A power prediction method and device based on photovoltaic module degradation characteristics

Abstract

The application relates to a power prediction method and device based on the attenuation characteristics of a photovoltaic module, which comprises the following steps: acquiring a prediction time and a commissioning time of the photovoltaic module; comparing the prediction time and the commissioning time of the photovoltaic module to obtain a time difference value of the prediction time and the commissioning time of the photovoltaic module; selecting a power prediction algorithm according to whether the time difference value exceeds a preset annual threshold; and predicting the power of the photovoltaic module according to the selected power prediction algorithm. According to the technical scheme, different power prediction algorithms are adopted for photovoltaic modules in different life cycles according to the attenuation characteristics of the photovoltaic modules, the problem that the predicted power is not corrected or is corrected depending on experience in the prior art is solved, the calculation method is simple and does not depend on machine learning, and prediction errors caused by pure data analysis are avoided.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of new energy power generation prediction, and particularly relates to a power prediction method and device based on attenuation characteristics of photovoltaic modules. BACKGROUND

[0002] Due to the randomness, intermittence and volatility of photovoltaic power generation, photovoltaic power prediction becomes very difficult. Statistical methods are currently the main prediction method. Statistical methods are based on a large amount of historical data, and the photovoltaic output power under similar weather conditions or after empirical correction is used as the prediction value. The aging and attenuation of the entire photovoltaic power generation system are not considered, resulting in increased prediction error. SUMMARY

[0003] Based on the above situation of the prior art, the purpose of the present application is to provide a power prediction method and device based on the attenuation characteristics of photovoltaic modules. According to the attenuation characteristics of photovoltaic modules, different power prediction algorithms are used for photovoltaic modules in different life cycles, thereby improving the reliability and accuracy of photovoltaic module power prediction.

[0004] To achieve the above purpose, according to one aspect of the present application, a power prediction method based on the attenuation characteristics of photovoltaic modules is provided, comprising the steps of:

[0005] obtaining a prediction time and a commissioning time of a photovoltaic module;

[0006] comparing the prediction time and the commissioning time of the photovoltaic module to obtain a time difference value of the prediction time and the commissioning time of the photovoltaic module;

[0007] selecting a power prediction algorithm according to whether the time difference value exceeds a preset age threshold;

[0008] predicting the power of the photovoltaic module according to the selected power prediction algorithm.

[0009] Further, selecting a power prediction algorithm according to whether the time difference value exceeds a preset age threshold comprises:

[0010] if the time difference value does not exceed the preset age threshold, using a first power prediction algorithm for power prediction;

[0011] if the time difference value exceeds the preset age threshold, selecting a power prediction algorithm according to weather conditions.

[0012] Further, the preset age threshold is 1 year.

[0013] Further, selecting a power prediction algorithm according to weather conditions comprises:

[0014] determine whether the occurrence time of the similar meteorological condition is within a preset operation life threshold; if yes, a second power prediction algorithm is used to perform power prediction; if no, a third power prediction algorithm is used to perform power prediction.

[0015] Further, the preset operation life threshold is 1 year.

[0016] Further, the first power prediction algorithm comprises calculating the predicted power according to the following formula:

[0017]

[0018] wherein, p t is the predicted power, p0 is the power of the photovoltaic module under the similar meteorological condition, k1 is the decay coefficient within the preset life threshold, and n0 is the interval days between the test time and the similar meteorological condition sample time.

[0019] Further, the second power prediction algorithm comprises calculating the predicted power according to the following formula:

[0020]

[0021] wherein, n1 is the number of days between the similar meteorological condition sample time and the operation of one year, k2 is the decay coefficient after exceeding the preset life threshold, x is the interval whole number of years between the prediction time and the similar meteorological condition sample time, and n2 is the interval days between the x whole number of years.

[0022] Further, the third power prediction algorithm comprises calculating the predicted power according to the following formula:

[0023]

[0024] Further, it further comprises: if the predicted meteorological factor is (R, T), wherein R is irradiance and T is temperature, the similar meteorological condition is set as (R±r, T±t);

[0025] If there are m similar meteorological condition records, the predicted power p t1 , p t2 ……p tm is calculated according to the first power prediction algorithm, the second power prediction algorithm and / or the third power prediction algorithm. t The total predicted power value p is calculated according to the following formula:

[0026]

[0027] According to another aspect of the present application, a power prediction device based on the degradation characteristics of a photovoltaic module is provided, comprising a data acquisition module, a time difference calculation module, a power prediction algorithm selection module and a power prediction module; wherein,

[0028] The data acquisition module is configured to acquire a prediction time and a commissioning time of the photovoltaic module.

[0029] The time difference calculation module is configured to compare the prediction time and the commissioning time of the photovoltaic module to obtain a time difference between the prediction time and the commissioning time of the photovoltaic module.

[0030] The power prediction algorithm selection module is configured to select a power prediction algorithm according to whether the time difference exceeds a preset threshold of years.

[0031] The power prediction module is configured to predict the power of the photovoltaic module according to the selected power prediction algorithm.

[0032] In summary, the present application provides a power prediction method and device based on the degradation characteristics of a photovoltaic module, which comprises the steps of: acquiring a prediction time and a commissioning time of the photovoltaic module; comparing the prediction time and the commissioning time of the photovoltaic module to obtain a time difference between the prediction time and the commissioning time of the photovoltaic module; selecting a power prediction algorithm according to whether the time difference exceeds a preset threshold of years; and predicting the power of the photovoltaic module according to the selected power prediction algorithm. The technical solution provided by the present application uses different power prediction algorithms for photovoltaic modules in different life cycles according to the degradation characteristics of the photovoltaic modules, thereby solving the problem of relying on experience in the prior art when predicting power without correction or correction, and the calculation method is simple and does not rely on machine learning, thereby avoiding prediction errors caused by relying solely on data analysis. BRIEF DESCRIPTION OF DRAWINGS

[0033] Figure 1 is a flowchart of the power prediction method based on the degradation characteristics of a photovoltaic module according to an embodiment of the present application;

[0034] Figure 2 is a photovoltaic module degradation rate curve when the prediction is within one year of commissioning;

[0035] Figure 3 is a photovoltaic module degradation rate curve when similar weather conditions occur within one year of commissioning;

[0036] Figure 4 is a photovoltaic module degradation rate curve when similar weather conditions occur outside one year of commissioning. DETAILED DESCRIPTION

[0037] In order to make the objects, technical solutions and advantages of the present application clearer, further detailed description will be made to the present application with reference to the specific embodiments and the accompanying drawings. It should be understood that these descriptions are only exemplary and are not intended to limit the scope of the present application. In addition, in the following description, the description of the well-known structures and technologies is omitted to avoid unnecessary confusion of the concept of the present application.

[0038] The technical solutions of the present application will be described in detail below with reference to the accompanying drawings. According to one embodiment of the present application, a power prediction method based on the degradation characteristics of a photovoltaic module is provided. A photovoltaic power generation system mainly consists of a photovoltaic module, a combiner box, an inverter and other links. Among them, the photovoltaic module is the core component affecting the photovoltaic power generation efficiency. The entire life cycle of the photovoltaic module is 20-25 years, and the degradation rate is about 8%-14%. The main factors affecting photovoltaic power generation are irradiance and temperature, therefore, under the condition of similar historical meteorological conditions, the power prediction method based on the degradation characteristics of the photovoltaic module is proposed in this embodiment.

[0039] From the degradation characteristics of the photovoltaic module, it can be known that the module degrades faster in the first year, which is set as k1; in the second year and thereafter, the degradation rate is basically consistent, which is set as k2. The flow chart of the power prediction method involved in this embodiment is shown in Figure 1 , which includes the following steps:

[0040] obtaining a prediction time d t and a commissioning time d t0 of the photovoltaic module;

[0041] comparing the prediction time and the commissioning time of the photovoltaic module to obtain a time difference value of the prediction time and the commissioning time of the photovoltaic module;

[0042] selecting a power prediction algorithm according to whether the time difference value exceeds a preset year limit threshold; in this embodiment, the preset year limit threshold is set to 1 year. If the time difference value does not exceed the preset year limit threshold, a first power prediction algorithm is used for power prediction; if the time difference value exceeds the preset year limit threshold, a power prediction algorithm is selected according to the meteorological condition: judging whether the occurrence time of similar meteorological conditions is within a preset commissioning year limit threshold; if yes, a second power prediction algorithm is used for power prediction; if no, a third power prediction algorithm is used for power prediction, and in this embodiment, the preset commissioning year limit threshold is set to 1 year.

[0043] predicting the power of the photovoltaic module according to the selected power prediction algorithm.

[0044] The following will be described in detail.

[0045] If d t -d t0≤1 year, similar meteorological conditions occur within one year of commissioning, the attenuation rate curve is as follows Figure 2 As shown. n0 is the number of days between the predicted time and the time of similar meteorological samples. In this case, we have:

[0046]

[0047] Therefore, under the first power prediction algorithm, the predicted power is calculated according to the following formula:

[0048]

[0049] Where, p t To predict the power, p0 is the power of the photovoltaic module under similar weather conditions, k1 is the attenuation coefficient within the preset annual threshold, and n0 is the number of days between the test time and the sample time under similar weather conditions.

[0050] If d t -d t0 If the timeframe is greater than 1 year, continue to determine whether the occurrence of similar meteorological conditions is within one year of operation. If the occurrence of similar meteorological conditions is within one year of operation, the attenuation rate curve is as follows: Figure 3 As shown. Therefore, under the second power prediction algorithm, the predicted power is calculated according to the following formula:

[0051]

[0052] Where n1 is the number of days between the sample time of similar meteorological conditions and the one-year time limit after commissioning, k2 is the attenuation coefficient after exceeding the preset annual threshold, x is the number of whole years between the predicted time and the sample time of similar meteorological conditions, and n2 is the number of days between the predicted time and the whole year time limit x.

[0053] If d t -d t0 >1 year, and the occurrence of similar meteorological conditions occurs more than one year after commissioning, the attenuation rate curve is as follows Figure 4 As shown. Therefore, under the third power prediction algorithm, the predicted power is calculated according to the following formula:

[0054]

[0055] Furthermore, if the predicted meteorological factor is (R, T), where R is irradiance (w / m2) and T is temperature (°C), and similar meteorological conditions are set as (R±r, T±t); if there are m similar meteorological condition records, then the predicted power p is calculated according to the first power prediction algorithm, the second power prediction algorithm, and / or the third power prediction algorithm. t1 p t2 ...p tm Then the total predicted power value p tThe value is calculated according to the following formula:

[0056]

[0057] According to another embodiment of the present application, a power prediction device based on the attenuation characteristics of a photovoltaic module is provided, comprising a data acquisition module, a time difference value calculation module, a power prediction algorithm selection module and a power prediction module; wherein,

[0058] The data acquisition module is configured to acquire a prediction time and a commissioning time of the photovoltaic module.

[0059] The time difference value calculation module is configured to compare the prediction time and the commissioning time of the photovoltaic module to obtain a time difference value between the prediction time and the commissioning time of the photovoltaic module.

[0060] The power prediction algorithm selection module is configured to select a power prediction algorithm according to whether the time difference value exceeds a preset age threshold.

[0061] The power prediction module is configured to predict the power of the photovoltaic module according to the selected power prediction algorithm.

[0062] The specific manner in which each module in the device of this embodiment implements each function is the same as that of the first embodiment of the present application, and will not be described here.

[0063] In summary, the present application relates to a power prediction method and device based on the attenuation characteristics of a photovoltaic module, the method comprising the steps of: acquiring a prediction time and a commissioning time of the photovoltaic module; comparing the prediction time and the commissioning time of the photovoltaic module to obtain a time difference value between the prediction time and the commissioning time of the photovoltaic module; selecting a power prediction algorithm according to whether the time difference value exceeds a preset age threshold; and predicting the power of the photovoltaic module according to the selected power prediction algorithm. The technical solution provided by the present application uses different power prediction algorithms for photovoltaic modules in different life cycles according to the attenuation characteristics of the photovoltaic modules, thereby solving the problem of relying on experience when predicting power without correction or correction in the prior art, and the calculation method is simple and does not rely on machine learning, thereby avoiding prediction errors caused by simply relying on data analysis.

[0064] Those skilled in the art will appreciate that embodiments of the present application can be provided as methods, systems, or computer program products. Therefore, the present application can take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can take the form of a computer program product implemented on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROMs, optical storage, etc.) containing computer-usable program code.

[0065] The computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart block or blocks. Figure 1 one or more flowcharts and / or blocks Figure 1 means for functionally implementing the steps listed in the flowchart block or blocks.

[0066] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instructions which implement the function specified in the flowchart block or blocks. Figure 1 one or more flowcharts and / or blocks Figure 1 means for functionally implementing the steps listed in the flowchart block or blocks.

[0067] The computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart block or blocks. Figure 1 one or more flowcharts and / or blocks Figure 1 means for functionally implementing the steps listed in the flowchart block or blocks.

[0068] Finally, it should be noted that: the above examples are only used to illustrate the technical solutions of the present application and not to limit the scope of protection, although the above-mentioned embodiments of the present application are described in detail, those skilled in the art should understand: the skilled person in the art can make various changes, modifications or equivalent replacements to the specific embodiments of the present application after reading the present application, but these changes, modifications or equivalent replacements are all within the scope of protection of the claims of the present application.

Claims

1. A power prediction method based on the degradation characteristics of photovoltaic modules, characterized in that, Including the following steps: Obtain the predicted time and the commissioning time of photovoltaic modules; The predicted time is compared with the commissioning time of the photovoltaic module to obtain the time difference between the predicted time and the commissioning time of the photovoltaic module; The power prediction algorithm is selected based on whether the time difference exceeds a preset age threshold, including: if the time difference does not exceed the preset age threshold, a first power prediction algorithm is used for power prediction; if the time difference exceeds the preset age threshold, a power prediction algorithm is selected based on meteorological conditions, including: determining whether the occurrence time of similar meteorological conditions is within a preset age threshold; if so, a second power prediction algorithm is used for power prediction; if not, a third power prediction algorithm is used for power prediction. The first power prediction algorithm includes calculating the predicted power according to the following formula: The second power prediction algorithm includes calculating the predicted power according to the following formula: The third power prediction algorithm includes calculating the predicted power according to the following formula: Where, p t For the predicted power, p0 is the power of the photovoltaic module under similar weather conditions, k1 is the attenuation coefficient within the preset annual threshold, n0 is the number of days between the test time and the sample time of similar weather conditions, n1 is the number of days between the sample time of similar weather conditions and one year after commissioning, k2 is the attenuation coefficient after exceeding the preset annual threshold, x is the number of whole years between the predicted time and the sample time of similar weather conditions, and n2 is the number of days between x and the whole year. The power of the photovoltaic module is predicted based on the selected power prediction algorithm.

2. The method according to claim 1, characterized in that, The preset time limit is 1 year.

3. The method according to claim 2, characterized in that, The preset operation period threshold is 1 year.

4. The method according to claim 3, characterized in that, Also includes: If the predicted meteorological factor is (R, T), where R is irradiance and T is temperature, then similar meteorological conditions are set as (R±r, T±t); If there are m similar meteorological condition records, then the predicted power p is calculated according to the first power prediction algorithm, the second power prediction algorithm, and / or the third power prediction algorithm. t1 p t2 ...p tm Then the total predicted power value p t The value is calculated using the following formula:

5. A power prediction device based on the degradation characteristics of photovoltaic modules, characterized in that, It includes a data acquisition module, a time difference calculation module, a power prediction algorithm selection module, and a power prediction module; among which, The data acquisition module is used to acquire the predicted time and the commissioning time of the photovoltaic modules; The time difference calculation module is used to compare the predicted time and the commissioning time of the photovoltaic module to obtain the time difference between the predicted time and the commissioning time of the photovoltaic module. The power prediction algorithm selection module is used to select a power prediction algorithm based on whether the time difference exceeds a preset age threshold, including: if the time difference does not exceed the preset age threshold, then a first power prediction algorithm is used for power prediction; if the time difference exceeds the preset age threshold, then a power prediction algorithm is selected based on meteorological conditions, including: determining whether the occurrence time of similar meteorological conditions is within a preset commissioning age threshold; if yes, then a second power prediction algorithm is used for power prediction; if no, then a third power prediction algorithm is used for power prediction. The first power prediction algorithm includes calculating the predicted power according to the following formula: The second power prediction algorithm includes calculating the predicted power according to the following formula: The third power prediction algorithm includes calculating the predicted power according to the following formula: Where, p t For the predicted power, p0 is the power of the photovoltaic module under similar weather conditions, k1 is the attenuation coefficient within the preset annual threshold, n0 is the number of days between the test time and the sample time of similar weather conditions, n1 is the number of days between the sample time of similar weather conditions and one year after commissioning, k2 is the attenuation coefficient after exceeding the preset annual threshold, x is the number of whole years between the predicted time and the sample time of similar weather conditions, and n2 is the number of days between x and the whole year. The power prediction module is used to predict the power of the photovoltaic module according to the selected power prediction algorithm.

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