Distributed photovoltaic power generation coordinated operation system and method based on artificial intelligence

Abstract

The invention belongs to the technical field of photovoltaic power generation management and control, and particularly relates to a distributed photovoltaic power generation coordinated operation system and method based on artificial intelligence, and the system comprises an omnibearing monitoring collection module, a power generation assembly operation quality judgment module, a power generation environment suitability analysis module, a coordinated operation controllability decision module and an intelligent supervision terminal. According to the method, the operation quality of each photovoltaic power generation assembly is accurately judged to realize scientific division of the optimal coordination assembly and the non-easy coordination assembly, the controllability of the photovoltaic power generation coordinated operation is comprehensively evaluated based on the operation quality information and the environmental influence factors, the supervision complexity and the response time are greatly reduced, and the efficiency of the photovoltaic power generation coordinated operation is improved. According to the method, refined evaluation of the individual operation quality of the photovoltaic power generation assembly is realized, environmental factors and overall cooperative control risk evaluation are also considered, passive fault handling is changed into active risk prevention and control, and the safety, stability and efficiency of distributed photovoltaic power generation coordinated operation are comprehensively improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of photovoltaic power generation management and control, in particular to a distributed photovoltaic power generation coordinated operation system and method based on artificial intelligence. BACKGROUND

[0002] In recent years, distributed photovoltaic power generation has played an important role in energy structure transformation due to its advantages of clean and environmental protection, and local consumption. However, the photovoltaic power generation components involved are easily affected by various factors, and the coordinated operation between components is significantly increased in difficulty. At present, there are related technologies of distributed photovoltaic cooperative control in the industry, but most of them are still focused on power optimization or voltage regulation, and the management and control dimension is limited, which is difficult to meet the needs of fine and proactive prevention and control.

[0003] For example, the Chinese invention patent with publication number CN116799867A discloses a distributed photovoltaic cooperative control method, system and device based on group internal pre-autonomy. The technical solution of the invention is to obtain the power prediction data of each photovoltaic point under the distributed photovoltaic aggregation point and the preset network topology model, calculate the power flow to obtain the key node voltage value, and then use the particle swarm algorithm to iteratively solve the objective function to obtain the maximum output value of the power operation state of each photovoltaic point, so as to realize power optimization and cooperative control.

[0004] However, the above-mentioned invention technical solution improves the cooperative effect in the aspects of power prediction and output optimization, but has significant defects in comprehensive and forward-looking management and control. First, it does not pay attention to the health status of photovoltaic power generation components and the actual implementation consistency of quantitative analysis of photovoltaic power generation strategy, which cannot accurately locate the cooperative risk caused by the health problem of component individuals and accurately judge the implementation effect of the strategy, and ignores the influence of environmental conditions on cooperative operation control, which cannot evaluate the superposition effect of environmental adaptability on overall cooperative risk, which is not conducive to meeting the needs of distributed photovoltaic power generation system for full-dimensional and proactive management and control. SUMMARY

[0005] The purpose of the present application is to provide a distributed photovoltaic power generation coordinated operation system and method based on artificial intelligence to solve the technical defects proposed in the background art.

[0006] To achieve the above-mentioned purpose, the present application provides the following technical solution: a distributed photovoltaic power generation coordinated operation system based on artificial intelligence, comprising a comprehensive monitoring and collecting module, a power generation component operation quality judgment module, a power generation environment adaptability analysis module, a coordinated operation controllability decision module and an intelligent supervision end.

[0007] The all-around monitoring and collecting module obtains all photovoltaic power generation components that need to be monitored, monitors all photovoltaic power generation components, and outputs monitoring data in real time; the power generation component operation quality judgment module comprehensively analyzes the operation quality of the photovoltaic power generation component, marks the corresponding photovoltaic power generation component as an optimal coordination component or a non-easy coordination component through analysis, and sends the marking information and the operation quality evaluation value of the corresponding photovoltaic power generation component to the coordination operation controllability decision module;

[0008] The power generation environment adaptability analysis module analyzes the environment of the photovoltaic power generation component, obtains an adaptability decision coefficient through analysis, and sends the adaptability decision coefficient to the coordination operation controllability decision module; the coordination operation controllability decision module is used for analyzing the cooperative control hidden danger degree of all photovoltaic power generation components, generating an operation controllability qualified signal or an operation controllability alarm signal according to the analysis, and sending the operation controllability qualified signal or the operation controllability alarm signal to the intelligent supervision end; when the intelligent supervision end receives the operation controllability alarm signal, the corresponding early warning is sent out.

[0009] Further, the power generation component operation quality judgment module is in communication connection with the power generation component condition evaluation module and the strategy execution fitness detection module; the power generation component condition evaluation module analyzes the equipment condition of the corresponding photovoltaic power generation component, obtains a power generation component risk coefficient through analysis, and transmits the power generation component risk coefficient to the power generation component operation quality judgment module;

[0010] The strategy execution fitness detection module is used for monitoring the execution process of the photovoltaic power generation component corresponding to the photovoltaic power generation strategy, analyzing the strategy execution fitness performance of the corresponding photovoltaic power generation component, obtaining a power generation component execution coefficient through analysis, and sending the power generation component execution coefficient to the power generation component operation quality judgment module.

[0011] Further, when the power generation component risk coefficient and the power generation component execution coefficient are received by the power generation component operation quality judgment module, the power generation component risk coefficient and the power generation component execution coefficient are weighted and summed to obtain the operation quality evaluation value of the corresponding photovoltaic power generation component;

[0012] The operation quality evaluation value is compared with a preset operation quality evaluation threshold value; if the operation quality evaluation value exceeds the preset operation quality evaluation threshold value, the corresponding photovoltaic power generation component is marked as a non-easy coordination component; if the operation quality evaluation value does not exceed the preset operation quality evaluation threshold value, the corresponding photovoltaic power generation component is marked as an optimal coordination component.

[0013] Further, the specific analysis process of the power generation component condition evaluation module includes:

[0014] The temperature values of a plurality of positions on the corresponding photovoltaic power generation component are obtained, and the temperature values of the corresponding positions are compared with the corresponding preset safety temperature threshold to obtain a temperature coefficient, and the temperature coefficients of all positions are averaged to obtain a component temperature risk characteristic value;

[0015] The historical total running time of the corresponding photovoltaic power generation component is collected and marked as a component running characteristic value, and the number of times that the photovoltaic power generation component is not maintained within the specified interval time in the historical stage is marked as a component maintenance characteristic value;

[0016] The total number of times of failure of the corresponding photovoltaic power generation component in the backtracking period is marked as a component failure characteristic value, and the component temperature risk characteristic value, the component running characteristic value, the component maintenance characteristic value and the component failure characteristic value are weighted and summed to obtain a power generation component quality difference coefficient.

[0017] Further, the specific analysis process of the strategy implementation consistency detection module includes:

[0018] The actual power generation power of the corresponding photovoltaic power generation component is collected, and the deviation of the actual power generation power from the set standard power generation power is marked as a power execution characteristic value, and the deviation of the inclination angle of the light receiving surface of the corresponding photovoltaic power generation component from the set standard angle is marked as a light receiving angle deviation characteristic value. The power execution characteristic value and the light receiving angle deviation characteristic value are respectively compared with the preset power execution characteristic threshold and the preset light receiving angle deviation characteristic threshold, and if the power execution characteristic value or the light receiving angle deviation characteristic value exceeds the corresponding preset threshold, it is judged that the corresponding photovoltaic power generation component is in a consistent abnormal state;

[0019] The total time length of the corresponding photovoltaic power generation component in the unit time in the consistent abnormal state is obtained and marked as a consistent abnormal time value, and the average of all power execution characteristic values of the corresponding photovoltaic power generation component in the unit time is calculated to obtain a power execution abnormal value, and the average of all light receiving angle deviation characteristic values of the corresponding photovoltaic power generation component in the unit time is calculated to obtain an angle adjustment execution abnormal value. The consistent abnormal time value, the power execution abnormal value and the angle adjustment execution abnormal value are weighted and summed to obtain a power generation component execution consistency coefficient.

[0020] Further, the specific analysis process of the power generation environment adaptability analysis module is as follows:

[0021] The non-adaptation zone measurement value is obtained by analysis, a plurality of preset non-adaptation zone measurement value ranges are set in advance, each preset non-adaptation zone measurement value range corresponds to a preset environmental impact weight value, the non-adaptation zone measurement value is compared with all preset non-adaptation zone measurement value ranges one by one, the preset non-adaptation zone measurement value range containing the corresponding non-adaptation zone measurement value is marked as a target range, and the preset environmental impact weight value corresponding to the target range is marked as an adaptability decision coefficient.

[0022] Further, the analysis method of the non-adaptation zone measurement value is specifically as follows:

[0023] The temperature, humidity and wind speed of the photovoltaic power generation environment are obtained, the temperature of the environment is subtracted from the corresponding standard temperature and the absolute value is taken to obtain a temperature adaptation coefficient, and similarly, a humidity adaptation coefficient and a wind speed adaptation coefficient are obtained; the temperature adaptation coefficient, the humidity adaptation coefficient and the wind speed adaptation coefficient are weighted and summed to obtain an adaptability evaluation value;

[0024] A rectangular coordinate system is established with time as the X-axis and the adaptability evaluation value as the Y-axis, an adaptability curve is drawn in the first quadrant of the rectangular coordinate system based on all adaptability evaluation values in a unit time, and the starting point of the adaptability curve is located on the Y-axis; an adaptability demarcation ray parallel to the X-axis and having an end point on the Y-axis is drawn in the first quadrant of the rectangular coordinate system, the area surrounded by the part of the adaptability curve above the adaptability demarcation ray and the adaptability demarcation ray is marked as a non-adaptation zone, and the areas of all non-adaptation zones are summed to obtain a non-adaptation zone measurement value.

[0025] Further, the specific analysis process of the coordinated operation controllability decision module includes:

[0026] The number of non-easy-to-coordinate components is obtained and is ratio calculated with the total number of photovoltaic power generation components required to be supervised to obtain a non-easy-to-coordinate measurement value, the non-easy-to-coordinate measurement value is compared with a preset non-easy-to-coordinate measurement threshold value, and if the non-easy-to-coordinate measurement value exceeds the preset non-easy-to-coordinate measurement threshold value, an operation controllability alarm signal is generated.

[0027] Further, if the non-easy-to-coordinate measurement value does not exceed the preset non-easy-to-coordinate measurement threshold value, the operation quality evaluation values of all photovoltaic power generation components are mean calculated to obtain an operation quality characteristic value, and the operation quality evaluation value with the largest value is marked as an operation quality amplitude value; the non-easy-to-coordinate measurement value, the operation quality characteristic value and the operation quality amplitude value are weighted and summed to obtain an operation quality decision value.

[0028] The system retrieves the adaptability decision coefficient, multiplies it by the operation quality decision value to obtain the controllability decision value, compares the controllability decision value with the preset controllability decision threshold, and generates an operation controllability alarm signal if the controllability decision value exceeds the preset controllability decision threshold; otherwise, it generates an operation controllability qualified signal.

[0029] This invention also proposes a method for coordinated operation of distributed photovoltaic power generation based on artificial intelligence, comprising the following steps:

[0030] Step 1: Monitor all photovoltaic power generation modules and output monitoring data in real time;

[0031] Step 2: Conduct a comprehensive analysis of the operational quality of the photovoltaic power generation modules;

[0032] Step 3: Analyze the environmental conditions of the photovoltaic power generation module's location;

[0033] Step 4: Analyze the degree of potential risks in the coordinated control of all photovoltaic power generation modules;

[0034] Step 5: When generating an operational controllability alarm signal, the intelligent monitoring terminal will issue an early warning.

[0035] Compared with the prior art, the beneficial effects of the present invention are:

[0036] 1. In this invention, the operating quality of each photovoltaic power generation component is accurately judged by the power generation component operation quality judgment module, and the power generation environment adaptability analysis module is accurately judged by the degree of environmental impact. Based on the operation quality information and environmental impact factors, the controllability of photovoltaic power generation coordinated operation is comprehensively evaluated, which greatly reduces the complexity of supervision and response time, shifts from passively responding to faults to actively preventing and controlling risks, and comprehensively improves the safety, stability and efficiency of distributed photovoltaic power generation coordinated operation.

[0037] 2. In this invention, the risk coefficient of the power generation component is quantified from the perspective of the health of the equipment itself, and the execution coefficient of the power generation component is calculated from the perspective of the strategy implementation effect. The two dimensions empower the power generation component operation quality judgment module to analyze and achieve the scientific division of well-coordinated components and non-coordinated components. This not only completes the fine control of the individual operation status of the component, but also clarifies the key focus objects for subsequent overall collaborative decision-making. Attached Figure Description

[0038] To facilitate understanding by those skilled in the art, the present invention will be further described below with reference to the accompanying drawings;

[0039] Figure 1 This is an overall system block diagram of the present invention;

[0040] Figure 2This is a flowchart of the method of the present invention. Detailed Implementation

[0041] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. 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.

[0042] Example 1: As Figure 1 As shown, the distributed photovoltaic power generation coordinated operation system based on artificial intelligence proposed in this invention includes an all-round monitoring and acquisition module, a power generation component operation quality judgment module, a power generation environment adaptability analysis module, a coordinated operation controllability decision-making module, and an intelligent monitoring terminal.

[0043] The comprehensive monitoring and acquisition module acquires all photovoltaic power generation components that need to be monitored, monitors all photovoltaic power generation components and outputs monitoring data in real time. By comprehensively covering all photovoltaic power generation components and outputting monitoring data in real time, it breaks through the limitations of monitoring single components or local data, ensures the integrity and real-time nature of the data, provides reliable raw data support for subsequent analysis and decision-making, and ensures the accuracy of the analysis results of each module from the source.

[0044] The photovoltaic (PV) power generation module operation quality assessment module comprehensively analyzes the operation quality of PV power generation modules. Through analysis, it marks corresponding PV power generation modules as either highly coordinated or uncoordinated modules. The marking information and operation quality assessment values ​​of these modules are then sent to the coordinated operation controllability decision-making module. This module analyzes the operation quality of each PV power generation module from two dimensions: "equipment condition" and "strategy execution performance." It reasonably assesses the operation quality of each PV power generation module and accurately distinguishes between highly coordinated and uncoordinated modules, avoiding the bias of a single-dimensional assessment. Furthermore, it provides information support to the coordinated operation controllability decision-making module's analysis process, ensuring its comprehensiveness and accuracy. The specific analysis process is as follows:

[0045] The photovoltaic module operation quality judgment module receives the risk coefficient and the compliance coefficient of the photovoltaic module. It calculates the operation quality assessment value of the photovoltaic module by weighted summation of the risk coefficient and the compliance coefficient. That is, it assigns the risk coefficient and the compliance coefficient to the corresponding preset weight coefficients, and multiplies the risk coefficient and the compliance coefficient by the corresponding preset weight coefficients. The two sets of product results are marked as the operation quality assessment value.

[0046] It should be noted that the higher the value of the operation quality assessment, the worse the overall operation quality of the corresponding photovoltaic power generation module. The operation quality assessment value is compared with the preset operation quality assessment threshold. If the operation quality assessment value exceeds the preset operation quality assessment threshold, it indicates that the overall operation quality of the corresponding photovoltaic power generation module is poor, and the corresponding photovoltaic power generation module is marked as a non-easily coordinated module. If the operation quality assessment value does not exceed the preset operation quality assessment threshold, it indicates that the overall operation quality of the corresponding photovoltaic power generation module is good, and the corresponding photovoltaic power generation module is marked as an excellent coordinated module.

[0047] The power generation environment adaptability analysis module analyzes the environmental conditions of the photovoltaic power generation modules, obtains adaptability decision coefficients through analysis, and sends these coefficients to the coordinated operation controllability decision module. This allows for a reasonable assessment of the impact of the power generation environment, effectively considering the influence of environmental factors on the coordinated operation of photovoltaic power generation. It fills the gap in traditional management that neglects environmental adaptability, and provides a key environmental dimension reference for the coordinated operation controllability decision module. The specific analysis process is as follows:

[0048] The temperature, humidity, and wind speed of the photovoltaic power generation environment are obtained. The temperature of the environment is calculated by comparing it with the corresponding standard temperature and the absolute value is taken to obtain the temperature adaptation coefficient. The humidity of the environment is calculated by comparing it with the corresponding standard humidity and the absolute value is taken to obtain the humidity adaptation coefficient. The wind speed of the environment is calculated by comparing it with the corresponding standard wind speed and the absolute value is taken to obtain the wind speed adaptation coefficient.

[0049] The suitability assessment value is obtained by weighted summation of the temperature, humidity, and wind speed adaptability coefficients. Specifically, each of the temperature, humidity, and wind speed adaptability coefficients is assigned a corresponding preset weight coefficient, and each coefficient is multiplied by its respective preset weight coefficient. The sum of these three products is then marked as the suitability assessment value. It should be noted that a higher suitability assessment value indicates a worse real-time power generation environment.

[0050] A rectangular coordinate system is established with time as the X-axis and adaptability assessment values ​​as the Y-axis. Adaptability curves are plotted in the first quadrant of the rectangular coordinate system based on all adaptability assessment values ​​within a unit time period, with the starting point of the adaptability curve located on the Y-axis. Adaptability boundary rays are drawn in the first quadrant of the rectangular coordinate system, parallel to the X-axis and with their endpoints on the Y-axis. The area enclosed by the portion of the adaptability curve above the adaptability boundary ray and the ray itself is marked as the non-adaptability region. The area of ​​all non-adaptability regions is summed to obtain the measured value of the non-adaptability region. Furthermore, the larger the measured value of the non-adaptability region, the greater the overall adverse impact of the power generation environment on the coordinated operation and management of power generation.

[0051] Several preset non-adaptive area measurement ranges are pre-defined, and each preset non-adaptive area measurement range corresponds to a preset environmental impact weight value with a value greater than zero. It should be noted that the larger the value of the corresponding preset non-adaptive area measurement range, the larger the value of the preset environmental impact weight value that matches it. The non-adaptive area measurement value is compared with all preset non-adaptive area measurement ranges one by one. The preset non-adaptive area measurement range containing the corresponding non-adaptive area measurement value is marked as the target range, and the preset environmental impact weight value corresponding to the target range is marked as the adaptability decision coefficient.

[0052] The coordinated operation controllability decision module analyzes the degree of potential risks in the coordinated control of all photovoltaic power generation modules. Based on this, it generates an operation controllability pass signal or an operation controllability alarm signal, which is then sent to the intelligent monitoring terminal. Upon receiving the operation controllability alarm signal, the intelligent monitoring terminal issues a corresponding warning. This approach can quickly respond to the explicit risk of excessive proportion of non-coordinated modules, and also uncover potential risks such as "superimposed risks of operational quality and environmental compatibility even if the proportion is not excessive." This significantly reduces the difficulty of supervision and helps to promptly remind regulatory personnel to strengthen photovoltaic power generation supervision and make reasonable improvement measures, ensuring the safety, stability, and efficiency of photovoltaic power generation. The specific analysis process is as follows:

[0053] The number of non-coordinating components is obtained and its ratio is calculated to the total number of photovoltaic power generation components to be monitored to obtain the non-coordinating prevalence value. The non-coordinating prevalence value is compared with the preset non-coordinating prevalence threshold. If the non-coordinating prevalence value exceeds the preset non-coordinating prevalence threshold, it indicates that the coordination operation control of all photovoltaic power generation components is at high risk, and an operation controllability alarm signal is generated.

[0054] Furthermore, if the non-coordination measurement value does not exceed the preset non-coordination measurement threshold, the average value of the operation quality assessment values ​​of all photovoltaic power generation modules is used to calculate the operation quality characteristic value, and the operation quality assessment value with the largest value is marked as the operation quality risk amplitude value.

[0055] The operational quality decision value is obtained by weighted summation of the non-coordination assessment value, operational quality characteristic value, and operational quality risk amplitude. Specifically, the non-coordination assessment value, operational quality characteristic value, and operational quality risk amplitude are each assigned a corresponding preset weight coefficient, and the non-coordination assessment value, operational quality characteristic value, and operational quality risk amplitude are multiplied by the corresponding preset weight coefficients. The sum of the three product results is then marked as the operational quality decision value.

[0056] In addition, the adaptability decision coefficient is retrieved, and the adaptability decision coefficient is multiplied by the operation quality decision value to obtain the controllability decision value. It should be noted that the larger the value of the controllability decision value, the higher the overall risk of coordinated operation and control of all photovoltaic power generation modules, and the less conducive it is to ensuring the safe, stable and efficient operation of all photovoltaic power generation modules.

[0057] The controllability decision value is compared with the preset controllability decision threshold. If the controllability decision value exceeds the preset controllability decision threshold, it indicates that the overall risk of coordinated operation control of all photovoltaic power generation components is relatively high, and an operation controllability alarm signal is generated. If the controllability decision value does not exceed the preset controllability decision threshold, it indicates that the overall risk of coordinated operation control of all photovoltaic power generation components is relatively low, and an operation controllability qualified signal is generated.

[0058] Example 2: Figure 1 As shown, the difference between this embodiment and Embodiment 1 is that the power generation component operation quality judgment module is communicatively connected to the power generation component condition assessment module and the strategy execution consistency detection module. The power generation component condition assessment module analyzes the equipment condition of the corresponding photovoltaic power generation component and obtains the power generation component quality risk coefficient through analysis.

[0059] Furthermore, transmitting the risk coefficient of the power generation components to the power generation component operation quality assessment module not only accurately quantifies the inherent health risks of each photovoltaic power generation component, but also provides data support for the analysis process of the power generation component operation quality assessment module, ensuring the accuracy of its analysis results; the specific analysis process of the power generation component condition assessment module is as follows:

[0060] The temperature values ​​at several locations on the corresponding photovoltaic power generation module are obtained. The temperature coefficient is calculated by comparing the temperature values ​​at the corresponding locations with the corresponding preset safe temperature threshold. The average of the temperature coefficients at all locations is then calculated to obtain the module temperature risk characteristic value.

[0061] The historical total operating time of the corresponding photovoltaic power generation module is collected and marked as the module operating characteristic value. The number of times the photovoltaic power generation module was not maintained within the specified interval in the historical period is marked as the module maintenance characteristic value. The current date is used as the end date to trace back and set the number of days as P1. Preferably, P1 is thirty days. The total number of times the corresponding photovoltaic power generation module failed during the traceback period is marked as the module failure characteristic value.

[0062] The structural anomaly coefficient of a photovoltaic (PV) module is calculated by weighting and summing the module's temperature hazard characteristics, operational characteristics, maintenance characteristics, and fault characteristics. Specifically, each of these four characteristics is assigned a pre-defined weighting coefficient, and each is then multiplied by its respective weighting coefficient. The sum of these four products is then labeled as the structural anomaly coefficient. It should be noted that a higher structural anomaly coefficient indicates a poorer overall condition of the PV module, making it more difficult to ensure its safe and stable operation.

[0063] Furthermore, the strategy execution consistency detection module monitors the execution process of photovoltaic (PV) modules in response to their corresponding PV power generation strategies, analyzes the strategy execution consistency performance of each PV module, obtains the consistency coefficient of the PV module through analysis, and sends the consistency coefficient to the PV module operation quality judgment module. This not only accurately reflects the consistency of each PV module's execution with the PV power generation strategy, but also provides data support for the analysis process of the PV module operation quality judgment module, ensuring the accuracy of its analysis results. The specific analysis process of the strategy execution consistency detection module is as follows:

[0064] The actual power generation of the corresponding photovoltaic power generation module is collected. The deviation of the actual power generation from the set standard power generation is marked as the power execution characteristic value. The deviation of the tilt angle of the light-receiving surface of the corresponding photovoltaic power generation module from the set standard angle is marked as the light-receiving angle deviation characteristic value. The power execution characteristic value and the light-receiving angle deviation characteristic value are compared with the preset power execution characteristic threshold and the preset light-receiving angle deviation characteristic threshold respectively. If the power execution characteristic value or the light-receiving angle deviation characteristic value exceeds the corresponding preset threshold, the corresponding photovoltaic power generation module is judged to be in an abnormal consistency state.

[0065] The total duration of the corresponding photovoltaic power generation module in the coincidence anomaly state within a unit time is obtained and marked as the coincidence time value. The power execution anomaly table value is obtained by averaging all the power execution characteristic values ​​of the corresponding photovoltaic power generation module within a unit time, and the angle adjustment execution anomaly table value is obtained by averaging all the light-receiving angle deviation characteristic values ​​of the corresponding photovoltaic power generation module within a unit time.

[0066] The matching coefficient of the photovoltaic module is calculated by weighting and summing the matching time difference value, the power execution difference value, and the angle adjustment difference value. Specifically, each of the matching time difference value, the power execution difference value, and the angle adjustment difference value is assigned a corresponding preset weight coefficient, and then each of these values ​​is multiplied by its corresponding preset weight coefficient. The sum of these three products is then marked as the matching coefficient of the photovoltaic module. It should be noted that the larger the matching coefficient of the photovoltaic module, the worse the overall performance of the corresponding photovoltaic module in implementing the photovoltaic power generation strategy.

[0067] Example 3: Figure 2 As shown, the difference between this embodiment and Embodiments 1 and 2 is that the distributed photovoltaic power generation coordinated operation method based on artificial intelligence includes the following steps:

[0068] Step 1: Monitor all photovoltaic power generation modules and output monitoring data in real time;

[0069] Step 2: Conduct a comprehensive analysis of the operational quality of the photovoltaic power generation modules;

[0070] Step 3: Analyze the environmental conditions of the photovoltaic power generation module's location;

[0071] Step 4: Analyze the degree of potential risks in the coordinated control of all photovoltaic power generation modules;

[0072] Step 5: When generating an operational controllability alarm signal, the intelligent monitoring terminal will issue an early warning.

[0073] The working principle of this invention is as follows: During use, the all-round monitoring and acquisition module covers all photovoltaic power generation components and outputs monitoring data in real time, avoiding analysis bias caused by missing or delayed data from the source. The power generation component operation quality judgment module accurately judges the operation quality of each photovoltaic power generation component, realizing the scientific classification of well-coordinated components and non-coordinated components. The power generation environment adaptability analysis module fills the gap in traditional management that ignores environmental impact. The coordinated operation controllability decision module comprehensively evaluates the controllability of photovoltaic power generation coordinated operation based on operation quality information and environmental impact factors, greatly reducing regulatory complexity and response time. It not only realizes the refined evaluation of the individual operation quality of photovoltaic power generation components, but also takes into account environmental factors and overall collaborative control risk assessment, shifting from passively responding to faults to actively preventing and controlling risks, and comprehensively improving the safety, stability and efficiency of distributed photovoltaic power generation coordinated operation.

[0074] In this invention, the threshold, preset value, or preset range settings are for result comparison and analysis to determine whether the result is good or bad. The magnitude of these values ​​is determined by a combination of large-scale model analysis of sample data and human experience, and can also be appropriately adjusted based on seasonal or common-sense influence conditions. Similarly, the preset weight coefficients and influence factors are assigned specific values ​​based on the magnitude of each parameter's influence on the result, ultimately reflecting the impact on the result. These settings are also determined by a combination of large-scale model analysis of sample data and human experience, and can also be appropriately adjusted based on seasonal or common-sense influence conditions.

[0075] The preferred embodiments of the present invention disclosed above are merely illustrative of the invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the invention to any specific implementation. Clearly, many modifications and variations can be made based on the content of this specification. This specification selects and specifically describes these embodiments to better explain the principles and practical applications of the invention, enabling those skilled in the art to better understand and utilize it. The invention is limited only by the claims and their full scope and equivalents.

Claims

1. A distributed photovoltaic power generation coordinated operation system based on artificial intelligence, characterized in that, It includes a comprehensive monitoring and data acquisition module, a power generation component operation quality judgment module, a power generation environment adaptability analysis module, a coordinated operation controllability decision-making module, and an intelligent monitoring terminal; The all-round monitoring and acquisition module acquires all photovoltaic power generation modules that need to be monitored, monitors all photovoltaic power generation modules, and outputs monitoring data in real time; the power generation module operation quality judgment module performs a comprehensive analysis of the operation quality of photovoltaic power generation modules, and marks the corresponding photovoltaic power generation modules as excellent coordination modules or non-easy coordination modules through analysis; The power generation environment adaptability analysis module analyzes the environmental conditions of the environment in which the photovoltaic power generation modules are located, and obtains the adaptability decision coefficient through the analysis; the coordinated operation controllability decision module is used to analyze the degree of collaborative control risks for all photovoltaic power generation modules, and sends the operation controllability qualified signal or operation controllability alarm signal to the intelligent monitoring terminal. When the intelligent monitoring terminal receives the operation controllability alarm signal, it issues an early warning.

2. The distributed photovoltaic power generation coordinated operation system based on artificial intelligence according to claim 1, characterized in that, The power generation component operation quality judgment module communicates with the power generation component condition assessment module and the strategy execution consistency detection module. The power generation component condition assessment module analyzes the equipment condition of the corresponding photovoltaic power generation component and transmits the power generation component risk coefficient to the power generation component operation quality judgment module. The strategy execution consistency detection module is used to monitor the execution process of photovoltaic power generation modules in response to the corresponding photovoltaic power generation strategies, analyze the strategy execution consistency performance of the corresponding photovoltaic power generation modules, and send the consistency coefficient of the power generation modules to the power generation module operation quality judgment module.

3. The distributed photovoltaic power generation coordinated operation system based on artificial intelligence according to claim 2, characterized in that, The photovoltaic module operation quality judgment module calculates the operation quality assessment value of the corresponding photovoltaic module by weighted summing of the risk coefficient and the compliance coefficient of the photovoltaic module. If the operation quality assessment value exceeds the preset operation quality assessment threshold, the corresponding photovoltaic module is marked as a non-coordinating module; otherwise, the corresponding photovoltaic module is marked as a well-coordinating module.

4. The distributed photovoltaic power generation coordinated operation system based on artificial intelligence according to claim 2, characterized in that, The specific analysis process of the power generation component condition assessment module includes: The temperature values ​​at several locations on the corresponding photovoltaic power generation module are obtained. The temperature coefficient is calculated by comparing the temperature values ​​at the corresponding locations with the corresponding preset safe temperature threshold. The average of the temperature coefficients at all locations is then calculated to obtain the module temperature risk characteristic value. The component quality coefficient is obtained by weighted summation of the component temperature risk characteristic value, component operation characteristic value, component maintenance characteristic value, and component fault characteristic value.

5. The distributed photovoltaic power generation coordinated operation system based on artificial intelligence according to claim 2, characterized in that, The specific analysis process of the strategy execution consistency detection module includes: The total duration of the corresponding photovoltaic power generation module in the coincidence anomaly state within a unit time is obtained and marked as the coincidence time value. The power execution anomaly value is obtained by averaging all power execution characteristic values ​​of the corresponding photovoltaic power generation module within a unit time, and the angle adjustment execution anomaly value is obtained by averaging all light-receiving angle deviation characteristic values ​​of the corresponding photovoltaic power generation module within a unit time. The coincidence time value, the power execution anomaly value, and the angle adjustment execution anomaly value are weighted and summed to obtain the power generation module coincidence coefficient.

6. The distributed photovoltaic power generation coordinated operation system based on artificial intelligence according to claim 1, characterized in that, The specific analysis process of the power generation environment adaptability analysis module is as follows: by analyzing the non-adaptable area measurement values, the preset non-adaptable area measurement value range containing the corresponding non-adaptable area measurement values ​​is marked as the target range, and the preset environmental impact weight value corresponding to the target range is marked as the adaptability decision coefficient.

7. The distributed photovoltaic power generation coordinated operation system based on artificial intelligence according to claim 1, characterized in that, The specific methods for analyzing and obtaining measurements in the non-fit region are as follows: The compatibility assessment value is obtained by weighted summation of temperature compatibility coefficient, humidity compatibility coefficient and wind speed compatibility coefficient. Based on all compatibility assessment values ​​per unit time, a compatibility curve is plotted in the first quadrant of the rectangular coordinate system. The area enclosed by the portion of the compatibility curve above the compatibility boundary ray and the compatibility boundary ray is marked as the non-fit area. The area of ​​all non-fit areas is summed to obtain the non-fit area measurement value.

8. The distributed photovoltaic power generation coordinated operation system based on artificial intelligence according to claim 1, characterized in that, The specific analysis process of the coordinated operation controllability decision module includes: The number of non-coordinating components is obtained and its ratio to the total number of photovoltaic power generation components to be monitored is calculated to obtain the non-coordinating prevalence value. If the non-coordinating prevalence value exceeds the preset non-coordinating prevalence threshold, an operational controllability alarm signal is generated.

9. The distributed photovoltaic power generation coordinated operation system based on artificial intelligence according to claim 8, characterized in that, If the non-coordination measurement value does not exceed the preset non-coordination measurement threshold, the operation quality decision value is calculated by weighting and summing the non-coordination measurement value, the operation quality characteristic value, and the operation quality risk amplitude value. The adaptability decision coefficient is then multiplied by the operation quality decision value to obtain the controllability decision value. If the controllability decision value exceeds the preset controllability decision threshold, an operation controllability alarm signal is generated; otherwise, an operation controllability qualified signal is generated.

10. A method for coordinated operation of distributed photovoltaic power generation based on artificial intelligence, characterized in that, Includes the following steps: Step 1: Monitor all photovoltaic power generation modules and output monitoring data in real time; Step 2: Conduct a comprehensive analysis of the operational quality of the photovoltaic power generation modules; Step 3: Analyze the environmental conditions of the environment in which the photovoltaic power generation modules are located; Step 4: Analyze the degree of potential risks in the coordinated control of all photovoltaic power generation modules; Step 5: When an operational controllability alarm signal is generated, issue an early warning at the intelligent monitoring terminal.

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