A photovoltaic power generation prediction and analysis system

Abstract

The present invention relates to the technical field of photovoltaic power generation prediction technology, and specifically discloses a photovoltaic power generation prediction and analysis system. The system is provided with a photovoltaic power station operation analysis module, a photovoltaic power station usage analysis module, a state signal matching module, an on-site meteorological feature acquisition module, an initial power prediction module, a photovoltaic power generation prediction power acquisition module, and a power system stability assessment module. The present invention solves the problem that traditional prediction models often assume that photovoltaic components and environmental conditions are in an ideal state, ignore many complex factors in actual conditions, and have imperfect feature extraction, resulting in insufficient real-time prediction capabilities and poor dynamic adaptability of photovoltaic power generation. The system effectively improves the accuracy of the prediction. By analyzing various influencing factors and determining the error power, the initial prediction can be corrected, so that the final prediction result is more consistent with the actual situation.

Description

Technical Field

[0001] The present invention relates to the technical field of photovoltaic power generation prediction, and in particular to a photovoltaic power generation prediction and analysis system. Background Art

[0002] As awareness of the limitations and environmental impacts of traditional fossil fuels deepens, countries around the world are actively seeking renewable energy alternatives. Solar energy, as a clean, renewable, and virtually inexhaustible energy resource, plays a key role in energy transition. The installed capacity of photovoltaic power generation is growing rapidly worldwide. Photovoltaic power generation output primarily depends on sunlight intensity, which is affected by a variety of factors. As the proportion of photovoltaic power generation in the power grid continues to increase, the uncertainty of its output poses a significant challenge to grid power balance. The power grid must maintain a real-time balance between power generation and consumption. Excessive photovoltaic power generation can lead to problems such as increased grid voltage and frequency fluctuations. To achieve economical and efficient operation of the power system, optimized scheduling of various power generation resources, including photovoltaic power generation, is necessary. Accurate photovoltaic power generation forecasts can help grid dispatchers plan the generation of other conventional power sources in advance and rationally allocate energy storage resources.

[0003] Nowadays, there are still some deficiencies in the research on the prediction and analysis of photovoltaic power generation. Specifically, traditional prediction models often assume that photovoltaic components and environmental conditions are in ideal conditions, ignoring many complex factors in actual situations. Feature extraction is imperfect, resulting in insufficient real-time prediction capabilities and poor dynamic adaptability of photovoltaic power generation. Summary of the Invention

[0004] In view of the shortcomings of the prior art, the present invention provides a photovoltaic power generation power prediction and analysis system, which can effectively solve the problems involved in the above-mentioned background technology.

[0005] To achieve the above objectives, the present invention is implemented through the following technical solutions: a photovoltaic power generation power prediction and analysis system, including a photovoltaic power station operation analysis module, a photovoltaic power station usage analysis module, a state signal matching module, an on-site meteorological feature acquisition module, an initial power prediction module, a photovoltaic power generation prediction power acquisition module and an electric power system stability assessment module, wherein: the photovoltaic power station operation analysis module is used to obtain photovoltaic power station operation status data, construct a photovoltaic power station operation status model, and output photovoltaic power station operation factors; the photovoltaic power station usage analysis module is used to analyze the photovoltaic power station usage status and obtain the photovoltaic power station usage factor; the state signal matching module is used to match the photovoltaic power station status signal based on the photovoltaic power station operation factor and the photovoltaic power station usage factor; the on-site meteorological feature acquisition module is used to collect photovoltaic power The on-site meteorological data of the photovoltaic power station is analyzed to obtain the on-site meteorological characteristics of the photovoltaic power station; the initial power prediction module is used to obtain the initial photovoltaic power generation prediction power based on the photovoltaic power station status signal and the on-site meteorological characteristics of the photovoltaic power station using the trained linear regression model; the photovoltaic power generation prediction power acquisition module is used to collect the power system operation status data, analyze the power system operation status signal, determine the photovoltaic power generation prediction error power, and obtain the photovoltaic power generation prediction power in combination with the initial photovoltaic power generation prediction power; the power system stability assessment module is used to assess the stability of the power system based on the photovoltaic power station operation status data, the on-site meteorological data of the photovoltaic power station, the photovoltaic power station aging factor and the power system operation status data after the photovoltaic power generation prediction power acquisition module obtains the photovoltaic power generation prediction power, and issue an early warning if the power system is unstable.

[0006] As a further solution, we obtain the operating status data of the photovoltaic power station, build the operating status model of the photovoltaic power station, and output the operating factors of the photovoltaic power station. The specific analysis process includes: installing power sensors at the output ends of each inverter of the photovoltaic power station, adding the output power of each inverter, and obtaining the overall power output P of the photovoltaic power station. total ;

[0007] Install a power sensor on the DC input side of the inverter to obtain the input DC power P dc , install a power sensor at the inverter output to obtain the output AC power P ac , calculate the inverter efficiency η inv :

[0008]

[0009] Measure the current and voltage of each photovoltaic module, calculate the power of each photovoltaic module, and find the maximum power P of each photovoltaic module. max and minimum power P min ; Based on the maximum power and minimum power of each photovoltaic module, calculate the photovoltaic module mismatch rate signal Msp :

[0010]

[0011] Where P0 is the reference power of the photovoltaic module stored in the database, and e is a natural constant;

[0012] The PV power station operating status data specifically includes the overall power output of the PV power station, inverter efficiency, and PV module mismatch rate signals. Based on the acquired PV power station operating status data, a PV power station operating status model is constructed, and the PV power station operating factors are output. The PV power station operating factors serve as the analysis basis for matching the PV power station status signals.

[0013] The specific analysis process of the photovoltaic power station operation status model is as follows:

[0014]

[0015] Where Gf yx It is the output of the photovoltaic power station operation status model, that is, the photovoltaic power station operation factor.

[0016] As a further solution, the use status of the photovoltaic power station is analyzed to obtain the photovoltaic power station use factor. The specific analysis process is: obtain the normal power generation time T of the photovoltaic power station during the statistical period can And the total duration of the statistical period T total , calculate the availability rate of photovoltaic power station T use :

[0017]

[0018] Get the running time T N Number of failures in the photovoltaic power station N qus , calculate the failure rate η of the photovoltaic power station gz :

[0019]

[0020] Analyze the reliability factor K of photovoltaic power station based on the availability rate and failure rate of photovoltaic power station g :

[0021]

[0022] Get the initial average maximum power point power Pcs of each photovoltaic module max And the current average maximum power point power Pdq of each photovoltaic module max , calculate the aging degree Lh of photovoltaic modules:

[0023]

[0024] Where, e is a natural constant;

[0025] Get the total number of years Dz that the photovoltaic power station has been put into use n ;

[0026] Calculate the aging factor L of the photovoltaic power station based on the aging degree of the photovoltaic components and the total number of years the photovoltaic power station has been in use. g :

[0027]

[0028] Based on the reliability factor and aging factor of the photovoltaic power station, a comprehensive analysis is conducted to obtain the photovoltaic power station utilization factor, which is used as the analysis basis for matching the photovoltaic power station status signal.

[0029] The specific analysis process of photovoltaic power station usage factors is as follows:

[0030]

[0031] Where Gf sy is the photovoltaic power station utilization factor, A1 is the set photovoltaic power station reliability factor K g A2 is the compensation factor of the photovoltaic power station, and L is the set aging factor of the photovoltaic power station. g compensation factor.

[0032] As a further solution, the photovoltaic power station status signal is matched based on the photovoltaic power station operation factor and the photovoltaic power station usage factor. The specific analysis process is: the photovoltaic power station operation factor and the photovoltaic power station usage factor are stored as specified tags; the specified tag-photovoltaic power station status signal mapping table pre-stored in the database is obtained, and the matching photovoltaic power station status signal is found according to the specified tag by searching the mapping table.

[0033] As a further solution, the on-site meteorological data of the photovoltaic power station is collected and analyzed to obtain the on-site meteorological characteristics of the photovoltaic power station. The specific analysis process is: obtaining the on-site meteorological data of the photovoltaic power station, which specifically includes the maximum light intensity at the photovoltaic power station, the average light intensity at the photovoltaic power station, the average temperature at the photovoltaic power station, and the average wind speed at the photovoltaic power station; based on the obtained on-site meteorological data of the photovoltaic power station, a comprehensive analysis is performed to obtain the on-site meteorological characteristics of the photovoltaic power station, and the on-site meteorological characteristics of the photovoltaic power station are used as the analysis basis for obtaining the initial photovoltaic power generation forecast power.

[0034] As a further solution, a trained linear regression model is used to obtain the initial photovoltaic power generation prediction based on the photovoltaic power station status signal and the on-site meteorological characteristics of the photovoltaic power station. The specific analysis process is as follows: the photovoltaic power station status signal and the on-site meteorological characteristics of the photovoltaic power station are input into the trained linear regression model;

[0035] The trained linear regression model is expressed as:

[0036] y=g0+g1*Jxr+g2*Qx t +b;

[0037] Where y is the initial photovoltaic power generation prediction power index, g0 is the intercept, g1 is the first slope, g2 is the second slope, Jxr is the photovoltaic power station status signal, Qx t is the on-site meteorological characteristics of the photovoltaic power station, b is the error term of photovoltaic power generation prediction;

[0038] Output the initial photovoltaic power generation prediction power index; obtain the initial photovoltaic power generation prediction power index-initial photovoltaic power generation prediction power mapping table pre-stored in the database, and find the matching initial photovoltaic power generation prediction power according to the initial photovoltaic power generation prediction power index by searching the mapping table.

[0039] As a further solution, the power system operation status data is collected, the power system operation status signal is obtained by analysis, the photovoltaic power generation prediction error power is determined, and the photovoltaic power generation prediction power is obtained by combining the initial photovoltaic power generation prediction power. The specific analysis process is: collecting the power system operation status data, the power system operation status data specifically includes the power system short-circuit capacity, the power system power factor, and the power system operating power; based on the collected power system operation status data, the power system operation status signal is analyzed to obtain the power system operation status signal, and the power system operation status signal is used as the analysis basis for determining the photovoltaic power generation prediction error power; obtaining the power system operation status signal-photovoltaic power generation prediction error power mapping table pre-stored in the database, and by searching the mapping table, according to the power system operation status signal, finding the matching photovoltaic power generation prediction error power; the initial photovoltaic power generation prediction power and the photovoltaic power generation prediction error power are accumulated to obtain the photovoltaic power generation prediction power.

[0040] As a further solution, the power system operation status signal, the specific analysis process is as follows:

[0041]

[0042] Where, Dl xi is the power system operating status signal, dlr is the power system short-circuit capacity, D ys is the power factor of the power system, Yx is the operating power of the power system, dlr0 is the defined short-circuit capacity of the power system stored in the database, Yx0 is the defined operating power of the power system stored in the database, and e is a natural constant.

[0043] As a further option:

[0044] The power system stability assessment is based on the PV power station operating status data, PV power station on-site meteorological data, PV power station aging factors, and power system operating status data, including the following steps:

[0045] The photovoltaic power station operation status data, photovoltaic power station on-site meteorological data, photovoltaic power station aging factor and power system operation status data are standardized respectively to obtain the standardized photovoltaic power station operation status data, standardized photovoltaic power station on-site meteorological data, standardized photovoltaic power station aging factor P en and standardized power system operating status data, including:

[0046] The standardized operating status data of the photovoltaic power station includes the standardized overall power output P of the photovoltaic power station. tn , Normalized inverter efficiency η tn , standardized photovoltaic module mismatch rate signal M tn ;

[0047] The standardized on-site meteorological data of the photovoltaic power station includes the standardized maximum light intensity I an , the average light intensity of the photovoltaic power station after standardization I mn , the average temperature of the photovoltaic power station after standardization T n , the average wind speed v at the photovoltaic power station site after standardization tn ;

[0048] The standardized power system operating status data includes the standardized power system short-circuit capacity S sn , Standardized power system power factor PF n , Standardized power system operating power P on ;

[0049] Based on the converted and standardized photovoltaic power station operating status data, standardized photovoltaic power station on-site meteorological data, and standardized photovoltaic power station aging factor P en The power system stability assessment factor S is calculated by combining the standardized power system operation status data in ;

[0050] If the power system stability assessment factor is greater than a threshold value of the stability assessment factor stored in the database, the power system is unstable.

[0051] As a further solution, the power system stability assessment factor S in The method to obtain is as follows:

[0052]

[0053] Among them, F1, F2, F3 and F4 are all transit functions, and a1 is (1-P tn) weight factor, a2 is (1-η tn ), a3 is the weight factor of M tn The weight factor, b1 is (1-I an ) weight factor, b2 is (1-I mn ), b3 is the weight factor of T n The weight factor, b4 is (1-v tn ), c1 is the weight factor of The weight factor, c2 is (1-PF n ), c3 is the weight factor of The weight factor of , e is a natural constant.

[0054] Compared with the prior art, the embodiments of the present invention have at least the following advantages or beneficial effects:

[0055] (1) The present invention provides a photovoltaic power generation prediction and analysis system that considers factors affecting photovoltaic power generation from multiple perspectives. The photovoltaic power station operation analysis module and the usage analysis module respectively obtain key factors based on the operation status and usage status of the power station itself, providing a solid data foundation for subsequent power prediction. Photovoltaic power generation is affected by both meteorological conditions and grid access conditions. Through the on-site meteorological feature acquisition module and the photovoltaic power generation prediction power acquisition module, external environmental factors and grid factors can be incorporated into the prediction system, effectively improving the accuracy of the prediction.

[0056] (2) The present invention collects power system operation status data, analyzes the power system operation status signal, determines the photovoltaic power generation prediction error power, and combines it with the initial photovoltaic power generation prediction power to obtain the photovoltaic power generation prediction power. The photovoltaic power generation prediction power acquisition module determines the photovoltaic power generation prediction error power by analyzing the power system operation status data, and then combines it with the initial prediction power to obtain the final photovoltaic power generation prediction power, which can effectively improve the prediction accuracy. Since the power system operation status may cause the actual output power of the photovoltaic power station to deviate from the initial prediction power, by analyzing these factors and determining the error power, the initial prediction can be corrected to make the final prediction result more consistent with the actual situation. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] The present invention is further described with reference to the accompanying drawings. However, the embodiments in the accompanying drawings do not constitute any limitation to the present invention. A person skilled in the art can obtain other drawings based on the following drawings without creative effort.

[0058] Figure 1 This is a schematic diagram of system module connections of the present invention.

[0059] Figure 2The present invention determines the photovoltaic power generation prediction error power and combines it with the initial photovoltaic power generation prediction power to obtain the photovoltaic power generation prediction power. DETAILED DESCRIPTION

[0060] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0061] See also Figure 1 An embodiment of the present invention provides a technical solution for predicting and analyzing photovoltaic power generation power: a photovoltaic power generation power prediction and analysis system, including a photovoltaic power station operation analysis module, a photovoltaic power station usage analysis module, a state signal matching module, an on-site meteorological feature acquisition module, an initial power prediction module, a photovoltaic power generation prediction power acquisition module and a power system stability assessment module.

[0062] The photovoltaic power station operation analysis module is used to obtain photovoltaic power station operation status data, build a photovoltaic power station operation status model, and output photovoltaic power station operation factors.

[0063] The specific analysis process includes: installing power sensors at the output terminals of each inverter in the photovoltaic power station, adding up the output power of each inverter, and obtaining the overall power output P of the photovoltaic power station. total ;

[0064] Install a power sensor on the DC input side of the inverter to obtain the input DC power P dc , install a power sensor at the inverter output to obtain the output AC power P ac , calculate the inverter efficiency η inv :

[0065]

[0066] Measure the current and voltage of each photovoltaic module, calculate the power of each photovoltaic module, and find the maximum power P of each photovoltaic module. max and minimum power P min ; Based on the maximum power and minimum power of each photovoltaic module, calculate the photovoltaic module mismatch rate signal M sp :

[0067]

[0068] Where P0 is the reference power of the photovoltaic module stored in the database, and e is a natural constant;

[0069] The operating status data of the photovoltaic power station specifically includes the overall power output of the photovoltaic power station, inverter efficiency, and photovoltaic module mismatch rate signals; based on the acquired photovoltaic power station operating status data, a photovoltaic power station operating status model is constructed, and the photovoltaic power station operating factors are output. The photovoltaic power station operating factors serve as the analysis basis for matching the photovoltaic power station status signals.

[0070] The specific analysis process of the photovoltaic power station operation status model is as follows:

[0071]

[0072] Where Gf yx It is the output of the photovoltaic power station operation status model, that is, the photovoltaic power station operation factor.

[0073] By installing power sensors at the output of each inverter in a PV power plant to measure overall power output, installing power sensors on the DC input and AC output sides of the inverters to measure input DC power and output AC power, and measuring the current and voltage of each PV module, we can comprehensively and meticulously collect key data from the PV plant's operation. This data, encompassing power information from the plant as a whole to the inverters and individual modules, provides a rich foundation for subsequent in-depth analysis, avoiding a one-sided understanding of the plant's operating status due to missing data.

[0074] A PV power plant operating status model is constructed based on acquired operating status data, including overall PV plant power output, inverter efficiency, and PV module mismatch rate signals. This model, which includes the PV plant operating factor, comprehensively considers multiple key operating parameters to more comprehensively and accurately reflect the actual operating status of the PV plant. Compared to single-parameter analysis, this comprehensive model avoids misjudgments of plant operating status due to the limitations of a single parameter, providing a more reliable basis for subsequent analysis and decision-making.

[0075] The photovoltaic power station usage analysis module is used to analyze the usage status of the photovoltaic power station and obtain the photovoltaic power station usage factor.

[0076] The specific analysis process is: obtain the normal power generation time T of the photovoltaic power station within the statistical period can And the total duration of the statistical period T total , calculate the availability rate of photovoltaic power station T use :

[0077]

[0078] Get the running time T N Number of failures in the photovoltaic power station N qus , calculate the failure rate η of the photovoltaic power station gz :

[0079]

[0080] Analyze the reliability factor K of photovoltaic power station based on the availability rate and failure rate of photovoltaic power station g :

[0081]

[0082] Get the initial average maximum power point power Pcs of each photovoltaic module max And the current average maximum power point power Pdq of each photovoltaic module max , calculate the aging degree Lh of photovoltaic modules:

[0083]

[0084] Get the total number of years Dz that the photovoltaic power station has been put into use n ;

[0085] Calculate the aging factor L of the photovoltaic power station based on the aging degree of the photovoltaic components and the total number of years the photovoltaic power station has been in use. g :

[0086]

[0087] Based on the reliability factor and aging factor of the photovoltaic power station, a comprehensive analysis is conducted to obtain the photovoltaic power station utilization factor, which is used as the analysis basis for matching the photovoltaic power station status signal.

[0088] The specific analysis process of photovoltaic power station usage factors is as follows:

[0089]

[0090] Where Gf sy is the photovoltaic power station utilization factor, A1 is the set K g The compensation factor, A2 is the set L g compensation factor.

[0091] By calculating the availability and failure rate of a PV power plant, we can clearly understand the actual operating time and failure rate of the PV plant during the statistical period. The availability rate directly reflects the proportion of time the plant is able to generate electricity normally, while the failure rate reflects the stability and reliability of the plant's operation. These quantitative indicators provide a clear basis for evaluating the operational status of a PV power plant, allowing operation and maintenance personnel to quickly understand the overall operation of the plant and promptly identify potential problems.

[0092] The reliability factor obtained based on the analysis of availability and failure rate comprehensively considers the operation time and failure frequency of the power station, and more comprehensively reflects the long-term reliability of the photovoltaic power station.

[0093] The resulting PV plant utilization factor combines the reliability factor and aging factor, and is adjusted using a predefined compensation factor, providing a comprehensive and integrated assessment of the PV plant's operational status. This factor serves as an important analytical basis for matching PV plant status signals. Within the entire PV power generation prediction and analysis system, combined with data and analysis results from other modules (such as the operation analysis module and the status signal matching module), it enables more accurate prediction of PV power generation and optimizes plant operation and management.

[0094] It should be noted that all the above compensation factors are obtained through a mapping set of historical data and compensation factors established in a database, that is, the corresponding compensation factors are obtained according to the current data.

[0095] The status signal matching module is used to match the photovoltaic power station status signal based on the photovoltaic power station operation factor and the photovoltaic power station usage factor.

[0096] The specific analysis process is as follows: storing the photovoltaic power station operation factor and the photovoltaic power station usage factor as specified tags; obtaining the specified tag-photovoltaic power station status signal mapping table pre-stored in the database, and finding the matching photovoltaic power station status signal according to the specified tag by searching the mapping table.

[0097] Storing PV plant operating and utilization factors as designated tags achieves data standardization and normalization. By accessing a pre-stored database mapping table of designated tags and PV plant status signals, matching PV plant status signals can be quickly found based on the stored designated tags. This avoids complex real-time calculation and analysis processes, significantly improving the speed of status signal matching and enabling timely response to changes in PV plant operating status, providing timely and accurate information support for subsequent decision-making and control.

[0098] The on-site meteorological characteristics acquisition module is used to collect on-site meteorological data of the photovoltaic power station and analyze the on-site meteorological characteristics of the photovoltaic power station.

[0099] The specific analysis process is as follows: obtaining the on-site meteorological data of the photovoltaic power station, which specifically includes the maximum light intensity at the photovoltaic power station, the average light intensity at the photovoltaic power station, the average temperature at the photovoltaic power station, and the average wind speed at the photovoltaic power station; based on the obtained on-site meteorological data of the photovoltaic power station, a comprehensive analysis is performed to obtain the on-site meteorological characteristics of the photovoltaic power station, and the on-site meteorological characteristics of the photovoltaic power station are used as the analysis basis for obtaining the initial photovoltaic power generation forecast power.

[0100] The specific calculation formula for the on-site meteorological characteristics of the photovoltaic power station is:

[0101]

[0102] Where, Qx tis the on-site meteorological characteristics of the photovoltaic power station, gz max is the highest light intensity at the photovoltaic power station site, gz pj is the average light intensity at the photovoltaic power station site, Jwd is the average temperature at the photovoltaic power station site, and Fsd is the average wind speed at the photovoltaic power station site.

[0103] We collect a variety of meteorological data from photovoltaic power station sites, including peak and average light intensity, average temperature, and average wind speed. This data covers the main meteorological factors affecting photovoltaic power generation, providing a comprehensive and detailed picture of on-site meteorological conditions. For example, light intensity directly determines the amount of light energy that photovoltaic modules can receive and is a key driver of power generation. Temperature affects the photovoltaic conversion efficiency of photovoltaic modules, with both high and low temperatures leading to a decrease in efficiency. Wind speed can affect the heat dissipation of photovoltaic modules and the accumulation of dust on the module surfaces, indirectly affecting power generation. Collecting this multi-dimensional meteorological data provides a rich information foundation for accurately predicting photovoltaic power generation.

[0104] The on-site meteorological characteristics of photovoltaic power plants serve as the analytical basis for the initial photovoltaic power generation forecast, clarifying the close connection between meteorological conditions and power generation forecasts. Since photovoltaic power generation is closely related to meteorological factors, incorporating on-site meteorological characteristics into the forecast model can make the forecast results more realistic, adapt to different meteorological conditions, and improve the accuracy and reliability of the forecast.

[0105] The initial power prediction module is used to obtain the initial photovoltaic power generation prediction power based on the photovoltaic power station status signal and the on-site meteorological characteristics of the photovoltaic power station using a trained linear regression model.

[0106] The specific analysis process is as follows: inputting the PV power station status signal and the PV power station on-site meteorological characteristics into the trained linear regression model;

[0107] The trained linear regression model can be expressed as:

[0108] y=g0+g1*Jxr+g2*Qx t +b;

[0109] Where y is the initial photovoltaic power generation prediction power index, g0 is the intercept, g1 is the first slope, g2 is the second slope, Jxr is the photovoltaic power station status signal, Qx t is the on-site meteorological characteristics of the photovoltaic power station, b is the error term of photovoltaic power generation prediction;

[0110] Output the initial photovoltaic power generation prediction power index; obtain the initial photovoltaic power generation prediction power index-initial photovoltaic power generation prediction power mapping table pre-stored in the database, and find the matching initial photovoltaic power generation prediction power according to the initial photovoltaic power generation prediction power index by searching the mapping table.

[0111] The prediction model uses both PV power station status signals and on-site meteorological characteristics as input, accounting for both internal and external factors affecting PV power generation. PV power station status signals reflect the operation and usage of the power station itself, such as the health and aging of the equipment; on-site meteorological characteristics encompass key meteorological factors such as light intensity, temperature, and wind speed. By incorporating these two factors into the prediction model, a more comprehensive and accurate prediction of PV power generation is possible. Even under the same meteorological conditions, PV power stations in different operating states will produce different power generation. The model integrates these factors to produce more realistic predictions.

[0112] By inputting PV power station status signals and on-site meteorological characteristics into a trained linear regression model, an initial PV power generation forecast indicator can be quickly output. This efficient forecasting method can meet application scenarios with high real-time requirements. By obtaining a pre-stored initial PV power generation forecast indicator-initial PV power forecast mapping table in the database, the matching initial PV power generation forecast indicator can be quickly found, further simplifying the process of obtaining forecast results.

[0113] The photovoltaic power generation prediction power acquisition module is used to collect power system operation status data, analyze and obtain power system operation status signals, determine the photovoltaic power generation prediction error power, and combine it with the initial photovoltaic power generation prediction power to obtain the photovoltaic power generation prediction power.

[0114] like Figure 2 As shown, the specific analysis process is: collecting power system operation status data, which specifically includes power system short-circuit capacity, power system power factor, and power system operating power; based on the collected power system operation status data, analyzing to obtain a power system operation status signal, which is used as an analysis basis for determining the photovoltaic power generation prediction error power; obtaining a power system operation status signal-photovoltaic power generation prediction error power mapping table pre-stored in the database, and finding a matching photovoltaic power generation prediction error power according to the power system operation status signal by searching the mapping table; accumulating the initial photovoltaic power generation prediction power and the photovoltaic power generation prediction error power to obtain the photovoltaic power generation prediction power.

[0115] The specific analysis process of the power system operation status signal is as follows:

[0116]

[0117] Where, Dl xi is the power system operating status signal, dlr is the power system short-circuit capacity, D ysis the power factor of the power system, Yx is the operating power of the power system, dlr0 is the defined short-circuit capacity of the power system stored in the database, and Yx0 is the defined operating power of the power system stored in the database.

[0118] Collect data on power system operation, including short-circuit capacity, power factor, and operating power, as these factors significantly impact photovoltaic power generation. The power system's short-circuit capacity affects the grid's ability to accommodate photovoltaic power generation; a low short-circuit capacity can limit the output power of a photovoltaic power station. The power factor reflects the power quality of the power system, and its variations indirectly affect the operating efficiency and power output of a photovoltaic power station. Operating power reflects the load on the grid and interacts with the power output of a photovoltaic power station. By comprehensively considering these power system operation data, a more comprehensive analysis of the interaction between photovoltaic power generation and the grid can be achieved, thereby improving forecast accuracy.

[0119] Based on the collected power system operation status data, the power system operation status signal is analyzed and the photovoltaic power generation prediction error power is determined by searching the pre-stored mapping table. The prediction error can be accurately quantified according to the actual operation of the power system.

[0120] Based on the power system's operating conditions and predicted power, power plants can rationally plan their power generation, increasing generation during periods when the power system can better accommodate photovoltaic power and adjusting output appropriately during periods when power is restricted, thus avoiding economic losses caused by power constraints. This also helps stabilize the interaction between the photovoltaic power plant and the grid, reducing the impact of power fluctuations on the grid and the power plant's own equipment, and improving the plant's operational stability and reliability.

[0121] The power system stability assessment module is used to assess the stability of the power system based on the operating status data of the photovoltaic power station, the on-site meteorological data of the photovoltaic power station, the aging factor of the photovoltaic power station and the operating status data of the power system after the photovoltaic power generation prediction power acquisition module obtains the photovoltaic power generation prediction power. If the power system is unstable, an early warning will be issued.

[0122] The photovoltaic power station operation status data, photovoltaic power station on-site meteorological data, photovoltaic power station aging factor and power system operation status data are standardized respectively to obtain the standardized photovoltaic power station operation status data, standardized photovoltaic power station on-site meteorological data, standardized photovoltaic power station aging factor P en and standardized power system operating status data, including:

[0123] The standardized operating status data of the photovoltaic power station includes the standardized overall power output P of the photovoltaic power station. tn , Normalized inverter efficiency η tn , standardized photovoltaic module mismatch rate signal M tn;

[0124] The standardized on-site meteorological data of the photovoltaic power station includes the standardized maximum light intensity I an , the average light intensity of the photovoltaic power station after standardization I mn , the average temperature of the photovoltaic power station after standardization T n , the average wind speed v at the photovoltaic power station site after standardization tn ;

[0125] The standardized power system operating status data includes the standardized power system short-circuit capacity S sn , Standardized power system power factor PF n , Standardized power system operating power P on ;

[0126] Based on the converted and standardized photovoltaic power station operating status data, standardized photovoltaic power station on-site meteorological data, and standardized photovoltaic power station aging factor P en The power system stability assessment factor S is calculated by combining the standardized power system operation status data in ;

[0127] If the power system stability assessment factor is greater than a threshold value of the stability assessment factor stored in the database, the power system is unstable.

[0128] Power system stability assessment factor S in The method to obtain is as follows:

[0129]

[0130] Among them, F1, F2, F3 and F4 are all transit functions, and a1 is (1-P tn ) weight factor, a2 is (1-η tn ), a3 is the weight factor of M tn The weight factor, b1 is (1-I an ) weight factor, b2 is (1-I mn ), b3 is the weight factor of T n The weight factor, b4 is (1-v tn ), c1 is the weight factor of The weight factor, c2 is (1-PF n ), c3 is the weight factor of The weight factor of .

[0131] As the share of photovoltaic power generation in the power system continues to rise, the uncertainty of its power generation poses a serious challenge to the stable operation of the power system. Stability assessments can monitor the extent to which the system is affected by photovoltaic power generation in real time, identifying potential risks in advance. By leveraging photovoltaic power plant operating status parameters, meteorological parameters, power system operating parameters, and photovoltaic power generation forecast error parameters, this assessment provides a comprehensive understanding of the operating status of all aspects of the system.

[0132] The above content is merely an example and explanation of the structure of the present invention. Those skilled in the art may make various modifications or additions to the described specific embodiments or replace them in a similar manner. As long as they do not deviate from the structure of the invention or exceed the scope defined by the claims, they should all fall within the scope of protection of the present invention.

Claims

1. A photovoltaic power generation power prediction and analysis system, characterized in that: It includes photovoltaic power station operation analysis module, photovoltaic power station usage analysis module, state signal matching module, on-site meteorological feature acquisition module, initial power prediction module, photovoltaic power generation prediction power acquisition module and power system stability assessment module, among which: The photovoltaic power station operation analysis module is used to obtain photovoltaic power station operation status data, build a photovoltaic power station operation status model, and output photovoltaic power station operation factors; The photovoltaic power station usage analysis module is used to analyze the usage status of the photovoltaic power station and obtain the photovoltaic power station usage factor; The state signal matching module is used to match the photovoltaic power station state signal based on the photovoltaic power station operation factor and the photovoltaic power station usage factor; The on-site meteorological characteristics acquisition module is used to collect on-site meteorological data of the photovoltaic power station and analyze and obtain on-site meteorological characteristics of the photovoltaic power station; The initial power prediction module is used to obtain the initial photovoltaic power generation prediction power based on the photovoltaic power station status signal and the on-site meteorological characteristics of the photovoltaic power station using a trained linear regression model; The photovoltaic power generation prediction power acquisition module is used to collect power system operation status data, analyze and obtain power system operation status signals, determine photovoltaic power generation prediction error power, and combine it with the initial photovoltaic power generation prediction power to obtain photovoltaic power generation prediction power; The power system stability assessment module is used to assess the stability of the power system based on the photovoltaic power station operating status data, photovoltaic power station on-site meteorological data, photovoltaic power station aging factor and power system operating status data after the photovoltaic power generation prediction power acquisition module obtains the photovoltaic power generation prediction power, and to issue an early warning if the power system is unstable.

2. The photovoltaic power generation prediction and analysis system according to claim 1, characterized in that: The process of obtaining the photovoltaic power station operation status data, building the photovoltaic power station operation status model, and outputting the photovoltaic power station operation factors includes: Install a power sensor at the output end of each inverter in the photovoltaic power station, add the output power of each inverter, and get the overall power output P of the photovoltaic power station. total ; Install a power sensor on the DC input side of the inverter to obtain the input DC power P dc , install a power sensor at the inverter output to obtain the output AC power P ac , calculate the inverter efficiency η inv : Measure the current and voltage of each photovoltaic module, calculate the power of each photovoltaic module, and find the maximum power P of each photovoltaic module. max and minimum power P min ; Based on the maximum power and minimum power of each photovoltaic module, the photovoltaic module mismatch rate signal M is calculated. sp : Where P0 is the reference power of the photovoltaic module stored in the database, and e is a natural constant; The operating status data of the photovoltaic power station specifically includes the overall power output of the photovoltaic power station, inverter efficiency, and photovoltaic module mismatch rate signals; Based on the acquired PV power station operation status data, a PV power station operation status model is constructed, and the PV power station operation factor is output. The PV power station operation factor serves as the analysis basis for matching the PV power station status signal; The specific analysis process of the photovoltaic power station operation status model is as follows: Where Gf yx It is the output of the photovoltaic power station operation status model, that is, the photovoltaic power station operation factor.

3. The photovoltaic power generation prediction and analysis system according to claim 1, characterized in that: The photovoltaic power station usage status is analyzed to obtain the photovoltaic power station usage factor. The specific analysis process is as follows: Get the duration T of normal power generation of the photovoltaic power station during the statistical period can And the total duration of the statistical period T total , calculate the availability rate of photovoltaic power station T use : Get the running time T N Number of failures in the photovoltaic power station N qus , calculate the failure rate η of the photovoltaic power station gz : Analyze the reliability factor K of photovoltaic power station based on the availability rate and failure rate of photovoltaic power station g : Get the initial average maximum power point power Pcs of each photovoltaic module max And the current average maximum power point power Pdq of each photovoltaic module max , calculate the aging degree Lh of photovoltaic modules: Where, e is a natural constant; Get the total number of years Dz that the photovoltaic power station has been put into use n ; Calculate the aging factor L of the photovoltaic power station based on the aging degree of the photovoltaic components and the total number of years the photovoltaic power station has been in use. g : Based on the reliability factor and aging factor of the photovoltaic power station, a comprehensive analysis is conducted to obtain the photovoltaic power station utilization factor, which is used as the analysis basis for matching the photovoltaic power station status signal. The specific analysis process of the photovoltaic power station usage factor is as follows: Where Gf sy is the photovoltaic power station utilization factor, A1 is the set photovoltaic power station reliability factor K g A2 is the compensation factor of the photovoltaic power station, and L is the set aging factor of the photovoltaic power station. g compensation factor.

4. The photovoltaic power generation power prediction and analysis system according to claim 1, characterized in that: The photovoltaic power station status signal is matched based on the photovoltaic power station operation factor and the photovoltaic power station usage factor. The specific analysis process is as follows: The photovoltaic power station operation factor and the photovoltaic power station usage factor are stored as specified tags; Obtain a mapping table of designated tags and photovoltaic power station status signals pre-stored in the database, and find a matching photovoltaic power station status signal according to the designated tag by searching the mapping table.

5. The photovoltaic power generation prediction and analysis system according to claim 1, characterized in that: The on-site meteorological data of the photovoltaic power station is collected and analyzed to obtain the on-site meteorological characteristics of the photovoltaic power station. The specific analysis process is as follows: Obtain on-site meteorological data for the photovoltaic power station, including the highest light intensity at the photovoltaic power station, the average light intensity at the photovoltaic power station, the average temperature at the photovoltaic power station, and the average wind speed at the photovoltaic power station; Based on the acquired on-site meteorological data of the photovoltaic power station, the on-site meteorological characteristics of the photovoltaic power station are obtained through comprehensive analysis. The on-site meteorological characteristics of the photovoltaic power station are used as the analysis basis for obtaining the initial photovoltaic power generation prediction power.

6. The photovoltaic power generation prediction and analysis system according to claim 1, characterized in that: Based on the photovoltaic power station status signal and the on-site meteorological characteristics of the photovoltaic power station, the trained linear regression model is used to obtain the initial photovoltaic power generation prediction power. The specific analysis process is as follows: Input the photovoltaic power station status signal and the photovoltaic power station on-site meteorological characteristics into the trained linear regression model; The trained linear regression model is expressed as: y=g0+g1*Jxr+g2*Qx t +b; Where y is the initial photovoltaic power generation prediction power index, g0 is the intercept, g1 is the first slope, g2 is the second slope, Jxr is the photovoltaic power station status signal, Qx t is the on-site meteorological characteristics of the photovoltaic power station, b is the error term of photovoltaic power generation prediction; Output the initial photovoltaic power generation prediction power index; Obtain an initial photovoltaic power generation prediction power index-initial photovoltaic power generation prediction power mapping table pre-stored in a database, and find a matching initial photovoltaic power generation prediction power according to the initial photovoltaic power generation prediction power index by searching the mapping table.

7. The photovoltaic power generation prediction and analysis system according to claim 1, characterized in that: The power system operation status data is collected, analyzed to obtain the power system operation status signal, and the photovoltaic power generation prediction error power is determined. The photovoltaic power generation prediction power is obtained by combining the initial photovoltaic power generation prediction power. The specific analysis process is as follows: Collecting power system operating status data, including power system short-circuit capacity, power system power factor, and power system operating power; Based on the collected power system operation status data, the power system operation status signal is analyzed and obtained, and the power system operation status signal is used as the analysis basis for determining the photovoltaic power generation prediction error power; Obtaining a mapping table of power system operation status signals and photovoltaic power generation prediction error power pre-stored in a database, and finding a matching photovoltaic power generation prediction error power according to the power system operation status signals by searching the mapping table; The initial photovoltaic power generation prediction power and the photovoltaic power generation prediction error power are accumulated to obtain the photovoltaic power generation prediction power.

8. The photovoltaic power generation prediction and analysis system according to claim 7, characterized in that: The specific analysis process of the power system operation status signal is as follows: Where, Dl xi is the power system operating status signal, dlr is the power system short-circuit capacity, D ys is the power factor of the power system, Yx is the operating power of the power system, dlr0 is the defined short-circuit capacity of the power system stored in the database, Yx0 is the defined operating power of the power system stored in the database, and e is a natural constant.

9. The photovoltaic power generation prediction and analysis system according to claim 1, characterized in that: The power system stability assessment is based on the PV power station operating status data, PV power station on-site meteorological data, PV power station aging factors, and power system operating status data, including the following steps: The photovoltaic power station operation status data, photovoltaic power station on-site meteorological data, photovoltaic power station aging factor and power system operation status data are standardized respectively to obtain the standardized photovoltaic power station operation status data, standardized photovoltaic power station on-site meteorological data, standardized photovoltaic power station aging factor P en and standardized power system operating status data, including: The standardized operating status data of the photovoltaic power station includes the standardized overall power output P of the photovoltaic power station. tn , Normalized inverter efficiency η tn , standardized photovoltaic module mismatch rate signal M tn ; The standardized on-site meteorological data of the photovoltaic power station includes the standardized maximum light intensity I an , the average light intensity of the photovoltaic power station after standardization I mn , the average temperature of the photovoltaic power station after standardization T n , the average wind speed v at the photovoltaic power station site after standardization tn ; The standardized power system operating status data includes the standardized power system short-circuit capacity S sn , Standardized power system power factor PF n , Standardized power system operating power P on ; Based on the converted and standardized photovoltaic power station operating status data, standardized photovoltaic power station on-site meteorological data, and standardized photovoltaic power station aging factor P en The power system stability assessment factor S is calculated by combining the standardized power system operation status data in ; If the power system stability assessment factor is greater than a threshold value of the stability assessment factor stored in the database, the power system is unstable.

10. The photovoltaic power generation prediction and analysis system according to claim 9, characterized in that: Power system stability assessment factor S in The method to obtain is as follows: Among them, F1, F2, F3 and F4 are all transit functions, and a1 is (1-P tn ) weight factor, a2 is (1-η tn ), a3 is the weight factor of M tn The weight factor, b1 is (1-I an ) weight factor, b2 is (1-I mn ), b3 is the weight factor of T n The weight factor, b4 is (1-v tn ), c1 is the weight factor of The weight factor, c2 is (1-PF n ), c3 is the weight factor of The weight factor of , e is a natural constant.

Images