power generation benefit.
4. The method of predicting photovoltaic power plant cleaning frequency of claim 2,
after the cost calculating step, the method further comprises:
and adjusting, namely adjusting the power threshold value and increasing the net yield of the photovoltaic power station to the highest value.
5. The method of predicting photovoltaic power plant cleaning frequency of claim 1,
the model construction step specifically comprises:
a sample classification step, wherein the data samples are randomly classified into two types, namely training samples and testing samples;
a primary model construction step of training and constructing a primary model by using the training sample;
a verification step, inputting the test sample into the primary model for verification;
and a primary model optimization step, namely optimizing the primary model according to the verification result of the verification step to obtain the prediction model.
6. The method for predicting the cleaning frequency of a photovoltaic power plant of claim 5, wherein the verifying step specifically comprises:
inputting test samples, namely inputting X test samples to the primary model to obtain X prediction results;
comparing the X prediction results with X generated power of the X test samples, and counting the number Y of the prediction results with the difference of the generated power exceeding a certain threshold;
and a calculating step, calculating the predicted error rate, and evaluating the model, wherein the error rate is the ratio of Y to the sample number X of the test sample.
7. The method of predicting photovoltaic power plant cleaning frequency of claim 1,
the time length of the preset historical time period is greater than or equal to 3 months.
8. The method of predicting photovoltaic power plant cleaning frequency of claim 1,
the data samples also include daily air quality data for the photovoltaic power plant.
9. The method of predicting photovoltaic power plant cleaning frequency of claim 1, further comprising:
and monitoring, namely monitoring the dust accumulation condition of each photovoltaic panel according to the curve of the predicted power and the air quality data.
10. A storage medium having computer-readable instructions stored thereon for execution by at least one processor to perform the method steps of any one of claims 1-9.
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106529723A (en) * | 2016-11-10 | 2017-03-22 | 上海许继电气有限公司 | Method for realizing photovoltaic power station cleaning period estimation based on monitoring platform |
| CN107578164A (en) * | 2017-08-31 | 2018-01-12 | 阳光电源股份有限公司 | A kind of solar panel cleaning method for early warning and device |
| 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 |
| CN109214552A (en) * | 2018-08-09 | 2019-01-15 | 上海安悦节能技术有限公司 | Intelligent O&M method based on the prediction of integrated study photovoltaic |
| US20190181793A1 (en) * | 2017-12-08 | 2019-06-13 | International Business Machines Corporation | Cognitively Predicting Dust Deposition on Solar Photovoltaic Modules |
| CN110649883A (en) * | 2019-09-29 | 2020-01-03 | 合肥阳光新能源科技有限公司 | Cleaning method and device and computer equipment |
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- 2020-03-10 CN CN202010160504.5A patent/CN111461407A/en active Pending
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106529723A (en) * | 2016-11-10 | 2017-03-22 | 上海许继电气有限公司 | Method for realizing photovoltaic power station cleaning period estimation based on monitoring platform |
| CN107578164A (en) * | 2017-08-31 | 2018-01-12 | 阳光电源股份有限公司 | A kind of solar panel cleaning method for early warning and device |
| 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 |
| US20190181793A1 (en) * | 2017-12-08 | 2019-06-13 | International Business Machines Corporation | Cognitively Predicting Dust Deposition on Solar Photovoltaic Modules |
| CN109214552A (en) * | 2018-08-09 | 2019-01-15 | 上海安悦节能技术有限公司 | Intelligent O&M method based on the prediction of integrated study photovoltaic |
| CN110649883A (en) * | 2019-09-29 | 2020-01-03 | 合肥阳光新能源科技有限公司 | Cleaning method and device and computer equipment |
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Application publication date: 20200728 |