Wireless channel switching method and system for microgrid intelligent grid connection

Abstract

The present invention relates to the field of wireless channel switching monitoring technology, and is a wireless channel switching method and system for intelligent microgrid grid connection. The specific method includes: collecting operating data of different microgrid areas and building a microgrid operation database; extracting and marking characteristic data of each operating stage of power equipment, monitoring the working status of power equipment; predicting the wireless channel switching threat coefficient and performing data matching tracing and prediction error compensation processing on the predicted wireless channel switching threat coefficient; performing wireless channel switching priority evaluation, and optimizing the channel switching management strategy for intelligent microgrid grid connection. The present invention solves the problem in the prior art that wireless channels are susceptible to environmental changes and interference, and the traditional method cannot switch channels in time, which easily leads to communication interruption. At the same time, the existing channel switching mechanism has a slow response speed, and is prone to data loss or increased delay during switching, and has poor communication stability.

Description

Technical Field

[0001] The invention relates to the technical field of wireless channel switching monitoring, and relates to a wireless channel switching method and system for intelligent grid connection of a microgrid. Background Art

[0002] With the rapid development of smart microgrid technology, wireless communication technology is increasingly used in microgrids. Through wireless communication technology, microgrids can exchange information and coordinate energy with the main grid and other microgrids. However, the wireless channels in microgrids are affected by environmental changes, interference, load and other factors, and often experience channel quality degradation, increased delays, and even communication interruptions. In this case, the traditional fixed channel transmission method is difficult to guarantee the reliability and stability of communication. Therefore, a wireless channel switching method and system for smart grid connection of microgrids are proposed, aiming to improve the stability and reliability of communication by dynamically switching wireless channels, and ensure the efficient operation of the smart grid connection function of the microgrid. Summary of the invention

[0003] The present invention solves the problem in the prior art that wireless channels are easily affected by environmental changes and interference, and the traditional method cannot switch channels in time, which easily leads to communication interruption. At the same time, the existing channel switching mechanism has a slow response speed and is prone to data loss or increased delay during switching, and has poor communication stability. A wireless channel switching method and system for intelligent grid connection of microgrids are proposed.

[0004] In order to achieve the above object, the technical solution of the wireless channel switching method for microgrid intelligent grid connection of the present invention includes the following steps:

[0005] S1: Collect the operation data of different microgrid areas and build a microgrid operation database;

[0006] S2: Monitor and photograph the power equipment in different microgrid areas from multiple angles, input them into the machine learning model, extract and mark the feature data of each operation stage of the power equipment, and monitor the working status of the power equipment;

[0007] S3: When the operating characteristic data of the power equipment is marked as switching, the operating status of the power equipment in different microgrid areas is evaluated, and the wireless channel switching threat coefficient is predicted according to the operating status evaluation strategy of the power equipment;

[0008] S4: Perform data matching and tracing processing and prediction error compensation processing on the predicted wireless channel switching threat coefficient through the historical data stored in the microgrid operation database;

[0009] S5: Based on the wireless channel status data that has completed the data matching tracing process and the prediction error compensation process, the wireless channel switching priority is evaluated, and the channel switching management strategy for the smart grid connection of the microgrid is optimized.

[0010] Specifically, in S1, the operation data of different microgrid areas include:

[0011] The total number of wireless channel switching in the microgrid area ; in the When the wireless channel is switched for the first time, the instantaneous load in the microgrid area and instantaneous electricity generation from renewable energy sources ;No. The actual operating time percentage of the power equipment in the microgrid area when wireless channel switching is performed and voltage fluctuation mean ;in, ;

[0012] The total number of times that equipment failures affect the stability of the wireless channel ; The impact of equipment failure on wireless channel stability ; The total number of times the interference signal affects the stability of the wireless channel ; The impact of interference signals on the stability of wireless channels ;in, , , Indicates the impact of the device failure on the stability of the wireless channel for the Nth time. It indicates the impact of the interference signal on the stability of the wireless channel when it affects the stability of the wireless channel for the Mth time.

[0013] Specifically, S2 includes the following specific steps:

[0014] S201: extracting power equipment monitoring data from different microgrid areas, and performing outlier removal and data alignment processing on the power equipment monitoring data from different microgrid areas;

[0015] S202: Based on the machine learning model, extract and classify the characteristic data of each operation stage of the power equipment according to the processed power equipment monitoring data of the microgrid area;

[0016] S203: Mark and output characteristic data of the power equipment at different operation stages, and monitor the working status of the power equipment in real time;

[0017] In S2, the machine learning model is a deep neural network model, a convolutional neural network model, or a recurrent neural network model.

[0018] Specifically, in S203, the operation phases include: a startup phase, a stable operation phase, a load fluctuation phase, a fault alarm phase, a maintenance phase, and a shutdown phase.

[0019] Specifically, in S3, the wireless channel switching threat coefficient is predicted according to the operation status evaluation strategy of the power equipment:

[0020] ;

[0021] in, For the The wireless channel switching threat coefficient when wireless channel switching is performed for the first time;

[0022] For the The influence of environmental factors on the state of the power equipment when the wireless channel is switched for the first time; the influence of environmental factors on the state of the power equipment The calculation strategy is: ;in, The first wireless channel switch is to the first wireless channel switch during the monitoring period of the power equipment. The instantaneous load average value of the microgrid area when the secondary wireless channel is switched; The first wireless channel switch is to the first wireless channel switch during the monitoring period of the power equipment. The average instantaneous power generation of renewable energy in the microgrid area when the secondary wireless channel is switched;

[0023] For the The impact of equipment failure and interference signals on the state of power equipment when the secondary wireless channel is switched; The impact of equipment failure and interference signals on the state of power equipment The calculation strategy is: ;

[0024] For the When the secondary wireless channel is switched, the influence of the operating parameters of the power equipment on the state of the power equipment; the influence of the operating parameters of the power equipment on the state of the power equipment The calculation strategy is: ;in, It is the minimum operating voltage of the electrical equipment; It is the maximum operating voltage of the power equipment; For the The deviation between the actual operating time percentage of the power equipment and the optimal operating time percentage when the wireless channel switching is performed for the first time.

[0025] Specifically, in S4, the prediction error compensation process includes:

[0026] ;

[0027] in, is the wireless channel switching threat coefficient after prediction error compensation;

[0028] is the compensation coefficient for the prediction error of the wireless channel switching threat coefficient caused by environmental factors, The calculation strategy is: ; Among them, the total number of historical switching of wireless channels matched and traced in the microgrid operation database is Y; and are the historical daily average load and the historical daily average power generation of renewable energy in the microgrid area where the power equipment is located at the time of the yth historical wireless channel switching;

[0029] A compensation coefficient for the prediction error of the wireless channel switching threat factor caused by equipment failure and interference signals;

[0030] The calculation strategy is:

[0031] ;in, and They are the historical impacts of equipment failures and interference signals on the stability of wireless channels matched and traced in the microgrid operation database;

[0032] A compensation coefficient for the prediction error of the wireless channel switching threat coefficient caused by the operating parameters of the power equipment;

[0033] The calculation strategy is: ;in, and They are respectively the historical voltage fluctuation mean and the actual historical operation time percentage of the microgrid area where the power equipment is located during the yth historical wireless channel switching.

[0034] Specifically, in S5, the wireless channel switching priority evaluation includes:

[0035] when When , the switching quality level for evaluating the wireless channel switching threat factor is the first-level channel quality;

[0036] when When , the switching quality level for evaluating the wireless channel switching threat factor is the secondary channel quality;

[0037] when When , the switching quality level for evaluating the wireless channel switching threat factor is the third-level channel quality;

[0038] when When , the switching quality level for evaluating the wireless channel switching threat factor is the fourth level of channel quality;

[0039] when When , the switching quality level for evaluating the wireless channel switching threat factor is level 5 channel quality;

[0040] in, They are the first-level channel quality evaluation threshold, the second-level channel quality evaluation threshold, the third-level channel quality evaluation threshold, the fourth-level channel quality evaluation threshold, and the fifth-level channel quality evaluation threshold of the switching quality of the wireless channel switching threat coefficient.

[0041] Specifically, in S5, the optimization of the channel switching management strategy includes:

[0042] S501: extracting wireless channel status evaluation data, and classifying the status of the wireless channel: when the switching quality level of the wireless channel switching threat coefficient is the first-level channel quality, the second-level channel quality, and the third-level channel quality, marking the wireless channel as an alternative switching channel, extracting the historical switching time of the alternative switching channel from the microgrid operation database, arranging the historical switching time in ascending order, and giving priority to switching to the alternative switching channel with the shortest historical switching time;

[0043] When the switching quality level of the wireless channel switching threat coefficient is level 4 channel quality or level 5 channel quality, the wireless channel is marked as a non-recommended switching channel, and step S502 is executed;

[0044] S502: Extracting the compensation coefficient of the prediction error of the wireless channel switching threat coefficient caused by environmental factors , compensation coefficient for prediction error of wireless channel switching threat coefficient caused by equipment failure and interference signal And the compensation coefficient of the prediction error of the wireless channel switching threat coefficient caused by the operating parameters of the power equipment ;

[0045] when When the wireless channel is switched, a pop-up message "Channel status does not meet the standard, it is recommended to adjust the antenna or amplify the signal" will appear in the background. The threshold for human intervention that poses a threat to wireless channel switching due to environmental factors;

[0046] when When the wireless channel is switched, a pop-up message "Channel status does not meet the standard, it is recommended to check the interference source and isolate the signal" will appear in the background. The human intervention threshold for equipment failure and interference signals posing a threat to wireless channel switching;

[0047] when When the wireless channel is switched, a pop-up message "Channel status does not meet the standard, it is recommended to optimize the system configuration or update the firmware" will appear in the background. The threshold for human intervention caused by the operating parameters of power equipment to threaten wireless channel switching.

[0048] In addition, the wireless channel switching system for microgrid intelligent grid connection of the present invention includes the following modules:

[0049] Power grid operation database construction module, equipment status monitoring module, channel switching threat prediction module, prediction error compensation module and channel switching management strategy optimization module;

[0050] The grid operation database construction module is used to collect operation data of different microgrid areas and construct a microgrid operation database;

[0051] The equipment status monitoring module is used to monitor and shoot the power equipment in different microgrid areas from multiple angles, input the data into the machine learning model, extract and mark the characteristic data of each operation stage of the power equipment, and monitor the working status of the power equipment;

[0052] The channel switching threat prediction module is used to evaluate the operating status of power equipment in different microgrid areas and predict the wireless channel switching threat coefficient according to the operating status evaluation strategy of the power equipment;

[0053] The prediction error compensation module performs data matching and tracing processing and prediction error compensation processing on the predicted wireless channel switching threat coefficient through the historical data stored in the microgrid operation database;

[0054] The channel switching management strategy optimization module performs wireless channel switching priority evaluation based on the wireless channel state data that has completed data matching tracing processing and prediction error compensation processing, and optimizes the channel switching management strategy for the smart grid connection of the microgrid.

[0055] A storage medium stores instructions, and when a computer reads the instructions, the computer executes the wireless channel switching method for intelligent microgrid grid connection.

[0056] An electronic device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the wireless channel switching method for intelligent grid connection of a microgrid is implemented.

[0057] Compared with the prior art, the technical effects of the present invention are as follows:

[0058] 1. The present invention improves communication stability: by real-time monitoring of channel quality, the system can quickly determine the channel status and immediately switch to the best channel when necessary to ensure continuous and stable communication.

[0059] 2. The present invention reduces the risk of communication interruption: through the intelligent channel switching mechanism, the system can switch to the backup channel in time when the channel quality deteriorates, thereby preventing communication interruption and improving the reliability of the system.

[0060] 3. The present invention reduces switching delay: The optimized channel switching algorithm can effectively reduce delay and data loss during channel switching, ensuring communication efficiency and data integrity. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. 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 creative labor. Among them:

[0062] Figure 1 It is a flow chart of a wireless channel switching method for intelligent microgrid grid connection of the present invention;

[0063] Figure 2 It is a structural schematic diagram of the wireless channel switching system for microgrid intelligent grid connection of the present invention. DETAILED DESCRIPTION

[0064] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the accompanying drawings.

[0065] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0066] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0067] Embodiment 1:

[0068] like Figure 1 As shown, the wireless channel switching method for microgrid intelligent grid connection according to the embodiment of the present invention is as follows: Figure 1 As shown, the specific steps are as follows:

[0069] S1: Collect the operation data of different microgrid areas and build a microgrid operation database;

[0070] In S1, the operation data of different microgrid areas include:

[0071] The total number of wireless channel switching in the microgrid area ; in the When the wireless channel is switched for the first time, the instantaneous load in the microgrid area and instantaneous electricity generation from renewable energy sources ;No. The actual operating time percentage of the power equipment in the microgrid area when the wireless channel switching is performed and voltage fluctuation mean ;in, ;

[0072] The total number of times that equipment failures affect the stability of the wireless channel ; The impact of equipment failure on wireless channel stability ; The total number of times the interference signal affects the stability of the wireless channel ; The impact of interference signals on the stability of wireless channels ;in, , , Indicates the impact of the device failure on the stability of the wireless channel for the Nth time. It indicates the impact of the interference signal on the stability of the wireless channel when it affects the stability of the wireless channel for the Mth time.

[0073] S2: Monitor and photograph the power equipment in different microgrid areas from multiple angles, input them into the machine learning model, extract and mark the feature data of each operation stage of the power equipment, and monitor the working status of the power equipment;

[0074] S2 includes the following specific steps:

[0075] S201: extracting power equipment monitoring data from different microgrid areas, and performing outlier removal and data alignment processing on the power equipment monitoring data from different microgrid areas;

[0076] S202: Based on the machine learning model, extract and classify the characteristic data of each operation stage of the power equipment according to the processed power equipment monitoring data of the microgrid area;

[0077] S203: Mark and output characteristic data of the power equipment at different operation stages, and monitor the working status of the power equipment in real time;

[0078] In S2, the machine learning model is a deep neural network model, a convolutional neural network model, or a recurrent neural network model.

[0079] In S203, the operation phases include: a startup phase, a stable operation phase, a load fluctuation phase, a fault alarm phase, a maintenance phase, and a shutdown phase.

[0080] S3: When the operating characteristic data of the power equipment is marked as switching, the operating status of the power equipment in different microgrid areas is evaluated, and the wireless channel switching threat coefficient is predicted according to the operating status evaluation strategy of the power equipment;

[0081] In S3, the wireless channel switching threat coefficient is predicted according to the operation status evaluation strategy of the power equipment:

[0082] ;

[0083] in, For the The wireless channel switching threat coefficient when wireless channel switching is performed for the first time;

[0084] For the The influence of environmental factors on the state of the power equipment when the wireless channel is switched for the first time; the influence of environmental factors on the state of the power equipment The calculation strategy is: ;in, The first wireless channel switch is to the first wireless channel switch during the monitoring period of the power equipment. The instantaneous load average value of the microgrid area when the secondary wireless channel is switched; The first wireless channel switch is to the first wireless channel switch during the monitoring period of the power equipment. The average instantaneous power generation of renewable energy in the microgrid area when the secondary wireless channel is switched;

[0085] For the The impact of equipment failure and interference signals on the state of power equipment when the secondary wireless channel is switched; The impact of equipment failure and interference signals on the state of power equipment The calculation strategy is: ;

[0086] For the When the secondary wireless channel is switched, the influence of the operating parameters of the power equipment on the state of the power equipment; the influence of the operating parameters of the power equipment on the state of the power equipment The calculation strategy is: ;in, It is the minimum operating voltage of the electrical equipment; It is the maximum operating voltage of the power equipment; For the The deviation between the actual operating time percentage of the power equipment and the optimal operating time percentage when the wireless channel switching is performed for the first time.

[0087] S4: Perform data matching and tracing processing and prediction error compensation processing on the predicted wireless channel switching threat coefficient through the historical data stored in the microgrid operation database;

[0088] In S4, the prediction error compensation process includes:

[0089] ;

[0090] in, is the wireless channel switching threat coefficient after prediction error compensation;

[0091] is the compensation coefficient for the prediction error of the wireless channel switching threat coefficient caused by environmental factors, The calculation strategy is: ; Among them, the total number of historical switching of wireless channels matched and traced in the microgrid operation database is Y; and are the historical daily average load and the historical daily average power generation of renewable energy in the microgrid area where the power equipment is located at the time of the yth historical wireless channel switching;

[0092] A compensation coefficient for the prediction error of the wireless channel switching threat factor caused by equipment failure and interference signals;

[0093] The calculation strategy is:

[0094] ;in, and They are the historical impacts of equipment failures and interference signals on the stability of wireless channels matched and traced in the microgrid operation database;

[0095] A compensation coefficient for the prediction error of the wireless channel switching threat coefficient caused by the operating parameters of the power equipment;

[0096] The calculation strategy is: ;in, and They are respectively the historical voltage fluctuation mean and the actual historical operation time percentage of the microgrid area where the power equipment is located during the yth historical wireless channel switching.

[0097] S5: Based on the wireless channel status data that has completed the data matching tracing process and the prediction error compensation process, the wireless channel switching priority is evaluated, and the channel switching management strategy for the smart grid connection of the microgrid is optimized.

[0098] In S5, the wireless channel switching priority evaluation includes:

[0099] when When , the switching quality level for evaluating the wireless channel switching threat factor is the first-level channel quality;

[0100] when When , the switching quality level for evaluating the wireless channel switching threat factor is the secondary channel quality;

[0101] when When , the switching quality level for evaluating the wireless channel switching threat factor is the third-level channel quality;

[0102] when When , the switching quality level for evaluating the wireless channel switching threat factor is the fourth level of channel quality;

[0103] when When , the switching quality level for evaluating the wireless channel switching threat factor is level 5 channel quality;

[0104] in, They are the first-level channel quality evaluation threshold, the second-level channel quality evaluation threshold, the third-level channel quality evaluation threshold, the fourth-level channel quality evaluation threshold, and the fifth-level channel quality evaluation threshold of the switching quality of the wireless channel switching threat coefficient.

[0105] In S5, the optimization of the channel switching management strategy includes:

[0106] S501: extracting wireless channel status evaluation data, and classifying the status of the wireless channel: when the switching quality level of the wireless channel switching threat coefficient is the first-level channel quality, the second-level channel quality, and the third-level channel quality, marking the wireless channel as an alternative switching channel, extracting the historical switching time of the alternative switching channel from the microgrid operation database, arranging the historical switching time in ascending order, and giving priority to switching to the alternative switching channel with the shortest historical switching time;

[0107] When the switching quality level of the wireless channel switching threat coefficient is level 4 channel quality or level 5 channel quality, the wireless channel is marked as a non-recommended switching channel, and step S502 is executed;

[0108] S502: Extracting the compensation coefficient of the prediction error of the wireless channel switching threat coefficient caused by environmental factors , compensation coefficient for prediction error of wireless channel switching threat coefficient caused by equipment failure and interference signal And the compensation coefficient of the prediction error of the wireless channel switching threat coefficient caused by the operating parameters of the power equipment ;

[0109] when When the wireless channel is switched, a pop-up message "Channel status does not meet the standard, it is recommended to adjust the antenna or amplify the signal" will appear in the background. The threshold for human intervention that poses a threat to wireless channel switching due to environmental factors;

[0110] when When the wireless channel is switched, a pop-up message "Channel status does not meet the standard, it is recommended to check the interference source and isolate the signal" will appear in the background. The human intervention threshold for equipment failure and interference signals posing a threat to wireless channel switching;

[0111] when When the wireless channel is switched, a pop-up message "Channel status does not meet the standard, it is recommended to optimize the system configuration or update the firmware" will appear in the background. The threshold for human intervention caused by the operating parameters of power equipment to threaten wireless channel switching.

[0112] Embodiment 2:

[0113] like Figure 2 As shown, the wireless channel switching system for microgrid intelligent grid connection according to the embodiment of the present invention is as follows: Figure 2 As shown, it includes the following modules:

[0114] Power grid operation database construction module, equipment status monitoring module, channel switching threat prediction module, prediction error compensation module and channel switching management strategy optimization module;

[0115] The grid operation database construction module is used to collect operation data of different microgrid areas and construct a microgrid operation database;

[0116] The equipment status monitoring module is used to monitor and shoot the power equipment in different microgrid areas from multiple angles, input the data into the machine learning model, extract and mark the characteristic data of each operation stage of the power equipment, and monitor the working status of the power equipment;

[0117] The channel switching threat prediction module is used to evaluate the operating status of power equipment in different microgrid areas and predict the wireless channel switching threat coefficient according to the operating status evaluation strategy of the power equipment;

[0118] The prediction error compensation module performs data matching and tracing processing and prediction error compensation processing on the predicted wireless channel switching threat coefficient through the historical data stored in the microgrid operation database;

[0119] The channel switching management strategy optimization module performs wireless channel switching priority evaluation based on the wireless channel state data that has completed data matching tracing processing and prediction error compensation processing, and optimizes the channel switching management strategy for the smart grid connection of the microgrid.

[0120] Embodiment three:

[0121] This embodiment provides an electronic device, including: a processor and a memory, wherein the memory stores a computer program that can be called by the processor;

[0122] The processor executes the above-mentioned wireless channel switching method for microgrid intelligent grid connection by calling the computer program stored in the memory.

[0123] The electronic device may have relatively large differences due to different configurations or performances, and may include one or more processors (Central Processing Units, CPU) and one or more memories, wherein the memory stores at least one computer program, which is loaded and executed by the processor to implement the wireless channel switching method for microgrid smart grid connection provided by the above method embodiment. The electronic device may also include other components for implementing the functions of the device, for example, the electronic device may also have components such as a wired or wireless network interface and an input and output interface to input and output data. This embodiment will not be described in detail here.

[0124] Embodiment 4:

[0125] This embodiment provides a computer-readable storage medium having a rewritable computer program stored thereon;

[0126] When the computer program runs on a computer device, the computer device is enabled to execute the above-mentioned wireless channel switching method for intelligent grid connection of a microgrid.

[0127] For example, the computer readable storage medium can be a read-only memory (ROM), a random access memory (RAM), a compact disc (CD-ROM), a magnetic tape, a floppy disk, an optical data storage device, etc.

[0128] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0129] It should be understood that determining B based on A does not mean determining B only based on A. B can also be determined based on A and / or other information.

[0130] The above embodiments can be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented by software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When a computer instruction or computer program is loaded or executed on a computer, a process or function according to an embodiment of the present invention is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. Computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, computer instructions can be transmitted from one website site, computer, server or data center to another website site, computer, server or data center through a wired network or / and a wireless network. The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state hard disk.

[0131] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed in the present invention can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0132] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0133] In the several embodiments provided by the present invention, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic, for example, the division of units is only one, and there may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0134] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0135] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0136] 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.

[0137] In summary, compared with the prior art, the technical effects of the present invention are as follows:

[0138] 1. The present invention improves communication stability: by real-time monitoring of channel quality, the system can quickly determine the channel status and immediately switch to the best channel when necessary to ensure continuous and stable communication.

[0139] 2. The present invention reduces the risk of communication interruption: through the intelligent channel switching mechanism, the system can switch to the backup channel in time when the channel quality deteriorates, thereby preventing communication interruption and improving the reliability of the system.

[0140] 3. The present invention reduces switching delay: The optimized channel switching algorithm can effectively reduce delay and data loss during channel switching, ensuring communication efficiency and data integrity.

[0141] The above shows and describes the basic principles and main features of the present invention and the advantages of the present invention. It should be understood by those skilled in the art that the present invention is not limited to the above embodiments. The above embodiments and descriptions are only for explaining the principles of the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, which fall within the scope of the present invention to be protected. The scope of protection of the present invention is defined by the attached claims and their equivalents.

Claims

1. A wireless channel switching method for intelligent microgrid connection, characterized in that: The method comprises the following specific steps: S1: Collect the operation data of different microgrid areas and build a microgrid operation database; S2: Monitor and photograph the power equipment in different microgrid areas from multiple angles, input them into the machine learning model, extract and mark the feature data of each operation stage of the power equipment, and monitor the working status of the power equipment; S3: When the operating characteristic data of the power equipment is marked as switching, the operating status of the power equipment in different microgrid areas is evaluated, and the wireless channel switching threat coefficient is predicted according to the operating status evaluation strategy of the power equipment; In S3, the wireless channel switching threat coefficient is predicted according to the operation status evaluation strategy of the power equipment: ； in, For the The wireless channel switching threat coefficient when wireless channel switching is performed for the first time; For the The influence of environmental factors on the state of the power equipment when the wireless channel is switched for the first time; the influence of environmental factors on the state of the power equipment The calculation strategy is: ;in, The first wireless channel switch is to the first wireless channel switch during the monitoring period of the power equipment. The instantaneous load average value of the microgrid area when the secondary wireless channel is switched; The first wireless channel switch is to the first wireless channel switch during the monitoring period of the power equipment. The average instantaneous power generation of renewable energy in the microgrid area when the secondary wireless channel is switched; For the The impact of equipment failure and interference signals on the state of power equipment when the secondary wireless channel is switched; The impact of equipment failure and interference signals on the state of power equipment The calculation strategy is: ； For the When the secondary wireless channel is switched, the influence of the operating parameters of the power equipment on the state of the power equipment; the influence of the operating parameters of the power equipment on the state of the power equipment The calculation strategy is: ;in, It is the minimum operating voltage of the electrical equipment; It is the maximum operating voltage of the power equipment; For the The deviation between the actual operating time percentage of the power equipment and the optimal operating time percentage when the wireless channel switching is performed for the first time; S4: Perform data matching and tracing processing and prediction error compensation processing on the predicted wireless channel switching threat coefficient through the historical data stored in the microgrid operation database; S5: Based on the wireless channel status data that has completed the data matching tracing process and the prediction error compensation process, the wireless channel switching priority is evaluated, and the channel switching management strategy for the smart grid connection of the microgrid is optimized.

2. The wireless channel switching method for microgrid intelligent grid connection according to claim 1, characterized in that: In S1, the operation data of different microgrid areas include: The total number of wireless channel switching in the microgrid area ; in the When the wireless channel is switched for the first time, the instantaneous load in the microgrid area and instantaneous electricity generation from renewable energy sources ;No. The actual operating time percentage of the power equipment in the microgrid area when the wireless channel switching is performed and voltage fluctuation mean ;in, ; The total number of times that equipment failures affect the stability of the wireless channel ; The impact of equipment failure on wireless channel stability ; The total number of times the interference signal affects the stability of the wireless channel ; The impact of interference signals on the stability of wireless channels ;in, , , Indicates the impact of the device failure on the stability of the wireless channel for the Nth time. It indicates the impact of the interference signal on the stability of the wireless channel when it affects the stability of the wireless channel for the Mth time.

3. The wireless channel switching method for microgrid intelligent grid connection according to claim 2, characterized in that: S2 includes the following specific steps: S201: extracting power equipment monitoring data from different microgrid areas, and performing outlier removal and data alignment processing on the power equipment monitoring data from different microgrid areas; S202: Based on the machine learning model, extract and classify the characteristic data of each operation stage of the power equipment according to the processed power equipment monitoring data of the microgrid area; S203: Mark and output characteristic data of the power equipment at different operation stages, and monitor the working status of the power equipment in real time; In S2, the machine learning model is a deep neural network model, a convolutional neural network model, or a recurrent neural network model.

4. The wireless channel switching method for microgrid intelligent grid connection according to claim 3, characterized in that: In S203, the operation phases include: a startup phase, a stable operation phase, a load fluctuation phase, a fault alarm phase, a maintenance phase, and a shutdown phase.

5. The wireless channel switching method for microgrid intelligent grid connection according to claim 4, characterized in that: In S4, the prediction error compensation process includes: ； in, is the wireless channel switching threat coefficient after prediction error compensation; is the compensation coefficient for the prediction error of the wireless channel switching threat coefficient caused by environmental factors, The calculation strategy is: ; Among them, the total number of historical switching of wireless channels matched and traced in the microgrid operation database is Y; and are the historical daily average load and the historical daily average power generation of renewable energy in the microgrid area where the power equipment is located at the time of the yth historical wireless channel switching; A compensation coefficient for the prediction error of the wireless channel switching threat factor caused by equipment failure and interference signals; The calculation strategy is: ;in, and They are the historical impacts of equipment failures and interference signals on the stability of wireless channels matched and traced in the microgrid operation database; A compensation coefficient for the prediction error of the wireless channel switching threat coefficient caused by the operating parameters of the power equipment; The calculation strategy is: ;in, and They are respectively the historical voltage fluctuation mean and the actual historical operation time percentage of the microgrid area where the power equipment is located during the yth historical wireless channel switching.

6. The wireless channel switching method for microgrid intelligent grid connection according to claim 5, characterized in that: In S5, the wireless channel switching priority evaluation includes: when When , the switching quality level for evaluating the wireless channel switching threat factor is the first-level channel quality; when When , the switching quality level for evaluating the wireless channel switching threat factor is the secondary channel quality; when When , the switching quality level for evaluating the wireless channel switching threat factor is the third-level channel quality; when When , the switching quality level for evaluating the wireless channel switching threat factor is the fourth level of channel quality; when When , the switching quality level for evaluating the wireless channel switching threat factor is level 5 channel quality; in, They are the first-level channel quality evaluation threshold, the second-level channel quality evaluation threshold, the third-level channel quality evaluation threshold, the fourth-level channel quality evaluation threshold, and the fifth-level channel quality evaluation threshold of the switching quality of the wireless channel switching threat coefficient.

7. The wireless channel switching method for microgrid intelligent grid connection according to claim 6, characterized in that: In S5, the optimization of the channel switching management strategy includes: S501: extracting wireless channel status evaluation data, and classifying the status of the wireless channel: when the switching quality level of the wireless channel switching threat coefficient is the first-level channel quality, the second-level channel quality, and the third-level channel quality, marking the wireless channel as an alternative switching channel, extracting the historical switching time of the alternative switching channel from the microgrid operation database, arranging the historical switching time in ascending order, and giving priority to switching to the alternative switching channel with the shortest historical switching time; When the switching quality level of the wireless channel switching threat coefficient is level 4 channel quality or level 5 channel quality, the wireless channel is marked as a non-recommended switching channel, and step S502 is executed; S502: Extracting the compensation coefficient of the prediction error of the wireless channel switching threat coefficient caused by environmental factors , compensation coefficient for prediction error of wireless channel switching threat coefficient caused by equipment failure and interference signal And the compensation coefficient of the prediction error of the wireless channel switching threat coefficient caused by the operating parameters of the power equipment ; when When the wireless channel is switched, a pop-up message "Channel status does not meet the standard, it is recommended to adjust the antenna or amplify the signal" will appear in the background. The threshold for human intervention that poses a threat to wireless channel switching due to environmental factors; when When the wireless channel is switched, a pop-up message "Channel status does not meet the standard, it is recommended to check the interference source and isolate the signal" will appear in the background. The human intervention threshold for equipment failure and interference signals posing a threat to wireless channel switching; when When the wireless channel is switched, a pop-up message "Channel status does not meet the standard, it is recommended to optimize the system configuration or update the firmware" will appear in the background. The threshold for human intervention caused by the operating parameters of power equipment to threaten wireless channel switching.

8. A wireless channel switching system for microgrid intelligent grid connection, which is used to implement the wireless channel switching method for microgrid intelligent grid connection as claimed in any one of claims 1 to 7, characterized in that: The system includes the following modules: Power grid operation database construction module, equipment status monitoring module, channel switching threat prediction module, prediction error compensation module and channel switching management strategy optimization module; The grid operation database construction module is used to collect operation data of different microgrid areas and construct a microgrid operation database; The equipment status monitoring module is used to monitor and shoot the power equipment in different microgrid areas from multiple angles, input the data into the machine learning model, extract and mark the characteristic data of each operation stage of the power equipment, and monitor the working status of the power equipment; The channel switching threat prediction module is used to evaluate the operating status of power equipment in different microgrid areas and predict the wireless channel switching threat coefficient according to the operating status evaluation strategy of the power equipment; The prediction error compensation module performs data matching and tracing processing and prediction error compensation processing on the predicted wireless channel switching threat coefficient through the historical data stored in the microgrid operation database; The channel switching management strategy optimization module performs wireless channel switching priority evaluation based on the wireless channel state data that has completed data matching tracing processing and prediction error compensation processing, and optimizes the channel switching management strategy for the smart grid connection of the microgrid.

Images