An artificial intelligence-based distributed photovoltaic power generation performance evaluation system

Abstract

The present application belongs to the field of distributed photovoltaic power generation, and relates to data analysis technology, which is used to solve the problem that the prior art cannot accurately and effectively evaluate the efficiency and stability of power generation by fully combining various operation data affecting the power generation system; in particular, the present application is a distributed photovoltaic power generation performance evaluation system based on artificial intelligence, which 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 connected in communication, and the data acquisition module is further connected in communication with the power generation stability evaluation module; the data acquisition module is used to acquire parameters in the system, and the power generation efficiency evaluation module and the power generation stability evaluation module are used to evaluate the efficiency and stability of power generation; the present application can train and fit operation data through an artificial intelligence model, thereby ensuring more accurate evaluation of the performance of distributed photovoltaic power generation.

Description

TECHNICAL FIELD

[0001] The application belongs to the field of distributed photovoltaic power generation performance evaluation, and relates to a data analysis technique, in particular to a distributed photovoltaic power generation performance evaluation system based on artificial intelligence. BACKGROUND

[0002] With the development of modern industry, global energy problems are increasingly prominent, and the demand for renewable energy is increasing. As a kind of ideal renewable energy, solar energy has become one of the fastest growing industries in the world, and distributed photovoltaic power generation has been widely used as a solution.

[0003] The existing distributed photovoltaic power generation performance evaluation system often relies on traditional power generation and manual analysis, lacks comprehensive consideration of various influencing factors affecting the power generation system, and the existing technology cannot fully combine the operation data to accurately and effectively evaluate the efficiency and stability of power generation, resulting in low utilization rate of photovoltaic power generation.

[0004] In view of the above technical problems, the present application provides a solution. SUMMARY

[0005] The application aims to provide a distributed photovoltaic power generation performance evaluation system based on artificial intelligence, which can solve the problem that the existing technology cannot fully combine various operation 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 application is how to provide a system that can fully combine various operation data affecting the power generation system to accurately and effectively evaluate the efficiency and stability of power generation.

[0007] The object of the application can be achieved by the following technical scheme:

[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 connected in communication, and the data acquisition module is further connected in communication with the power generation stability evaluation module.

[0009] The data acquisition module is used to acquire data in the distributed photovoltaic power generation system: a group of power generation devices in the distributed photovoltaic power generation system is marked as a photovoltaic component, and each natural day's power generation period is marked as a monitoring period. The temperature, light intensity, voltage DY and current DL of the input end of the inverter on the surface of the photovoltaic cell are obtained at regular intervals in the monitoring period, and the voltage DY and 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 by the artificial intelligence model to obtain the ideal power generation efficiency formula LX=k1*GZ*[1-k2*(WD-t)], wherein k1 and k2 are proportional coefficients, t is the standard working 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: the fluctuation ratio BB of each collection time point is obtained, the power generation stability coefficient WX of the photovoltaic module in the monitoring period is calculated according to the fluctuation ratios BB of all collection time points, and the power generation stability is evaluated according to the power generation stability coefficient WX.

[0013] Further, in the monitoring period, the temperature and the light intensity on the surface of the photovoltaic cell are collected by the temperature sensor and the light sensor, the voltage DY and the current DL of the input end of the inverter are collected by the voltage sensor and the current sensor, the collection frequency is set to 1 time per 5 seconds, the voltage DY and the current DL are multiplied to obtain the power GL, and the collected data is 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] Further, the power GL is taken as the Y-axis of the coordinate system, the collection time is taken as the X-axis of the coordinate system, the power GL-time curve is drawn, the actual power generation amount FD of the monitoring period is obtained by integral calculation of 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 amount 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 in 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] Further, the temperature and the light intensity on the surface of the photovoltaic cell collected in the monitoring period are averaged to obtain the average temperature WD and the average light intensity GZ of the monitoring period.

[0016] Further, the average temperature WD and the average light intensity GZ of the monitoring period are substituted into the ideal power generation efficiency formula to calculate 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 to obtain the power generation loss rate SS of the photovoltaic module in the monitoring period; the power generation loss rate SS and the preset power generation loss threshold SSmax are compared: 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 in 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 in the monitoring period does not meet the requirements, and the photovoltaic power generation system has hidden dangers, an alarm signal is generated and sent to the mobile terminal of the management personnel, and optimization measures are taken before the next monitoring period begins.

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

[0018] Further, the fluctuation ratio BB of each collection 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 determined that the power generation power of the photovoltaic module at the collection time point is stable, and the power stable value GW is added by 1; if the fluctuation ratio BB is greater than or equal to the fluctuation ratio threshold BBmax, it is determined that the power generation power of the photovoltaic module at the collection time point is unstable, and the power unstable value GN is added by 1; the power stable value GW and the power unstable 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 and the preset power stability threshold WXmax are compared: if the power stability coefficient WX is greater than the power stability threshold WXmax, it is determined that the power generation stability of the photovoltaic module in the monitoring period meets the requirements and does not need to be processed; if the power stability coefficient WX is less than or equal to the power stability threshold WXmax, it is determined that the power generation stability of the photovoltaic module in the monitoring period does not meet the requirements, an alarm signal is generated and sent to the mobile terminal of the management personnel, and optimization measures are taken before the next monitoring period begins.

[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 traditional evaluation methods is avoided. This allows for the plotting of power-time curves and the calculation of the actual power generation efficiency of photovoltaic modules, ensuring a more accurate and comprehensive evaluation of the efficiency of distributed photovoltaic power generation.

[0021] 2. By training and fitting the actual power generation efficiency, temperature and light intensity in the operating data through artificial intelligence models, the ideal power generation efficiency calculation formula is obtained. This allows for the assessment of the loss between the actual power generation efficiency and the ideal power generation efficiency, accurately and intuitively reflecting the power generation performance of photovoltaic modules.

[0022] 3. By combining the environmental fluctuation coefficient and power fluctuation coefficient at each collection time point to calculate the fluctuation ratio, 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. The influence of light intensity and temperature is fully considered to ensure the accuracy of the power generation stability assessment. Attached Figure Description

[0023] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0024] Figure 1 This is a system block diagram of the entire invention. Detailed Implementation

[0025] The technical solution of the present invention will be clearly and completely described below with reference to the embodiments. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0026] like Figure 1 As shown, an artificial intelligence-based distributed photovoltaic power generation performance evaluation system 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. The data acquisition module is also connected in communication with the power generation stability evaluation module.

[0027] The data acquisition module is used for acquiring data in the distributed photovoltaic power generation system: a group of power generation devices in the distributed photovoltaic power generation system is marked as a photovoltaic assembly, the photovoltaic assembly includes photovoltaic cells and an inverter; each natural day of the distributed photovoltaic power generation system is marked as a monitoring period; in the monitoring period, the temperature and the light intensity on the surface of the photovoltaic cells are acquired by a temperature sensor and a light sensor, the voltage DY and the current DL of the input end of the inverter are acquired by a voltage sensor and a current sensor, the acquisition frequency is set to once every 5 seconds, the voltage DY and the current DL acquired in the monitoring period are multiplied to obtain the power GL; the acquired data is transmitted to the power generation efficiency calculation module, the power generation efficiency evaluation module and the power generation stability evaluation module through a corresponding communication interface; by comprehensively acquiring the temperature, the light intensity, the current and the voltage of the photovoltaic assembly 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 for calculating the index of the distributed photovoltaic power generation performance: at the end of the monitoring period, the power GL is taken as the Y-axis of the coordinate system, the acquisition time is taken as the X-axis of the coordinate system, the power GL-time curve is drawn, 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 assembly 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 by the formula SX=FD / (P0*T) to obtain the actual power generation efficiency SX of the photovoltaic assembly in the monitoring period, and the actual power generation efficiency SX is transmitted to the power generation efficiency evaluation module through a corresponding communication interface; by drawing the power GL-time curve, the actual power generation efficiency of the photovoltaic assembly is calculated, so that the distributed photovoltaic power generation efficiency can be more accurately and comprehensively evaluated.

[0029] The power generation efficiency evaluation module is used for evaluating the power generation efficiency of the photovoltaic module: the temperature and the light intensity on the surface of the photovoltaic cell collected in the monitoring period are averaged to obtain the average temperature WD and the average light intensity GZ in the monitoring period; the average temperature WD, the average light intensity GZ and the corresponding actual power generation efficiency SX of the historical monitoring period in the database are trained by the artificial intelligence model, the relationship model of the power generation efficiency-temperature and the light intensity is fitted, and the ideal power generation efficiency formula LX=k1*GZ*[1-k2*(WD-t) is obtained, wherein k1 and k2 are proportional coefficients, and k1>k2>0, and t is the standard working temperature of the photovoltaic module; the average temperature WD and the average light intensity GZ in the monitoring period are substituted into the ideal power generation efficiency formula for calculation to obtain the ideal power generation efficiency LX in the monitoring period; the actual power generation efficiency SX and the ideal power generation efficiency LX are numerically calculated by the formula SS=LX-SX / LX to obtain the power generation loss rate SS of the photovoltaic module in 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 in 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 in the monitoring period does not meet the requirements, and the photovoltaic power generation system has hidden dangers, an alarm signal is generated and sent to the mobile terminal of the management personnel, and optimization measures are taken before the next monitoring period starts: the surface of the photovoltaic cell is cleaned and the light angle is adjusted; the actual power generation efficiency, the temperature and the light intensity in the operation data are trained and fitted by the artificial intelligence model, and then the loss between the actual power generation efficiency and the ideal power generation efficiency can be evaluated, and the power generation performance of the photovoltaic module is accurately and intuitively reflected.

[0030] The power generation stability evaluation module is used for evaluating the power generation stability of the photovoltaic module: in the monitoring period, the temperature, the light intensity and the power collected each time are respectively marked as the current temperature WD1, the current light intensity GZ1 and the current power GL1, and the temperature, the light intensity and the power collected last time are respectively marked as the last temperature WD0, the last light intensity GZ0 and the last power GL0; the environmental fluctuation coefficient HB is obtained by numerically calculating the formula , wherein α1 and α2 are proportional coefficients, and α1>α2>1; the power generation stability coefficient is obtained by numerically calculating the formula The current power GL1 and the last power GL0 are calculated to obtain a power fluctuation coefficient GB; the environmental fluctuation coefficient HB and the power fluctuation coefficient GB are divided to obtain a fluctuation ratio BB, and the fluctuation ratio BB of each collection time point is compared with a 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 of the photovoltaic module at the collection time point is stable, and the power stability value GW is added 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 of the photovoltaic module at the collection time point is unstable, and the power instability value GN is added by 1; the power stability value GW and the power instability value GN in the monitoring period are calculated by the formula WX = GW / (GN+GW) to obtain a power stability coefficient WX; the power stability coefficient WX is compared with a 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 module 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 module in the monitoring period does not meet the requirements, an alarm signal is generated and sent to the mobile terminal of the management personnel, and optimization measures are taken before the next monitoring period starts: the line is checked and the aged power generation component is updated; the fluctuation ratio is calculated by combining the environmental fluctuation coefficient and the power fluctuation coefficient of each collection time point to judge the power generation stability at the time point, and the power generation stability of the photovoltaic module in the monitoring period is further evaluated, the influence of the light intensity and the temperature is fully considered, and the accuracy of the power generation stability evaluation is ensured.

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

[0032] The above content is only an example and description of the structure of the application, and those skilled in the art can make various modifications or supplements to the described specific embodiments or replace them with similar ways, as long as they do not deviate from the structure of the application or exceed the scope defined by the present claims, which shall belong to the protection scope of the application.

[0033] The above formulas are obtained by collecting a large amount of data for software simulation and selecting one formula close to the true value, and the coefficients in the formula are set by the person skilled in the art according to the actual situation; for example: formula LX=k1*GZ*[1-k2*(WD-t)]; a plurality of sample data are collected by the person skilled in the art, and the corresponding ideal power generation efficiency of each sample data is set; the set ideal power generation efficiency and the collected sample data are substituted into the formula, and any two formulas constitute a two-element linear equation group, the calculated coefficients are screened and the mean value is taken, and the values of k1 and k2 are 2.26 and 0.14 respectively;

[0034] The size of the coefficient is a specific value obtained by quantifying each parameter for subsequent comparison, and the size of the coefficient depends on the number of sample data and the preliminary setting of the corresponding harmful coefficient of each sample data by the person skilled in the art; as long as it does not affect the proportional relationship between the parameter and the quantized value, such as the positive correlation between the ideal power generation efficiency and the light intensity.

[0035] In the description of the present specification, the description of the terms "one embodiment", "example", "specific example" and the like 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 application. In the present specification, the illustrative description 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 application disclosed above are only used to help explain the application. The preferred embodiments do not describe all the details and limit the application to the specific embodiments. Obviously, many modifications and changes can be made according to the content of the present specification. The present specification selects and describes these embodiments in order to better explain the principles and practical applications of the application, so that the person skilled in the art can well understand and utilize the application. The application is limited by the claims and their entire scope and equivalents.

Claims

1. An artificial intelligence-based distributed photovoltaic power generation performance evaluation system, characterized in that, The system 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 connected in communication; the data acquisition module is further connected in communication with the power generation stability evaluation module; The data acquisition module is configured to acquire data in the distributed photovoltaic power generation system; a group of power generation devices in the distributed photovoltaic power generation system is marked as a photovoltaic assembly; a power generation period of each natural day is marked as a monitoring period; the temperature of the surface of a photovoltaic cell, the light intensity, the voltage DY and the current DL of the input end of an inverter are acquired at the monitoring period; and the voltage DY and the current DL are multiplied to obtain the power GL. The temperature of the surface of the photovoltaic cell and the light intensity acquired at the monitoring period are averaged to obtain the average temperature WD and the average light intensity GZ of the monitoring period. The power generation efficiency calculation module is configured to calculate an index of the distributed photovoltaic power generation performance; a power GL-time curve is drawn and integrated to obtain the actual power generation efficiency SX of the monitoring period. The power generation efficiency evaluation module is used for evaluating the power generation efficiency of the photovoltaic module: training and fitting data in the database by an artificial intelligence model to obtain an ideal power generation efficiency formula LX=k1 GZ [1-k2 (WD-t)], wherein k1 and k2 are both proportional coefficients, t is a standard working temperature of the photovoltaic module, and an actual power generation efficiency SX is evaluated. The power generation stability evaluation module is configured to evaluate the power generation stability of the photovoltaic assembly; the fluctuation ratio BB of each acquisition time point is acquired; the fluctuation ratio BB of all acquisition time points is used to calculate the power generation stability coefficient WX of the photovoltaic assembly of the monitoring period; and the power generation stability is evaluated according to the power generation stability coefficient WX.

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

3. The distributed photovoltaic power generation performance evaluation system based on artificial intelligence according to claim 2, characterized in that, The power GL is used as the Y-axis of a coordinate system, the acquisition time is used as the X-axis of the coordinate system, a power GL-time curve is drawn, the power GL-time curve is integrated to obtain the actual power generation FD of the monitoring period; the rated power P0 of the photovoltaic assembly and the power generation time T of the monitoring period are acquired; 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 assembly of the monitoring period; and the actual power generation efficiency SX is transmitted to the power generation efficiency evaluation module through a corresponding communication interface.

4. The distributed photovoltaic power generation performance evaluation system based on artificial intelligence according to claim 3, characterized in that, The average temperature WD and the average light intensity GZ of the monitoring period are substituted into the ideal power generation efficiency formula to calculate 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 to obtain the power generation loss rate SS of the photovoltaic module in 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 in 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 in the monitoring period does not meet the requirements, and the photovoltaic power generation system has hidden dangers, an alarm signal is generated and sent to the mobile terminal of the management personnel.

5. The distributed photovoltaic power generation performance evaluation system based on artificial intelligence according to claim 4, characterized in that, In 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 respectively, 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 current temperature WD1, the current light intensity GZ1, the last temperature WD0 and the last light intensity GZ0 are numerically calculated to obtain the environmental fluctuation coefficient HB; the current power GL1 and the last power GL0 are numerically calculated to obtain the power fluctuation coefficient GB; the environmental fluctuation coefficient HB and the power fluctuation coefficient GB are divided to obtain the fluctuation ratio BB.

6. The distributed photovoltaic power generation performance evaluation system based on artificial intelligence according to claim 5, characterized in that, The fluctuation ratio BB of each collection 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 determined that the power generation power of the photovoltaic module at this collection time point is stable, and the power stable value GW is increased by 1; if the fluctuation ratio BB is greater than or equal to the fluctuation ratio threshold BBmax, it is determined that the power generation power of the photovoltaic module at this collection time point is unstable, and the power unstable value GN is increased by 1; the power stable value GW and the power unstable 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 determined that the power generation stability of the photovoltaic module in the monitoring period meets the requirements and does not need to be processed; if the power stability coefficient WX is less than or equal to the power stability threshold WXmax, it is determined that the power generation stability of the photovoltaic module in the monitoring period does not meet the requirements, an alarm signal is generated and sent to the mobile terminal of the management personnel, and optimization measures are taken before the next monitoring period begins.

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