Distributed photovoltaic power generation performance evaluation system based on artificial intelligence

Abstract

The invention belongs to the field of distributed photovoltaic power generation, relates to a data analysis technology, and is used for solving the problem that the power generation efficiency and stability cannot be accurately and effectively evaluated by fully combining various operation data influencing a power generation system in the prior art. The distributed photovoltaic power generation performance evaluation system based on artificial intelligence comprises a data acquisition module, a power generation efficiency calculation module, a power generation efficiency evaluation module, a power generation stability evaluation module and a database. The data acquisition module, the power generation efficiency calculation module, the power generation efficiency evaluation module and the database are sequentially in communication connection, and the data acquisition module is further in communication connection with a power generation stability evaluation module; the data acquisition module is used for acquiring parameters in the system; the power generation efficiency evaluation module and the power generation stability evaluation module are used for evaluating the power generation efficiency and stability; according to the invention, the operation data can be trained and fitted through the artificial intelligence model, and the distributed photovoltaic power generation performance can be evaluated more accurately.

Description

Technical Field

[0001] The present invention belongs to the field of distributed photovoltaic power generation performance evaluation, relates to data analysis technology, and specifically is a distributed photovoltaic power generation performance evaluation system based on artificial intelligence. Background Art

[0002] With the development of modern industry, global energy issues are becoming increasingly prominent and the demand for renewable energy is increasing. Solar energy, as one of the most ideal renewable energy sources, and its related industries have become one of the fastest growing industries in the world. Distributed photovoltaic power generation has been widely used as a solution.

[0003] Existing distributed photovoltaic power generation performance evaluation systems often rely on traditional power generation and manual analysis, lacking comprehensive consideration of the various factors affecting the power generation system. In addition, existing technologies cannot fully combine operating data to accurately and effectively evaluate the efficiency and stability of power generation, resulting in low photovoltaic power generation utilization.

[0004] In view of the above technical problems, this application proposes a solution. Summary of the invention

[0005] The purpose of the present invention is to provide a distributed photovoltaic power generation performance evaluation system based on artificial intelligence, which is used to solve the problem that the existing technology cannot fully combine various operating data affecting the power generation system to accurately and effectively evaluate the efficiency and stability of power generation;

[0006] The technical problem to be solved by the present invention is: how to provide a method that can fully combine various operating data that affect the power generation system to accurately and effectively evaluate the efficiency and stability of power generation.

[0007] The purpose of the present invention can be achieved through the following technical solutions:

[0008] A distributed photovoltaic power generation performance evaluation system based on artificial intelligence, comprising a data acquisition module, a power generation efficiency calculation module, a power generation efficiency evaluation module, a power generation stability evaluation module and a database; the data acquisition module, the power generation efficiency calculation module, the power generation efficiency evaluation module and the database are sequentially communicatively connected, and the data acquisition module is also communicatively connected to the power generation stability evaluation module;

[0009] The data acquisition module is used to collect data in the distributed photovoltaic power generation system: a group of power generation devices in the distributed photovoltaic power generation system is marked as photovoltaic modules, and the power generation period of each natural day is marked as a monitoring period. During the monitoring period, the temperature of the photovoltaic cell surface, the light intensity, the voltage DY and the current DL at the inverter input end are obtained regularly, and the voltage DY and the current DL are multiplied to obtain the power GL;

[0010] The power generation efficiency calculation module is used to calculate the index of distributed photovoltaic power generation performance: by drawing the power GL-time curve and performing integral calculation, the actual power generation efficiency SX of the monitoring period is obtained;

[0011] The power generation efficiency evaluation module is used to evaluate the power generation efficiency of the photovoltaic module: the data in the database is trained and fitted through the artificial intelligence model to obtain the ideal power generation efficiency formula LX=k1*GZ*[1-k2*(WD-t)], where k1 and k2 are both proportional coefficients, t is the standard operating temperature of the photovoltaic module, and the actual power generation efficiency SX is evaluated;

[0012] The power generation stability evaluation module is used to evaluate the power generation stability of the photovoltaic module: obtain the fluctuation ratio BB at each acquisition time point, calculate the power generation stability coefficient WX of the photovoltaic module during the monitoring period according to the fluctuation ratios BB of all acquisition time points, and evaluate the power generation stability according to the power generation stability coefficient WX.

[0013] Furthermore, during the monitoring period, the temperature and light intensity on the surface of the photovoltaic cell are collected through the temperature sensor and the light sensor, and the voltage DY and the current DL at the input end of the inverter are collected through the voltage sensor and the current sensor. The collection frequency is set to once every 5 seconds, and the voltage DY and the current DL are multiplied to obtain the power GL; the collected data are transmitted to the power generation efficiency calculation module, the power generation efficiency evaluation module and the power generation stability evaluation module through the corresponding communication interface.

[0014] Furthermore, the power GL is used as the Y-axis of the coordinate system, and the acquisition time is used as the X-axis of the coordinate system to draw a power GL-time curve. By integrating the power GL-time curve, the actual power generation FD of the monitoring period is obtained. The rated power P0 of the photovoltaic module and the power generation time T of the monitoring period are obtained, and the actual power generation FD, the rated power P0 and the power generation time T are numerically calculated to obtain the actual power generation efficiency SX of the photovoltaic module during the monitoring period, and the actual power generation efficiency SX is transmitted to the power generation efficiency evaluation module through the corresponding communication interface.

[0015] Furthermore, the temperature and light intensity of the surface of the photovoltaic cell collected during the monitoring period are averaged to calculate the average temperature WD and the average light intensity GZ of the monitoring period.

[0016] Furthermore, the average temperature WD and the average light intensity GZ during the monitoring period are substituted into the ideal power generation efficiency formula for calculation to obtain the ideal power generation efficiency LX during the monitoring period; the actual power generation efficiency SX and the ideal power generation efficiency LX are numerically calculated to obtain the power generation loss rate SS of the photovoltaic module during the monitoring period; the power generation loss rate SS is compared with the preset power generation loss threshold SSmax: if the power generation loss rate SS is less than the power generation loss threshold SSmax, it is determined that the power generation efficiency of the photovoltaic module during the monitoring period meets the requirements, and the collected temperature, light intensity and actual power generation efficiency SX are transmitted to the database through the corresponding communication interface for storage; if the power generation loss rate SS is greater than or equal to the power generation loss threshold SSmax, it is determined that the power generation efficiency of the photovoltaic module during the monitoring period does not meet the requirements, and there are hidden dangers in the photovoltaic power generation system, an alarm signal is generated and sent to the mobile phone terminal of the management personnel, and optimization measures are taken before the start of the next monitoring period.

[0017] Furthermore, during the monitoring period, the temperature, light intensity and power collected each time are marked as the current temperature WD1, the current light intensity GZ1 and the current power GL1, and the temperature, light intensity and power collected last time are marked as the last temperature WD0, the last light intensity GZ0 and the last power GL0, respectively; the environmental fluctuation coefficient HB is obtained by numerically calculating the current temperature WD1, the current light intensity GZ1, the last temperature WD0 and the last light intensity GZ0; the current power GL1 and the last power GL0 are numerically calculated to obtain the power fluctuation coefficient GB; the environmental fluctuation coefficient HB is divided by the power fluctuation coefficient GB to obtain the fluctuation ratio BB.

[0018] Furthermore, the fluctuation ratio BB at each acquisition time point is compared with a preset fluctuation ratio threshold BBmax: if the fluctuation ratio BB is less than the fluctuation ratio threshold BBmax, the power generation power of the photovoltaic component at the acquisition time point is judged to be stable, and the power stability value GW is increased by 1; if the fluctuation ratio BB is greater than or equal to the fluctuation ratio threshold BBmax, the power generation power of the photovoltaic component at the acquisition time point is judged to be unstable, and the power instability value GN is increased by 1; the power stability value GW and the power instability value GN in the monitoring period are numerically calculated by the formula WX=GW / (GN+GW) to obtain the power stability coefficient WX; the power stability coefficient WX is compared with the preset power stability threshold WXmax: if the power stability coefficient WX is greater than the power stability threshold WXmax, it is judged that the power generation stability of the photovoltaic component during the monitoring period meets the requirements and no processing is required; if the power stability coefficient WX is less than or equal to the power stability threshold WXmax, it is judged that the power generation stability of the photovoltaic component during the monitoring period does not meet the requirements, an alarm signal is generated and sent to the mobile phone terminal of the manager, and optimization measures are taken before the start of the next monitoring period.

[0019] The present invention has the following beneficial effects:

[0020] 1. By comprehensively collecting the temperature, light intensity, current and voltage of photovoltaic modules in the distributed photovoltaic power generation system, the problem of single data in the traditional evaluation method is avoided, and then the power GL-time curve can be drawn to calculate the actual power generation efficiency of the photovoltaic modules, ensuring a more accurate and comprehensive evaluation of the distributed photovoltaic power generation efficiency;

[0021] 2. The actual power generation efficiency, temperature and light intensity in the operating data are trained and fitted through the artificial intelligence model to obtain the ideal power generation efficiency calculation formula, which can then evaluate the loss between the actual power generation efficiency and the ideal power generation efficiency, accurately and intuitively reflecting the power generation performance of the photovoltaic modules;

[0022] 3. By calculating the fluctuation ratio by combining the environmental fluctuation coefficient and the power fluctuation coefficient at each collection time point, the power generation stability at that time point is determined, and the power generation stability of the photovoltaic modules during the monitoring period is further evaluated, fully considering the influence of light intensity and temperature, and ensuring the accuracy of the power generation stability evaluation. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0024] Figure 1 The figure is a system block diagram of the present invention as a whole. DETAILED DESCRIPTION

[0025] The technical solution of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0026] like Figure 1 As shown, a distributed photovoltaic power generation performance evaluation system based on artificial intelligence includes a data acquisition module, a power generation efficiency calculation module, a power generation efficiency evaluation module, a power generation stability evaluation module and a database; the data acquisition module, the power generation efficiency calculation module, the power generation efficiency evaluation module and the database are communicatively connected in sequence, and the data acquisition module is also communicatively connected to the power generation stability evaluation module.

[0027] The data acquisition module is used to collect data in the distributed photovoltaic power generation system: a group of power generation devices in the distributed photovoltaic power generation system is marked as photovoltaic modules, which include photovoltaic cells and inverters; the power generation period of the distributed photovoltaic power generation system in each natural day is marked as a monitoring period; during the monitoring period, the temperature and light intensity on the surface of the photovoltaic cells are collected through temperature sensors and light sensors, and the voltage DY and current DL at the input end of the inverter are collected through voltage sensors and current sensors. The collection frequency is set to once every 5 seconds, and the voltage DY and current DL collected during the monitoring period are multiplied to obtain the power GL; the collected data are transmitted to the power generation efficiency calculation module, the power generation efficiency evaluation module and the power generation stability evaluation module through the corresponding communication interface; by comprehensively collecting the temperature, light intensity, current and voltage of the photovoltaic modules in the distributed photovoltaic power generation system, the problem of single data in the traditional evaluation method is avoided.

[0028] The power generation efficiency calculation module is used to calculate the indicators of distributed photovoltaic power generation performance: at the end of the monitoring period, the power GL is used as the Y-axis of the coordinate system, and the acquisition time is used as the X-axis of the coordinate system to draw the power GL-time curve. By integrating the power GL-time curve, the actual power generation FD of the monitoring period is obtained; the rated power P0 of the photovoltaic module and the power generation time T of the monitoring period are obtained, and the actual power generation FD, the rated power P0 and the power generation time T are numerically calculated by the formula SX=FD / (P0*T) to obtain the actual power generation efficiency SX of the photovoltaic module during the monitoring period, and the actual power generation efficiency SX is transmitted to the power generation efficiency evaluation module through the corresponding communication interface; by drawing the power GL-time curve, the actual power generation efficiency of the photovoltaic module is calculated, which ensures that the distributed photovoltaic power generation efficiency can be evaluated more accurately and comprehensively.

[0029] The power generation efficiency evaluation module is used to evaluate the power generation efficiency of photovoltaic modules: the temperature and light intensity of the photovoltaic cell surface collected during the monitoring period are averaged to obtain the average temperature WD and the average light intensity GZ of the monitoring period; the average temperature WD, the average light intensity GZ of the historical monitoring period in the database and the corresponding actual power generation efficiency SX are trained through the artificial intelligence model, and the relationship model between power generation efficiency, temperature and light intensity is fitted to obtain the ideal power generation efficiency formula LX=k1*GZ*[1-k2*(WD-t)], where k1 and k2 are both proportional coefficients, and k1>k2>0, and t is the standard operating temperature of the photovoltaic module; the average temperature WD and the average light intensity GZ of the monitoring period are substituted into the ideal power generation efficiency formula for calculation to obtain the ideal power generation efficiency LX of the monitoring period; the actual power generation efficiency SX and the ideal power generation efficiency LX are numerically calculated through the formula SS=LX-SX / LX, The power generation loss rate SS of the photovoltaic module during the monitoring period is obtained; the power generation loss rate SS is compared with the preset power generation loss threshold SSmax: if the power generation loss rate SS is less than the power generation loss threshold SSmax, it is determined that the power generation efficiency of the photovoltaic module during the monitoring period meets the requirements, and the collected temperature, light intensity and actual power generation efficiency SX are transmitted to the database through the corresponding communication interface for storage; if the power generation loss rate SS is greater than or equal to the power generation loss threshold SSmax, it is determined that the power generation efficiency of the photovoltaic module during the monitoring period does not meet the requirements, and there are hidden dangers in the photovoltaic power generation system, an alarm signal is generated and sent to the mobile phone terminal of the manager, and optimization measures are taken before the start of the next monitoring period: clean the surface of the photovoltaic cell and adjust the light angle; the actual power generation efficiency, temperature and light intensity in the operating data are trained and fitted through the artificial intelligence model, and then the loss between the actual power generation efficiency and the ideal power generation efficiency can be evaluated, which accurately and intuitively reflects the power generation performance of the photovoltaic module.

[0030] The power generation stability evaluation module is used to evaluate the power generation stability of photovoltaic modules: during the monitoring period, the temperature, light intensity and power collected each time are marked as the current temperature WD1, the current light intensity GZ1 and the current power GL1, and the temperature, light intensity and power collected last time are marked as the last temperature WD0, the last light intensity GZ0 and the last power GL0; through the formula Numerical calculations are performed to obtain the environmental fluctuation coefficient HB, where α1 and α2 are both proportional coefficients, and α1>α2>1; through the formula The current power GL1 and the previous power GL0 are numerically calculated to obtain the power fluctuation coefficient GB; the environmental fluctuation coefficient HB is divided by the power fluctuation coefficient GB to obtain the fluctuation ratio BB, and the fluctuation ratio BB at each acquisition time point is compared with the preset fluctuation ratio threshold BBmax: if the fluctuation ratio BB is less than the fluctuation ratio threshold BBmax, it is judged that the power generation power of the photovoltaic component at the acquisition time point is stable, and the power stability value GW is increased by 1; if the fluctuation ratio BB is greater than or equal to the fluctuation ratio threshold BBmax, it is judged that the power generation power of the photovoltaic component at the acquisition time point is unstable, and the power instability value GN is increased by 1; the power stability value GW and the power instability value GN in the monitoring period are numerically calculated by the formula WX=GW / (GN+GW) to obtain the power stability coefficient WX; the power stability coefficient WX is compared with the preset power The power stability coefficient WX is compared with the power stability threshold WXmax: if the power stability coefficient WX is greater than the power stability threshold WXmax, it is judged that the power generation stability of the PV module during the monitoring period meets the requirements and no processing is required; if the power stability coefficient WX is less than or equal to the power stability threshold WXmax, it is judged that the power generation stability of the PV module during the monitoring period does not meet the requirements, an alarm signal is generated and sent to the mobile phone terminal of the manager, and optimization measures are taken before the start of the next monitoring period: check the line and update the aging power generation components; calculate the fluctuation ratio by combining the environmental fluctuation coefficient and the power fluctuation coefficient at each collection time point, judge the power generation stability at that time point, and further evaluate the power generation stability of the PV module during the monitoring period, fully considering the influence of light intensity and temperature, and ensuring the accuracy of the power generation stability evaluation.

[0031] A distributed photovoltaic power generation performance evaluation system based on artificial intelligence collects the temperature of the photovoltaic cell surface, light intensity, voltage DY and current DL at the inverter input terminal during the monitoring period, and draws the power GL-time curve for integral calculation to obtain the actual power generation FD and actual power generation efficiency SX during the monitoring period; the relationship model between power generation efficiency, temperature and light intensity is fitted through an artificial intelligence model to obtain the ideal power generation efficiency formula, and the power generation loss rate SS is calculated in combination with the actual power generation to evaluate the power generation performance; finally, the power generation stability coefficient WX of the photovoltaic module during the monitoring period is calculated by combining the light intensity, temperature and power, and the power generation stability of the photovoltaic module is evaluated.

[0032] The above contents are merely examples and explanations of the structure of the present invention. The technicians in this technical field may make various modifications or additions to the specific embodiments described 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 protection scope of the present invention.

[0033] The above formulas are obtained by collecting a large amount of data for software simulation and selecting a formula close to the actual value. The coefficients in the formula are set by technicians in this field according to actual conditions; such as: formula LX = k1*GZ*[1-k2*(WD-t)]; technicians in this field collect multiple groups of sample data and set the corresponding ideal power generation efficiency for each group of sample data; substitute the set ideal power generation efficiency and the collected sample data into the formula, any two formulas constitute a two-variable linear equation group, screen the calculated coefficients and take the average, and obtain the values ​​of k1 and k2 as 2.26 and 0.14 respectively;

[0034] The size of the coefficient is to quantify each parameter to obtain a specific value for subsequent comparison. The size of the coefficient depends on the amount of sample data and the preliminary setting of the corresponding harmful coefficient for each set of sample data by technical personnel in this field; as long as it does not affect the proportional relationship between the parameter and the quantified value, such as the ideal power generation efficiency is proportional to the value of light intensity.

[0035] In the description of this specification, the description with reference to the terms "one embodiment", "example", "specific example", etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0036] The preferred embodiments of the present invention disclosed above are only used to help explain the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to only specific implementation methods. Obviously, many modifications and changes can be made according to the content of this specification. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the present invention, so that those skilled in the art can understand and use the present invention well. The present invention is limited only by the claims and their full scope and equivalents.

Claims

1. A distributed photovoltaic power generation performance evaluation system based on artificial intelligence, characterized in that: It includes a data acquisition module, a power generation efficiency calculation module, a power generation efficiency evaluation module, a power generation stability evaluation module and a database; the data acquisition module, the power generation efficiency calculation module, the power generation efficiency evaluation module and the database are sequentially connected in communication, and the data acquisition module is also connected in communication with the power generation stability evaluation module; The data acquisition module is used to collect data in the distributed photovoltaic power generation system: a group of power generation devices in the distributed photovoltaic power generation system is marked as photovoltaic modules, and the power generation period of each natural day is marked as a monitoring period. During the monitoring period, the temperature of the photovoltaic cell surface, the light intensity, the voltage DY and the current DL at the inverter input end are obtained regularly, and the voltage DY and the current DL are multiplied to obtain the power GL; The power generation efficiency calculation module is used to calculate the index of distributed photovoltaic power generation performance: by drawing the power GL-time curve and performing integral calculation, the actual power generation efficiency SX of the monitoring period is obtained; The power generation efficiency evaluation module is used to evaluate the power generation efficiency of the photovoltaic module: the data in the database is trained and fitted through the artificial intelligence model to obtain the ideal power generation efficiency formula LX=k1*GZ*[1-k2*(WD-t)], where k1 and k2 are both proportional coefficients, t is the standard operating temperature of the photovoltaic module, and the actual power generation efficiency SX is evaluated; The power generation stability evaluation module is used to evaluate the power generation stability of the photovoltaic module: obtain the fluctuation ratio BB at each acquisition time point, calculate the power generation stability coefficient WX of the photovoltaic module during the monitoring period according to the fluctuation ratios BB of all acquisition time points, and evaluate the power generation stability according to the power generation stability coefficient WX.

2. According to claim 1, a distributed photovoltaic power generation performance evaluation system based on artificial intelligence is characterized in that: During the monitoring period, the temperature and light intensity on the surface of the photovoltaic cell are collected through temperature sensors and light sensors, and the voltage DY and current DL at the input end of the inverter are collected through voltage sensors and current sensors. The collection frequency is set to once every 5 seconds, and the voltage DY and current DL are multiplied to obtain the power GL; the collected data are transmitted to the power generation efficiency calculation module, the power generation efficiency evaluation module and the power generation stability evaluation module through the corresponding communication interface.

3. The distributed photovoltaic power generation performance evaluation system based on artificial intelligence according to claim 2 is characterized in that: The power GL is used as the Y-axis of the coordinate system and the acquisition time is used as the X-axis of the coordinate system to draw the power GL-time curve. The actual power generation FD of the monitoring period is obtained by integrating the power GL-time curve. The rated power P0 of the photovoltaic module and the power generation time T of the monitoring period are obtained. The actual power generation FD, the rated power P0 and the power generation time T are numerically calculated to obtain the actual power generation efficiency SX of the photovoltaic module during the monitoring period, and the actual power generation efficiency SX is transmitted to the power generation efficiency evaluation module through the corresponding communication interface.

4. The distributed photovoltaic power generation performance evaluation system based on artificial intelligence according to claim 3 is characterized in that: The temperature and light intensity on the surface of the photovoltaic cell collected during the monitoring period are averaged to calculate the average temperature WD and the average light intensity GZ of the monitoring period.

5. The distributed photovoltaic power generation performance evaluation system based on artificial intelligence according to claim 4 is characterized in that: The average temperature WD and the average light intensity GZ during the monitoring period are substituted into the ideal power generation efficiency formula for calculation to obtain the ideal power generation efficiency LX during the monitoring period; the actual power generation efficiency SX and the ideal power generation efficiency LX are numerically calculated to obtain the power generation loss rate SS of the photovoltaic module during the monitoring period; the power generation loss rate SS is compared with the preset power generation loss threshold SSmax: if the power generation loss rate SS is less than the power generation loss threshold SSmax, it is determined that the power generation efficiency of the photovoltaic module during the monitoring period meets the requirements, and the collected temperature, light intensity and actual power generation efficiency SX are transmitted to the database through the corresponding communication interface for storage; if the power generation loss rate SS is greater than or equal to the power generation loss threshold SSmax, it is determined that the power generation efficiency of the photovoltaic module during the monitoring period does not meet the requirements, and there are hidden dangers in the photovoltaic power generation system, and an alarm signal is generated and sent to the mobile phone terminal of the manager.

6. The distributed photovoltaic power generation performance evaluation system based on artificial intelligence according to claim 5 is characterized in that: During the monitoring period, the temperature, light intensity and power collected each time are marked as the current temperature WD1, the current light intensity GZ1 and the current power GL1, and the temperature, light intensity and power collected last time are marked as the last temperature WD0, the last light intensity GZ0 and the last power GL0; The environment fluctuation coefficient HB is obtained by numerically calculating the current temperature WD1, the current light intensity GZ1, the last temperature WD0 and the last light intensity GZ0; the power fluctuation coefficient GB is obtained by numerically calculating the current power GL1 and the last power GL0; the fluctuation ratio BB is obtained by dividing the environment fluctuation coefficient HB and the power fluctuation coefficient GB.

7. The distributed photovoltaic power generation performance evaluation system based on artificial intelligence according to claim 6 is characterized in that: The fluctuation ratio BB at each acquisition time point is compared with the preset fluctuation ratio threshold BBmax: if the fluctuation ratio BB is less than the fluctuation ratio threshold BBmax, the power generation power of the photovoltaic component at the acquisition time point is judged to be stable, and the power stability value GW is increased by 1; if the fluctuation ratio BB is greater than or equal to the fluctuation ratio threshold BBmax, the power generation power of the photovoltaic component at the acquisition time point is judged to be unstable, and the power instability value GN is increased by 1; the power stability value GW and the power instability value GN in the monitoring period are numerically calculated by the formula WX=GW / (GN+GW) to obtain the power stability coefficient WX; the power stability coefficient WX is compared with the preset power stability threshold WXmax: if the power stability coefficient WX is greater than the power stability threshold WXmax, it is judged that the power generation stability of the photovoltaic component in the monitoring period meets the requirements and no processing is required; if the power stability coefficient WX is less than or equal to the power stability threshold WXmax, it is judged that the power generation stability of the photovoltaic component in the monitoring period does not meet the requirements, an alarm signal is generated and sent to the mobile phone terminal of the manager, and optimization measures are taken before the start of the next monitoring period.

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