A photovoltaic power grid anti-reverse flow control method based on artificial intelligence

Abstract

The present invention belongs to the field of anti-backflow control and involves data analysis technology. It is used to solve the problem that the prior art cannot analyze the adjustment mode in combination with the overall state of the inverter when power adjustment is required. Specifically, it is an artificial intelligence-based photovoltaic power grid anti-backflow control method, comprising the following steps: building a Bluetooth Mesh network, connecting all inverters and electric meters in the photovoltaic power grid in the same communication environment through the Bluetooth Mesh network; the electric meter uses the Bluetooth Mesh network to obtain the output power and working mode of each inverter in real time, and detects the grid power in real time, and marks the inverter as a necessary adjustment object or a non-necessary adjustment object when the grid power reverse flow is detected; the present invention can obtain the power information of the inverter in real time, realize accurate anti-backflow control, and ensure the stable operation of the power system; the adjustment mode is screened according to the differentiated marking results, taking into account the operating stability of the photovoltaic power grid and the timeliness of the reverse flow control.

Description

Technical Field

[0001] The present invention belongs to the field of backflow prevention control and relates to data analysis technology, and specifically is a photovoltaic power grid backflow prevention control method based on artificial intelligence. Background Art

[0002] The impact of reverse current on the photovoltaic system itself is mainly reflected in that when the power generation of the photovoltaic system is greater than the power consumption of the local load, the excess power may flow back to the grid, causing the voltage of the photovoltaic system to increase, thereby affecting the life and performance of the photovoltaic equipment.

[0003] The photovoltaic power grid anti-backflow control method in the prior art generally uses 485 communication to feedback the grid energy flow, so as to achieve the purpose of controlling the backflow; however, such methods usually require complex wiring and communication systems, which affects the timeliness of anti-backflow detection, feedback, and control; at the same time, the traditional photovoltaic power grid anti-backflow control method cannot analyze the adjustment mode in combination with the overall state of the inverter when power adjustment is required, resulting in the inability to improve the timeliness of inverter power adjustment of the photovoltaic power grid anti-backflow control method. Summary of the invention

[0004] The purpose of the present invention is to provide a photovoltaic power grid anti-backflow control method based on artificial intelligence, which is used to solve the problem that the existing technology cannot analyze the adjustment mode in combination with the overall state of the inverter when power adjustment is required;

[0005] The technical problem to be solved by the present invention is: how to provide an artificial intelligence-based photovoltaic power grid anti-backflow control method that can analyze the adjustment mode in combination with the overall state of the inverter when power adjustment is required.

[0006] The purpose of the present invention can be achieved through the following technical solutions:

[0007] A photovoltaic power grid anti-backflow control method based on artificial intelligence, including an anti-backflow control adjustment method and a control adjustment optimization method;

[0008] The anti-backflow control and adjustment method comprises the following steps:

[0009] Step 1: Build a Bluetooth Mesh network to connect all inverters and meters in the photovoltaic power grid in the same communication environment through the Bluetooth Mesh network;

[0010] Step 2: The electric meter uses the Bluetooth Mesh network to obtain the output power and working mode of each inverter in real time, and detects the grid power in real time. When the grid power reverse flow is detected, step 3 is executed;

[0011] Step 3: Mark the inverter as a necessary regulation object or a non-necessary regulation object;

[0012] Step 4: Analyze the adjustment mode of the necessary adjustment object: when the adjustment mode is marked as a flexible adjustment mode, execute step 5; when the adjustment mode is marked as a rigid adjustment mode, execute step 6;

[0013] Step 5: Use the flexible adjustment mode to perform anti-backflow control adjustment: send control instructions to the necessary adjustment objects through the Bluetooth Mesh network, and adjust the output power of the necessary adjustment objects according to the control instructions;

[0014] Step 6: Use rigid regulation mode for anti-backflow control: disconnect the grid-connected switches of all necessary regulation objects, and connect them to the grid after the output power of the necessary regulation objects is regulated.

[0015] Furthermore, in step three, the specific process of marking the inverter as a necessary adjustment object or a non-necessary adjustment object includes: obtaining the output power of all inverters and marking it as the monitoring value of the inverter, marking the rated power of the electric meter as the rated value, marking the difference between the rated value and the monitoring value as the necessary adjustment value of the inverter, and comparing the necessary adjustment value with the preset necessary adjustment threshold: if the necessary adjustment value is less than the necessary adjustment threshold, marking the corresponding inverter as a necessary adjustment object; if the necessary adjustment value is greater than or equal to the necessary adjustment threshold, marking the corresponding inverter as a non-necessary adjustment object.

[0016] Furthermore, in step four, the specific process of marking the adjustment mode includes: marking the number of necessary adjustment objects as the adjustment requirement value TS, and summing the adjustment necessary values ​​of all necessary adjustment objects to obtain the adjustment amplitude value TF; obtaining the adjustment coefficient TJ through the formula TJ=k1*TS-100*k2*TF, wherein k1 and k2 are both proportional coefficients, and k2>k1>1; comparing the adjustment coefficient TJ with the preset adjustment threshold TJmax: if the adjustment coefficient TJ is less than the adjustment threshold TJmax, the adjustment mode is marked as a flexible adjustment mode; if the adjustment coefficient TJ is greater than or equal to the adjustment threshold TJmax, the adjustment mode is marked as a rigid adjustment mode.

[0017] Furthermore, the control adjustment optimization method comprises the following steps:

[0018] Step P1: Generate an optimization analysis cycle, mark the inverter output power adjustment process of the photovoltaic power grid within the optimization analysis cycle as an analysis process, generate an analysis period for the analysis process, the duration of the analysis period is L1 seconds, and the end time of the analysis period is the start execution time of the analysis process;

[0019] Step P2: the difference between the maximum value and the minimum value of the load of the photovoltaic power grid during the analysis period is marked as the load floating value, and the analysis process of power regulation using the flexible regulation mode is marked as a flexible process; the analysis process of power regulation using the rigid regulation mode is marked as a rigid process;

[0020] Step P3: Obtaining a flexible load switching range and a rigid load switching range;

[0021] Step P4: Compare the flexible load switching range with the rigid load switching range;

[0022] Step P5: Optimize the anti-backflow control adjustment method using a flexible load switching range and a rigid load switching range: In step 2 of the anti-backflow control adjustment method, when a power grid backflow is detected, the optimization period is composed of L1 seconds before the current moment, and the difference between the maximum and minimum load values ​​of the photovoltaic power grid during the optimization period is marked as a pattern matching value. The execution process of the anti-backflow control adjustment method is optimized through the pattern matching value.

[0023] Further, in step P3, the process of acquiring the flexible load switching range includes: forming a flexible floating set by the load floating values ​​corresponding to the flexible process within the optimization analysis period, performing variance calculation on all elements in the flexible floating set to obtain a flexible concentrated value, and comparing the flexible concentrated value with a preset concentrated analysis threshold: if the flexible concentrated value is less than the concentrated analysis threshold, it is determined that the flexible floating set meets the concentrated distribution requirements, and the flexible load switching range is formed by the maximum element and the minimum element in the flexible floating set; if the flexible concentrated value is greater than or equal to the concentrated analysis threshold, it is determined that the flexible floating set does not meet the concentrated distribution requirements, the maximum element and the minimum element in the flexible floating set are eliminated, and then the flexible concentrated value of the flexible floating set is recalculated, and so on, until the flexible concentrated value is less than the concentrated analysis threshold.

[0024] Further, in step P3, the process of obtaining the rigid load switching range includes: forming a rigid floating set from the load floating values ​​corresponding to the rigid process within the optimization analysis period, performing variance calculation on all elements in the rigid floating set to obtain a rigid concentrated value, and comparing the rigid concentrated value with a preset concentrated analysis threshold: if the rigid concentrated value is less than the concentrated analysis threshold, it is determined that the rigid floating set meets the concentrated distribution requirements, and the rigid load switching range is formed by the maximum element and the minimum element in the rigid floating set; if the rigid concentrated value is greater than or equal to the concentrated analysis threshold, it is determined that the rigid floating set does not meet the concentrated distribution requirements, the maximum element and the minimum element in the rigid floating set are eliminated, and then the rigid concentrated value of the rigid floating set is recalculated, and so on, until the rigid concentrated value is less than the concentrated analysis threshold.

[0025] Furthermore, in step P4, the specific process of comparing the flexible load switching range with the rigid load switching range includes: if there is no intersection between the flexible load switching range and the rigid load switching range, no processing is performed; if there is an intersection between the flexible load switching range and the rigid load switching range, the intersection part of the flexible load switching range and the rigid load switching range is eliminated.

[0026] Furthermore, in step P5, the specific process of optimizing the execution flow of the anti-backflow control adjustment method includes: determining whether the pattern matching value is within the flexible load switching range: if so, executing step five of the anti-backflow control adjustment method; if not, determining whether the pattern matching value is within the rigid load switching range: if so, executing step six of the anti-backflow control adjustment method; if not, executing step four of the anti-backflow control adjustment method.

[0027] The present invention has the following beneficial effects:

[0028] 1. The communication method based on Bluetooth Mesh network is adopted, which cancels the traditional RS485 wiring system and reduces the system cost. At the same time, the system can obtain the power information of the inverter in real time, realize accurate anti-backflow control, and ensure the stable operation of the power system;

[0029] 2. When the reverse flow of power in the power grid is detected, the inverter is first differentiated according to the output power, and then the adjustment mode is screened according to the differentiated marking results. Different adjustment modes are selected according to different power adjustment application scenarios, taking into account the operational stability of the photovoltaic power grid and the timeliness of reverse flow control;

[0030] 3. By comprehensively analyzing multiple parameters corresponding to the analysis process within the optimization analysis cycle, the flexible load switching range and the rigid load switching range are obtained. The inverter power control mode is screened from the grid load end according to the flexible load switching range and the rigid load switching range. This process does not need to be based on the inverter differentiation marking results. It can be directly executed when the grid power reverse flow is detected, which shortens the inverter power adjustment process and further improves the timeliness of reverse flow control. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0032] Figure 1 This is a flow chart of a method according to Embodiment 1 of the present invention;

[0033] Figure 2 This is a flow chart of the method of Embodiment 2 of the present invention. DETAILED DESCRIPTION

[0034] The technical solution of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0035] A photovoltaic power grid anti-backflow control method based on artificial intelligence, including an anti-backflow control regulation method and a control regulation optimization method.

[0036] Embodiment 1: Figure 1 As shown, the anti-backflow control and adjustment method includes the following steps:

[0037] Step 1: Build a communication network: Through Bluetooth Mesh networking, modules are connected to each other. Modules that are far away from the router can be connected to modules that are directly connected to the router. All inverters and meters in the photovoltaic power grid are connected in the same communication environment through the Bluetooth Mesh network. The Bluetooth Mesh network adopts a distributed topology structure, in which each device can act as a node, so that devices can communicate with each other and transmit data. The communication method based on the Bluetooth Mesh network cancels the traditional RS485 wiring system and reduces the system cost. At the same time, the system can obtain the power information of the inverter in real time, realize accurate anti-backflow control, and ensure the stable operation of the power system.

[0038] Step 2: The electric meter uses the Bluetooth Mesh network to obtain the output power and working mode of each inverter in real time, and detects the grid power in real time. When the grid power reverse flow is detected, step 3 is executed;

[0039] Step 3: Obtain the output power of all inverters and mark it as the monitoring value of the inverter, mark the rated power of the meter as the rated value, mark the difference between the rated value and the monitoring value as the necessary adjustment value of the inverter, and compare the necessary adjustment value with the preset necessary adjustment threshold: if the necessary adjustment value is less than the necessary adjustment threshold, mark the corresponding inverter as a necessary adjustment object; if the necessary adjustment value is greater than or equal to the necessary adjustment threshold, mark the corresponding inverter as a non-necessary adjustment object;

[0040] Step 4: Analyze the regulation mode of the necessary regulation objects: mark the number of necessary regulation objects as the regulation demand value TS, and sum the necessary regulation values ​​of all necessary regulation objects to obtain the regulation amplitude value TF; obtain the regulation coefficient TJ through the formula TJ=k1*TS-100*k2*TF, where k1 and k2 are both proportional coefficients, and k2>k1>1; compare the regulation coefficient TJ with the preset regulation threshold TJmax: if the regulation coefficient TJ is less than the regulation threshold TJmax, the regulation mode is marked as a flexible regulation mode, and step five is executed; if the regulation coefficient TJ is greater than or equal to the regulation threshold TJmax, the regulation mode is marked as a rigid regulation mode, and step six is ​​executed; when the reverse flow of grid power is detected, the inverter is first differentiated according to the output power, and then the regulation mode is screened according to the differentiated marking results, and different regulation modes are selected according to different power regulation application scenarios, taking into account the operation stability of the photovoltaic power grid and the timeliness of reverse flow control;

[0041] Step 5: Use the flexible adjustment mode to perform anti-backflow control adjustment: send control instructions to the necessary adjustment objects through the Bluetooth Mesh network, and adjust the output power of the necessary adjustment objects according to the control instructions;

[0042] Step 6: Use rigid regulation mode for anti-backflow control: disconnect the grid-connected switches of all necessary regulation objects, and connect them to the grid after the output power of the necessary regulation objects is regulated.

[0043] Embodiment 2: Figure 2 As shown, the control adjustment optimization method includes the following steps:

[0044] Step P1: Generate an optimization analysis cycle, mark the inverter output power adjustment process of the photovoltaic power grid within the optimization analysis cycle as an analysis process, generate an analysis period for the analysis process, the duration of the analysis period is L1 seconds, and the end time of the analysis period is the start execution time of the analysis process;

[0045] Step P2: the difference between the maximum value and the minimum value of the load of the photovoltaic power grid during the analysis period is marked as the load floating value, and the analysis process of power regulation using the flexible regulation mode is marked as a flexible process; the analysis process of power regulation using the rigid regulation mode is marked as a rigid process;

[0046] Step P3: The load floating values ​​corresponding to the flexible processes within the optimization analysis cycle constitute a flexible floating set, and the variance of all elements in the flexible floating set is calculated to obtain a flexible concentrated value, and the flexible concentrated value is compared with a preset concentrated analysis threshold: if the flexible concentrated value is less than the concentrated analysis threshold, it is determined that the flexible floating set meets the concentrated distribution requirements, and the maximum element and the minimum element in the flexible floating set form a flexible load switching range; if the flexible concentrated value is greater than or equal to the concentrated analysis threshold, it is determined that the flexible floating set does not meet the concentrated distribution requirements, and the maximum element and the minimum element in the flexible floating set are removed, and then the flexible concentrated value of the flexible floating set is recalculated, and so on, until the flexible concentrated value is less than the concentrated analysis threshold. Threshold; then the load floating values ​​corresponding to the rigid process within the optimization analysis cycle constitute a rigid floating set, the variance of all elements in the rigid floating set is calculated to obtain a rigid concentrated value, and the rigid concentrated value is compared with the preset concentrated analysis threshold: if the rigid concentrated value is less than the concentrated analysis threshold, it is determined that the rigid floating set meets the concentrated distribution requirements, and the maximum element and the minimum element in the rigid floating set form the rigid load switching range; if the rigid concentrated value is greater than or equal to the concentrated analysis threshold, it is determined that the rigid floating set does not meet the concentrated distribution requirements, the maximum element and the minimum element in the rigid floating set are removed, and then the rigid concentrated value of the rigid floating set is recalculated, and so on, until the rigid concentrated value is less than the concentrated analysis threshold;

[0047] Step P4: Compare the flexible load switching range with the rigid load switching range: if there is no intersection between the flexible load switching range and the rigid load switching range, no processing is performed; if there is an intersection between the flexible load switching range and the rigid load switching range, the intersection between the flexible load switching range and the rigid load switching range is removed;

[0048] Step P5: Optimize the anti-backflow control adjustment method using the flexible load switching range and the rigid load switching range: In step 2 of the anti-backflow control adjustment method, when the grid power reverse flow is detected, the optimization period is composed of L1 seconds before the current moment, and the difference between the maximum and minimum load values ​​of the photovoltaic power grid in the optimization period is marked as the pattern matching value, and it is determined whether the pattern matching value is within the flexible load switching range: if so, step five of the anti-backflow control adjustment method is executed; if not, it is determined whether the pattern matching value is within the rigid load switching range: if so, step six of the anti-backflow control adjustment method is executed; if not, step four of the anti-backflow control adjustment method is executed; it should be noted that in the optimized anti-backflow control adjustment method, after the grid power reverse flow is detected in step 2, step three of the anti-backflow control adjustment method and step P5 of the control adjustment optimization method are executed simultaneously. When the pattern matching value can match the flexible load switching range or the rigid load switching range, the adjustment mode marking process in step four of the anti-backflow control adjustment method can be omitted to improve the anti-backflow control efficiency;

[0049] A comprehensive analysis is performed on multiple parameters corresponding to the analysis process within the optimization analysis cycle to obtain the flexible load switching range and the rigid load switching range. The inverter power control mode is screened from the grid load end based on the flexible load switching range and the rigid load switching range. This process does not need to be based on the inverter differentiation marking results, and can be directly executed when grid power reverse flow is detected, shortening the inverter power adjustment process and further improving the timeliness of reverse flow control.

[0050] A photovoltaic power grid anti-backflow control method based on artificial intelligence is provided. When working, a communication network is established, and the electric meter uses the Bluetooth Mesh network to obtain the output power and working mode of each inverter in real time, and detects the power of the power grid in real time. When the power grid reverse flow is detected, the inverter is marked as a necessary adjustment object or a non-necessary adjustment object; the adjustment demand value TS and the adjustment amplitude value TF are obtained and numerically calculated to obtain the adjustment coefficient TJ, and the corresponding adjustment mode is selected according to the adjustment coefficient TJ to perform inverter power adjustment control.

[0051] The above contents are merely examples and explanations of the structure of the present invention. The technicians in this technical field may make various modifications or additions to the specific embodiments described or replace them in a similar manner. As long as they do not deviate from the structure of the invention or exceed the scope defined by the claims, they should all fall within the protection scope of the present invention.

[0052] The above formulas are obtained by collecting a large amount of data for software simulation and selecting a formula close to the real value. The coefficients in the formula are set by technicians in this field according to the actual situation; for example: formula TJ = k1*TS-100*k2*TF; technicians in this field collect multiple groups of sample data and set corresponding adjustment coefficients for each group of sample data; substitute the set adjustment coefficients and the collected sample data into the formula, any two formulas constitute a set of two-variable linear equations, screen the calculated coefficients and take the average, and obtain the values ​​of k1 and k2 as 3.05 and 2.83 respectively;

[0053] The size of the coefficient is to quantify each parameter to obtain a specific value for subsequent comparison. The size of the coefficient depends on the amount of sample data and the initial setting of the corresponding adjustment coefficient for each group of sample data by technical personnel in this field; as long as it does not affect the proportional relationship between the parameter and the quantified value, such as the adjustment coefficient is proportional to the value of the adjustment demand value.

[0054] In the description of this specification, the description with reference to the terms "one embodiment", "example", "specific example", etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0055] The preferred embodiments of the present invention disclosed above are only used to help explain the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to only specific implementation methods. Obviously, many modifications and changes can be made according to the content of this specification. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the present invention, so that those skilled in the art can understand and use the present invention well. The present invention is limited only by the claims and their full scope and equivalents.

Claims

1. A photovoltaic power grid anti-backflow control method based on artificial intelligence, characterized in that: Including anti-backflow control and regulation methods and control and regulation optimization methods; The anti-backflow control and adjustment method comprises the following steps: Step 1: Build a Bluetooth Mesh network to connect all inverters and meters in the photovoltaic power grid in the same communication environment through the Bluetooth Mesh network; Step 2: The electric meter uses the Bluetooth Mesh network to obtain the output power and working mode of each inverter in real time, and detects the grid power in real time. When the grid power reverse flow is detected, step 3 is executed; Step 3: Mark the inverter as a necessary regulation object or a non-necessary regulation object; Step 4: Analyze the adjustment mode of the necessary adjustment object: when the adjustment mode is marked as a flexible adjustment mode, execute step 5; when the adjustment mode is marked as a rigid adjustment mode, execute step 6; Step 5: Use the flexible adjustment mode to perform anti-backflow control adjustment: send control instructions to the necessary adjustment objects through the Bluetooth Mesh network, and adjust the output power of the necessary adjustment objects according to the control instructions; Step 6: Use rigid regulation mode to perform anti-backflow control regulation: disconnect the grid-connected switches of all necessary regulation objects, and connect them to the grid after the output power of the necessary regulation objects is regulated; In step three, the specific process of marking the inverter as a necessary adjustment object or a non-necessary adjustment object includes: obtaining the output power of all inverters and marking it as the monitoring value of the inverter, marking the rated power of the meter as the rated value, marking the difference between the rated value and the monitoring value as the necessary adjustment value of the inverter, and comparing the necessary adjustment value with the preset necessary adjustment threshold: if the necessary adjustment value is less than the necessary adjustment threshold, marking the corresponding inverter as a necessary adjustment object; if the necessary adjustment value is greater than or equal to the necessary adjustment threshold, marking the corresponding inverter as a non-necessary adjustment object; In step four, the specific process of marking the adjustment mode includes: marking the number of necessary adjustment objects as the adjustment requirement value TS, and summing the adjustment necessary values ​​of all necessary adjustment objects to obtain the adjustment amplitude value TF; obtaining the adjustment coefficient TJ through the formula TJ=k1*TS-100*k2*TF, where k1 and k2 are both proportional coefficients, and k2>k1>1; comparing the adjustment coefficient TJ with the preset adjustment threshold TJmax: if the adjustment coefficient TJ is less than the adjustment threshold TJmax, the adjustment mode is marked as a flexible adjustment mode; if the adjustment coefficient TJ is greater than or equal to the adjustment threshold TJmax, the adjustment mode is marked as a rigid adjustment mode.

2. The photovoltaic power grid anti-backflow control method based on artificial intelligence according to claim 1 is characterized in that: The control and regulation optimization method comprises the following steps: Step P1: Generate an optimization analysis cycle, mark the inverter output power adjustment process of the photovoltaic power grid within the optimization analysis cycle as an analysis process, generate an analysis period for the analysis process, the duration of the analysis period is L1 seconds, and the end time of the analysis period is the start execution time of the analysis process; Step P2: the difference between the maximum value and the minimum value of the load of the photovoltaic power grid during the analysis period is marked as the load floating value, and the analysis process of power regulation using the flexible regulation mode is marked as a flexible process; the analysis process of power regulation using the rigid regulation mode is marked as a rigid process; Step P3: Obtaining a flexible load switching range and a rigid load switching range; Step P4: Compare the flexible load switching range with the rigid load switching range; Step P5: Optimize the anti-backflow control adjustment method using a flexible load switching range and a rigid load switching range: In step 2 of the anti-backflow control adjustment method, when a power grid backflow is detected, the optimization period is composed of L1 seconds before the current moment, and the difference between the maximum and minimum load values ​​of the photovoltaic power grid during the optimization period is marked as a pattern matching value. The execution process of the anti-backflow control adjustment method is optimized through the pattern matching value.

3. The photovoltaic power grid anti-backflow control method based on artificial intelligence according to claim 2 is characterized in that: In step P3, the process of obtaining the flexible load switching range includes: forming a flexible floating set from the load floating values ​​corresponding to the flexible process within the optimization analysis period, performing variance calculation on all elements in the flexible floating set to obtain a flexible concentrated value, and comparing the flexible concentrated value with a preset concentrated analysis threshold: if the flexible concentrated value is less than the concentrated analysis threshold, it is determined that the flexible floating set meets the concentrated distribution requirements, and the flexible load switching range is formed by the maximum element and the minimum element in the flexible floating set; if the flexible concentrated value is greater than or equal to the concentrated analysis threshold, it is determined that the flexible floating set does not meet the concentrated distribution requirements, the maximum element and the minimum element in the flexible floating set are eliminated, and then the flexible concentrated value of the flexible floating set is recalculated, and so on, until the flexible concentrated value is less than the concentrated analysis threshold.

4. The photovoltaic power grid anti-backflow control method based on artificial intelligence according to claim 2 is characterized in that: In step P3, the process of obtaining the rigid load switching range includes: forming a rigid floating set from the load floating values ​​corresponding to the rigid process within the optimization analysis period, performing variance calculation on all elements in the rigid floating set to obtain a rigid concentrated value, and comparing the rigid concentrated value with a preset concentrated analysis threshold: if the rigid concentrated value is less than the concentrated analysis threshold, it is determined that the rigid floating set meets the concentrated distribution requirements, and the rigid load switching range is formed by the maximum element and the minimum element in the rigid floating set; if the rigid concentrated value is greater than or equal to the concentrated analysis threshold, it is determined that the rigid floating set does not meet the concentrated distribution requirements, the maximum element and the minimum element in the rigid floating set are eliminated, and then the rigid concentrated value of the rigid floating set is recalculated, and so on, until the rigid concentrated value is less than the concentrated analysis threshold.

5. The photovoltaic power grid anti-backflow control method based on artificial intelligence according to claim 2 is characterized in that: In step P4, the specific process of comparing the flexible load switching range with the rigid load switching range includes: if there is no intersection between the flexible load switching range and the rigid load switching range, no processing is performed; if there is an intersection between the flexible load switching range and the rigid load switching range, the intersection part of the flexible load switching range and the rigid load switching range is eliminated.

6. The photovoltaic power grid anti-backflow control method based on artificial intelligence according to claim 5 is characterized in that: In step P5, the specific process of optimizing the execution flow of the anti-backflow control adjustment method includes: determining whether the pattern matching value is within the flexible load switching range: if so, executing step five of the anti-backflow control adjustment method; if not, determining whether the pattern matching value is within the rigid load switching range: if so, executing step six of the anti-backflow control adjustment method; if not, executing step four of the anti-backflow control adjustment method.

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