A distributed photovoltaic fusion control method and device

Abstract

The present invention relates to a fusion control method and device for distributed photovoltaics, and relates to the field of photovoltaic control. When no islanding effect is triggered at a photovoltaic site, device control parameters are obtained for state mapping to obtain a baseline state of device operation; device state information is received according to a default period, and if the device state information is consistent with the device operation baseline state, a device state fluctuation curve is constructed to perform same-modal high-frequency abnormal cycle indexing to obtain a device state monitoring cycle to update a default period; when an islanding effect is triggered at a photovoltaic site, or when the device state information is inconsistent with the device operation baseline state, off-grid control of the photovoltaic site is performed through a photovoltaic circuit breaker, thereby solving the technical problems of response lag and inflexible control caused by fixed-period monitoring in photovoltaic management.

Description

Technical Field

[0001] The present invention relates to the field of photovoltaic control, and in particular to a distributed photovoltaic fusion control method and device. Background Art

[0002] With the growing global emphasis on and demand for renewable energy, distributed photovoltaic power generation has been widely used as a clean and efficient way to utilize energy. However, the effective management and monitoring of distributed photovoltaic sites has become an urgent issue.

[0003] Traditional distributed photovoltaic management typically relies on fixed-cycle monitoring methods, which exhibit significant lags and make it difficult to respond promptly to abnormalities at PV sites. For example, when islanding occurs at a PV site or equipment operating abnormalities occur, traditional monitoring methods are unable to quickly detect and implement control measures, leading to equipment damage, power outages, and other problems. Furthermore, existing PV site monitoring and control solutions often lack intelligence and adaptability. They typically operate according to preset parameters and rules, lacking the ability to adjust flexibly. This limits further improvements in PV site power generation efficiency and power supply stability. Summary of the Invention

[0004] The present invention aims to solve the technical problems of delayed response and inflexible control caused by fixed-cycle monitoring in photovoltaic management in the prior art, and provides a distributed photovoltaic fusion control method and device to solve the problems.

[0005] The technical solution of the present invention to solve the above technical problems is as follows:

[0006] In a first aspect, the present invention provides a fusion control method for distributed photovoltaics, which is applied to a master station, and the master station is communicatively connected to a photovoltaic site. The method includes: when the photovoltaic site does not trigger an islanding effect, obtaining device control parameters for state mapping to obtain a baseline state of device operation; receiving device status information according to a default period, and if the device status information is consistent with the device operation baseline state, constructing a device status fluctuation curve for same-modal high-frequency abnormal cycle indexing, and obtaining a device status monitoring period to update a default period; when an islanding effect is triggered at the photovoltaic site, or when the device status information is inconsistent with the device operation baseline state, performing off-grid control of the photovoltaic site through a photovoltaic circuit breaker.

[0007] In the second aspect, the present invention provides a distributed photovoltaic fusion control device, which is applied to a master station, and the master station is communicatively connected to the photovoltaic site. The device includes: a parameter acquisition module, which is used to obtain device control parameters for state mapping and obtain the equipment operation baseline state when the photovoltaic site does not trigger the island effect; a state judgment module, which is used to receive device status information according to a default period, and if the device status information is consistent with the device operation baseline state, construct a device status fluctuation curve for the same modal high-frequency abnormal cycle index, and obtain the device status monitoring cycle to update the default period; an off-grid control module, which is used to perform off-grid control of the photovoltaic site through a photovoltaic circuit breaker when the photovoltaic site triggers the island effect, or when the device status information is inconsistent with the device operation baseline state.

[0008] The beneficial effects of the present invention are: through dynamic periodic monitoring and intelligent control strategies, real-time, efficient and accurate management of photovoltaic sites is achieved, thereby ensuring the stability of photovoltaic power supply and quickly responding to site abnormalities to avoid failures. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] Figure 1 A schematic flow chart of a distributed photovoltaic fusion control method provided by the present invention.

[0010] Figure 2 This is a structural schematic diagram of a distributed photovoltaic fusion control device provided by the present invention.

[0011] Description of reference numerals: parameter acquisition module 11 , state determination module 12 , off-grid control module 13 . DETAILED DESCRIPTION

[0012] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without making any creative efforts shall fall within the scope of protection of the present invention.

[0013] In the description of the present invention, the terms "first" and "second" are used for descriptive purposes only and should not be understood to indicate or imply relative importance or implicitly specify the number of the technical features indicated. Therefore, a feature specified as "first" or "second" may explicitly or implicitly include one or more of the specified features. In the description of the present invention, "plurality" means two or more, unless otherwise specifically defined.

[0014] In the description of the present invention, the term "for example" is used to mean "used as an example, illustration or illustration". Any embodiment of the present invention described as "for example" is not necessarily to be construed as being more preferred or advantageous than other embodiments. The following description is given to enable any person skilled in the art to implement and use the present invention. In the following description, details are listed for the purpose of explanation. It should be understood that a person of ordinary skill in the art can recognize that the present invention can be implemented without using these specific details. In other examples, well-known structures and processes are not elaborated in detail to avoid obscuring the description of the present invention with unnecessary details. Therefore, the present invention is not intended to be limited to the embodiments shown, but is consistent with the widest scope consistent with the principles and features disclosed herein.

[0015] Example 1:

[0016] like Figure 1 As shown, an embodiment of the present invention provides a distributed photovoltaic integrated control method, which is applied to a master station, the master station is communicatively connected with a photovoltaic site, and the method includes:

[0017] S10: When the islanding effect is not triggered at the photovoltaic site, the device control parameters are obtained for state mapping to obtain the device operation baseline state.

[0018] S20: Receive device status information according to a default period. If the device status information is consistent with the device operating baseline status, construct a device status fluctuation curve to perform a same-modal high-frequency abnormal period index, and obtain a device status monitoring period to update the default period.

[0019] S30: When an islanding effect is triggered at the photovoltaic site, or when the device status information is inconsistent with the device operation baseline status, off-grid control of the photovoltaic site is performed through a photovoltaic circuit breaker.

[0020] Exemplarily, the master station is responsible for receiving data from each photovoltaic site, performing data analysis and processing, and sending control instructions to the photovoltaic site based on the analysis results to achieve efficient and stable operation of the entire distributed photovoltaic system. The photovoltaic site, that is, the installation location of the photovoltaic power generation system, is the basic unit in the distributed photovoltaic system. It is mainly composed of solar cell modules, batteries, controllers, inverters and other components, and can independently complete the collection, conversion and storage of solar energy. The photovoltaic site converts solar energy into electrical energy through solar cell modules, and converts electrical energy into alternating current suitable for use in the power grid through controllers and inverters. At the same time, the photovoltaic site also has data collection and communication functions, and can upload its own operating status and data to the master station in real time so that the master station can perform unified management and control. The control method of the present application is implemented by applying it to the master station and relying on the communication connection between the master station and the photovoltaic site.

[0021] Specifically, the islanding effect refers to the phenomenon in which, under certain conditions, a photovoltaic power generation system, even after being disconnected from the grid, continues to supply power to independent loads, forming a self-sufficient power supply island. When the photovoltaic site does not trigger the islanding effect, it means that the connection between the photovoltaic power generation system and the grid is normal, and the electricity generated by photovoltaic power generation can be smoothly transmitted to the grid or obtained from the grid to meet load demand. At this time, the master station can send query commands through the communication connection with the photovoltaic site to obtain the current device control parameters of the photovoltaic inverter. These parameters include limit control, fixed value control, proportional control, and timing control, which are used to control key indicators such as the output power and power factor of the photovoltaic inverter to ensure stable operation of the photovoltaic site. Limit control refers to the master station issuing limit control commands to limit the output active power and reactive power of the photovoltaic inverter to preset set points. This is to prevent the photovoltaic inverter from overloading or generating excessive reactive power, which could affect the stable operation of the grid. Fixed value control refers to the master station issuing fixed value control commands to adjust the power factor of the photovoltaic inverter to a preset set point. This helps maintain power balance and voltage stability in the power grid. Proportional control refers to the master station issuing proportional control commands based on the inverter's rated power, ensuring that the active and reactive power of the PV inverter do not exceed set proportional values. This control method allows flexible adjustment of output power based on the actual conditions of the PV site to accommodate varying lighting conditions and grid requirements. Timed control refers to the master station issuing timed control commands, which in turn causes the PV data collection and monitoring unit to control the PV inverter using limit control or proportional control within a preset time period. This control method enables scheduled start and stop times or power adjustments at the PV site to accommodate varying operational requirements. The master station then maps the operating status of the PV inverter based on the acquired device control parameters and real-time operating data from the PV site, such as active power, reactive power, and power factor. State mapping aims to transform complex operating data into easily understandable and analyzable metrics, enabling the master station to monitor and manage the PV site's operating status. Through state mapping, the master station can determine baseline parameters for the PV inverter under normal operating conditions. These baseline parameters refer to a series of preset or actual operating parameters for key equipment, primarily the PV inverter, under normal operating conditions at the PV site. These parameters form the baseline or reference point for PV site operations, used to assess equipment status, provide fault warnings, and optimize performance. These baseline parameters include, but are not limited to, set and actual operating values ​​for active power, reactive power, and power factor. Establishing this baseline provides the foundation for subsequent equipment monitoring, fault warnings, and performance optimization. In summary, by acquiring device control parameters and performing state mapping, the master station can establish a baseline for PV site equipment operation, providing strong support for subsequent equipment monitoring, fault warnings, and performance optimization.

[0022] Furthermore, in a device monitoring system, the system is configured to receive status information from devices at a predetermined default period. This period is manually set to periodically check and update device status information. This status information includes various device operating parameters and reflects the device's current condition. Each time the system receives this status information, it compares it with a previously established baseline operating state for the device. This baseline state is derived from historical data from normal device operation and represents the ideal operating parameter range for the device. During this comparison, the state parameters of each attribute (such as power, temperature, and pressure) in the status information are compared one by one. If a state parameter for a particular attribute exceeds the baseline range, and the percentage of time that this exceeds the range is greater than or equal to a preset threshold, the device is considered inconsistent with the baseline state. Conversely, if all state parameters for any attribute do not exceed the baseline range, or the percentage of time that they exceed the range is less than the threshold, the device is considered consistent with the baseline state. For example, consider a device with a power parameter whose baseline range is 80%-120% of rated power. The preset threshold is 10%. Currently, device status information is received according to the default cycle (e.g., once a day). On a given day, the power parameters received for the device are as follows: 5% of the time is below 80% of the rated power, 85% of the time is between 80% and 120% of the rated power, and 10% of the time is above 120% of the rated power. Since the 10% of time exceeds the baseline interval equals the 10% threshold, if the 10% of time exceeds the baseline interval, it is considered inconsistent with the baseline state. If the device is consistent with the baseline state, a device status fluctuation curve is constructed. This curve reflects the temporal trend of device status parameters. Analysis of this curve can identify high-frequency anomaly cycles with the same modality within the device state—i.e., those that occur frequently and have similar patterns. Finally, based on information from these anomaly cycles, the device status monitoring cycle is updated. This new cycle may be shorter or longer than the default, depending on the frequency and pattern of device status changes. The updated cycle serves as the new default cycle for the next device status information reception and comparison. For example, if the system detects frequent fluctuations in device status and similar patterns, it might shorten the monitoring cycle to half a day or hourly to more promptly detect and address abnormal conditions. Conversely, if the device status is stable and anomalies are rare, the system might extend the monitoring cycle to every two days or once a week. In summary, setting a default period for receiving device status information allows for regular, automatic acquisition of the device's current status without manual intervention, thereby increasing the level of automation in operations and maintenance.When the device is consistent with its baseline status, the system requires no further action, reducing unnecessary intervention and wasted resources. When an anomaly is detected, the system responds quickly, notifying operations and maintenance personnel for immediate action, shortening the time required to discover and repair the fault. Comparison with the device's baseline operating status accurately determines whether the device is operating normally, avoiding operational risks caused by false alarms or missed alerts. Analysis of device status fluctuation curves identifies high-frequency anomaly cycles with the same modality within the device's status, providing operators with more accurate fault warning and location information. Furthermore, the monitoring cycle can be dynamically adjusted based on changes in device status, avoiding the resource waste and blind spots associated with fixed-cycle monitoring. When the device's status is stable, extending the monitoring cycle reduces the frequency of data transmission and processing, lowering system energy consumption and operational costs. When the device's status is abnormal, shortening the monitoring cycle allows for more timely detection and resolution of the anomaly, improving system reliability and security.

[0023] In one specific embodiment, when a photovoltaic site triggers an islanding effect or device status information deviates from its baseline operating state, this indicates that the site or its equipment may have encountered a problem, requiring immediate action to prevent potential safety risks. In this case, the system will, based on pre-set safety policies, implement off-grid control of the photovoltaic site using a photovoltaic circuit breaker. A photovoltaic circuit breaker is a switching device specifically designed for photovoltaic systems. It can quickly disconnect the circuit upon detecting an abnormal condition, ensuring safe isolation of the photovoltaic site from the grid.

[0024] The embodiment of the present invention provides a distributed photovoltaic integrated control method, which has at least the following technical effects:

[0025] 1. When the PV site is not experiencing islanding, acquiring device control parameters and performing state mapping accurately establishes a baseline for device operation, providing a reliable reference for subsequent anomaly detection. Real-time status monitoring also ensures timely updates of device status, helping to identify potential issues and prevent them from escalating.

[0026] 2. When the device is in normal condition, the device status monitoring cycle can be intelligently adjusted by constructing a device status fluctuation curve and indexing the same-modal high-frequency anomaly cycles. This intelligent cycle adjustment mechanism not only reduces unnecessary data transmission and processing, lowering system energy consumption, but also improves the accuracy and timeliness of anomaly detection.

[0027] 3. When a PV site triggers an islanding effect or equipment status information deviates from baseline, the system responds quickly, disconnecting the site from the grid via the PV circuit breaker. This rapid response ensures safe operation of the PV site under abnormal conditions, avoiding voltage and frequency instability caused by islanding or equipment failure.

[0028] In a preferred embodiment, the obtaining of device control parameters includes: when the photovoltaic site does not trigger the island effect, when the first fluctuation value of the monitored grid load and the pre-stored grid load is greater than or equal to the first fluctuation threshold, or / and the second fluctuation value of the monitored environmental factor and the pre-stored environmental factor is greater than or equal to the second fluctuation threshold, the monitored grid load and the monitored environmental factor are input into the historical control database to retrieve the device control parameters; the pre-stored grid load is updated using the monitored grid load, the pre-stored environmental factor is updated using the monitored environmental factor, and the device control parameters are sent down to the photovoltaic site for control initialization.

[0029] Specifically, when the system is not experiencing islanding, it dynamically adjusts device control parameters based on fluctuations in grid load and environmental factors. During normal operation, the PV plant continuously monitors grid load and environmental factors (such as light intensity and temperature) and compares these data with pre-stored grid load and environmental factor data. This pre-stored data serves as the baseline for the system's last stable operation. When a first fluctuation between the monitored grid load and the pre-stored grid load reaches or exceeds a preset first fluctuation threshold, or a second fluctuation between the monitored environmental factor and the pre-stored environmental factor reaches or exceeds a preset second fluctuation threshold, the system deems the current state to have changed significantly and requires control parameter optimization. To obtain the optimal device control parameters, the current monitored grid load and monitored environmental factors are used as inputs to search a historical control database. This database stores control parameters from successful PV plants operating under similar conditions. This search identifies the historical control parameters that best match the current state, and these parameters are considered the optimal control strategy for the current state. Once suitable device control parameters are retrieved, the pre-stored grid load and environmental factor data are immediately updated with these new parameters to ensure an updated baseline for the next comparison. At the same time, these new device control parameters are distributed to the PV site for initialization or adjustment of the site's control strategy. A crucial prerequisite for this entire process is whether the PV site triggers an islanding effect. Islanding occurs when, during a power outage, the PV system continues to supply power to the affected grid line, creating a self-sufficient power supply island beyond the control of the power company. To detect islanding, PV sites employ various methods, such as active frequency shift detection, active phase shift detection, and voltage positive feedback islanding detection. These methods apply small perturbations to the grid and observe the response to determine whether an islanding effect has occurred. Once an islanding effect is detected, appropriate protective measures are immediately implemented to ensure the safety of personnel and equipment. Specific detection methods are known in the art and will not be detailed here. In summary, this process is essentially an intelligent control strategy based on historical database retrieval. It can quickly retrieve optimal device control parameters and make corresponding adjustments when grid load and environmental factors fluctuate, ensuring stable operation and optimized control of the PV site. Furthermore, by continuously monitoring and updating pre-stored data, it can continuously adapt to changes in the external environment, improving overall control efficiency and accuracy.

[0030] In a preferred embodiment, the step of inputting the grid load and the environmental factors into a historical control database to retrieve the device control parameters comprises: inputting the grid load and the environmental factors into the historical control database to retrieve multiple sets of initial device control parameters for the photovoltaic site, wherein the multiple sets of initial device control parameters have multiple fitness identifiers, and the greater the fitness, the better the control parameters; using the multiple sets of initial device control parameters as the initial population, performing population iteration according to the multiple fitness identifiers to obtain updated device control parameters; configuring the grid load, the environmental factors, and the photovoltaic site layout topology as background conditions, setting the updated device control parameters as foreground conditions, and searching online for the power factor of the control samples that meet the background conditions and the foreground conditions. The power factor eigenvalue, the control stability eigenvalue and the dynamic response speed eigenvalue are processed by a fitness evaluation function to obtain an updated parameter fitness identifier; when the updated parameter fitness identifier is less than or equal to the minimum value of the multiple fitness identifiers, a loop iteration is performed based on the multiple groups of initial device control parameters; when the updated parameter fitness identifier is greater than the minimum value of the multiple fitness identifiers, the updated device control parameter is used to replace the initial device control parameter with the minimum value of the fitness identifier of the multiple groups of initial device control parameters, and an updated population is obtained for loop iteration; when the number of loop iterations meets the preset number, the device control parameter with the maximum value of the fitness identifier is output.

[0031] Optionally, real-time monitored grid load and environmental factors are used as input conditions and fed into a historical control database. This database stores a large number of historical PV site device control parameters under similar conditions. These parameters are assigned fitness indicators, with larger indicators indicating better control parameter performance. Multiple sets of initial device control parameters matching the current conditions can be retrieved from the database. These parameters form the initial population for subsequent optimization. The population is then iteratively updated based on the fitness indicators of these parameters. During the iterative process, parameters are continuously adjusted to achieve optimal control results. To more comprehensively evaluate parameter performance, the current grid load, environmental factors, and the PV site layout and topology are used as background conditions, and the iteratively updated device control parameters are used as foreground conditions. An online search is conducted to identify control samples that meet these conditions. Background conditions are relatively stable, unchangeable, and significantly impact PV site operation. These factors serve as the baseline for online control sample retrieval, filtering out historical data or cases similar to the current situation. Grid load, as such, refers to the power demand on the grid at a given moment. The magnitude and variability of grid load directly impacts a PV site's power generation plan and output. Environmental factors, including natural factors such as sunlight intensity, temperature, humidity, and wind speed, directly impact the efficiency and performance of PV modules. The PV site layout topology refers to the layout and connection of key components within the site, including PV modules, inverters, and energy storage devices. This configuration determines the paths for power transmission and conversion, significantly impacting the overall performance and stability of the system. Foreground conditions are relatively flexible and adjustable factors used to optimize the operational performance of a PV site. When searching for control samples online, these factors are set as optimization targets to identify optimal control parameters that meet specific requirements. When searching for control samples online, the system searches and matches historical databases based on the specified background and foreground conditions. These conditions together determine the search direction and target, identifying historical data or cases that meet the requirements. Key eigenvalues ​​(such as power factor, control stability, and dynamic response speed) are then extracted from these historical data or cases for evaluation and comparison to determine the optimal control parameters. Among these, the control stability eigenvalue is a particularly important indicator, characterized by the system's minimum eigenvalue. In a grid-connected photovoltaic system, the minimum eigenvalue reflects the system's stability in the face of disturbances. When the minimum eigenvalue approaches 0, the system may be unstable, so this metric warrants special attention. Once these eigenvalues ​​are obtained, they are processed through a fitness evaluation function to obtain the fitness indicator of the updated parameter, which reflects the parameter's overall performance under current conditions. Next, the fitness indicator of the updated parameter is compared with the fitness indicator of the parameter in the initial population.If the fitness indicator of the updated parameter is less than or equal to the minimum fitness indicator in the initial population, the current iterative update has not resulted in improved performance, and further iterations are required based on the initial population. If the fitness indicator of the updated parameter is greater than the minimum fitness indicator in the initial population, the current iterative update is valid. The updated device control parameters replace the parameters with the minimum fitness indicator in the initial population, creating a new population and continuing the iterations. This iterative process continues until the preset number of iterations is met. Finally, the device control parameters with the maximum fitness indicator are selected from the population and output as the optimal control parameters for the current conditions. In summary, the entire process is essentially achieved through a networked optimization algorithm. By retrieving and analyzing big data, we can more accurately identify device control parameters that are optimal for current conditions, improve control stability and accuracy, and avoid the randomness of historical decisions. This strategy not only improves the operational efficiency of photovoltaic sites but also reduces operation and maintenance costs, providing strong support for intelligent management in the photovoltaic industry.

[0032] In a preferred embodiment, the multiple sets of initial device control parameters are used as the initial population, and population iteration is performed according to the multiple fitness identifiers to obtain updated device control parameters, including: setting search steps for the multiple sets of initial device control parameters according to the multiple fitness identifiers to obtain multiple search steps, wherein the search step setting principle is: the greater the fitness, the smaller the search step; distributing the multiple sets of initial device control parameters to the particle distribution space to obtain multiple control particle distribution coordinates, and setting search directions for the multiple control particle distribution coordinates according to the multiple fitness identifiers to obtain multiple search directions, wherein the search direction setting principle is: taking any one of the control particle distribution coordinates as the control particle distribution coordinate with the maximum fitness within a sphere neighborhood with a preset distance from the sphere center as its search direction, when the sphere neighborhood center is the maximum fitness, taking a random position in the sphere neighborhood as its search direction; updating the multiple sets of initial device control parameters according to the multiple search directions and the multiple search steps to obtain the updated device control parameters.

[0033] Furthermore, a fitness-based iterative search method is employed to optimize the control parameters of photovoltaic plant equipment. First, multiple sets of initial equipment control parameters are used as the initial population, representing different operating strategies and configurations. Then, based on the actual performance of these initial parameters, their fitness indicators are calculated. These indicators reflect the degree of impact of each parameter combination on system performance. Next, a search step size is set for each set of initial equipment control parameters based on the fitness indicator. The principle behind setting the search step size is that solutions with higher fitness have a greater probability of having a more optimal solution nearby. Therefore, a smaller search step size is set to allow for a detailed search near potentially more optimal solutions. Conversely, solutions with lower fitness still require a certain amount of search opportunity, although the probability of having a more optimal solution nearby is relatively small. The search step size is then increased to allow for a wider range of exploration. Subsequently, the multiple sets of initial equipment control parameters are distributed into a particle distribution space, with each parameter combination corresponding to a control particle distribution coordinate. Within this space, a search direction is set for each control particle based on its fitness indicator. The principle of setting the search direction is to use any control particle distribution coordinate as the center of a sphere within a preset distance. If there is a control particle distribution coordinate with greater fitness, then this coordinate will be used as the search direction for the current control particle. If the center of the sphere neighborhood itself is the maximum fitness, then in order to avoid falling into a local optimal solution, a position within the sphere neighborhood can be randomly selected as its search direction. Finally, multiple sets of initial device control parameters are updated according to the set multiple search directions and search steps, thereby obtaining updated device control parameters. These updated parameter combinations usually have improved fitness because they are obtained after a detailed search near a potential better solution or after exploring a wider range. In summary, the entire process performs iterative search based on fitness, and by dynamically adjusting the search step and search direction, it gradually approaches the optimal solution, thereby optimizing the device control parameters of the photovoltaic site.

[0034] In a preferred embodiment, the power factor characteristic value, the control stability characteristic value, and the dynamic response speed characteristic value are processed by a fitness evaluation function to obtain an update parameter fitness identifier, including: when the power factor characteristic value, the control stability characteristic value, and the dynamic response speed characteristic value have abnormal control records exceeding a preset number of times in historical cross-sectional data, the update parameter fitness identifier is set to 0; otherwise, the power factor characteristic value, the control stability characteristic value, and the dynamic response speed characteristic value are processed according to the fitness evaluation function to obtain an update parameter fitness identifier; the fitness evaluation function is: ;in, Characterize the control stability eigenvalue, Characterize the dynamic response speed characteristic value, Characterize the power factor characteristic value, 、 and Characterize the weight parameters, Characterizes the expected value of control stability, Characterize the expected value of dynamic response speed, represents the expected value of the power factor, e represents the natural constant, and fit() represents the fitness evaluation function, which is used to evaluate the comprehensive characteristic value of the stability characteristic value, dynamic response speed characteristic value and power factor characteristic value.

[0035] Specifically, during the optimization of PV site equipment control parameters, a fitness evaluation function is introduced to quantify the performance of the updated parameter combination in actual operation. This function comprehensively considers three key indicators: the power factor eigenvalue, the control stability eigenvalue, and the dynamic response speed eigenvalue. First, the three eigenvalues ​​are checked for abnormal control records in the historical cross-sectional data. Abnormal control records may indicate that the parameter combination is unable to operate stably or performs poorly under certain conditions. If any of the power factor eigenvalue, the control stability eigenvalue, or the dynamic response speed eigenvalue exhibits abnormalities more than a preset number of times in the historical data, the updated parameter combination is deemed unqualified and its fitness flag is set to 0. This setting ensures that only parameter combinations that perform stably in actual operation are considered further. If none of the three eigenvalues ​​exhibit abnormal control records in the historical data, the fitness evaluation function is used to calculate the fitness flag of the updated parameters. This function is a weighted sum, where each eigenvalue is scored based on the difference between its actual value and the expected value, weighted by a corresponding weight parameter. The specific formula is: Among them, the fitness evaluation function 、 and represent the actual values ​​of control stability characteristic value, dynamic response speed characteristic value and power factor characteristic value respectively, and 、 and These three characteristic values ​​are set according to the operation requirements and performance targets of the photovoltaic site. 、 and The weight parameter is used to adjust the relative importance of different eigenvalues ​​in the fitness evaluation. e represents a natural constant. Ultimately, the fitness evaluation function returns a fitness indicator between 0 and a maximum value, reflecting the overall performance of the updated parameter combination in terms of power factor, control stability, and dynamic response speed. By comparing the fitness indicators of different parameter combinations, the optimal parameter combination can be selected as the final optimization result.

[0036] For a more intuitive explanation, the following example illustrates the fitness identification process obtained by calculating the fitness evaluation function. The specific representation symbols of the example are different from the fitness formula symbols, as shown in the following table:

[0037]

[0038] In the above table, the "Power Factor Characteristic Value (X)", "Control Stability Characteristic Value (Y)", and "Dynamic Response Speed ​​Characteristic Value (Z)" columns represent the actual performance of different parameter combinations in historical cross-sectional data. The "Number of Abnormal Control Records" column indicates whether abnormal control records have occurred for this parameter combination in the historical data. If the number of abnormal records exceeds the preset number (for example, set to 1), the fitness flag is set to 0. The "Fitness Evaluation Function Result (F)" column indicates the fitness score calculated based on the fitness evaluation function. The "Fitness Flag" column is derived from the fitness evaluation function result and the number of abnormal control records. If the number of abnormal records exceeds the preset number, the fitness flag is "low" (or 0). Otherwise, based on the result of the fitness evaluation function, the fitness flag is classified as "high", "medium", or "low" (or a specific numerical range).

[0039] In summary, by comprehensively considering the optimization objectives, historical data, system requirements and potential improvement space of the photovoltaic site, setting reasonable expected values ​​and weight parameters, and using the fitness evaluation function to quantitatively evaluate different parameter combinations, the technical effect of accurately guiding the optimization of the control parameters of the photovoltaic site equipment was achieved, effectively improving the system's power generation efficiency, stability and dynamic response speed.

[0040] In a preferred embodiment, the device control parameters are obtained for state mapping to obtain the device operation baseline state, including: the device control parameters are processed by a state mapping network corresponding to the photovoltaic site to output the device operation baseline state; the state mapping network is obtained through multiple sets of data based on machine learning training, wherein any set of the multiple sets of data includes: device control parameter recording data and label data identifying the device operation baseline state.

[0041] For example, during the optimization of control parameters for photovoltaic (PV) site equipment, a state mapping network (SMN)-based approach is used to obtain the equipment's baseline operating state. The core of this operation lies in the state mapping network's ability to quickly and accurately process the equipment's control parameters, thereby outputting the equipment's baseline operating state. Specifically, the SMN is trained using machine learning techniques. To train this network, multiple data sets are first collected. Each set contains recorded data on the equipment's control parameters and labeled data identifying the equipment's baseline operating state. This data is primarily collected from the PV site's real-time monitoring system, which records the equipment's control parameters under different operating conditions and their corresponding baseline states. After sufficient data has been collected, an appropriate machine learning algorithm and model are selected to construct the SMN. Common machine learning algorithms include neural networks and support vector machines. In this scenario, a deep learning model, such as a convolutional neural network (CNN) or a recurrent neural network (RNN), is suitable because these models excel at processing complex data and recognizing patterns. Next, the SMN is trained using the collected data. The training process involves inputting data into the network, calculating the output through forward propagation, and then comparing it with the actual labeled data to calculate the error. The network's weights and biases are then adjusted using a backpropagation algorithm to minimize errors. This process is repeated multiple times until the network's performance reaches a stable level, accurately outputting the device baseline state based on the device control parameters. The trained state mapping network can quickly process new device control parameters and output the corresponding device baseline state. Because the network has learned patterns and regularities within a large amount of data, it performs well on new data, achieving high mapping efficiency and accuracy. In summary, the state mapping network can quickly process a large number of device control parameters and output the device baseline state in real time, significantly improving mapping efficiency. The state mapping network, trained through machine learning, accurately identifies the mapping relationship between device control parameters and the baseline state, thereby improving mapping accuracy. Furthermore, once the accurate baseline state is obtained, the device control parameters can be further optimized to improve the power generation efficiency, stability, and dynamic response speed of the photovoltaic site.

[0042] In a preferred embodiment, the multiple data collection steps include: obtaining equipment control parameter record data of the photovoltaic site; retrieving several groups of historical operating status monitoring data of normal operation based on the equipment control parameter record data; performing interval analysis of the same attribute status data on the several groups of historical operating status monitoring data to obtain the label data that identifies the baseline operating status of the equipment.

[0043] Optionally, to extract valuable information from the PV site's historical operating data and provide a solid foundation for subsequent machine learning tasks, recorded data on device control parameters is first collected from the site's monitoring system. This data typically includes key parameters such as voltage, current, power factor, and temperature, reflecting the device's status under different operating conditions. This data is collected in real time, ensuring the latest updates on device operation. Next, based on the collected device control parameter records, the PV site's historical database is used to retrieve normal operating status monitoring data corresponding to these parameters. This historical data records the normal operating status of the device over a period of time (e.g., several months or years), including its performance under various operating conditions. Data marked as "normal" or "stable" is selected to ensure the accuracy and reliability of subsequent analysis. After obtaining several sets of normal operating status monitoring data, a clustered interval analysis is performed on data with the same attributes. This step aims to identify the value range or distribution characteristics of each control parameter under normal conditions. Statistical methods (such as mean, standard deviation, maximum, and minimum values) are used to analyze the distribution of this data and determine a reasonable interval that covers the majority of data points under normal conditions, representing the clustered interval of the data. Subsequently, based on the results of the centralized interval analysis of state data with the same attributes, labeled data identifying the baseline operating state of the equipment can be generated. For each set of historical operating state monitoring data, its control parameters are checked to see if they fall within the previously determined normal value range. If all or most parameters fall within this range, the data set is labeled as "normal" or "baseline state." Conversely, if some parameters deviate from the normal range, the data set may be labeled as "abnormal" or "non-baseline state." In summary, the multiple sets of labeled baseline equipment operating state datasets extracted from the historical operating data of the PV site not only provide valuable labeled data for training the state mapping network but also provide an important reference for subsequent optimization of equipment control parameters. It is important to emphasize that the entire data collection process requires strict quality control to ensure data accuracy, completeness, and consistency. Furthermore, as PV site operating conditions continue to change and new data is continuously generated, the data collection process needs to be regularly updated and optimized to adapt to new needs and challenges.

[0044] In a preferred embodiment, the device state fluctuation curve is constructed to perform homomodal high-frequency abnormal cycle indexing to obtain a device state monitoring period, including: networking indexing the device state fluctuation record curve before the triggering fault of the photovoltaic power station when the number of triggered faults exceeds the triggering fault number threshold, wherein the first time zone length of the device state fluctuation record curve is greater than the second time zone length of the device state fluctuation curve; performing differential feature extraction on the device state fluctuation record curve to obtain a first differential feature sequence; performing differential feature extraction on the device state fluctuation curve to obtain a second differential feature sequence; based on the second differential feature sequence, intercepting equal-length sequences from the first differential feature sequence for Euclidean distance comparison to obtain a Euclidean distance evaluation value; when the Euclidean distance evaluation value is less than or equal to the Euclidean distance threshold, statistically extracting the interval between the end point of the intercepted sequence and the triggering fault moment and storing it; when the number of stored interval time lengths is greater than or equal to the preset statistical number, taking the minimum value of the non-outlier points of all interval time lengths as the device state monitoring period.

[0045] Specifically, devices with fault triggering times exceeding a preset threshold are screened from the PV power plant's historical database. State fluctuation curves for these devices prior to each fault triggering are then obtained. These curves record changes in key parameters, such as voltage, current, and power, over a period of time (e.g., hours, days, or weeks) prior to a fault. Notably, to capture state changes at different timescales, two time zones are used: the first, longer, captures long-term trends; the second, shorter, captures short-term fluctuations. Next, differential features are extracted from each device state fluctuation curve. A differential feature is the amount of change between parameter values ​​at adjacent time points, reflecting the rate of change and trend of the device state. Differential feature extraction is performed on the curves with the first and second time zones, respectively, yielding two differential feature sequences: the first differential feature sequence and the second differential feature sequence. Then, using the second differential feature sequence as a reference, sequences of equal length from the first differential feature sequence are cut off and compared using Euclidean distance. Euclidean distance measures the similarity between two vectors; smaller distances indicate greater similarity. By comparing these trends, we can identify long-term trend segments that resemble short-term fluctuations and assess their similarity. When the Euclidean distance evaluation value is less than or equal to a preset Euclidean distance threshold, the long-term trend segment is considered to share modal characteristics with the short-term fluctuation, potentially indicating an impending failure. The interval between the end of the sequence and the fault triggering moment is statistically calculated and stored. This interval reflects the time interval from the device status anomaly to the actual failure. Finally, when the number of stored intervals reaches a preset number, these intervals are analyzed. To eliminate the influence of outliers (i.e., abnormal values), the minimum value of all non-outlier intervals is taken as the device status monitoring period. This period represents the shortest historical interval from the device status anomaly to the failure. Finally, using the shortest historical triggering interval as the monitoring period significantly increases the probability of timely detection of device abnormalities. This is because during this period, device status changes are most drastically, making it most likely to expose potential fault hazards. Regularly monitoring device status and implementing appropriate preventive measures can effectively reduce the incidence of equipment failures and improve the operational efficiency and safety of photovoltaic power plants. In summary, the method of constructing equipment status fluctuation curves for homomodal high-frequency abnormal period indexing provides a scientific and effective means of equipment status monitoring. It can not only detect equipment abnormalities in a timely manner, but also guide the formulation of reasonable maintenance plans and preventive measures, providing strong guarantees for the long-term stable operation of photovoltaic power stations.

[0046] Example 2:

[0047] like Figure 2As shown, based on the same inventive concept as the distributed photovoltaic fusion control method provided in Example 1, an embodiment of the present invention further provides a distributed photovoltaic fusion control device, the device comprising:

[0048] The parameter acquisition module 11 is used to obtain the device control parameters for state mapping to obtain the device operation baseline state when the photovoltaic site does not trigger the islanding effect.

[0049] The state identification module 12 is used to receive device state information according to a default period. If the device state information is consistent with the device operating baseline state, a device state fluctuation curve is constructed to perform a same-modal high-frequency abnormal cycle index, and the device state monitoring cycle is obtained to update the default cycle.

[0050] The off-grid control module 13 is configured to perform off-grid control of the photovoltaic site through a photovoltaic circuit breaker when an islanding effect is triggered at the photovoltaic site or the device status information is inconsistent with the device operating baseline status.

[0051] Furthermore, the parameter acquisition module 11 is further configured to perform the following steps:

[0052] When the photovoltaic site does not trigger the island effect, when the first fluctuation value of the monitored grid load and the pre-stored grid load is greater than or equal to the first fluctuation threshold, or / and the second fluctuation value of the monitored environmental factor and the pre-stored environmental factor is greater than or equal to the second fluctuation threshold, the monitored grid load and the monitored environmental factor are input into the historical control database to retrieve the device control parameters; the monitored grid load is used to update the pre-stored grid load, and the monitored environmental factor is used to update the pre-stored environmental factor, and at the same time, the device control parameters are sent to the photovoltaic site for control initialization.

[0053] Furthermore, the parameter acquisition module 11 is further configured to perform the following steps:

[0054] The grid load and the environmental factors are input into the historical control database, and multiple groups of initial device control parameters of the photovoltaic site are retrieved, wherein the multiple groups of initial device control parameters have multiple fitness identifiers, and the greater the fitness, the better the control parameters; the multiple groups of initial device control parameters are used as the initial population, and population iteration is performed according to the multiple fitness identifiers to obtain updated device control parameters; the grid load, the environmental factors and the photovoltaic site layout topology configuration background conditions are set, and the updated device control parameters are set as foreground conditions, and the power factor characteristic value, control stability characteristic value and dynamic response speed characteristic value of the control samples that meet the background conditions and the foreground conditions are retrieved online. value; processing the power factor eigenvalue, the control stability eigenvalue and the dynamic response speed eigenvalue through a fitness evaluation function to obtain an updated parameter fitness identifier; when the updated parameter fitness identifier is less than or equal to the minimum value of the multiple fitness identifiers, performing loop iteration based on the multiple groups of initial device control parameters; when the updated parameter fitness identifier is greater than the minimum value of the multiple fitness identifiers, using the updated device control parameter to replace the initial device control parameter with the minimum value of the fitness identifier of the multiple groups of initial device control parameters, obtaining an updated population for loop iteration; when the number of loop iterations meets the preset number, outputting the device control parameter with the maximum value of the fitness identifier.

[0055] Furthermore, the parameter acquisition module 11 is further configured to perform the following steps:

[0056] According to the multiple fitness identifiers, search steps are set for the multiple groups of initial device control parameters to obtain multiple search steps, wherein the search step setting principle is: the greater the fitness, the smaller the search step; the multiple groups of initial device control parameters are distributed to the particle distribution space to obtain multiple control particle distribution coordinates, and search directions are set for the multiple control particle distribution coordinates according to the multiple fitness identifiers to obtain multiple search directions, wherein the search direction setting principle is: the control particle distribution coordinate with the maximum fitness value within a spherical neighborhood with a preset distance from any one of the control particle distribution coordinates as the sphere center is used as its search direction, and when the center of the sphere neighborhood is the maximum fitness value, the random position of the sphere neighborhood is used as its search direction; according to the multiple search directions and the multiple search steps, the multiple groups of initial device control parameters are updated to obtain the updated device control parameters.

[0057] Furthermore, the parameter acquisition module 11 is further configured to perform the following steps:

[0058] When the power factor characteristic value, the control stability characteristic value, and the dynamic response speed characteristic value have abnormal control records exceeding a preset number of times in the historical cross-sectional data, the update parameter fitness flag is set to 0; otherwise, the power factor characteristic value, the control stability characteristic value, and the dynamic response speed characteristic value are processed according to the fitness evaluation function to obtain the update parameter fitness flag; the fitness evaluation function is: ;in, Characterize the control stability eigenvalue, Characterize the dynamic response speed characteristic value, Characterize the power factor characteristic value, 、 and Characterize the weight parameters, Characterizes the expected value of control stability, Characterize the expected value of dynamic response speed, represents the expected value of the power factor, e represents the natural constant, and fit() represents the fitness evaluation function, which is used to evaluate the comprehensive characteristic value of the stability characteristic value, dynamic response speed characteristic value and power factor characteristic value.

[0059] Furthermore, the parameter acquisition module 11 is further configured to perform the following steps:

[0060] The device control parameters are processed by a state mapping network corresponding to the photovoltaic site to output the device operation baseline state; the state mapping network is obtained through multiple sets of data based on machine learning training, wherein any set of the multiple sets of data includes: device control parameter recording data and label data identifying the device operation baseline state.

[0061] Furthermore, the parameter acquisition module 11 is further configured to perform the following steps:

[0062] Obtain equipment control parameter record data of the photovoltaic site; retrieve several groups of historical operating status monitoring data of normal operation based on the equipment control parameter record data; perform interval analysis of the same attribute status data on the several groups of historical operating status monitoring data to obtain the label data identifying the baseline operating status of the equipment.

[0063] Furthermore, the state determination module 12 is further configured to perform the following steps:

[0064] The network indexing photovoltaic power station has a device state fluctuation record curve before the triggering fault when the number of triggered faults exceeds the triggering fault number threshold, wherein the length of the first time zone of the device state fluctuation record curve is greater than the length of the second time zone of the device state fluctuation curve; differential feature extraction is performed on the device state fluctuation record curve to obtain a first differential feature sequence; differential feature extraction is performed on the device state fluctuation curve to obtain a second differential feature sequence; according to the second differential feature sequence, an equal-length sequence is intercepted from the first differential feature sequence for Euclidean distance comparison to obtain a Euclidean distance evaluation value; when the Euclidean distance evaluation value is less than or equal to the Euclidean distance threshold, the interval between the end point of the intercepted sequence and the triggering fault is statistically stored; when the number of stored interval time lengths is greater than or equal to the preset statistical number, the minimum value of the non-outlier points of all interval time lengths is taken and set as the device state monitoring period.

[0065] Through the detailed description of a distributed photovoltaic fusion control method in the foregoing specification, those skilled in the art can clearly understand a distributed photovoltaic fusion control device in this embodiment. For the device disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the method part description.

[0066] The above description of the disclosed embodiments is intended to enable one skilled in the art to implement or use the present application. Various modifications to these embodiments will be readily apparent to one skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application is not limited to the embodiments shown herein, but is intended to conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A distributed photovoltaic fusion control method, characterized in that: Applied to the master station, the master station communicates with the photovoltaic site, including: When the islanding effect is not triggered at the photovoltaic site, the device control parameters are obtained for state mapping to obtain the baseline status of the device operation; Receive device status information according to a default period, and if the device status information is consistent with the device operating baseline status, construct a device status fluctuation curve to perform a same-modal high-frequency abnormal period index, and obtain a device status monitoring period to update the default period; When an islanding effect is triggered at a photovoltaic site, or the device status information is inconsistent with the device operating baseline status, off-grid control of the photovoltaic site is performed through a photovoltaic circuit breaker; Among them, obtaining the device control parameters for state mapping and obtaining the device operation baseline status includes: The device control parameters are processed by a state mapping network corresponding to the photovoltaic site to output the device operation baseline state; The state mapping network is obtained through machine learning training based on multiple sets of data, wherein any set of the multiple sets of data includes: device control parameter recording data and label data identifying the baseline operating state of the device.

2. The method according to claim 1, wherein Obtain device control parameters, including: When the photovoltaic site does not trigger an islanding effect, when a first fluctuation value between the monitored grid load and the pre-stored grid load is greater than or equal to a first fluctuation threshold, or / and a second fluctuation value between the monitored environmental factor and the pre-stored environmental factor is greater than or equal to a second fluctuation threshold, the monitored grid load and the monitored environmental factor are input into a historical control database to retrieve a device control parameter; The monitored grid load is used to update the pre-stored grid load, the monitored environmental factors are used to update the pre-stored environmental factors, and the device control parameters are sent to the photovoltaic site for control initialization.

3. The method according to claim 2, wherein Input grid load and environmental factors into the historical control database to retrieve equipment control parameters, including: Inputting the grid load and the environmental factors into the historical control database, and retrieving multiple sets of initial device control parameters for the photovoltaic site, wherein the multiple sets of initial device control parameters have multiple fitness identifiers, and the greater the fitness, the better the control parameters; Using the multiple groups of initial device control parameters as an initial population, performing population iteration according to the multiple fitness identifiers to obtain updated device control parameters; The grid load, the environmental factors, and the photovoltaic site layout topology are configured as background conditions, the update device control parameters are set as foreground conditions, and the power factor characteristic value, the control stability characteristic value, and the dynamic response speed characteristic value of the control samples that meet the background conditions and the foreground conditions are retrieved online; Processing the power factor characteristic value, the control stability characteristic value, and the dynamic response speed characteristic value through a fitness evaluation function to obtain an updated parameter fitness identifier; When the updated parameter fitness identifier is less than or equal to the minimum value of the multiple fitness identifiers, performing cyclic iteration based on the multiple sets of initial device control parameters; When the fitness indicator of the updated parameter is greater than the minimum value of the multiple fitness indicators, the updated device control parameter is used to replace the initial device control parameter with the minimum value of the fitness indicators of the multiple groups of initial device control parameters, and an updated population is obtained to perform cyclic iteration; When the number of loop iterations meets the preset number, the device control parameter with the maximum fitness indicator is output.

4. The method according to claim 3, wherein The multiple sets of initial device control parameters are used as an initial population, and population iteration is performed according to the multiple fitness identifiers to obtain updated device control parameters, including: According to the multiple fitness identifiers, search step lengths are set for the multiple groups of initial device control parameters to obtain multiple search step lengths, wherein the search step length setting principle is: the greater the fitness, the smaller the search step length; The multiple sets of initial device control parameters are distributed to the particle distribution space to obtain multiple control particle distribution coordinates, and search directions are set for the multiple control particle distribution coordinates according to the multiple fitness identifiers to obtain multiple search directions, wherein the search direction setting principle is: the control particle distribution coordinate with the maximum fitness value within a sphere neighborhood with a preset distance from any control particle distribution coordinate as the sphere center is used as the search direction; when the center of the sphere neighborhood is the maximum fitness value, a random position in the sphere neighborhood is used as the search direction; The multiple groups of initial device control parameters are updated according to the multiple search directions and the multiple search step sizes to obtain the updated device control parameters.

5. The method according to claim 3, wherein Processing the power factor characteristic value, the control stability characteristic value, and the dynamic response speed characteristic value through a fitness evaluation function to obtain an update parameter fitness identifier includes: When the power factor characteristic value, the control stability characteristic value, and the dynamic response speed characteristic value have abnormal control records exceeding a preset number of times in the historical cross-sectional data, the update parameter fitness flag is set to 0; Otherwise, processing the power factor characteristic value, the control stability characteristic value, and the dynamic response speed characteristic value according to the fitness evaluation function to obtain an updated parameter fitness identifier; The fitness evaluation function is: in, Characterize the control stability eigenvalue, Characterize the dynamic response speed characteristic value, Characterize the power factor characteristic value, 、 and Characterize the weight parameters, Characterizes the expected value of control stability, Characterize the expected value of dynamic response speed, represents the expected value of the power factor, e represents the natural constant, and fit() represents the fitness evaluation function, which is used to evaluate the comprehensive characteristic value of the stability characteristic value, dynamic response speed characteristic value and power factor characteristic value.

6. The method according to claim 1, wherein The multiple data collection steps include: Obtain equipment control parameter record data of photovoltaic sites; Retrieving several groups of historical operating status monitoring data of normal operation based on the equipment control parameter record data; Performing interval analysis on the plurality of groups of historical operating status monitoring data in the same attribute status data to obtain label data identifying the baseline operating status of the equipment.

7. The method according to claim 1, wherein Construct the equipment status fluctuation curve to index the same-mode high-frequency abnormal period and obtain the equipment status monitoring period, including: A device state fluctuation record curve before a fault is triggered, wherein the number of triggered faults of the network-indexed photovoltaic power station exceeds a threshold value of the number of triggered faults, wherein the length of the first time zone of the device state fluctuation record curve is greater than the length of the second time zone of the device state fluctuation curve; Performing differential feature extraction on the device state fluctuation record curve to obtain a first differential feature sequence; performing differential feature extraction on the device state fluctuation curve to obtain a second differential feature sequence; According to the second differential feature sequence, a sequence of equal length is intercepted from the first differential feature sequence to perform a Euclidean distance comparison to obtain a Euclidean distance evaluation value; When the Euclidean distance evaluation value is less than or equal to the Euclidean distance threshold, the interval between the end point of the interception sequence and the fault triggering time is statistically collected and stored; When the stored number of interval durations is greater than or equal to the preset statistical number, the minimum value of the non-outlier points of all interval durations is taken as the device status monitoring period.

8. A distributed photovoltaic fusion control device, characterized in that: The device is applied to a master station, the master station is communicatively connected with a photovoltaic site, and is used to implement a distributed photovoltaic integration control method according to any one of claims 1 to 7, the device comprising: The parameter acquisition module is used to obtain the equipment control parameters for state mapping and obtain the equipment operating baseline status when the photovoltaic site does not trigger the islanding effect; A state identification module is used to receive device state information according to a default period. If the device state information is consistent with the device operating baseline state, a device state fluctuation curve is constructed to perform a same-modal high-frequency abnormal cycle index, and a default period is updated to obtain a device state monitoring cycle. The off-grid control module is used to control the off-grid operation of the photovoltaic site through the photovoltaic circuit breaker when the island effect is triggered at the photovoltaic site or the device status information is inconsistent with the device operation baseline status.

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