Distribution automation control method and device based on virtual bus communication

Abstract

The invention discloses a power distribution automation control method and device based on virtual bus communication, and relates to the technical field of power distribution automation, and the method comprises the steps: registering a physical communication interface to a drive chain table, and achieving the standardized operation of the physical interface through a drive layer; configuring a physical communication interface as a virtual communication interface, registering the virtual communication interface to a virtual bus linked list, and realizing unified scheduling and data interaction of multiple interfaces through a virtual bus layer; physical communication equipment is configured as virtual communication equipment, the virtual communication equipment is registered to a virtual equipment linked list, and equipment function mapping and protocol adaptation are completed through a virtual equipment layer; registering a physical equipment instance to an equipment instance linked list, and realizing data transceiving and control instruction execution through an equipment instance layer; based on a virtual bus architecture, different communication protocols and transmission modes are dynamically matched so as to communicate heterogeneous equipment. According to the application, diversified field application requirements can be met, and the communication configuration efficiency between the power distribution automation terminal and the external equipment is improved.

Description

Technical Field

[0001] The present application relates to the technical field of power distribution automation, and in particular to a power distribution automation control method and device based on virtual bus communication. Background Art

[0002] With the rapid intelligent development of power grids, distribution automation terminals such as switchgear terminal equipment DTU (Data Transfer Unit) need to connect to more and more types of external communication devices, resulting in increasingly complex interface types and communication protocols. A physical communication interface of a traditional distribution automation terminal can usually only be bound to a specific communication device. This means that every time an external device is replaced or added, the corresponding communication interface and communication protocol need to be reconfigured, which is highly dependent on the hardware platform. The communication configuration efficiency between the distribution automation terminal and external devices is relatively low, making it difficult to adapt to diverse on-site application requirements. Therefore, there is room for improvement. Summary of the Invention

[0003] In order to meet diverse on-site application needs and improve the communication configuration efficiency between distribution automation terminals and external devices, the present application provides a distribution automation control method and device based on virtual bus communication.

[0004] In the first aspect, the invention objectives of this application are achieved by adopting the following technical solutions: A distribution automation control method based on virtual bus communication, comprising: Register the physical communication interface to the driver list and implement standardized operations of the physical interface through the driver layer; Configure the physical communication interface as a virtual communication interface, register it to the virtual bus list, and implement unified scheduling and data interaction of multiple interfaces through the virtual bus layer; Configure the physical communication device as a virtual communication device, register it to the virtual device list, and complete device function mapping and protocol adaptation through the virtual device layer; Register the physical device instance to the device instance list, and implement data transmission and reception and control instruction execution through the device instance layer; based on the virtual bus architecture, dynamically match different communication protocols and transmission modes to connect heterogeneous devices.

[0005] By adopting the above technical solution, the standardized operations of the physical interface include opening, reading, writing and control operations; a distribution automation data communication method based on virtual bus communication is provided, which realizes support for communication equipment of various types and protocols. By abstracting the physical interface into a virtual interface and the physical communication equipment into a virtual communication equipment, the hardware and software decoupling is realized, and the interconnection and data exchange process between software components in the distribution automation system is simplified. It can not only reduce the dependence on the hardware platform, but also improve the flexibility and adaptability of the distribution automation system, and facilitate cross-platform transplantation between distribution automation terminals and external devices. This application improves data processing efficiency and system stability through a virtualized layered architecture, which is conducive to resource optimization, so that the distribution automation terminal can be flexibly applied to various distribution scenarios such as power management, environmental monitoring and equipment control; further, by dynamically matching different communication protocols (such as IEC104, Modbus, DNP3.0, etc.) and transmission modes (polling, event triggering), the interconnection and interoperability between heterogeneous devices are completed; to adapt to different transmission priorities and real-time requirements, it can meet diverse field application needs and improve the communication configuration efficiency between distribution automation terminals and external devices.

[0006] In a preferred embodiment of the present application, the method further includes: Obtaining power distribution system configuration information and design parameters, and determining a first predicted load and a second predicted load of each node in the power distribution network under different load conditions based on the power distribution system configuration information and design parameters; generating a power distribution network adjustment rule for adjusting an operating state of a power distribution network according to the design parameters, the first predicted load, and the second predicted load; Acquiring an operation variation reference range representing a distribution network adjustment threshold, and generating a distribution network control model according to the operation variation reference range and the distribution network adjustment rule; Real-time load data is acquired and input into the power distribution network control model to adjust the operation of the power distribution network.

[0007] By adopting the above technical solution, the present application obtains the distribution system configuration information (grid topology, substation capacity, etc.) and design parameters (standard load distribution, safety limit), dynamically determines the node load prediction value under different load conditions, and generates adjustment rules; specifically, different from the traditional static load model, the present application combines real-time network topology and equipment characteristics to achieve accurate prediction of load during low / peak periods, and automatically generates distribution network adjustment rules through the linkage between design parameters and predicted load, reducing manual intervention and improving reconstruction efficiency, and further introducing safe operation limit parameters to ensure that the adjustment rules are executed within the safety threshold and reduce the risk of exceeding the limit.

[0008] In a preferred example of the present application, the obtaining of power distribution system configuration information and design parameters, and determining, based on the power distribution system configuration information and design parameters, a first predicted load and a second predicted load of each node in the power distribution network under different load conditions, specifically includes: Obtaining power distribution system configuration information and design parameters for the target monitoring area, wherein the power distribution system configuration information includes grid topology, substation capacity, line impedance, and user-end device characteristics, and the design parameters include standard load distribution, maximum allowable load, and safe operating limits; Determining a first predicted load during an off-peak period based on the standard load distribution, the grid topology, the line impedance, and user-end device characteristics; A second predicted load during a peak period is determined according to the power grid topology, the substation capacity, the maximum allowable load and the safe operation limit.

[0009] By adopting the above technical solutions, it is clear that the prediction values during off-peak periods are based on standard load distribution and real-time network parameter corrections, and the prediction values during peak periods are based on substation capacity and safety limit constraints; the load prediction values are calculated independently in different time periods to analyze the load difference status and prediction deviation in different time periods; the prediction process is constrained by physical parameters such as grid topology and line impedance to ensure that the prediction results match the actual network characteristics.

[0010] In a preferred example of the present application, generating a distribution network adjustment rule for adjusting the operating state of the distribution network based on the design parameters, the first predicted load, and the second predicted load specifically includes: Calculating an expected load distribution time based on the grid topology, the first predicted load, and the second predicted load; and obtaining an ideal load distribution time based on the standard load distribution and the maximum allowable load; Based on the expected load distribution time, the ideal load distribution time and the safe operation limit, the distribution network security operation state is evaluated to obtain a distribution network security prediction value; Calculating a ratio of the first predicted load to the second predicted load to obtain a load ratio coefficient for evaluating an actual load change trend; Calculating a time difference between the expected load distribution time and the ideal load distribution time, and calculating a safety difference between the predicted safe operation state of the power distribution network and the safe operation limit; Obtaining a load state correspondence relationship for determining an actual operating state of a power distribution network according to the time difference and the safety difference; According to the load proportion coefficient and the load state correspondence, a distribution network adjustment rule for adjusting the operating state of the distribution network is generated.

[0011] By adopting the above-mentioned technical solution, this application generates distribution network adjustment rules based on parameters such as the time difference between expected and ideal load distribution and the safety difference, and dynamically adjusts operations through real-time data; through quantitative assessment of the safety status of the power grid, and based on the correspondence between the load proportion coefficient and the status, the distribution network adjustment rules for adjusting the operating status of the distribution network are automatically triggered to improve the response speed of the distribution automation system.

[0012] In a preferred example of the present application, before determining the first predicted load during the off-peak period based on the standard load distribution, the grid topology, the line impedance, and user-side device characteristics, the method further includes: Acquire real-time network load data of a target monitoring area, and determine a network load adjustment coefficient based on the power grid topology and the real-time network load data; Obtaining a first load correction coefficient for adjusting a first predicted load according to the network load adjustment coefficient; A first predicted load during an off-peak period is determined based on the first load correction coefficient, the standard load distribution, the grid topology, the line impedance, and user-end device characteristics.

[0013] By adopting the above technical solution, real-time network load data is introduced, and the low-peak prediction value is dynamically corrected to resolve the impact of uncertain factors such as photovoltaic output fluctuations. The real-time network status is quantified through the load adjustment coefficient, making the prediction model more in line with the actual operation scenario. Real-time disturbances are taken into account in the prediction stage, reducing the number of iterations for generating subsequent adjustment rules, which is conducive to improving the overall control efficiency of the distribution automation system.

[0014] In a preferred example of the present application, when determining the first predicted load and the second predicted load of each node in the power distribution network under different load conditions, the calculation formula used includes: The first forecast load P during the off-peak period L : Among them, S i is the standard load capacity of the i-th node; η i is the first load correction factor of the i-th node, T L is the total duration of the off-peak period; The second predicted load P during peak hours H : Among them, C i is the substation capacity of the i-th node, β i is the safe operation coefficient of the i-th node, calculated based on the safe operation limit, T H The total duration of peak hours.

[0015] By adopting the above technical solution, the first predicted load and the second predicted load are calculated in a quantitative manner based on the specific parameters of each node (such as standard load capacity, substation capacity, safe operation factor, etc.), making the load forecast more precise and better able to adapt to different distribution network conditions.

[0016] In a preferred example of the present application, when generating the distribution network adjustment rule, the following formula is used to calculate the distribution network security prediction value S P And the operational feasibility coefficient F: Distribution network security prediction value S P : S P =1-|T e -T i | / T i Among them, T e is the expected load distribution time, T i is the ideal load distribution time; Operational feasibility coefficient F: F=S P / 1+|P H -P L | / P L Among them, P H is the second predicted load, P L is the first predicted load; when the operation feasibility coefficient F is greater than the preset coefficient threshold, the load even operation is performed.

[0017] By adopting the above technical solution, the difference between the expected load distribution time and the ideal load distribution time is calculated, the safe operation status of the distribution network is evaluated, and the feasibility of performing load evenly distributed operations is evaluated by calculating the operation feasibility coefficient F. When F is greater than the preset threshold, it indicates that the system has the ability to evenly distribute the load, thereby ensuring the effectiveness and safety of the operation.

[0018] In the second aspect, the invention objective of this application is achieved by adopting the following technical solutions: A distribution automation control device based on virtual bus communication, used to execute the distribution automation control method based on virtual bus communication as described above, the device comprising: The virtual bus is used to register the physical communication interface to the driver list and implement standardized operations of the physical interface through the driver layer; the physical communication interface is configured as a virtual communication interface and registered to the virtual bus list, and the unified scheduling and data interaction of multiple interfaces are implemented through the virtual bus layer; Virtual devices are used to configure physical communication devices as virtual communication devices and register them in the virtual device list, completing device function mapping and protocol adaptation through the virtual device layer; and register physical device instances in the device instance list, and implement data transmission and reception and control instruction execution through the device instance layer; The virtual device dynamically matches different communication protocols and transmission modes based on the virtual bus architecture at the communication protocol layer to connect heterogeneous devices.

[0019] By adopting the above technical solutions, a complete distribution automation control system is formed, which realizes the conversion from physical communication interface to virtual communication interface, mapping of equipment functions and protocol adaptation, as well as data interaction based on virtual bus architecture and communication protocol matching between heterogeneous devices. The distribution automation control device based on virtual bus communication not only improves the flexibility and scalability of the system, but also enhances the efficiency and accuracy of distribution network management.

[0020] In a third aspect, the invention objective of this application is achieved by adopting the following technical solutions: A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of the above-mentioned distribution automation control method based on virtual bus communication.

[0021] Fourthly, the invention objectives of this application are achieved by adopting the following technical solutions: A computer program product, when running on a distribution automation system, enables the distribution automation system to execute the above-mentioned distribution automation control method based on virtual bus communication.

[0022] In summary, this application includes at least one of the following beneficial technical effects: 1. A virtualized layered architecture improves data processing efficiency and system stability, facilitating resource optimization and enabling flexible application of distribution automation terminals in diverse power distribution scenarios, including power management, environmental monitoring, and equipment control. Furthermore, by dynamically matching different communication protocols (such as IEC104, Modbus, and DNP3.0) with transmission modes (polling and event-triggered), interconnection between heterogeneous devices is achieved. 2. By independently calculating load forecast values in different time periods, the load difference status and forecast deviation in different time periods can be analyzed; the forecast process is constrained by physical parameters such as grid topology and line impedance to ensure that the forecast results match the actual network characteristics; 3. A complete distribution automation control system is formed, realizing the conversion from physical communication interface to virtual communication interface, mapping of device functions and protocol adaptation, as well as data interaction based on virtual bus architecture and communication protocol matching between heterogeneous devices. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 This is a flow chart of a distribution automation control method based on virtual bus communication in one embodiment of the present application; Figure 2 This is a communication framework diagram of an application embodiment of a distribution automation control method based on virtual bus communication in one embodiment of the present application; Figure 3 This is another flow chart of a distribution automation control method based on virtual bus communication in one embodiment of the present application. DETAILED DESCRIPTION

[0024] The present application is further described in detail below with reference to the accompanying drawings.

[0025] In one embodiment, if Figure 1 As shown, the present application discloses a distribution automation control method based on virtual bus communication, which specifically includes the following steps: S1: Register the physical communication interface to the driver list and implement standardized operations of the physical interface through the driver layer.

[0026] In this embodiment, standardized operations include opening, reading, writing and controlling operations; the physical communication interface refers to the actual hardware communication interface (such as RS485, Ethernet, etc.) in the power distribution system; the driver layer is responsible for managing all physical communication interfaces, providing a unified interface standard, and registering the information of each physical interface (such as type, address, etc.) in the driver linked list for subsequent management and calling; by providing a standardized interface, the upper-level application does not need to care about the specific physical interface implementation of the underlying layer, and registers the driver to the driver linked list, and manages the interface life cycle through the linked list to achieve software and hardware decoupling.

[0027] Specifically, if Figure 2 As shown in the figure, it is a communication framework diagram of the present application in actual application, in which multiple communication interfaces such as serial port, network port, CAN, USB, SPI and II are used, and different communication interfaces are suitable for different device transmission requirements; virtual bus communication supports multiple communication protocols, such as IEC104, IEC101, MODBUS, DNP3.0, AT command, GPS (NMEA protocol), and different protocols are suitable for different data transmission requirements.

[0028] S2: Configure the physical communication interface as a virtual communication interface, register it to the virtual bus list, and implement unified scheduling and data interaction of multiple interfaces through the virtual bus layer.

[0029] In this embodiment, the virtual communication interface refers to a layer of logical interface abstracted above the driver layer to facilitate data exchange across devices; the virtual bus layer is responsible for coordinating data transmission and scheduling between multiple virtual communication interfaces.

[0030] Specifically, a virtual bus protocol (such as a virtual fiber optic bus) is defined; the physical interface is registered as a virtual bus node and a unique identifier is assigned; bandwidth resources are dynamically allocated based on a priority algorithm (such as polling or preemptive); the virtual bus layer maintains a node status table and monitors the interface load in real time; and supports dynamic addition / deletion of virtual nodes to adapt to device expansion needs. This application shields physical layer differences through a virtual bus and supports communication between heterogeneous devices (such as RS485 and CAN bus interconnection).

[0031] S3: Configure the physical communication device as a virtual communication device, register it to the virtual device list, and complete device function mapping and protocol adaptation through the virtual device layer.

[0032] In this embodiment, the virtual communication device refers to a logical device abstracted from the functions of a physical device, used to support higher-level applications; the virtual device layer is responsible for processing protocol conversion and function mapping between devices.

[0033] Specifically, the functional parameters of physical devices (such as switch input / output, analog acquisition) are extracted, the functional parameters are mapped to virtual device service interfaces, and communication protocols (such as IEC104, Modbus) are automatically matched according to the device type; in actual application, a protocol feature library is constructed to store key parameters such as message structure and timing rules, and automatic protocol identification is achieved through device fingerprints (MAC address, firmware version).

[0034] S4: Register the physical device instance to the device instance list, and implement data transmission and reception and control instruction execution through the device instance layer.

[0035] In this embodiment, the device instance layer directly faces the management layer of the physical device and is responsible for specific data transmission and reception and instruction execution; Specifically, it binds the device functional interface and control logic (such as remote control and remote adjustment); uses a message queue mechanism to isolate instruction sending and receiving from business processing; supports multi-level caching (memory / disk) of instruction execution results, and parses instructions and executes feedback through a state machine.

[0036] S5: Based on a virtual bus architecture, it dynamically matches different communication protocols and transmission modes to connect heterogeneous devices.

[0037] In this embodiment, the communication protocol and transmission mode (such as polling and event triggering) are automatically selected based on the real-time status of the network; the application monitors network quality indicators (latency, packet loss rate, bandwidth utilization); constructs a protocol performance evaluation matrix (such as IEC104 is suitable for low-latency scenarios, Modbus is suitable for high-throughput scenarios); dynamically switches protocols and transmission modes to generate the optimal communication strategy.

[0038] In one embodiment, if Figure 3 As shown, a distribution automation control method based on virtual bus communication also includes: S10: obtaining distribution system configuration information and design parameters, and judging the first predicted load and the second predicted load of each node in the distribution network under different load conditions based on the distribution system configuration information and design parameters.

[0039] In this embodiment, the distribution system configuration information includes the grid topology (node connection relationship, line parameters), substation capacity, line impedance, user-end equipment characteristics (such as transformer model, load type), etc.; design parameters refer to standard load distribution, maximum allowable load, safe operation limits (such as voltage fluctuation threshold, overload protection threshold), and other basic data used for load forecasting.

[0040] Specifically, step S10 includes: S101: Obtaining the distribution system configuration information and design parameters of the target monitoring area. The distribution system configuration information includes the grid topology, substation capacity, line impedance, and user-end equipment characteristics. The design parameters include standard load distribution, maximum allowable load, and safe operation limits.

[0041] Specifically, the grid topology (node number, line connection relationship) and equipment inventory information (such as transformer model, CT / PT ratio) are obtained, the standard load distribution curve is analyzed (such as "peak load on weekdays = 1.2 times the daily average") and the safe operating limits are imported (such as the voltage and frequency ranges specified in the IEEE1459 standard).

[0042] S102: Determine a first predicted load during a low-peak period based on standard load distribution, grid topology, line impedance, and user-end device characteristics.

[0043] In this embodiment, based on the characteristics of user-side devices and the grid topology, the load during off-peak periods is predicted, with an emphasis on the impact of distributed energy (such as photovoltaics). Modeling is first performed on the user side: for example, a dynamic model is established for each user terminal (such as a photovoltaic inverter, an energy storage system) to predict its output curve. Environmental factors such as temperature and light intensity are considered (such as photovoltaic output = rated power × light coefficient). The predicted load on the user side is then superimposed on the grid node, and the node injection power is calculated in combination with the line impedance. In combination with distributed computing, the predicted data of each user is processed in parallel based on the edge computing nodes. The randomness of photovoltaic output is simulated and quantified to generate a probability distribution curve, and the first predicted load of each user-side device during the off-peak period is determined.

[0044] Specifically, before step S102, a distribution automation control method based on virtual bus communication also includes: S1021: obtaining real-time network load data of the target monitoring area, and determining the network load adjustment coefficient according to the power grid topology and the real-time network load data.

[0045] Specifically, weights are assigned to each node based on the grid topology (e.g., node importance, line length); the dynamic weighting formula is: Network load adjustment coefficient Among them, W i is the topological weight of node i (e.g. trunk line weight = 2, branch line weight = 1); L i (t) is the real-time load rate of node i, and the network load adjustment coefficient α is mapped to [0.8, 1.2], which represents the load adjustment range.

[0046] S1022: Obtain a first load correction coefficient for adjusting the first predicted load according to the network load adjustment coefficient.

[0047] In this embodiment, based on the network load adjustment coefficient α and historical load characteristics, a coefficient for correcting the off-peak predicted load is calculated to quantify the impact of the real-time network status on the prediction.

[0048] Specifically, a load curve library is constructed to store load data under the same season and weather conditions in the past three years. Time series decomposition (STL) is used to extract trend, season, and residual components, and the standard deviation of historical loads is calculated. The calculation formula is: the first load correction coefficient β = γ × α × σ, where γ is the sensitivity coefficient, ranging from 0.5 to 1.5; σ is the standard deviation of historical loads.

[0049] S1023: Determine a first predicted load during a low-peak period based on a first load correction coefficient, standard load distribution, grid topology, line impedance, and user-end device characteristics.

[0050] In this embodiment, a dynamic predicted load that takes into account the real-time network status is generated by combining standard load distribution, grid topology, line impedance, user-end device characteristics, and correction coefficients.

[0051] Specifically, a benchmark load curve is constructed based on the design parameters (e.g., "weekday low peak = 60% of the daily average") and a seasonal adjustment factor (e.g., summer air conditioning load increase) is introduced; the maximum allowable current is calculated based on the line impedance Z. Where V is the rated voltage of the user-side equipment, which limits long-term overload based on the equipment aging model.

[0052] S103: Determine a second predicted load during the peak period based on the grid topology, substation capacity, maximum allowable load, and safe operation limit.

[0053] In this embodiment, based on the substation capacity and safety limit, the maximum load during peak hours is predicted to prevent equipment overload. In actual application, capacity constraint analysis can be performed, such as calculating the maximum allowable output of the substation main transformer under the N-1 safety criterion, and constructing a line thermal stability model (such as the equivalent power method) to evaluate the load peak by simulating extreme scenarios (such as all distributed power sources being disconnected from the grid); using a genetic algorithm to generate multiple groups of load combinations, and selecting the worst operating condition as the prediction upper limit to obtain a second predicted load based on different usage scenarios.

[0054] Furthermore, when determining the first predicted load and the second predicted load of each node in the power distribution network under different load conditions, the calculation formula used includes: The first forecast load P during the off-peak period L : Among them, S i is the standard load capacity of the i-th node; η i is the first load correction factor of the i-th node, T L is the total duration of the off-peak period; The second predicted load P during peak hours H : Among them, C i is the substation capacity of the i-th node, β i is the safe operation coefficient of the i-th node, calculated based on the safe operation limit, T H The total duration of peak hours.

[0055] S20: Generate a distribution network adjustment rule for adjusting an operating state of the distribution network according to the design parameters, the first predicted load, and the second predicted load.

[0056] In this embodiment, the distribution network adjustment rules are used to formulate dynamic adjustment strategies based on the predicted load and design parameters, and to clarify the equipment operation thresholds and priorities in different scenarios; combined with the design parameters (such as standard load distribution, maximum allowable load, etc.), the first predicted load (off-peak period) and the second predicted load (peak period), the optimal operation strategy is determined through analysis, and then specific adjustment rules are formulated to determine when to increase or decrease the power supply and how to distribute the load.

[0057] Specifically, step S20 includes: S201: Calculate the expected load distribution time based on the grid topology, the first predicted load, and the second predicted load; and obtain the ideal load distribution time based on the standard load distribution and the maximum allowable load.

[0058] In this embodiment, the load distribution path is quantified by the grid topology structure, and the actual load time (expected time) of the equipment is calculated in combination with the predicted load, and compared with the theoretical optimal time (ideal time) to reflect the grid operation efficiency.

[0059] Specifically, the grounding connection relationship and line parameters are obtained based on the grid topology, and the power flow of each node is calculated based on the power flow equation, where the expected load distribution time Among them, P i is the predicted load of node i, L i is the equivalent line length from the node to the power supply, P total is the total load.

[0060] Furthermore, based on the standard load distribution curve, assuming no line loss and congestion, the theoretical shortest transmission time t is calculated. ideal , using the following calculation formula: Where v is the speed of power transmission (50%-80% of the speed of light, depending on the medium).

[0061] S202: Based on the expected load distribution time and the ideal load distribution time, the safe operation limit is evaluated to obtain a distribution network security prediction value.

[0062] Specifically, by comparing the expected and ideal times and combining the safety margins, the risk level of the current operating status of the power grid is quantified.

[0063] S203: Calculate the ratio of the first predicted load to the second predicted load to obtain a load ratio coefficient for evaluating the actual load change trend.

[0064] S204: Calculate the time difference between the expected load distribution time and the ideal load distribution time, and calculate the safety difference between the predicted safe operation state of the power distribution network and the safe operation limit.

[0065] Specifically, the safety difference is the difference between the maximum allowable safety index and the safety margin of the current safety index. The deviation tolerance range can be automatically updated according to seasonal changes; the time difference reflects the degree of deviation between the actual load distribution and the ideal state; the safety difference is used to quantify the gap between the current safety state of the system and the safety boundary.

[0066] S205: Obtaining a load state correspondence for determining an actual operating state of the power distribution network according to the time difference and the safety difference.

[0067] Specifically, the load state correspondence: the time difference and the safety difference are mapped to specific system operation states, such as "normal", "needs adjustment", and "urgent"; the threshold range is set: for example, the time difference threshold: such as [0, 10%] indicates normal, [10%, 20%] indicates adjustment is required, and >20% indicates emergency; for example, the safety difference threshold: such as [0, 5%] indicates safety, [-5%, 0] indicates warning is required, and <-5% indicates immediate intervention is required. Based on the combination of the time difference and the safety difference, an operation state label is generated (such as "power supply needs to be increased during off-peak hours" and "current limitation is required during peak hours").

[0068] S206: Generate a distribution network adjustment rule for adjusting the operation state of the distribution network according to the corresponding relationship between the load proportion coefficient and the load state.

[0069] Specifically, the distribution network adjustment rules include specific operating instructions (such as adjusting transformer output, switching lines, etc.); combined with the load proportion coefficient (trend analysis) and the load state correspondence (current state), the adjustment instructions are generated through the rule engine.

[0070] For example: If the load ratio factor is greater than 2 (peak load increases significantly) and the safety difference is less than 0 (exceeds the safety margin), then the “start backup power supply + reduce power supply to non-critical areas” action will be triggered.

[0071] Furthermore, when generating the distribution network adjustment rules, the following formula is used to calculate the distribution network security prediction value S P And the operational feasibility coefficient F: Distribution network security prediction value S P : S P =1-|T e -T i | / T i Among them, T e is the expected load distribution time, T i is the ideal load distribution time; Operational feasibility coefficient F: F=S P / 1+|P H -P L | / P L Among them, P H is the second predicted load, P L is the first predicted load; when the operation feasibility coefficient F is greater than the preset coefficient threshold (such as the operation feasibility coefficient F is greater than 0.8), the load evenly distributed operation is performed.

[0072] S30: Acquire an operation change reference range representing a distribution network adjustment threshold, and generate a distribution network control model according to the operation change reference range and the distribution network adjustment rule.

[0073] In this embodiment, the operation change reference range refers to the operation adjustment range allowed by the system (such as voltage regulation range ±5%, frequency fluctuation ±0.5Hz); deep reinforcement learning (DRL) is used to construct a dynamic control model, and the state space includes indicators such as load, voltage, and frequency; the action space is defined as a set of device operations (such as "disconnect line 3" and "put the capacitor bank into operation") to generate a distribution network control model based on a deep learning algorithm.

[0074] S40: Acquire real-time load data, and input the real-time load data into a distribution network control model to adjust the operation of the distribution network.

[0075] In this embodiment, real-time load data refers to the actual load value (such as current, voltage, and power) of each node at the current moment; the adjustment operation is used to make adjustments based on specific instructions output by the control model (such as adjusting transformer taps and switching lines).

[0076] It should be understood that the serial numbers of the steps in the above embodiments do not imply the order of execution. The order of execution 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.

[0077] In one embodiment, a distribution automation control device based on virtual bus communication is provided. The distribution automation control device based on virtual bus communication corresponds to the distribution automation control method based on virtual bus communication in the above embodiment.

[0078] A distribution automation control device based on virtual bus communication includes a virtual bus and virtual devices. The detailed description of each functional module is as follows: The virtual bus is used to register the physical communication interface to the driver list and implement standardized operations of the physical interface through the driver layer; the physical communication interface is configured as a virtual communication interface and registered to the virtual bus list, and the unified scheduling and data interaction of multiple interfaces are implemented through the virtual bus layer; Virtual devices are used to configure physical communication devices as virtual communication devices and register them in the virtual device list, completing device function mapping and protocol adaptation through the virtual device layer; and register physical device instances in the device instance list, and implement data transmission and reception and control instruction execution through the device instance layer; At the communication protocol layer, virtual devices dynamically match different communication protocols and transmission modes based on the virtual bus architecture to connect heterogeneous devices.

[0079] For the specific definition of a distribution automation control device based on virtual bus communication, please refer to the definition of a distribution automation control method based on virtual bus communication in the above text, which will not be repeated here; the various modules in the above-mentioned distribution automation control device based on virtual bus communication can be implemented in whole or in part through software, hardware and their combination; the above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the corresponding operations of the above modules.

[0080] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the following steps are implemented: S1: Register the physical communication interface to the driver list and implement standardized operations of the physical interface through the driver layer.

[0081] S2: Configure the physical communication interface as a virtual communication interface, register it to the virtual bus list, and implement unified scheduling and data interaction of multiple interfaces through the virtual bus layer.

[0082] S3: Configure the physical communication device as a virtual communication device, register it to the virtual device list, and complete device function mapping and protocol adaptation through the virtual device layer.

[0083] S4: Register the physical device instance to the device instance list, and implement data transmission and reception and control instruction execution through the device instance layer.

[0084] S5: Based on a virtual bus architecture, it dynamically matches different communication protocols and transmission modes to connect heterogeneous devices.

[0085] Those skilled in the art will appreciate that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0086] In one embodiment, in particular, according to an embodiment of the present invention, the process described above with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present invention includes a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a computer program for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network via a communication module, and / or installed from a removable medium. When the computer program is executed by a central processing unit (CPU), the various functions defined in the present invention are performed.

[0087] Those skilled in the art will clearly understand that for the sake of convenience and brevity of description, only the division of the above-mentioned functional units and modules is used as an example. In actual applications, the above-mentioned functions can be distributed and completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above.

[0088] The above-described embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, it should be understood by those skilled in the art that the technical solutions described in the aforementioned embodiments may still be modified, or some of the features thereof may be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present application, and should all be included in the scope of protection of the present application.

Claims

1. A distribution automation control method based on virtual bus communication, characterized in that: include: Register the physical communication interface to the driver list and implement standardized operations of the physical interface through the driver layer; Configure the physical communication interface as a virtual communication interface, register it to the virtual bus list, and implement unified scheduling and data interaction of multiple interfaces through the virtual bus layer; Configure the physical communication device as a virtual communication device, register it to the virtual device list, and complete device function mapping and protocol adaptation through the virtual device layer; Register the physical device instance to the device instance list, and implement data transmission and reception and control instruction execution through the device instance layer; Based on the virtual bus architecture, different communication protocols and transmission modes are dynamically matched to connect heterogeneous devices.

2. A distribution automation control method based on virtual bus communication according to claim 1, characterized in that: The method also includes: Obtaining power distribution system configuration information and design parameters, and determining a first predicted load and a second predicted load of each node in the power distribution network under different load conditions based on the power distribution system configuration information and design parameters; generating a power distribution network adjustment rule for adjusting an operating state of a power distribution network according to the design parameters, the first predicted load, and the second predicted load; Acquiring an operation variation reference range representing a distribution network adjustment threshold, and generating a distribution network control model according to the operation variation reference range and the distribution network adjustment rule; Real-time load data is acquired and input into the power distribution network control model to adjust the operation of the power distribution network.

3. A distribution automation control method based on virtual bus communication according to claim 2, characterized in that: The obtaining of power distribution system configuration information and design parameters, and determining, based on the power distribution system configuration information and design parameters, a first predicted load and a second predicted load of each node in the power distribution network under different load conditions, specifically includes: Obtaining power distribution system configuration information and design parameters for the target monitoring area, wherein the power distribution system configuration information includes grid topology, substation capacity, line impedance, and user-end device characteristics, and the design parameters include standard load distribution, maximum allowable load, and safe operating limits; Determining a first predicted load during an off-peak period based on the standard load distribution, the grid topology, the line impedance, and user-end device characteristics; A second predicted load during a peak period is determined according to the power grid topology, the substation capacity, the maximum allowable load and the safe operation limit.

4. A distribution automation control method based on virtual bus communication according to claim 3, characterized in that: The generating, based on the design parameters, the first predicted load, and the second predicted load, a distribution network adjustment rule for adjusting the operating state of the distribution network specifically includes: Calculating an expected load distribution time based on the grid topology, the first predicted load, and the second predicted load; and obtaining an ideal load distribution time based on the standard load distribution and the maximum allowable load; Based on the expected load distribution time, the ideal load distribution time and the safe operation limit, the distribution network security operation state is evaluated to obtain a distribution network security prediction value; Calculating a ratio of the first predicted load to the second predicted load to obtain a load ratio coefficient for evaluating an actual load change trend; Calculating a time difference between the expected load distribution time and the ideal load distribution time, and calculating a safety difference between the predicted safe operation state of the power distribution network and the safe operation limit; Obtaining a load state correspondence relationship for determining an actual operating state of a power distribution network according to the time difference and the safety difference; According to the load proportion coefficient and the load state correspondence, a distribution network adjustment rule for adjusting the operating state of the distribution network is generated.

5. A distribution automation control method based on virtual bus communication according to claim 3, characterized in that: Before determining a first predicted load during an off-peak period based on the standard load distribution, the grid topology, the line impedance, and user-side device characteristics, the method further includes: Acquire real-time network load data of a target monitoring area, and determine a network load adjustment coefficient based on the power grid topology and the real-time network load data; Obtaining a first load correction coefficient for adjusting a first predicted load according to the network load adjustment coefficient; A first predicted load during an off-peak period is determined based on the first load correction coefficient, the standard load distribution, the grid topology, the line impedance, and user-end device characteristics.

6. A distribution automation control method based on virtual bus communication according to claim 2, characterized in that: When determining the first predicted load and the second predicted load of each node in the power distribution network under different load conditions, the calculation formula used includes: The first forecast load P during the off-peak period L : Among them, S i is the standard load capacity of the i-th node; η i is the first load correction factor of the i-th node, T L is the total duration of the off-peak period; The second predicted load P during peak hours H : Among them, C i is the substation capacity of the i-th node, β i is the safe operation coefficient of the i-th node, calculated based on the safe operation limit, T H The total duration of peak hours.

7. A distribution automation control method based on virtual bus communication according to claim 4 or 5, characterized in that: When generating the distribution network adjustment rules, the following formula is used to calculate the distribution network security prediction value S P And operational feasibility factor F: distribution network security prediction value S P : S P =1-|T e -T i | / T i Among them, T e is the expected load distribution time, T i is the ideal load distribution time; Operational feasibility coefficient F: F=S P / 1+|P H -P L | / P L Among them, P H is the second predicted load, P L is the first predicted load; when the operation feasibility coefficient F is greater than the preset coefficient threshold, the load even operation is performed.

8. A power distribution automation control device based on virtual bus communication, characterized in that: The device is used to execute a distribution automation control method based on virtual bus communication as claimed in any one of claims 1 to 7, the device comprising: The virtual bus is used to register the physical communication interface to the driver list and implement standardized operations of the physical interface through the driver layer; the physical communication interface is configured as a virtual communication interface and registered to the virtual bus list, and the unified scheduling and data interaction of multiple interfaces are implemented through the virtual bus layer; Virtual devices are used to configure physical communication devices as virtual communication devices and register them in the virtual device list, completing device function mapping and protocol adaptation through the virtual device layer; and register physical device instances in the device instance list, and implement data transmission and reception and control instruction execution through the device instance layer; The virtual device dynamically matches different communication protocols and transmission modes based on the virtual bus architecture at the communication protocol layer to connect heterogeneous devices.

9. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the distribution automation control method based on virtual bus communication as claimed in any one of claims 1 to 7 are implemented.

10. A computer program product, characterized in that When the computer program product is run on a distribution automation system, the distribution automation system is enabled to execute the distribution automation control method based on virtual bus communication according to any one of claims 1 to 7.

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