Distribution network distributed photovoltaic voltage regulation and control fusion method, device, storage medium, server and system

Abstract

The invention relates to the field of solar power generation protection control device and equipment manufacturing and the like in the new energy industry, in particular to a power distribution network distributed photovoltaic voltage regulation and control fusion method, device, storage medium, server and system. The power distribution network distributed photovoltaic voltage regulation and control fusion method comprises the steps of obtaining irradiance prediction data in a future preset time period; inputting the irradiance prediction data and the rated power of each photovoltaic panel into a photovoltaic panel output prediction model to obtain photovoltaic panel output prediction data in a future preset time period; for each load node, obtaining load prediction data of a future preset time period corresponding to the load node; solving by utilizing a power flow equation to obtain predicted voltage of each photovoltaic node and each load node in a future preset time period; and calculating to obtain a regulation and control instruction of each photovoltaic node in a future preset time period. Compared with the prior art, the input of the photovoltaic output model is prevented from being influenced, and the accuracy of photovoltaic output prediction is improved.

Description

Technical Field

[0001] The present invention relates to the fields of solar power generation protection control devices and equipment manufacturing in the new energy industry, and in particular to a method, device, computer-readable storage medium, server, and system for integrating distributed photovoltaic voltage regulation in a distribution network. Background Art

[0002] The modern smart distribution network is a new type of distribution network form of the power system. Through the deep integration of modern information and communication technologies such as cloud computing, big data, the Internet of Things, mobile Internet, and intelligent chains with active distribution networks, it promotes the evolution of the three forms of "grid structure, digital management and control, and commercial operation" to meet the needs of high-quality electricity supply, green energy transformation, and optimal resource allocation.

[0003] At present, new elements such as distributed photovoltaics, energy storage, and electric vehicle charging piles have been connected to the medium and low voltage distribution networks. Among them, distributed photovoltaics will cause problems such as reverse overload in the substation and excessive voltage at the substation grid connection point, which will seriously affect the quality of residents' electricity and the service life of power equipment and household appliances. Summary of the invention

[0004] 1. Technical issues to be solved

[0005] The present invention is expected to at least partially solve one of the above technical problems.

[0006] 2. Technical Solution

[0007] A first aspect of the present invention provides a method for integrating distributed photovoltaic voltage regulation in a distribution network. The method for integrating distributed photovoltaic voltage regulation in a distribution network comprises:

[0008] Step A, inputting the historical meteorological data of the current date and the previous N days into the irradiance prediction model to obtain the irradiance prediction data for the future preset time period, N≥2;

[0009] Step B, inputting the irradiance prediction data and the rated power of each photovoltaic panel into the photovoltaic panel output prediction model to obtain the photovoltaic panel output prediction data for a future preset time period;

[0010] Step C: for each load node, input the current date, the historical meteorological data of the previous N days, and the historical load data of the previous N days into the load forecasting model corresponding to the load node to obtain the load forecasting data for the future preset time period corresponding to the load node;

[0011] Step D, using the distribution network information of the substation area, the irradiance prediction data, the photovoltaic panel output prediction data, and the load prediction data of each load node as boundary conditions, and using the power flow equation to solve the predicted voltage of each photovoltaic node and load node in the future preset time period;

[0012] Step E, using the predicted voltage of each photovoltaic node in a future preset time period and the upper voltage threshold of the grid connection point as inputs of the droop control algorithm, and calculating the control instructions of each photovoltaic node in the future preset time period;

[0013] Step F: issuing corresponding control instructions to each photovoltaic node.

[0014] In some embodiments of the present invention, in step B, for the photovoltaic panel output prediction model: its model architecture is constructed using two-dimensional convolution, including a total of W convolution modules, each convolution module includes: a convolution layer, a batch normalization layer and an activation layer, W ≥ 5; its input data dimension is: B×96×5×U, wherein "B" is the batch size; "96" is the time dimension; "5" is the attribute dimension, including: irradiance, rated power, installed capacity, brand, and the ratio of service life to life span; "U" is the number of photovoltaic panels; its output data dimension is: B×96×U, wherein "B" is the batch size; "96" is the time dimension; "U" is the number of photovoltaic panels.

[0015] In some embodiments of the present invention, N=7; and / or, historical meteorological data includes one or more of the following: temperature, sunshine, atmospheric pressure, wind speed; and / or, the irradiance prediction model, the load prediction model, and the photovoltaic panel output prediction model are all: time series prediction models; and / or, the irradiance prediction model is generated through training with irradiance data for no less than 2 years.

[0016] In some embodiments of the present invention, the future prediction time period is the future day; wherein: in step A, the irradiance prediction data for the future preset time period is: 96-point irradiance prediction data for the future day; in step B, the photovoltaic panel output prediction data for the future preset time period is: 96-point photovoltaic panel output prediction data for the future day; in step C, the load prediction data for the future preset time period is: 96-point load prediction data for the future day; in step E, the control instructions for the future preset time period are: 96-point voltage control instructions for the future day.

[0017] In some embodiments of the present invention, in the solution of the power flow equation in step D, the power flow equation is solved by using the Newton-Raphson method; when the power flow equation solution process satisfies one of the preset target error δ and the maximum number of iterations K, the power flow equation solution iteration process is exited, and the current power flow calculation result is used as the predicted voltage of each photovoltaic node and load node in the future preset time period, where: δ≤10 -3 ; K≥200.

[0018] In some embodiments of the present invention, step D includes: sub-step D1, inputting the information of the distribution network in the substation area, the distribution network information in the substation area includes: the topological information of the photovoltaic nodes and the load nodes in the distribution network in the substation area; sub-step D2, forming a node admittance matrix according to the distribution network information in the substation area; sub-step D3, updating the node parameters, including: using the photovoltaic panel output forecast data of the future preset time period and the load forecast data of the future forecast time period of each load node as the active power and reactive power of the PQ node in the power flow calculation, wherein the PQ node includes: photovoltaic nodes and load nodes; sub-step D4, assigning initial values ​​to the voltages of each load node and photovoltaic node; sub-step D5 , set the maximum number of iterations and the target error; sub-step D6, solve the power flow equation, and calculate the error between the power flow equation solution results of the current time step and the previous time step; sub-step D7, if the error is less than the target error δ, execute sub-step D8, otherwise, execute sub-step D10; sub-step D8, obtain the predicted voltage of each photovoltaic node and load node; sub-step D9, output the power flow calculation result, and the process ends; sub-step D10, iteratively solve the power flow equation; sub-step D11, determine whether the number of iterations k satisfies: k>K, if so, execute sub-step D12; otherwise, execute step D8; sub-step D12, end the iteration, and the process ends.

[0019] In some embodiments of the present invention, in step F, for each photovoltaic node, the following logic is executed: sub-step F1, obtaining the real-time voltage of the photovoltaic node; sub-step F2, determining whether the target voltage of the control instruction to be executed on the photovoltaic node is inconsistent with the real-time voltage, and if there is no contradiction, executing sub-step F3; otherwise, executing sub-step F4; sub-step F3, taking the control instruction as the instruction to be issued, and executing sub-step F6; sub-step F4, for the photovoltaic node, obtaining a new second control instruction through a real-time control method; sub-step F5, using the second control instruction to replace the control instruction at the most recent moment as the instruction to be issued, and executing sub-step F6; sub-step F6, issuing the instruction to be issued to the photovoltaic node.

[0020] In some embodiments of the present invention, in sub-step F4, the real-time control method is: if it is found that the current voltage exceeds the voltage upper limit threshold, a second control instruction to reduce the photovoltaic output power is sent to the photovoltaic node until the current voltage is below the voltage upper limit threshold.

[0021] The second aspect of the present invention provides a distributed photovoltaic voltage regulation and fusion device for a distribution network. The distributed photovoltaic voltage regulation and fusion device for a distribution network includes: an irradiance prediction module, which is used to input the current date and the historical meteorological data of the previous N days into the irradiance prediction model to obtain the irradiance prediction data for a preset time period in the future, N≥2; a photovoltaic panel output prediction module, which is used to input the irradiance prediction data and the rated power of each photovoltaic panel into the photovoltaic panel output prediction model to obtain the photovoltaic panel output prediction data for a preset time period in the future; a load prediction module, which is used to input the current date, the historical meteorological data of the previous N days, and the historical load data of the previous N days into the load prediction model corresponding to the load node for each load node, and obtain the load prediction data corresponding to the load node. The module is used to calculate the load forecast data for a preset time period in the future; the flow calculation module is used to use the distribution network information of the substation, the irradiance forecast data, the photovoltaic panel output forecast data, and the load forecast data of each load node as boundary conditions, and use the flow equation to solve the predicted voltage of each photovoltaic node and load node in the preset time period in the future; the droop control instruction generation module is used to use the predicted voltage of each photovoltaic node in the preset time period in the future and the upper limit threshold of the grid connection point voltage as the input of the droop control algorithm, and calculate the control instructions of each photovoltaic node in the preset time period in the future; the instruction issuing module is used to issue the corresponding control instructions to each photovoltaic node.

[0022] In some embodiments of the present invention, the instruction issuing module includes: a real-time voltage acquisition submodule, a contradiction judgment submodule, a to-be-issued instruction determination submodule, a second control instruction generation submodule, a new to-be-issued instruction determination submodule, and an issuing submodule; wherein, for each photovoltaic node: a real-time voltage acquisition submodule, used to obtain the real-time voltage of the photovoltaic node; a contradiction judgment submodule, used to determine whether the target voltage of the control instruction to be executed on the photovoltaic node is inconsistent with the real-time voltage, if there is no contradiction, transfer to the to-be-issued instruction determination submodule; otherwise, transfer to the second control instruction generation submodule; the to-be-issued instruction determination submodule, used to use the control instruction as the to-be-issued instruction, and transfer to the issuing submodule; the second control instruction generation submodule, used to obtain a new second control instruction for the photovoltaic node through a real-time control method; the new to-be-issued instruction determination submodule, used to replace the control instruction at the most recent moment with the second control instruction as the to-be-issued instruction, and transfer to the issuing submodule; the issuing submodule, used to issue the to-be-issued instruction to the photovoltaic node.

[0023] A third aspect of the present invention provides a computer-readable storage medium. The computer storage medium stores a computer program, and when the computer program is executed by a processor, the above-mentioned distributed photovoltaic voltage regulation and fusion method for distribution network is implemented.

[0024] A fourth aspect of the present invention provides a computer device, which includes: a processor; a memory on which a computer program is stored; wherein the processor executes the computer program to implement the above-mentioned distribution network distributed photovoltaic voltage regulation and fusion method.

[0025] The fifth aspect of the present invention provides a server for the fusion of distributed photovoltaic voltage regulation in a distribution network. The server includes: an AI module for storing an irradiance prediction model, a load prediction model, and M photovoltaic panel output prediction models, and performing reasoning operations related to the three prediction models; and a fusion terminal for calling the AI ​​module to execute the above-mentioned fusion method for distributed photovoltaic voltage regulation in a distribution network.

[0026] In some embodiments of the present invention, the AI ​​module is set independently from the fusion terminal; the AI ​​module is provided with a GPU for model training and reasoning operations.

[0027] The sixth aspect of the present invention provides a distributed photovoltaic voltage regulation and fusion system for a distribution network. The distributed photovoltaic voltage regulation and fusion system for a distribution network includes: a meteorological instrument for obtaining historical meteorological data of the substation; P photovoltaic nodes, each photovoltaic node includes: a photovoltaic inverter and a photovoltaic panel, P ≥ 1; and a server as above, whose signal is connected to the meteorological instrument and the P photovoltaic nodes.

[0028] In some embodiments of the present invention, the photovoltaic inverter also executes the following control logic: sub-step G1, determines whether the control instruction issued by the fusion terminal is received, if so, executes sub-step G2, otherwise, executes sub-step G3; sub-step G2, executes the control instruction, and the process ends; sub-step G3, executes the original control instruction in chronological order.

[0029] 3. Beneficial Effects

[0030] It can be seen from the above technical solution that the present invention has at least one of the following beneficial effects compared with the prior art:

[0031] 1. In the existing technology, a photovoltaic output model is established based on historical photovoltaic data. However, after the voltage of photovoltaic grid-connected points in the distribution network is regulated, the actual output no longer conforms to the natural conditions and can no longer be used as an input for the photovoltaic output prediction model.

[0032] Compared with the prior art, the present invention realizes photovoltaic output prediction by establishing an irradiance prediction model and a photovoltaic panel output model, thereby avoiding the influence of the photovoltaic output model input and improving the accuracy of photovoltaic output prediction.

[0033] 2. In the prior art, the control instructions generated by the server are directly sent to the photovoltaic nodes.

[0034] In the present invention, if the current control instruction to be issued is not inconsistent with the real-time voltage, the control instruction is issued, otherwise the real-time control instruction is used to replace the control instruction at the most recent moment. Through the above setting, it is possible to avoid the control error of the photovoltaic node caused by inappropriate control instructions.

[0035] 3. In the present invention, the control instruction is issued in advance, and the original control instruction is executed when the control instruction is not issued in time.

[0036] Through the improvements in the above two aspects, the present invention can better solve the problem of poor PLC communication stability without increasing the implementation cost, and has a better prospect for promotion and application.

[0037] 4. The distributed photovoltaic voltage control method of the distribution network of the present invention has the following calculation process for the control instructions at 96 points in the future day: creating an irradiance prediction model, a photovoltaic panel output prediction model, and a load prediction model. The above model prediction data is used as boundary conditions, and the voltage distribution of each node in the future period is obtained based on the flow calculation. Combined with the upper voltage threshold of the photovoltaic grid-connected point, the droop control method is used to obtain the prediction control instructions of each node in the future period.

[0038] Compared with the prior art, the method of the present invention has the advantages of high intelligence, accurate and reliable voltage control, and has good prospects for promotion and application.

[0039] 5. In the prior art, the prediction model is set in the fusion terminal, but the structure of the fusion terminal is not suitable for training and logical reasoning.

[0040] In the present invention, an independent AI module is expanded on the fusion terminal, and a GPU for model training and reasoning operations is set in the AI ​​module. This setting can accelerate the reasoning speed of artificial neural networks such as irradiance prediction models, photovoltaic panel power generation models, and load prediction models, and improve real-time performance.

[0041] 6. In the prior art, the meteorological data type used for photovoltaic grid voltage regulation is single and has poor real-time performance.

[0042] The present invention adds a micro-meteorological instrument and provides the local real-time meteorological data collected by the micro-meteorological instrument to the irradiance prediction model and the photovoltaic panel output model, which can ensure the accuracy of irradiance prediction and photovoltaic panel processing prediction, and further ensure the accuracy of distributed photovoltaic voltage regulation. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 It is a structural schematic diagram of a distributed photovoltaic voltage control fusion system for a distribution network according to an embodiment of the present invention.

[0044] Figure 2It is the control logic executed by the photovoltaic inverter of the photovoltaic node in the distributed photovoltaic voltage regulation and fusion system of the distribution network according to the embodiment of the present invention.

[0045] Figure 3 The present invention is a flowchart of a method for integrating distributed photovoltaic voltage control in a distribution network according to an embodiment of the present invention.

[0046] Figure 4 for Figure 3 The detailed flow chart of step A and step B in the distributed photovoltaic voltage control fusion method of the distribution network is shown.

[0047] Figure 5 for Figure 3 The detailed flow chart of step C in the distributed photovoltaic voltage control fusion method of the distribution network is shown.

[0048] Figure 6 for Figure 3 Detailed flow chart of step D in the distributed photovoltaic voltage control fusion method for distribution network shown.

[0049] Figure 7 for Figure 3 The detailed flow chart of step F in the distributed photovoltaic voltage control fusion method of the distribution network is shown.

[0050] Figure 8 It is a structural schematic diagram of a distributed photovoltaic voltage regulation and fusion device for a distribution network in an embodiment of the present invention.

[0051] Fig. 9 A schematic diagram of a computer-readable storage medium according to an embodiment of the present invention.

[0052] Fig.10 FIG. 4 is a schematic diagram of a computer device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0053] The present invention proposes a solution for the integration of distributed photovoltaic voltage regulation in distribution networks, and creates an irradiance prediction model, a photovoltaic panel output model, and a load prediction model. Combined with the prediction data of these models, the voltage distribution of each node in the future period is obtained based on the solution of the power flow equation, and the prediction and control instructions of each node in the future period are obtained by the droop control method. During actual regulation, whether to use the preset control instruction or the real-time control instruction is determined by judging whether the prediction and control instructions are inconsistent with the real-time voltage situation.

[0054] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with specific implementation methods and with reference to the accompanying drawings.

[0055] A first aspect of the present invention provides a distributed photovoltaic voltage control fusion system for a distribution network. Figure 1Schematic diagram of the structure of the distributed photovoltaic voltage control fusion system for the power distribution network according to the embodiment of the present invention. Figure 1 As shown, the distributed photovoltaic voltage control fusion system of the distribution network of this embodiment includes:

[0056] Meteorological instrument, used to obtain historical meteorological data of the area;

[0057] P photovoltaic nodes, each photovoltaic node includes: a photovoltaic inverter and a photovoltaic panel, P ≥ 1;

[0058] The server has signals connected to the meteorological instrument and P photovoltaic nodes.

[0059] In this embodiment, for the substations under the jurisdiction of the server, a micro-meteorological instrument is used to collect meteorological data, and the historical meteorological data here include one or more of the following data: temperature, sunshine, atmospheric pressure, wind speed, etc.

[0060] In the process of realizing the present invention, the applicant found that meteorological data is crucial for distributed photovoltaic voltage regulation, which not only determines the intensity of the radiation illumination of the day, but also determines the level of the load. More importantly, only the accuracy of meteorological data can guarantee the accuracy of radiation illumination prediction. Therefore, the present invention adds a micro-meteorological instrument, and provides the irradiance prediction model and the photovoltaic panel output model based on the historical meteorological data collected by the micro-meteorological instrument, which can ensure the accuracy of irradiance prediction and photovoltaic panel processing prediction, and thus ensure the accuracy of distributed photovoltaic voltage regulation.

[0061] Those skilled in the art should understand that in other embodiments of the present invention, meteorological data may be collected by using a meteorological instrument, or obtained from a third-party channel, all of which can implement the present invention and are within the protection scope of the present invention.

[0062] In this embodiment, each photovoltaic node includes: a photovoltaic inverter and a photovoltaic panel. The photovoltaic inverter is used to adjust the voltage and frequency output by the photovoltaic panel to ensure power supply stability and optimize output efficiency. Those skilled in the art should understand that each photovoltaic node may also include two or more photovoltaic panels, which share the same photovoltaic inverter, and the present invention can also be implemented.

[0063] In the prior art, distribution network communication often uses PLC communication, but PLC communication has poor stability and communication anomalies may occur. The usual processing method is to pause and wait for PLC communication to resume and obtain the correct instructions before executing.

[0064] In order to overcome the above defects, this embodiment improves the control logic of the photovoltaic inverter. Figure 2 The control logic executed by the photovoltaic inverter of the photovoltaic node in the distributed photovoltaic voltage control fusion system of the distribution network according to the embodiment of the present invention. Figure 2 As shown, the photovoltaic inverter executes the following control logic:

[0065] Sub-step G1, determining whether the control instruction issued by the fusion terminal has been received, if yes, executing sub-step G2, otherwise, executing sub-step G3;

[0066] Sub-step G2, updating the 96-point control instructions and executing the latest control instructions, and the process ends;

[0067] Sub-step G3, executing the original 96-point control instructions in chronological order.

[0068] It can be seen that in view of the above-mentioned PLC communication stability problem, this embodiment has two improvements:

[0069] (1) Regulatory instructions are issued in advance

[0070] In this embodiment, based on the photovoltaic output and user load forecast data, photovoltaic control instructions for a period of time in the future are given, and these instructions are sent to the photovoltaic inverter controller in advance to avoid untimely control due to communication anomalies.

[0071] (2) Execute the original control order when the control order is not issued in time

[0072] In this embodiment, the logic of the photovoltaic inverter executing the control command when the communication is abnormal is considered. When the communication is normal, the 96-point control command in the photovoltaic inverter is executed; when the communication is abnormal, the original 96-point control command is executed in chronological order.

[0073] In this embodiment, through the above two improvements, the problem of poor PLC communication stability can be better solved without increasing the implementation cost, and the prospect of promotion and application is better.

[0074] A second aspect of the present invention provides a distributed photovoltaic voltage control fusion server for a distribution network. Figure 1 As shown, in this embodiment, the server includes: an AI module for storing an irradiance prediction model, a load prediction model, Q photovoltaic panel output prediction models, and performing reasoning operations related to the three prediction models; and a fusion terminal for calling the AI ​​module to execute a fusion method for distributed photovoltaic voltage regulation in a distribution network. Where Q is the number of photovoltaic nodes, Q≥1, and each photovoltaic node corresponds to one photovoltaic panel output model.

[0075] In the prior art, prediction models are all set in the fusion terminal, but the structure of the fusion terminal is not suitable for training and logical reasoning. In the present invention, an independent AI module is expanded on the fusion terminal, and a GPU for model training and reasoning operations is set in the AI ​​module. Such a setting can accelerate the reasoning speed of artificial neural networks such as irradiance prediction models, photovoltaic panel power generation models, and load prediction models, and improve real-time performance.

[0076] The distribution network distributed photovoltaic voltage control fusion method performed by the fusion terminal will be described in detail in subsequent embodiments and will not be repeated here.

[0077] The third aspect of the present invention provides a method for integrating distributed photovoltaic voltage regulation in a distribution network. Those skilled in the art should understand that the method for integrating distributed photovoltaic voltage regulation in a distribution network in the present invention can be used in Figure 1 The process is executed on the server shown, and may also be executed on other servers. For example, an AI module is disposed on a server in a fusion terminal, which may also implement the present invention and is also within the scope of protection of the present invention.

[0078] Figure 3 This is a flow chart of the method for integrating voltage regulation of distributed photovoltaic power distribution network according to an embodiment of the present invention. Figure 3 As shown, the distributed photovoltaic voltage control fusion method of the distribution network in this embodiment includes:

[0079] Step A, inputting the historical meteorological data of the current date and the previous N days into the irradiance prediction model to obtain the irradiance prediction data for the future preset time period, N≥2;

[0080] Step B, inputting the irradiance prediction data and the rated power of each photovoltaic panel into a photovoltaic panel output prediction model to obtain photovoltaic panel output prediction data for a future preset time period;

[0081] Step C: for each load node, input the current date, the historical meteorological data of the previous T days, and the historical load data of the previous N days into the load forecasting model corresponding to the load node to obtain the load forecasting data for the future preset time period corresponding to the load node;

[0082] Step D, using the distribution network information of the substation area, the irradiance prediction data, the photovoltaic panel output prediction data, and the load prediction data of each load node as boundary conditions, and using the power flow equation to solve the predicted voltage of each photovoltaic node and load node in the future preset time period;

[0083] Step E, using the predicted voltage of each photovoltaic node in a future preset time period and the upper voltage threshold of the grid connection point as inputs of the droop control algorithm, and calculating the control instructions of each photovoltaic node in the future preset time period;

[0084] Step F: issuing corresponding control instructions to each photovoltaic node.

[0085] In this embodiment, the distribution network distributed photovoltaic voltage control fusion method includes an irradiance prediction model, a photovoltaic panel output prediction model and a load prediction model, and combines the power flow calculation and droop control method to obtain the control instructions for 96 points in the future day. Compared with the existing technology, this embodiment is more scientific and effective, the prediction results are more accurate, the control instructions are more targeted, and the photovoltaic utilization efficiency can be improved.

[0086] The following describes in detail the various steps, processes and principles of the distributed photovoltaic voltage control fusion method for the distribution network of this embodiment.

[0087] In this embodiment, it is first necessary to collect the historical irradiance and load data of the substation, and establish an irradiance prediction model and a load prediction model respectively, both of which respectively use the historical 7-day data to predict the next day's data. On this basis, it is also necessary to collect the substation irradiance data and photovoltaic output data, establish a photovoltaic panel output model, and obtain the relationship between the photovoltaic panel output and parameters such as irradiance and photovoltaic panel rated power, so as to use the historical 7-day data to predict the photovoltaic output data for the next day.

[0088] In this embodiment, various historical data select 7 days of historical data, but the present invention is not limited thereto. Those skilled in the art should understand that the number of days of historical meteorological data can be adjusted according to actual scene requirements. Generally, N≥3, where N is the number of days of historical data.

[0089] As mentioned above, in this embodiment, a separate micro-meteorological instrument is provided to obtain meteorological data of the substation, and the meteorological data includes: temperature, sunshine, atmospheric pressure, and wind speed. Those skilled in the art should understand that the type and quantity of meteorological data can be selected according to the actual scene requirements, and the present invention is not limited to the above items.

[0090] In order to be compatible with the existing distribution network distributed photovoltaic voltage control fusion method, in this embodiment, the future prediction time period is the next day, and all kinds of data are 96 points of data in one day, specifically:

[0091] (1) In step A, the irradiance prediction data for the future preset time period is: the irradiance prediction data of 96 points in the future day;

[0092] (2) In step B, the photovoltaic panel output prediction data for the future preset time period is: the photovoltaic panel output prediction data at 96 points in the future day;

[0093] (3) In step C, the load forecast data for the future preset time period is: 96-point load forecast data for the next day;

[0094] (4) In step E, the control instruction for the future preset time period is: 96-point voltage control instruction for the next day.

[0095] Of course, those skilled in the art should understand that the above "96 points*data" means one point of prediction data corresponding to every 15 minutes, and 96 points of prediction data corresponding to 24 hours a day. In actual scenarios, the future prediction time period and data type can also be adjusted according to the needs of the actual scenario, and the present invention can also be implemented and is also within the protection scope of the present invention.

[0096] In this embodiment, the irradiance prediction model, load prediction model, and photovoltaic panel output prediction model are all time series prediction models, and all three are generated through training with no less than 2 years of historical data. Of course, those skilled in the art can train the model according to actual needs, and the present invention does not limit this. It should be noted that in this embodiment, for the photovoltaic panel output prediction model:

[0097] (1) Model Architecture

[0098] In this embodiment, the model architecture of the photovoltaic panel output prediction model is constructed using two-dimensional convolution, which includes W convolution modules in total. Each convolution module includes: a convolution layer, a batch normalization layer (Batch Normalization), and an activation layer, where W≥5.

[0099] (2) Input data dimensions

[0100] In this embodiment, the input data dimension of the photovoltaic panel output prediction model is: B×96×5×U, where "B" is the batch size; "96" is the time dimension, specifically: 96 points of data in the next day; "5" is the attribute dimension, including: irradiance, rated power, installed capacity, brand, and the ratio of service life to lifespan; "U" is the number of photovoltaic panels;

[0101] (3) Output data dimension

[0102] In this embodiment, the output data dimension of the photovoltaic panel output prediction model is: B×96×U, where "B" is the batch size; "96" is the time dimension, specifically: 96 points of data in the next day; and "U" is the number of photovoltaic panels.

[0103] Figure 4 for Figure 3 The detailed flow chart of step A and step B in the distributed photovoltaic voltage regulation fusion method of the distribution network is shown in FIG. Figure 4As shown, in this embodiment, meteorological data are first obtained from the micro-meteorological instrument. When the meteorological data is accumulated for 7 days, these meteorological data are input into the irradiance prediction model to obtain the irradiance prediction data for the next day; then the predicted irradiance prediction data, the rated power of the photovoltaic panel and the current date, temperature, atmospheric pressure, wind speed and other meteorological data are input into the photovoltaic panel output model to obtain the corresponding photovoltaic panel output data at 96 points in the next day.

[0104] It should be noted that in this embodiment, a photovoltaic output prediction model is not directly established. This is because the photovoltaic output prediction model often requires historical photovoltaic output as input, and the actual output after the photovoltaic grid-connected point voltage regulation is deployed in the distribution network of the substation no longer conforms to the natural situation and can no longer be used as the input of the photovoltaic output prediction model. This embodiment realizes photovoltaic output prediction by establishing an irradiance prediction model and a photovoltaic panel output model, avoiding the influence of the photovoltaic output model input and improving the accuracy of photovoltaic output prediction.

[0105] Figure 5 for Figure 3 The detailed flow chart of step C in the distributed photovoltaic voltage regulation fusion method of the distribution network is shown in FIG. Figure 5 As shown, in this embodiment, the irradiance prediction model simultaneously accumulates 7 days of historical load data and 7 days of historical meteorological data, and the historical load data and historical meteorological data are spliced ​​and given as a whole to the load prediction model to obtain 96 points of load data for the corresponding load node in the next day.

[0106] In step D of this embodiment, the Newton-Raphson method is used to solve the power flow equation; when the power flow equation solving process satisfies one of the preset target error δ and the maximum number of iterations K, the power flow equation solving iteration process is exited, and the current power flow calculation result is used as the predicted voltage of each photovoltaic node and load node in the future preset time period, where: δ≤10 -3 ; K≥200.

[0107] Figure 6 for Figure 3 The detailed flow chart of step D in the distributed photovoltaic voltage regulation fusion method of the distribution network is shown in FIG. Figure 6 As shown, in this embodiment, step D includes:

[0108] Sub-step D1, inputting the area distribution network information, the area distribution network information includes: topological information of photovoltaic nodes and load nodes in the area distribution network;

[0109] Sub-step D2, forming a node admittance matrix according to the distribution network information of the substation area;

[0110] Sub-step D3, updating node parameters, including: using the photovoltaic panel output forecast data of the future preset time period and the load forecast data of each load node in the future forecast time period as the active power and reactive power of the PQ node in the power flow calculation, wherein the PQ node includes: photovoltaic nodes and load nodes;

[0111] Sub-step D4, assigning initial values ​​to the voltages of each load node and photovoltaic node;

[0112] Sub-step D5, setting the maximum number of iterations and target error;

[0113] Sub-step D6, solving the power flow equation, and calculating the error △ between the power flow equation solution results of the current time step and the previous time step;

[0114] Sub-step D7: Is the error △ less than the target error δ (△<δ?)? If yes, execute sub-step D8; otherwise, execute sub-step D10;

[0115] Sub-step D8, obtaining the predicted voltage of each photovoltaic node and load node;

[0116] Sub-step D9, output the power flow calculation results, and the process ends;

[0117] Sub-step D10, iterative solution of the power flow equation;

[0118] Sub-step D11, determine whether the number of iterations k satisfies: k>K, if yes, execute sub-step D12; otherwise, execute step D8;

[0119] Sub-step D12, ends the iteration and the process ends.

[0120] In step E of this embodiment, the node information at each time of the next day and the upper voltage threshold of the grid connection point are used as the input of the droop control algorithm, the active and reactive control instructions of each photovoltaic panel are calculated, and the control instructions of each photovoltaic grid connection point at 96 points in the next day are issued to the corresponding photovoltaic inverter. Among them, the upper voltage threshold of the grid connection point is the upper voltage limit value of the photovoltaic node.

[0121] In step F of this embodiment, if the current instruction to be executed is not inconsistent with the real-time voltage, the 96-point droop control instruction is issued, otherwise the actual control instruction is used to replace the droop control instruction at the most recent moment, and the final 96-point control instruction is issued to the photovoltaic inverter. Through the above settings, it is possible to avoid photovoltaic node control errors caused by inappropriate control instructions.

[0122] Figure 7 for Figure 3 The detailed flow chart of step F in the distributed photovoltaic voltage regulation fusion method of the distribution network is shown in FIG. Figure 7As shown, in this embodiment, step F further includes:

[0123] Sub-step F1, obtaining the real-time voltage of the photovoltaic node;

[0124] Sub-step F2, determining whether the target voltage of the control instruction to be executed on the photovoltaic node is inconsistent with the real-time voltage, if there is no contradiction, executing sub-step F3; otherwise, executing sub-step F4;

[0125] Among them, the control instructions are 96-point voltage control instructions for the next day.

[0126] Sub-step F3, taking the control instruction as the instruction to be issued, and executing sub-step F6;

[0127] Sub-step F4, obtaining a new second control instruction for the photovoltaic node through a real-time control method;

[0128] Sub-step F5, using the second control instruction to replace the most recent control instruction as the instruction to be issued, and executing sub-step F6;

[0129] Sub-step F6, sending the instruction to be sent to the photovoltaic node.

[0130] Specifically, the real-time control method is: if it is found that the current voltage exceeds the voltage upper limit threshold, then a second control instruction to reduce the photovoltaic output power is sent to the photovoltaic node until the current voltage is below the voltage upper limit threshold.

[0131] In summary, the distributed photovoltaic voltage control method of the distribution network of this embodiment has a flow chart for calculating the control instructions for 96 points in the future day: creating an irradiance prediction model, a photovoltaic panel output prediction model, and a load prediction model. The above model prediction data is used as boundary conditions, and the voltage distribution of each node in the future period is obtained based on the flow calculation. After the voltage of each photovoltaic grid-connected point is obtained, the droop control method is used in combination with the upper threshold of the photovoltaic grid-connected point voltage to obtain the prediction control instructions for each node in the future period. This embodiment has the advantages of high intelligence and accurate and reliable voltage control, and has good prospects for promotion and application.

[0132] A fourth aspect of the present invention provides a distributed photovoltaic voltage control and fusion device for a distribution network. Figure 8 Schematic diagram of the structure of the distributed photovoltaic voltage control fusion device for the distribution network in the embodiment of the present invention. Figure 8 As shown, the distributed photovoltaic voltage control and fusion device for the distribution network in this embodiment includes:

[0133] The irradiance prediction module 10 is used to input the historical meteorological data of the current date and the previous N days into the irradiance prediction model to obtain the irradiance prediction data for the future preset time period, N≥2;

[0134] The photovoltaic panel output prediction module 20 is used to input the irradiance prediction data and the rated power of each photovoltaic panel into the photovoltaic panel output prediction model to obtain the photovoltaic panel output prediction data for a preset time period in the future;

[0135] The load forecasting module 30 is used to input the current date, the historical meteorological data of the previous N days, and the historical load data of the previous N days into the load forecasting model corresponding to the load node for each load node, and obtain the load forecasting data of the future preset time period corresponding to the load node;

[0136] The power flow calculation module 40 is used to use the power distribution network information, irradiance prediction data, photovoltaic panel output prediction data, and load prediction data of each load node as boundary conditions, and use the power flow equation to solve the predicted voltage of each photovoltaic node and load node in the future preset time period;

[0137] The droop control instruction generation module 50 is used to use the predicted voltage of each photovoltaic node in the future preset time period and the upper threshold of the grid connection point voltage as the input of the droop control algorithm, and calculate the control instructions of each photovoltaic node in the future preset time period;

[0138] The instruction issuing module 60 is used to issue corresponding control instructions to each photovoltaic node.

[0139] Further, the instruction issuing module 60 includes: a real-time voltage acquisition submodule 61, a contradiction judgment submodule 62, a to-be-issued instruction determination submodule 63, a second control instruction generation submodule 64, a new to-be-issued instruction determination submodule 65, and an issuing submodule 66. For each photovoltaic node: the real-time voltage acquisition submodule 61 is used to obtain the real-time voltage of the photovoltaic node; the contradiction judgment submodule 62 is used to determine whether the target voltage of the control instruction to be executed on the photovoltaic node is inconsistent with the real-time voltage, if there is no contradiction, transfer to the to-be-issued instruction determination submodule 63; otherwise, transfer to the second control instruction generation submodule 64; the to-be-issued instruction determination submodule 63 is used to take the control instruction as the to-be-issued instruction and transfer to the issuing submodule; the second control instruction generation submodule 64 is used to obtain a new second control instruction for the photovoltaic node through a real-time control method; the new to-be-issued instruction determination submodule 65 is used to replace the control instruction at the most recent moment with the second control instruction as the to-be-issued instruction and transfer to the issuing submodule 66; the issuing submodule is used to send the to-be-issued instruction to the photovoltaic node.

[0140] A fifth aspect of the present invention provides a computer-readable storage medium. Fig. 9 Schematic diagram of a computer-readable storage medium according to an embodiment of the present invention. Fig. 9As shown, the computer storage medium of this embodiment stores a computer program, and when the computer program is executed by the processor, the distribution network distributed photovoltaic voltage control fusion method as described in the above embodiment is implemented.

[0141] A sixth aspect of the present invention provides a computer device. Fig.10 Schematic diagram of a computer device according to an embodiment of the present invention. Fig.10 As shown, the computer device of this embodiment includes: a processor; a memory, on which a computer program is stored; wherein the processor executes the computer program to implement the distribution network distributed photovoltaic voltage control fusion method as described in the above embodiment.

[0142] For the specific contents of the distribution network distributed photovoltaic voltage regulation and fusion device, computer readable storage medium, and computer device of the present invention, reference may be made to the previous embodiments of the distribution network distributed photovoltaic voltage regulation and fusion method, server, and system of the present invention. All the contents of the previous embodiments are incorporated into the following embodiments and will not be described again.

[0143] So far, the various embodiments of the present invention have been introduced. According to the above description, those skilled in the art should have a clear understanding of the present invention.

[0144] The ordinal numbers used in the specification and claims, such as "first", "second", as well as Arabic numerals, letters, etc., to modify the corresponding steps are intended only to make a step with a certain name clearly distinguishable from another step with the same name, and do not mean that the steps have any ordinal numbers, nor do they represent the order of one step and another step. At the same time, unless the steps are specifically described or must occur in sequence, the order of the above steps is not limited to the above list, and can be changed or rearranged according to the required design.

[0145] The present invention may also be implemented as a device or apparatus program (e.g., a computer program and a computer program product) for performing a portion or all of the methods described herein. Such a program implementing the present invention may be stored on a computer-readable medium, or may be in the form of one or more signals. Such a signal may be downloaded from an Internet website, or provided on a carrier signal, or provided in any other form.

[0146] The present invention can be implemented by means of hardware including several different elements and by means of a suitably programmed computer. The various component embodiments of the present invention can be implemented in hardware, or in software modules running on one or more processors, or in a combination thereof. The physical implementation of the hardware structure includes but is not limited to physical devices, which include but are not limited to transistors, memristors, DNA computers, single-chip microcomputers, microprocessors or digital signal processors (DSPs). In addition, the present invention is not directed to any specific programming language. It should be understood that the content of the present invention can be implemented using various programming languages, and the description of specific languages ​​herein is to disclose the best implementation of the present invention.

[0147] Those skilled in the art should understand that in the claims and description of the present invention, the word "comprising" does not exclude the existence of elements (or steps) not listed in the claims. The word "a" or "an" preceding an element (or step) does not exclude the existence of multiple such elements (or steps).

[0148] For certain implementations, if they are not the key content of the present invention and are well known to ordinary technicians in the relevant technical field, they are not described in detail in the drawings or text of the specification due to space limitations. In this case, reference can be made to relevant existing technologies for understanding.

[0149] Furthermore, the above embodiments are provided merely to enable the present invention to satisfy legal requirements, and the present invention may be embodied in many different forms and should not be construed as limited to the embodiments set forth herein.

[0150] Similarly, it should be understood that in order to simplify the present invention, in the above description of the exemplary embodiments of the present invention, the various features of the present invention are sometimes grouped together into a single embodiment, figure, or description thereof. However, the method of the invention should not be interpreted as reflecting the following intention: the claimed invention requires more features than the features explicitly stated in each claim. More specifically, as reflected in the claims, each inventive aspect lies in less than all the features of the previous single embodiment. Moreover, the embodiments can be mixed and matched with each other or with other embodiments based on design and reliability considerations, that is, the technical features in different embodiments can be freely combined to form more embodiments. Therefore, the claims following the specific embodiment are hereby explicitly incorporated into the specific embodiment, wherein each claim itself serves as a separate embodiment of the present invention.

[0151] The above specific embodiments provide a detailed description of the objectives, technical means and beneficial effects of the present invention. It should be understood that the purpose of the detailed description is to enable those skilled in the art to understand the present invention more clearly, and it is not used to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A distributed photovoltaic voltage control fusion method for a distribution network, characterized in that: include: Step A, inputting the historical meteorological data of the current date and the previous N days into the irradiance prediction model to obtain the irradiance prediction data for the future preset time period, N≥2; Step B, inputting the irradiance prediction data and the rated power of each photovoltaic panel into a photovoltaic panel output prediction model to obtain photovoltaic panel output prediction data for a future preset time period; Step C: for each load node, input the current date, the historical meteorological data of the previous N days, and the historical load data of the previous N days into the load forecasting model corresponding to the load node to obtain the load forecasting data for the future preset time period corresponding to the load node; Step D, using the distribution network information of the substation area, the irradiance prediction data, the photovoltaic panel output prediction data, and the load prediction data of each load node as boundary conditions, and using the power flow equation to solve the predicted voltage of each photovoltaic node and load node in the future preset time period; Step E, using the predicted voltage of each photovoltaic node in a future preset time period and the upper voltage threshold of the grid connection point as inputs of the droop control algorithm, and calculating the control instructions of each photovoltaic node in the future preset time period; Step F: issuing corresponding control instructions to each photovoltaic node.

2. The method for fusion voltage regulation of distributed photovoltaic power distribution network according to claim 1, characterized in that: In step B, for the photovoltaic panel output prediction model: The model architecture is built using two-dimensional convolution, which includes W convolution modules. Each convolution module includes: convolution layer, batch normalization layer and activation layer, W ≥ 5; The input data dimensions are: B×96×5×U, where "B" is the batch size; "96" is the time dimension; "5" is the attribute dimension, including: irradiance, rated power, installed capacity, brand, and the ratio of service life to lifespan; "U" is the number of photovoltaic panels; The output data dimension is: B×96×U, where "B" is the batch size; "96" is the time dimension; and "U" is the number of photovoltaic panels.

3. The method for fusion voltage regulation of distributed photovoltaic power distribution network according to claim 1, characterized in that: N＝7； And / or, the historical meteorological data includes one or more of the following: temperature, sunshine, atmospheric pressure, wind speed; And / or, the irradiance prediction model, load prediction model, and photovoltaic panel output prediction model are all: time series prediction models; And / or, the irradiance prediction model is generated by training with irradiance data of no less than 2 years.

4. The method for fusion voltage regulation of distributed photovoltaic power distribution network according to claim 1, characterized in that: The future prediction time period is the next day; wherein: In step A, the irradiance prediction data for the future preset time period is: irradiance prediction data at 96 points in the future day; In step B, the photovoltaic panel output prediction data for the future preset time period is: photovoltaic panel output prediction data at 96 points in the future day; In step C, the load forecast data for the future preset time period is: 96-point load forecast data for the next day; In step E, the control instructions for the future preset time period are: 96-point voltage control instructions for the next day.

5. The method for fusion voltage regulation of distributed photovoltaic power distribution network according to claim 1, characterized in that: In the step D, the power flow equation is solved; The Newton-Raphson method is used to solve the power flow equation; When the power flow equation solution process meets one of the preset target error δ and the maximum number of iterations K, the power flow equation solution iteration process is exited, and the current power flow calculation result is used as the predicted voltage of each photovoltaic node and load node in the future preset time period, where: δ≤10 -3 ; K≥200.

6. The method for fusion voltage regulation of distributed photovoltaic power distribution network according to claim 5, characterized in that: Step D includes: Sub-step D1, inputting the area distribution network information, the area distribution network information includes: topological information of photovoltaic nodes and load nodes in the area distribution network; Sub-step D2, forming a node admittance matrix according to the distribution network information of the substation area; Sub-step D3, updating node parameters, including: using the photovoltaic panel output forecast data of the future preset time period and the load forecast data of each load node in the future forecast time period as the active power and reactive power of the PQ node in the power flow calculation, wherein the PQ node includes: photovoltaic nodes and load nodes; Sub-step D4, assigning initial values ​​to the voltages of each load node and photovoltaic node; Sub-step D5, setting the maximum number of iterations and target error; Sub-step D6, solving the power flow equation and calculating the error between the power flow equation solution results of the current time step and the previous time step; Sub-step D7, if the error is less than the target error δ, execute sub-step D8, otherwise, execute sub-step D10; Sub-step D8, obtaining the predicted voltage of each photovoltaic node and load node; Sub-step D9, output the power flow calculation results, and the process ends; Sub-step D10, iterative solution of the power flow equation; Sub-step D11, determine whether the number of iterations k satisfies: k>K, if yes, execute sub-step D12; otherwise, execute step D8; Sub-step D12, ends the iteration and the process ends.

7. The method for fusion voltage regulation of distributed photovoltaic power distribution network according to claim 1, characterized in that: In step F, for each photovoltaic node, the following logic is executed: Sub-step F1, obtaining the real-time voltage of the photovoltaic node; Sub-step F2, determining whether the target voltage of the control instruction to be executed on the photovoltaic node is inconsistent with the real-time voltage, if there is no contradiction, executing sub-step F3; otherwise, executing sub-step F4; Sub-step F3, taking the control instruction as the instruction to be issued, and executing sub-step F6; Sub-step F4, obtaining a new second control instruction for the photovoltaic node through a real-time control method; Sub-step F5, using the second control instruction to replace the most recent control instruction as the instruction to be issued, and executing sub-step F6; Sub-step F6, sending the instruction to be sent to the photovoltaic node.

8. The method for fusion voltage regulation of distributed photovoltaic power distribution network according to claim 7, characterized in that: In said sub-step F4, The real-time control method is: if it is found that the current voltage exceeds the voltage upper limit threshold, a second control instruction to reduce the photovoltaic output power is sent to the photovoltaic node until the current voltage is below the voltage upper limit threshold.

9. A distributed photovoltaic voltage control and fusion device for a distribution network, characterized in that: include: The irradiance prediction module is used to input the historical meteorological data of the current date and the previous N days into the irradiance prediction model to obtain the irradiance prediction data for the future preset time period, N≥2; A photovoltaic panel output prediction module, used to input the irradiance prediction data and the rated power of each photovoltaic panel into a photovoltaic panel output prediction model to obtain photovoltaic panel output prediction data for a future preset time period; The load forecasting module is used to input the current date, the historical meteorological data of the previous N days, and the historical load data of the previous N days into the load forecasting model corresponding to the load node for each load node, and obtain the load forecasting data of the future preset time period corresponding to the load node; The power flow calculation module is used to use the distribution network information of the substation area, the irradiance prediction data, the photovoltaic panel output prediction data, and the load prediction data of each load node as boundary conditions, and use the power flow equation to solve the predicted voltage of each photovoltaic node and load node in the future preset time period; A droop control instruction generation module is used to use the predicted voltage of each photovoltaic node in a future preset time period and the upper threshold of the grid connection point voltage as inputs of the droop control algorithm to calculate the control instructions of each photovoltaic node in the future preset time period; The instruction issuing module is used to issue the corresponding control instructions to each photovoltaic node.

10. The distributed photovoltaic voltage control and fusion device for distribution network according to claim 9, characterized in that: The instruction issuing module includes: a real-time voltage acquisition submodule, a contradiction judgment submodule, a pending instruction determination submodule, a second control instruction generation submodule, a new pending instruction determination submodule, and an issuing submodule; wherein, for each photovoltaic node: A real-time voltage acquisition submodule is used to obtain the real-time voltage of the photovoltaic node; A contradiction judgment submodule is used to judge whether the target voltage of the control instruction to be executed on the photovoltaic node is inconsistent with the real-time voltage. If there is no contradiction, the submodule for determining the instruction to be sent is transferred; otherwise, the submodule for generating the second control instruction is transferred; A to-be-issued instruction determination submodule is used to take the control instruction as an instruction to be issued and transfer it to the issuing submodule; A second control instruction generating submodule is used to obtain a new second control instruction for the photovoltaic node through a real-time control method; A new pending instruction determination submodule is used to replace the most recent control instruction with the second control instruction as the pending instruction, and transfer it to the issuing submodule; The sending submodule is used to send the instruction to be sent to the photovoltaic node.

11. A computer-readable storage medium, characterized in that: The computer storage medium stores a computer program, which, when executed by a processor, implements the distributed photovoltaic voltage regulation and fusion method for a distribution network as described in any one of claims 1 to 8.

12. A computer device, characterized in that: include: processor; a memory having a computer program stored thereon; Wherein, the processor executes the computer program to implement the distributed photovoltaic voltage regulation and fusion method for the distribution network as described in any one of claims 1 to 8.

13. A server for integrating distributed photovoltaic voltage regulation in a distribution network, characterized in that: include: The AI ​​module is used to store the irradiance prediction model, the load prediction model, and the M photovoltaic panel output prediction models, and perform reasoning operations related to the three prediction models; A fusion terminal is used to call the AI ​​module to execute the distribution network distributed photovoltaic voltage control fusion method as described in any one of claims 1 to 8.

14. The server according to claim 13, characterized in that: The AI ​​module is independently configured from the fusion terminal; The AI ​​module is provided with a GPU for model training and reasoning operations.

15. A distributed photovoltaic voltage control fusion system for distribution network, characterized in that: include: Meteorological instrument, used to obtain historical meteorological data of the area; P photovoltaic nodes, each photovoltaic node includes: a photovoltaic inverter and a photovoltaic panel, P ≥ 1; The server as claimed in claim 13 or 14, wherein the signal thereof is connected to the meteorological instrument and P photovoltaic nodes.

16. The distributed photovoltaic voltage control fusion system for distribution network according to claim 15, characterized in that: The photovoltaic inverter also performs the following control logic: Sub-step G1, determining whether the control instruction issued by the fusion terminal has been received, if yes, executing sub-step G2, otherwise, executing sub-step G3; Sub-step G2, executing the control instruction, and the process ends; Sub-step G3, executing the original control instructions in chronological order.

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