Photovoltaic power station weather type identification method and system based on theoretical power fluctuation

Abstract

The invention provides a photovoltaic power station weather type identification method and system based on theoretical power fluctuation, and the method comprises the steps: obtaining the full-field theoretical power of a photovoltaic power station on a to-be-identified day based on the equipment fixed parameters of the photovoltaic power station and the equipment operation parameters of the to-be-identified day; calculating a weather type identification index according to the full-field theoretical power; identifying the weather type of the day to be identified according to the weather type identification index; wherein the weather type identification indexes comprise the daily power generation capacity, the daily maximum power generation capacity and the daily theoretical power fluctuation characteristics of the photovoltaic power station; according to the method and the system, the whole theoretical power of the photovoltaic power station and the corresponding weather type identification indexes are calculated, and the refined weather type of the day can be obtained without collecting meteorological data, so that the weather type identification when the meteorological data are missing is realized, a weather type basis is provided for power prediction modeling, resource evaluation, consumption analysis and the like, and the method and the system are suitable for popularization and application. And the new energy prediction level is supported, and the new energy consumption capability is improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of new energy, and particularly relates to a method for identifying weather types of a photovoltaic power station based on theoretical power fluctuations. Background Art

[0002] Due to the large-scale development and construction of photovoltaic power stations, the volatility, randomness, and intermittency inherent in photovoltaic power generation itself, which vary with seasons and daily output changes, will have a serious impact on the stability of the power grid and the power quality when connected to the grid on a large scale. Photovoltaic power consumption has become an important obstacle to the further development of the photovoltaic industry, and power prediction is an effective way to solve the problem of photovoltaic power consumption. On the one hand, power prediction can provide important transient power information for coordinated control and grid optimal dispatching. On the other hand, power prediction can improve the photovoltaic power consumption capacity and increase the return on investment of photovoltaic power stations.

[0003] The output power of a photovoltaic power generation system is closely related to the surrounding meteorological conditions. Currently, research on photovoltaic power prediction generally improves the generalization ability of the photovoltaic power prediction model by establishing sub-models, thereby improving the accuracy of photovoltaic power prediction. Generally, sub-models use data with high similarity. That is to say, historical data is first divided into several categories according to similarity, and then sub-models are established for each category of data. The main role of weather type identification is to classify data samples with similar meteorological conditions into one category, establish sub-models to exclude interference factors, and improve the prediction accuracy.

[0004] However, most photovoltaic power stations are unmanned and automatically maintained, and are far from urban areas. The urban weather cannot represent the actual weather at the location of the photovoltaic power station. The meteorological monitoring equipment of photovoltaic power stations may also malfunction or have inaccurate data collection, and the power often fluctuates during the day, increasing the difficulty of identification. This brings difficulties to the establishment of power prediction models, especially power prediction models based on weather types. Therefore, identifying the weather type of a photovoltaic power station is of great significance for improving the level of new energy prediction and the ability of new energy consumption. Summary of the Invention

[0005] To overcome the deficiencies of the above-mentioned prior art, the present invention proposes a method for identifying weather types of a photovoltaic power station based on theoretical power fluctuations, including:

[0006] Based on the fixed parameters of the equipment of the photovoltaic power station and the operating parameters of the equipment on the day to be identified, obtain the full-field theoretical power of the photovoltaic power station on the day to be identified;

[0007] According to the full-field theoretical power, calculate the weather type identification index;

[0008] Identify the weather type of the day to be identified according to the weather type identification index;

[0009] Among them, the weather type recognition indicators include the daily power generation capacity, the daily maximum power generation capacity, and the daily theoretical power fluctuation characteristics of the photovoltaic power station.

[0010] Preferably, obtaining the full-field theoretical power of the photovoltaic power station on the day to be recognized based on the fixed parameters of the equipment of the photovoltaic power station and the operating parameters of the equipment on the day to be recognized includes:

[0011] Obtaining the full-field initial theoretical power according to the fixed parameters of the equipment and the operating parameters of the equipment;

[0012] Verifying and checking the full-field initial theoretical power with the full-field actual power on the day to be recognized to obtain the full-field theoretical power;

[0013] Among them, the fixed parameters of the equipment include the installed capacity of the photovoltaic power station and the installed capacity of the sample machine, and the operating parameters of the equipment include the actual power of the sample machine of the photovoltaic power station and the full-field actual power.

[0014] Preferably, the process of obtaining the full-field initial theoretical power according to the fixed parameters of the equipment and the operating parameters of the equipment satisfies the following formula:

[0015]

[0016] Among them, P i ll is the full-field initial theoretical power at the i-th power sampling moment; C qc is the installed capacity of the photovoltaic power station; C yb is the installed capacity of the sample machine of the photovoltaic power station; P i yb is the actual power of the sample machine at the i-th power sampling moment.

[0017] Preferably, verifying and checking the full-field initial theoretical power with the full-field actual power on the day to be recognized to obtain the full-field theoretical power includes:

[0018] Calculating the correlation coefficient between the full-field actual power and the full-field initial theoretical power;

[0019] When the correlation coefficient is greater than or equal to the set coefficient threshold, the larger value of the full-field actual power and the full-field initial theoretical power at the i-th power sampling moment is recorded as the full-field theoretical power at the i-th power sampling moment;

[0020] When the correlation coefficient is less than the set coefficient threshold, the full-field theoretical power is determined according to the fixed parameters of the equipment and the operating parameters of the equipment on the day to be recognized.

[0021] Preferably, the process of recording the larger value of the actual full-field power and the initial theoretical full-field power at the i-th power sampling moment as the theoretical full-field power at the i-th power sampling moment satisfies the following expression:

[0022]

[0023] where P i Yll is the theoretical full-field power at the i-th power sampling moment, and P i qc is the actual full-field power at the i-th power sampling moment; P i ll is the initial theoretical full-field power at the i-th power sampling moment;

[0024] Preferably, the process of determining the theoretical full-field power according to the fixed parameters of the device and the device operation parameters on the day to be identified satisfies the following expression:

[0025]

[0026] where K1 is the aging loss coefficient of the photovoltaic modules in the photovoltaic power station, and K2 is the dust shielding loss coefficient of the photovoltaic modules; η inv is the grid-connected inverter efficiency; P i yb is the actual power of the reference machine at the i-th power sampling moment; C qc is the installed capacity of the photovoltaic power station; C yb is the installed capacity of the reference machine in the photovoltaic power station.

[0027] Preferably, the identification of the weather type on the day to be identified according to the weather type identification parameters includes:

[0028] Obtaining the weather cloud amount level of the day to be identified according to the daily power generation capacity and the daily maximum power generation capacity;

[0029] Obtaining the cloud amount fluctuation level of the day to be identified according to the daily theoretical power fluctuation characteristics;

[0030] Obtaining the weather type of the day to be identified according to the weather cloud amount level and the cloud amount fluctuation level.

[0031] Preferably, the daily power generation capacity is represented by the average value of the theoretical full-field power on the day to be identified.

[0032] Preferably, the maximum daily power generation capacity is represented by the daily maximum clear sky index, and the daily maximum clear sky index is determined according to the maximum value of the theoretical full-field power.

[0033] Preferably, the process of determining the daily maximum clear sky index according to the maximum theoretical power of the whole field satisfies the following expression:

[0034]

[0035] where K d is the daily maximum clear sky index, P Yll (MAX) is the maximum theoretical power of the whole field on the day to be identified, and C qc is the installed capacity of the photovoltaic power station.

[0036] Preferably, the daily theoretical power fluctuation characteristic is represented by the standard deviation of the second-order difference of the whole-field theoretical power on the day to be identified, and the standard deviation is determined according to the second-order difference of the whole-field theoretical power.

[0037] Preferably, the process of determining the standard deviation according to the second-order difference of the whole-field theoretical power satisfies the following expression:

[0038]

[0039] where σ is the standard deviation, N is the number of samples of the whole-field theoretical power with output greater than 0 on the day to be identified; Δ2P i Yll is the second-order difference of the whole-field theoretical power at the i-th power sampling moment, where i ∈ [1, N - 2]; is the mean value of the second-order difference of the whole-field theoretical power.

[0040] Based on the same inventive concept, the present invention also provides a photovoltaic power station weather type recognition system based on theoretical power fluctuation, including:

[0041] A first calculation module, configured to obtain the whole-field theoretical power of the photovoltaic power station on the day to be identified based on the fixed parameters of the equipment of the photovoltaic power station and the operating parameters of the equipment on the day to be identified;

[0042] A second calculation module, configured to calculate a weather type recognition index according to the whole-field theoretical power;

[0043] A weather type recognition module, configured to recognize the weather type of the day to be identified according to the weather type recognition index;

[0044] where the weather type recognition index includes the daily power generation capacity, the daily maximum power generation capacity, and the daily theoretical power fluctuation characteristic of the photovoltaic power station.

[0045] Preferably, the first calculation module is specifically configured to:

[0046] Obtain the whole-field initial theoretical power according to the fixed parameters of the equipment and the operating parameters of the equipment;

[0047] Verify and check the full - field initial theoretical power with the actual full - field power on the day to be recognized, and obtain the full - field theoretical power;

[0048] Among them, the fixed parameters of the equipment include the installed capacity of the photovoltaic power station and the installed capacity of the sample machine, and the operating parameters of the equipment include the actual power of the sample machine and the actual full - field power of the photovoltaic power station.

[0049] Preferably, the process of obtaining the full - field initial theoretical power according to the fixed parameters of the equipment and the operating parameters of the equipment in the first calculation module satisfies the following formula:

[0050]

[0051] Among them, P i ll is the full - field initial theoretical power at the i - th power sampling moment; C qc is the installed capacity of the photovoltaic power station; C yb is the installed capacity of the sample machine of the photovoltaic power station; P i yb is the actual power of the sample machine at the i - th power sampling moment.

[0052] Preferably, the process of verifying and checking the full - field initial theoretical power with the actual full - field power on the day to be recognized in the first calculation module to obtain the full - field theoretical power includes:

[0053] Calculate the correlation coefficient between the actual full - field power and the full - field initial theoretical power;

[0054] When the correlation coefficient is greater than or equal to the set coefficient threshold, record the larger value of the actual full - field power and the full - field initial theoretical power at the i - th power sampling moment as the full - field theoretical power at the i - th power sampling moment;

[0055] When the correlation coefficient is less than the set coefficient threshold, determine the full - field theoretical power according to the fixed parameters of the equipment and the operating parameters of the equipment on the day to be recognized.

[0056] Preferably, the process of the first calculation module recording the larger value of the actual full - field power and the full - field initial theoretical power at the i - th power sampling moment as the full - field theoretical power at the i - th power sampling moment satisfies the following expression:

[0057]

[0058] Among them, P i Yll is the full - field theoretical power at the i - th power sampling moment, Pi qc is the actual power of the whole field at the i-th power sampling moment; P i ll is the initial theoretical power of the whole field at the i-th power sampling moment;

[0059] Preferably, the process by which the first calculation module determines the theoretical power of the whole field according to the fixed parameters of the device and the device operation parameters on the day to be identified satisfies the following expression:

[0060]

[0061] where K1 is the aging loss coefficient of the photovoltaic modules in the photovoltaic power station, and K2 is the dust shielding loss coefficient of the photovoltaic modules; η inv is the grid-connected inverter efficiency; P i yb is the actual power of the reference machine at the i-th power sampling moment; C qc is the installed capacity of the photovoltaic power station; C yb is the installed capacity of the reference machine in the photovoltaic power station.

[0062] Preferably, the second calculation module is specifically used for:

[0063] obtaining the weather cloud cover level of the day to be identified according to the daily power generation capacity and the maximum daily power generation capacity;

[0064] obtaining the cloud cover fluctuation level of the day to be identified according to the daily theoretical power fluctuation characteristics;

[0065] obtaining the weather type of the day to be identified according to the weather cloud cover level and the cloud cover fluctuation level.

[0066] Preferably, the daily power generation capacity in the second calculation module is represented by the average value of the theoretical power of the whole field on the day to be identified.

[0067] Preferably, the maximum daily power generation capacity in the second calculation module is represented by the daily maximum clear sky index, and the daily maximum clear sky index is determined according to the maximum value of the theoretical power of the whole field.

[0068] Preferably, the process of determining the daily maximum clear sky index according to the maximum value of the theoretical power of the whole field satisfies the following expression:

[0069]

[0070] where K d is the daily maximum clear sky index, P Yll (MAX) is the maximum value of the theoretical power of the whole field on the day to be identified, C qc is the installed capacity of the photovoltaic power station.

[0071] Preferably, the daily theoretical power fluctuation feature in the second calculation module is represented by the standard deviation of the second-order difference of the full-field theoretical power on the day to be identified, and the standard deviation is determined according to the second-order difference of the full-field theoretical power.

[0072] Preferably, the process of determining the standard deviation according to the second-order difference of the full-field theoretical power satisfies the following expression:

[0073]

[0074] where σ is the standard deviation, N is the number of samples of the full-field theoretical power with output greater than 0 within the day to be identified; Δ2P i Yll is the second-order difference of the full-field theoretical power at the i-th power sampling moment, where i ∈ [1, N - 2]; is the mean value of the second-order differences of the full-field theoretical power.

[0075] Based on the same inventive concept, the present invention also provides a computer device, including: one or more processors;

[0076] a memory for storing one or more programs;

[0077] When the one or more programs are executed by the one or more processors, the method for identifying the weather type of a photovoltaic power station based on theoretical power fluctuation as described above is implemented.

[0078] Based on the same inventive concept, the present invention also provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed, the method for identifying the weather type of a photovoltaic power station based on theoretical power fluctuation as described above is implemented.

[0079] Compared with the closest prior art, the beneficial effects of the present invention are as follows:

[0080] The present invention provides a method and system for identifying weather types of a photovoltaic power station based on theoretical power fluctuations, including obtaining the full-field theoretical power of the photovoltaic power station on the day to be identified based on the fixed parameters of the equipment of the photovoltaic power station and the operating parameters of the equipment on the day to be identified; calculating weather type identification indicators according to the full-field theoretical power; and identifying the weather type of the day to be identified according to the weather type identification indicators; wherein the weather type identification indicators include the daily power generation capacity, the daily maximum power generation capacity, and the daily theoretical power fluctuation characteristics of the photovoltaic power station; by calculating the full-field theoretical power of the photovoltaic power station, the corresponding weather type identification indicators are calculated, the influence of power curtailment, operation and maintenance, and equipment damage of the photovoltaic power station on the acquisition of the full-field theoretical power and the accuracy of subsequent weather type identification is eliminated, and the refined weather type of the day can be obtained without collecting meteorological data, so as to realize the identification of weather types in the case of missing meteorological data, provide a weather type basis for power prediction modeling, resource assessment, consumption analysis, etc., and support the improvement of the new energy prediction level and the new energy consumption capacity. BRIEF DESCRIPTION OF THE DRAWINGS

[0081] Figure 1 It is a schematic flow chart of a method for identifying weather types of a photovoltaic power station based on theoretical power fluctuations provided by the present invention;

[0082] Figure 2 It is a data verification diagram of a certain photovoltaic power station provided by the present invention for several days;

[0083] Figure 3 It is a schematic structural diagram of a system for identifying weather types of a photovoltaic power station based on theoretical power fluctuations provided by the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0084] The following further elaborates on the specific embodiments of the present invention with reference to the accompanying drawings.

[0085] Embodiment 1:

[0086] A method for identifying weather types of a photovoltaic power station based on theoretical power fluctuations provided by the present invention, as Figure 1 shown, includes:

[0087] S1. Based on the fixed parameters of the equipment of the photovoltaic power station and the operating parameters of the equipment on the day to be identified, obtain the full-field theoretical power of the photovoltaic power station on the day to be identified;

[0088] S2. Calculate weather type identification indicators according to the full-field theoretical power;

[0089] S3. Identify the weather type of the day to be identified according to the weather type identification indicators;

[0090] Among them, the weather type recognition indicators include the daily power generation capacity, the daily maximum power generation capacity, and the daily theoretical power fluctuation characteristics of the photovoltaic power station.

[0091] Step S1 specifically includes:

[0092] Obtain the initial theoretical power of the whole field according to the equipment fixed parameters and the equipment operation parameters;

[0093] Verify and check the initial theoretical power of the whole field with the actual power of the whole field on the day to be recognized, and obtain the theoretical power of the whole field;

[0094] Among them, the equipment fixed parameters include the installed capacity of the photovoltaic power station and the installed capacity of the sample machine, and the equipment operation parameters include the actual power of the sample machine and the actual power of the whole field of the photovoltaic power station.

[0095] In this embodiment, the equipment fixed parameters further include the longitude and latitude of the photovoltaic power station, and the equipment operation parameters further include the date of the day to be recognized, and the date format is YYYY-MM-DD.

[0096] Both the actual power of the sample machine and the actual power of the whole field are data for the whole day (0:00 - 23:45 BTC) every 15 minutes.

[0097] The day to be recognized is a historical date or the current day. When the day to be recognized is the current day, after 23:45, after the last power sampling of the current day is completed, the weather type recognition is carried out.

[0098] In order to remove the influence of power rationing, operation and maintenance, and equipment damage on the power of the whole field, the initial theoretical power of the whole field on the day to be recognized is used to replace the actual output of the whole field. The process of obtaining the initial theoretical power of the whole field according to the equipment fixed parameters and the equipment operation parameters satisfies the following formula:

[0099]

[0100] Among them, P i ll is the initial theoretical power of the whole field at the i-th power sampling moment; C qc is the installed capacity of the photovoltaic power station; C yb is the installed capacity of the sample machine of the photovoltaic power station; P i yb is the actual power of the sample machine at the i-th power sampling moment.

[0101] In this embodiment, the power sampling interval is 15 minutes, and the sampling time is 0:00 - 23:45 BTC of the day to be recognized, and BTC is the time of the eighth time zone east.

[0102] There may be certain deviations in the calculation of the initial theoretical power of the whole field. To reduce the deviations, the initial theoretical power of the whole field is verified here. First, the correlation coefficient between the initial theoretical power of the whole field and the actual power is calculated, and then the discrimination is carried out according to the situation of the correlation coefficient;

[0103] Verifying and checking the initial theoretical power of the whole field by using the actual power of the whole field on the day to be recognized to obtain the theoretical power of the whole field, including:

[0104] Calculating the correlation coefficient between the actual power of the whole field and the initial theoretical power of the whole field;

[0105] When the correlation coefficient is greater than or equal to the set coefficient threshold, the larger value of the actual power of the whole field and the initial theoretical power of the whole field at the i-th power sampling moment is recorded as the theoretical power of the whole field at the i-th power sampling moment;

[0106] When the correlation coefficient is less than the set coefficient threshold, the theoretical power of the whole field is determined according to the fixed parameters of the equipment and the operating parameters of the equipment on the day to be recognized.

[0107] Among them, the process of solving the correlation coefficient is the verification process, which is used to verify the correlation between the actual power of the whole field and the initial theoretical power of the whole field; the process of determining the final theoretical power of the whole field according to the correlation coefficient is the checking process, which removes the influence of power restriction, operation and maintenance, and equipment damage on the theoretical power of the whole field.

[0108] In this embodiment, the correlation coefficient is expressed as:

[0109]

[0110] Among them, r is the correlation coefficient, P i ll is the initial theoretical power of the whole field at the i-th power sampling moment, is the average value of the initial theoretical power of the whole field, P i qc is the actual power of the whole field at the i-th power sampling moment, is the average value of the actual power of the whole field, and n is the number of power samplings on the day to be recognized.

[0111] The process of recording the larger value of the actual power of the whole field and the initial theoretical power of the whole field at the i-th power sampling moment as the theoretical power of the whole field at the i-th power sampling moment satisfies the following expression:

[0112]

[0113] Among them, P i Yll is the theoretical power of the whole field at the i-th power sampling moment, Pi qc is the actual power of the whole field at the i-th power sampling moment; P i ll is the initial theoretical power of the whole field at the i-th power sampling moment;

[0114] The process of determining the theoretical power of the whole field according to the fixed parameters of the device and the device operation parameters on the day to be identified satisfies the following expression:

[0115]

[0116] where K1 is the aging loss coefficient of the photovoltaic modules in the photovoltaic power station, dimensionless, and decreases at a certain ratio every year;

[0117] Specifically, K1 = 1 - k × y a , where y a is the annual attenuation rate of different solar cell materials, based on the relevant attenuation rate parameters provided by the solar cell manufacturer, and k is the number of years since the grid-connected photovoltaic power station was put into use;

[0118] K2 is the dust occlusion loss coefficient of the photovoltaic modules, dimensionless, which can be obtained by querying the feasibility study report; η inv is the grid-connected inverter efficiency, dimensionless, and is equivalent using the European standard EN 50530; P i yb is the actual power of the prototype at the i-th power sampling moment; C qc is the installed capacity of the photovoltaic power station; C yb is the installed capacity of the prototype in the photovoltaic power station.

[0119] In this embodiment, the set coefficient threshold is exemplified as 0.35*.

[0120] In step S2, the daily power generation capacity is represented by the average value of the theoretical power of the whole field on the day to be identified, which is used to evaluate the daily power generation capacity of the photovoltaic power station.

[0121] In this embodiment, the average value of the theoretical power of the whole field is expressed as:

[0122]

[0123] where, is the average value of the theoretical power of the whole field, N is the number of samples of the theoretical power of the whole field with output greater than 0 on the day to be identified;

[0124] In step S2, the maximum daily power generation capacity is represented by the daily maximum clear sky index, which is used to evaluate the maximum daily power generation capacity of the photovoltaic power station, and the daily maximum clear sky index is determined according to the maximum value of the theoretical power of the whole field.

[0125] The process of determining the daily maximum clear sky index according to the maximum theoretical power of the whole field satisfies the following expression:

[0126]

[0127] where K d is the daily maximum clear sky index, and P Yll (MAX) is the maximum theoretical power of the whole field of the day to be identified, and C qc is the installed capacity of the photovoltaic power station.

[0128] In step S2, the daily theoretical power fluctuation characteristics are represented by the standard deviation of the second-order difference of the whole-field theoretical power of the day to be identified, which is used to evaluate the fluctuation characteristics of the whole-field theoretical power of the photovoltaic power station within a day. The standard deviation is determined according to the second-order difference of the whole-field theoretical power.

[0129] The process of determining the standard deviation according to the second-order difference of the whole-field theoretical power satisfies the following expression:

[0130]

[0131] where σ is the standard deviation, N is the number of samples of the whole-field theoretical power with output greater than 0 within the day to be identified; Δ2P i Yll is the second-order difference of the whole-field theoretical power at the i-th power sampling moment, where i ∈ [1, N - 2]; is the mean value of the second-order difference of the whole-field theoretical power.

[0132] Among them, before calculating the second-order difference, first calculate the first-order difference of the whole-field theoretical power, that is, data normalization processing, which is expressed as:

[0133]

[0134] where is the first-order difference of the whole-field theoretical power at the (N - 1)-th power sampling moment;

[0135] Subsequently, calculate the second-order difference according to the first-order difference, which is expressed as:

[0136]

[0137] where N is the number of samples of the whole-field theoretical power with output greater than 0 within the day to be identified; Δ2P i Yll is the second-order difference of the whole-field theoretical power at the i-th power sampling moment.

[0138] Step S3 specifically includes:

[0139] Obtain the weather cloud cover level of the day to be identified based on the daily power generation capacity and the daily maximum power generation capacity;

[0140] Obtain the cloud cover fluctuation level of the day to be identified based on the daily theoretical power fluctuation characteristics;

[0141] Based on the weather cloud cover level and the cloud cover fluctuation level, obtain the weather type of the day to be identified.

[0142] Calculate the daily maximum clear sky index K d , the average value of the full-field theoretical power and the standard deviation σ, then conduct weather type classification, query the weather type classification table to determine the weather type, and the weather type classification table is shown in Table 1;

[0143] Table 1 Weather type classification table

[0144]

[0145] In the table, the code column represents the weather cloud cover level, and the number column represents the cloud cover fluctuation level;

[0146] Based on the data statistics, the weather type criterion is obtained as follows:

[0147] A1 represents sunny day, cloudless;

[0148] A2-3 represents sunny day, few clouds;

[0149] A4-6 represents cloudy day, but sunny at noon;

[0150] B1 represents cloudy day, the sun is relatively obvious, and the clouds do not change much;

[0151] B2-3 represents cloudy day, the sun is not obvious, and the clouds change slowly;

[0152] B4-6 represents cloudy day, the sun is not obvious, and the clouds change quickly;

[0153] C1 represents cloudy day, the sun is not obvious, and the clouds do not change much;

[0154] C2-3 represents cloudy day, the sun is not obvious, and the clouds change slowly;

[0155] C4-6 represents cloudy day, the sun is not obvious, and the clouds change quickly;

[0156] D1 represents cloudy turning to overcast / cloudy day, the clouds do not change much;

[0157] D2-3 represents cloudy turning to overcast / cloudy day, the clouds change slowly;

[0158] D4-6 indicates cloudy turning to overcast / overcast, with relatively fast cloud changes;

[0159] E1-6 indicates rainy days.

[0160] In this embodiment, as Figure 2 shown, with the sampling time series as the abscissa and the full-field theoretical power as the ordinate, the verification using data from several whole days of a certain photovoltaic power station is as follows:

[0161] Figure 2 - A1 and A2 are characterized by high peak values, high integrated power, and low volatility, and usually correspond to sunny days;

[0162] Figure 2 - B1 is characterized by high peak values and relatively high integrated power, etc., and usually corresponds to combined weather conditions such as cloudy turning to sunny, sunny turning to cloudy, etc.;

[0163] Figure 2 - C3 is characterized by high peak values, low integrated power, and moderate volatility, and usually corresponds to mostly cloudy weather with occasional sunny spells;

[0164] Figure 2 - D4 is characterized by relatively high peak values and strong volatility, and usually corresponds to overcast days;

[0165] Figure 2 - E3 is characterized by relatively high peak values and strong volatility, and usually corresponds to rainy days.

[0166] Embodiment 2:

[0167] Based on the same inventive concept, the present invention also provides a photovoltaic power station weather type recognition system based on theoretical power fluctuations, as Figure 3 shown, including:

[0168] A first calculation module, configured to obtain the full-field theoretical power of the photovoltaic power station on the day to be recognized based on the fixed parameters of the equipment of the photovoltaic power station and the operating parameters of the equipment on the day to be recognized;

[0169] A second calculation module, configured to calculate a weather type recognition index according to the full-field theoretical power;

[0170] A weather type recognition module, configured to recognize the weather type of the day to be recognized according to the weather type recognition index;

[0171] Wherein, the weather type recognition index includes the daily power generation capacity, the daily maximum power generation capacity, and the daily theoretical power fluctuation characteristics of the photovoltaic power station.

[0172] The first calculation module is specifically configured to:

[0173] Obtain the initial theoretical power of the whole field based on the device fixed parameters and the device operation parameters;

[0174] Verify and check the initial theoretical power of the whole field with the actual power of the whole field on the day to be identified, and obtain the theoretical power of the whole field;

[0175] Among them, the device fixed parameters include the installed capacity of the photovoltaic power station and the installed capacity of the sample machine, and the device operation parameters include the actual power of the sample machine and the actual power of the whole field of the photovoltaic power station.

[0176] The process of obtaining the initial theoretical power of the whole field according to the device fixed parameters and the device operation parameters in the first calculation module satisfies the following formula:

[0177]

[0178] Among them, P i ll is the initial theoretical power of the whole field at the i-th power sampling moment; C qc is the installed capacity of the photovoltaic power station; C yb is the installed capacity of the sample machine of the photovoltaic power station; P i yb is the actual power of the sample machine at the i-th power sampling moment.

[0179] The process of verifying and checking the initial theoretical power of the whole field with the actual power of the whole field on the day to be identified in the first calculation module to obtain the theoretical power of the whole field includes:

[0180] Calculate the correlation coefficient between the actual power of the whole field and the initial theoretical power of the whole field;

[0181] When the correlation coefficient is greater than or equal to the set coefficient threshold, record the larger value of the actual power of the whole field and the initial theoretical power of the whole field at the i-th power sampling moment as the theoretical power of the whole field at the i-th power sampling moment;

[0182] When the correlation coefficient is less than the set coefficient threshold, determine the theoretical power of the whole field according to the device fixed parameters and the device operation parameters on the day to be identified.

[0183] The process of the first calculation module recording the larger value of the actual power of the whole field and the initial theoretical power of the whole field at the i-th power sampling moment as the theoretical power of the whole field at the i-th power sampling moment satisfies the following expression:

[0184]

[0185] Among them, P i Yllis the full - field theoretical power at the i - th power sampling moment, P i qc is the full - field actual power at the i - th power sampling moment; P i ll is the full - field initial theoretical power at the i - th power sampling moment;

[0186] The process of the first calculation module determining the full - field theoretical power according to the fixed parameters of the device and the device operation parameters on the day to be identified satisfies the following expression:

[0187]

[0188] where K1 is the aging loss coefficient of the photovoltaic modules in the photovoltaic power station, K2 is the dust shielding loss coefficient of the photovoltaic modules; η inv is the grid - connected inverter efficiency; P i yb is the actual power of the reference machine at the i - th power sampling moment; C qc is the installed capacity of the photovoltaic power station; C yb is the installed capacity of the reference machine in the photovoltaic power station.

[0189] The second calculation module is specifically used for:

[0190] Obtaining the weather cloud cover level of the day to be identified according to the daily power generation capacity and the daily maximum power generation capacity;

[0191] Obtaining the cloud cover fluctuation level of the day to be identified according to the daily theoretical power fluctuation characteristics;

[0192] Obtaining the weather type of the day to be identified according to the weather cloud cover level and the cloud cover fluctuation level.

[0193] The daily power generation capacity in the second calculation module is represented by the average value of the full - field theoretical power on the day to be identified.

[0194] The maximum daily power generation capacity in the second calculation module is represented by the daily maximum clear - sky index, and the daily maximum clear - sky index is determined according to the maximum value of the full - field theoretical power.

[0195] The process of determining the daily maximum clear - sky index according to the maximum value of the full - field theoretical power satisfies the following expression:

[0196]

[0197] where K d is the daily maximum clear - sky index, P Yll (MAX) is the maximum value of the full - field theoretical power on the day to be identified, C qc is the installed capacity of the photovoltaic power station.

[0198] In the second calculation module, the daily theoretical power fluctuation feature is represented by the standard deviation of the second-order difference of the full-field theoretical power on the day to be identified, and the standard deviation is determined according to the second-order difference of the full-field theoretical power.

[0199] The process of determining the standard deviation according to the second-order difference of the full-field theoretical power satisfies the following expression:

[0200]

[0201] where σ is the standard deviation, N is the number of samples of the full-field theoretical power with output greater than 0 within the day to be identified; Δ2P i Yll is the second-order difference of the full-field theoretical power at the i-th power sampling moment, where i ∈ [1, N - 2]; is the mean value of the second-order differences of the full-field theoretical power.

[0202] Embodiment 3:

[0203] Based on the same inventive concept, the present invention also provides a computer device, which includes a processor and a memory. The memory is used to store a computer program, and the computer program includes program instructions. The processor is used to execute the program instructions stored in the computer storage medium. The processor may be a Central Processing Unit (CPU), or may also be other general-purpose processors, Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field-Programmable Gate Arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. It is the computing core and control core of the terminal, and is suitable for implementing one or more instructions. Specifically, it is suitable for loading and executing one or more instructions in the computer storage medium to implement the corresponding method flow or corresponding function, so as to implement the steps of a method for identifying the weather type of a photovoltaic power station based on theoretical power fluctuation in the above embodiment.

[0204] Embodiment 4:

[0205] Based on the same inventive concept, the present invention also provides a storage medium, specifically a computer-readable storage medium (Memory). The computer-readable storage medium is a memory device in a computer device and is used to store programs and data. It can be understood that the computer-readable storage medium here can include both the built-in storage medium in the computer device and, of course, the extended storage medium supported by the computer device. The computer-readable storage medium provides a storage space, and the operating system of the terminal is stored in this storage space. Moreover, one or more instructions suitable for being loaded and executed by the processor are stored in this storage space, and these instructions can be one or more computer programs (including program codes). It should be noted that the computer-readable storage medium here can be a high-speed RAM memory or a non-volatile memory, such as at least one disk memory. One or more instructions stored in the computer-readable storage medium can be loaded and executed by the processor to implement the steps of a method for identifying weather types of a photovoltaic power station based on theoretical power fluctuations in the above embodiments.

[0206] Those skilled in the art should understand that the embodiments of the present invention can be provided as a method, a system, or a computer program product. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk memories, CD-ROMs, optical memories, etc.) containing computer-usable program codes.

[0207] The present invention is described with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products according to embodiments of the present invention. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, and the combination of flows and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, such that the instructions executed by the processor of the computer or other programmable data processing devices generate a device for implementing the specified functions in Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0208] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, such that the instructions stored in the computer-readable memory generate a manufactured article including an instruction device that implements the specified functions in Figure 1 one flow or multiple flows and / or blocksFigure 1 The functions specified in one or more boxes.

[0209] These computer program instructions can also be loaded onto a computer or other programmable data processing device, so that a series of operation steps are executed on the computer or other programmable device to generate a computer-implemented process. Thus, the instructions executed on the computer or other programmable device provide for implementing the steps of the functions specified in one or more processes and / or boxes Figure 1 One or more processes and / or boxes Figure 1 The steps of the functions specified in one or more boxes.

[0210] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit the scope of its protection. Although the present invention has been described in detail with reference to the above embodiments, those of ordinary skill in the art should understand that after reading the present invention, various changes, modifications or equivalent replacements can still be made to the specific implementation manners of the application. However, these changes, modifications or equivalent replacements are all within the scope of the protection of the claims of the present invention.

Claims

1. A method for identifying weather types of a photovoltaic power station based on theoretical power fluctuations, characterized in that, Including: Based on the fixed parameters of the equipment in the photovoltaic power station and the equipment operation parameters on the day to be identified, obtain the full-field theoretical power of the photovoltaic power station on the day to be identified; Calculate the weather type identification index according to the full-field theoretical power; Identify the weather type on the day to be identified according to the weather type identification index; Wherein, the weather type identification index includes the daily power generation capacity, the daily maximum power generation capacity and the daily theoretical power fluctuation characteristics of the photovoltaic power station.

2. The method according to claim 1, characterized in that, The obtaining of the full-field theoretical power of the photovoltaic power station on the day to be identified based on the fixed parameters of the equipment in the photovoltaic power station and the equipment operation parameters on the day to be identified includes: Obtain the full-field initial theoretical power according to the fixed parameters of the equipment and the operation parameters of the equipment; Verify and check the full-field initial theoretical power with the full-field actual power on the day to be identified to obtain the full-field theoretical power; Wherein, the fixed parameters of the equipment include the installed capacity of the photovoltaic power station and the installed capacity of the sample machine, and the operation parameters of the equipment include the actual power of the sample machine in the photovoltaic power station and the full-field actual power.

3. The method according to claim 2, characterized in that The process of obtaining the full-field initial theoretical power according to the fixed parameters of the equipment and the operation parameters of the equipment satisfies the following formula: Among them, P i ll is the initial theoretical power of the whole field at the i-th power sampling moment; C qc is the installed capacity of the photovoltaic power station; C yb is the installed capacity of the reference machine of the photovoltaic power station; P i yb is the actual power of the reference machine at the i-th power sampling moment.

4. The method according to claim 2 or 3, characterized in that, The verifying and checking the full-field initial theoretical power with the full-field actual power on the day to be identified to obtain the full-field theoretical power includes: Calculate the correlation coefficient between the full-field actual power and the full-field initial theoretical power; When the correlation coefficient is greater than or equal to the set coefficient threshold, record the larger value of the full-field actual power and the full-field initial theoretical power at the i-th power sampling moment as the full-field theoretical power at the i-th power sampling moment; When the correlation coefficient is less than the set coefficient threshold, determine the full-field theoretical power according to the fixed parameters of the equipment and the operation parameters of the equipment on the day to be identified.

5. The method according to claim 4, characterized in that The process of recording the larger value of the full-field actual power and the full-field initial theoretical power at the i-th power sampling moment as the full-field theoretical power at the i-th power sampling moment satisfies the following expression: Among them, P i Yll is the full-field theoretical power at the i-th power sampling moment, P i qc is the full-field actual power at the i-th power sampling moment; P i ll is the full-field initial theoretical power at the i-th power sampling moment.

6. The method according to claim 4, wherein The process of determining the full-field theoretical power according to the fixed parameters of the equipment and the operation parameters of the equipment on the day to be identified satisfies the following expression: Among them, K1 is the aging loss coefficient of the photovoltaic modules of the photovoltaic power station, and K2 is the dust shielding loss coefficient of the photovoltaic modules; η inv is the grid-connected inverter efficiency; P i yb is the actual power of the prototype at the i-th power sampling moment; C qc is the installed capacity of the photovoltaic power station; C yb is the installed capacity of the prototype of the photovoltaic power station.

7. The method according to claim 1, characterized in that, The identifying the weather type on the day to be identified according to the weather type identification parameter includes: Obtain the weather cloud amount level on the day to be identified according to the daily power generation capacity and the daily maximum power generation capacity; Obtain the cloud amount fluctuation level on the day to be identified according to the daily theoretical power fluctuation characteristics; Obtain the weather type on the day to be identified according to the weather cloud amount level and the cloud amount fluctuation level.

8. The method according to claim 1 or 7, characterized in that, The daily power generation capacity is represented by the average value of the full-field theoretical power on the day to be identified.

9. The method according to claim 1 or 7, characterized in that, The maximum daily power generation capacity is represented by the daily maximum clear sky index, and the daily maximum clear sky index is determined according to the maximum value of the full-field theoretical power.

10. The method according to claim 9, wherein The process of determining the daily maximum clear sky index according to the maximum value of the full-field theoretical power satisfies the following expression: Among them, K d is the daily maximum clear sky index, and P Yll (MAX) is the maximum theoretical power of the whole field for the day to be identified, and C qc is the installed capacity of the photovoltaic power station.

11. The method according to claim 1 or 7, characterized in that, The daily theoretical power fluctuation characteristics are represented by the standard deviation of the second-order difference of the full-field theoretical power on the day to be identified, and the standard deviation is determined according to the second-order difference of the full-field theoretical power.

12. The method according to claim 11, wherein The process of determining the standard deviation according to the second-order difference of the full-field theoretical power satisfies the following expression: Where, σ is the standard deviation, and N is the number of samples of the theoretical power of the whole field with output greater than 0 on the day to be recognized; Δ2P i Yll is the second-order difference of the theoretical power of the whole field at the i-th power sampling moment, where i ∈ [1, N - 2]; is the mean value of the second-order differences of the theoretical power of the whole field.

13. A photovoltaic power station weather type recognition system based on theoretical power fluctuation, characterized in that Including: A first calculation module, configured to obtain the full-field theoretical power of the photovoltaic power station on the day to be identified based on the fixed parameters of the equipment of the photovoltaic power station and the operating parameters of the equipment on the day to be identified; A second calculation module, configured to calculate a weather type identification index according to the full-field theoretical power; A weather type identification module, configured to identify the weather type of the day to be identified according to the weather type identification index; Wherein, the weather type identification index includes the daily power generation capacity, the daily maximum power generation capacity and the daily theoretical power fluctuation characteristics of the photovoltaic power station.

14. A computer device, characterized in that, Including: One or more processors; A memory, configured to store one or more programs; When the one or more programs are executed by the one or more processors, the method for identifying the weather type of a photovoltaic power station based on theoretical power fluctuation according to any one of claims 1 to 12 is implemented.

15. A computer-readable storage medium, characterized in that, There is a computer program stored thereon, and when the computer program is executed, the method for identifying the weather type of a photovoltaic power station based on theoretical power fluctuation according to any one of claims 1 to 12 is implemented.

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