Multi-port cooperative control method and system combined with hardware framework structure

Abstract

The application discloses a multi-port cooperative control method and system combined with a hardware framework structure, relates to the technical field of power distribution network cooperative control, and comprises the following steps: traversing a hardware framework structure to activate a data acquisition port and collecting a power distribution network in real time; performing multi-port cooperative analysis based on an operation parameter data set and the hardware framework structure, generating a topology reconstruction instruction; performing distributed control on the power distribution network according to the topology reconstruction instruction, generating a topology reconstruction result; and performing cooperative control on the power distribution network based on the topology reconstruction result, formulating a multi-port cooperative control strategy and optimizing intelligent operation of the power distribution network. The application solves the technical problems of slow response, poor fault tolerance and insufficient flexibility of centralized control in the prior art, and is difficult to cope with high-proportion distributed energy access and complex and variable operation conditions, and achieves the technical effects of realizing real-time and accurate optimization of an operation state, improving the control response speed and flexibility of the power distribution network and the operation efficiency of the power distribution network.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power distribution network collaborative control, and particularly relates to a multi-port collaborative control method and system combined with a hardware framework structure. BACKGROUND

[0002] As an important part of the power system, the operation efficiency and reliability of the power distribution network are crucial. The traditional power distribution network structure usually adopts centralized control, relying on a single data acquisition port and a central processing unit for monitoring and control of the operation state. In the face of complex and variable power distribution network environment, it often shows slow response speed, poor fault tolerance and insufficient scalability, especially when a high proportion of distributed energy is accessed and load fluctuation is frequent, it is difficult to achieve fast and accurate regulation and control, affecting the overall operation performance of the power distribution network.

[0003] Therefore, in the related art, there are technical problems of slow response of centralized control, poor fault tolerance, insufficient flexibility, and difficulty in coping with high proportion of distributed energy access and complex and variable operating conditions. SUMMARY

[0004] The present application provides a multi-port collaborative control method and system combined with a hardware framework structure, which solves the technical problems of slow response of centralized control, poor fault tolerance, insufficient flexibility, and difficulty in coping with high proportion of distributed energy access and complex and variable operating conditions in the prior art, and achieves the technical effects of real-time and accurate optimization of operation state, improvement of power distribution network control response speed and flexibility, and power distribution network operation efficiency.

[0005] The present application provides a multi-port collaborative control method combined with a hardware framework structure, which comprises: traversing the hardware framework structure to activate a data acquisition port, collecting the power distribution network in real time through the data acquisition port to obtain a set of operation parameter data; performing multi-port collaborative analysis based on the set of operation parameter data combined with the hardware framework structure to generate a topology reconstruction instruction; performing distributed control on the power distribution network according to the topology reconstruction instruction to generate a topology reconstruction result; and performing collaborative control on the power distribution network based on the topology reconstruction result to develop a multi-port collaborative control strategy for intelligent operation optimization of the power distribution network.

[0006] In a possible implementation, the multi-port cooperative control method combined with the hardware framework structure further performs the following processing: the main control unit performs port configuration scanning by traversing the hardware framework structure, determines the data acquisition port according to the scanning result; performs acquisition conflict analysis based on the data acquisition port, sets the port activation timing; sends an activation instruction to the data acquisition port according to the port activation timing, and performs state monitoring on the data acquisition port to determine the port activation state; starts the data acquisition port to perform multi-dimensional synchronous acquisition on the key nodes of the power distribution network according to the port activation state, and obtains a multi-dimensional original operation data set; performs data validity verification based on the multi-dimensional original operation data set, and generates the operation parameter data set.

[0007] In a possible implementation, the multi-port cooperative control method combined with the hardware framework structure further performs the following processing: performs state confirmation on the data acquisition port according to the multi-port activation state, and generates a state confirmation result; performs operation influence analysis on the power distribution network based on the hardware framework structure, and determines the key nodes of the power distribution network; performs sampling coordination analysis on the data acquisition port based on the state confirmation result, and sets the sampling frequency; starts the data acquisition port to perform multi-dimensional synchronous acquisition on the key nodes of the power distribution network according to the sampling frequency, and obtains a port synchronization data set; performs overlap analysis based on the port synchronization data set, performs interference evaluation based on the overlap data, and generates an interference score; integrates the port synchronization data set according to the interference score, and constructs the multi-dimensional original operation data set.

[0008] In a possible implementation, the multi-port cooperative control method combined with the hardware framework structure further performs the following processing: performs operation evaluation based on the operation parameter data set according to the time dimension, and obtains a first operation state evaluation result; performs operation evaluation based on the operation parameter data set according to the space dimension, and obtains a second operation state evaluation result; performs operation identification on the power distribution network according to the first operation state evaluation result and the second operation state evaluation result, divides a plurality of operation regions according to the identification; performs topology analysis by scanning the hardware framework structure, constructs hardware topology structure data, performs power distribution network graph theory analysis based on the hardware topology structure data, and constructs a weighted connected graph; maps the plurality of operation regions to the weighted connected graph to perform multi-port cooperative learning, and sets a topology optimization target; performs topology reconstruction analysis according to the topology optimization target, and constructs the topology reconstruction instruction.

[0009] In a possible implementation, the multi-port cooperative control method combined with the hardware framework structure further performs the following processing: scanning a device connection relationship of the hardware framework structure to identify a communication link of a hardware component, and obtaining physical connection topology information; performing device state analysis on the hardware component based on the physical connection topology information, constructing hardware topology structure data, and the hardware topology structure data containing device running state data; performing power distribution network graph theory modeling based on the hardware topology structure data: S1: mapping the hardware component to a graph theory node based on the hardware topology structure data; S2: mapping the device connection relationship to a graph theory branch based on the hardware topology structure data; S3: associating the graph theory node with the graph theory branch, constructing an association matrix, and constructing a power distribution network graph theory framework according to the association matrix; performing calculation based on the device running state data and the graph theory node, constructing a node weight factor, performing weight calculation based on the device connection relationship and the graph theory branch, and constructing a branch weight factor; and mapping the node weight factor and the branch weight factor to the power distribution network graph theory framework, and constructing the weighted connected graph.

[0010] In a possible implementation, the multi-port cooperative control method combined with the hardware framework structure further performs the following processing: performing real-time operation collection on the power distribution network based on the topology reconstruction result, and obtaining real-time operation parameters; performing multi-port AGC automatic control gain on the real-time operation parameters: S10: performing multi-port error detection based on the real-time operation parameters, calculating a multi-port control deviation value, performing multi-port distributed gain adjustment based on the multi-port control deviation value, and setting a multi-port control signal amplification multiple; S20: performing multi-path isolation output on the power distribution network multi-port according to the multi-port control signal amplification multiple and the multi-port control deviation value, and generating an initial control signal; performing multi-port cooperative control on the power distribution network by using the initial control signal, and generating a control effect parameter; synchronizing the control effect parameter to a feedback network to perform feedback adjustment on the initial control signal, and formulating a multi-port cooperative control strategy.

[0011] In a possible implementation, the multi-port cooperative control method combined with the hardware framework structure further performs the following processing: performing multi-port operation analysis on the power distribution network, setting an operation parameter target value, the operation parameter target value corresponding to the multi-port of the power distribution network; performing instantaneous deviation calculation on the real-time operation parameter and the operation parameter target value to obtain an instantaneous deviation value; performing cumulative calculation on the instantaneous deviation value according to the operation time length to obtain a cumulative deviation value; performing multi-port coordination evaluation according to the instantaneous deviation value and the cumulative deviation value to obtain a multi-port control deviation value; extracting a deviation characteristic parameter according to the multi-port control deviation value based on the multi-port of the power distribution network; performing multi-port distributed gain analysis according to the deviation characteristic parameter to obtain a gain parameter; performing coupling analysis on the multi-port of the power distribution network to obtain a multi-port coupling relationship; performing cooperative gain optimization according to the gain parameter to set gain parameter upper and lower limit protection values; and performing constraint on the gain parameter according to the gain parameter upper and lower limit protection values to set a multi-port control signal amplification multiple.

[0012] In a possible implementation, the multi-port cooperative control method combined with the hardware framework structure further performs the following processing: distributing the initial control signal to an execution terminal of the multi-port of the power distribution network to construct a multi-port control time sequence coordination matrix; performing control logic analysis on the initial control signal according to the multi-port control time sequence coordination matrix to set a control action dependency relationship; performing execution monitoring on the initial control signal based on the control action dependency relationship to obtain real-time execution state information; performing control response analysis according to the real-time execution state information to generate the control effect parameter.

[0013] In a possible implementation, the multi-port cooperative control method combined with the hardware framework structure further performs the following processing: constructing a multi-level distributed feedback network, the multi-level distributed feedback network including a local feedback layer and a global feedback layer; synchronizing the control effect parameter to the local feedback layer to perform control analysis, obtaining a first control deviation feature, performing dynamic correction analysis on the initial control signal according to the first control deviation feature to construct a first correction factor; synchronizing the control effect parameter to the global feedback layer to perform control analysis, obtaining a second control deviation feature, performing dynamic correction analysis on the initial control signal according to the second control deviation feature to construct a second correction factor; performing fuzzy reasoning optimization based on the first correction factor and the second correction factor to determine a control signal adjustment amount; performing feedback adjustment on the initial control signal according to the control signal adjustment amount to formulate the multi-port cooperative control strategy.

[0014] The application also provides a multi-port cooperative control system combined with a hardware framework structure, which comprises: an operating parameter acquisition module, configured to traverse a hardware framework structure to activate a data acquisition port, collect power distribution network in real time through the data acquisition port, and obtain an operating parameter data set; a multi-port cooperative analysis module, configured to perform multi-port cooperative analysis based on the operating parameter data set and the hardware framework structure, and generate a topology reconstruction instruction; a topology reconstruction module, configured to perform distributed control on the power distribution network according to the topology reconstruction instruction, and generate a topology reconstruction result; and a cooperative control module, configured to perform cooperative control on the power distribution network based on the topology reconstruction result, and formulate a multi-port cooperative control strategy to optimize intelligent operation of the power distribution network.

[0015] The multi-port cooperative control method and system combined with the hardware framework structure provided in the application traverse the hardware framework structure to activate a data acquisition port, collect the power distribution network in real time, perform multi-port cooperative analysis based on the operating parameter data set and the hardware framework structure, generate a topology reconstruction instruction, perform distributed control on the power distribution network according to the topology reconstruction instruction, generate a topology reconstruction result, perform cooperative control on the power distribution network based on the topology reconstruction result, and formulate a multi-port cooperative control strategy to optimize intelligent operation of the power distribution network. The technical problems of slow response, poor fault tolerance, insufficient flexibility, and difficulty in coping with high proportion of distributed energy access and complex and variable operating conditions in the prior art are solved, and the technical effects of realizing real-time and accurate optimization of operating state, improving control response speed and flexibility of the power distribution network, and improving operating efficiency of the power distribution network are achieved. BRIEF DESCRIPTION OF DRAWINGS

[0016] In order to more clearly illustrate the technical solutions of the embodiments of the present disclosure, the drawings of the embodiments of the present disclosure will be briefly introduced below. In the present application, a flowchart is used to illustrate the operations performed by the system according to the embodiments of the present application. It should be understood that the foregoing or the following operations are not necessarily performed in sequence. On the contrary, various steps can be processed in reverse order or simultaneously as needed. Meanwhile, other operations can be added to these processes, or one or more steps of operations can be removed from these processes.

[0017] Figure 1 The multi-port cooperative control method combined with the hardware framework structure provided by the embodiments of the present application is shown in the flowchart.

[0018] Figure 2 The multi-port cooperative control system combined with the hardware framework structure provided by the embodiments of the present application is shown in the structural schematic diagram.

[0019] Legend: operating parameter acquisition module 10, multi-port cooperative analysis module 20, topology reconstruction module 30, and cooperative control module 40. DETAILED DESCRIPTION

[0020] To further clarify the technical means and effects taken by the present application to achieve the predetermined inventive purpose, the following will be described in detail according to the specific embodiments, structures, features and effects of the present application, with reference to the accompanying drawings and preferred embodiments.

[0021] The embodiment of the present application provides a multi-port cooperative control method combined with a hardware framework structure, as shown in the figure, the method comprises the following steps: Figure 1 The method comprises the following steps:

[0022] In step S100, the data acquisition ports of the hardware framework structure are traversed, the power distribution network is collected in real time through the data acquisition ports, and a running parameter data set is obtained.

[0023] Step S100 further comprises the following steps: in step S110, the hardware framework structure is traversed by the master control unit to perform port configuration scanning, and the data acquisition ports are identified and determined according to the scanning results; in step S120, collection conflict analysis is performed based on the data acquisition ports, and a port activation time sequence is set; in step S130, an activation instruction is sent to the data acquisition ports according to the port activation time sequence, the state of the data acquisition ports is monitored, and the port activation state is determined; in step S140, the data acquisition ports are started according to the port activation state to perform multi-dimensional synchronous collection on the key nodes of the power distribution network, and a multi-dimensional original running data set is obtained; in step S150, data validity verification is performed based on the multi-dimensional original running data set, and the running parameter data set is generated.

[0024] Preferably, the master control unit performs port configuration scanning in the hardware framework structure, and identifies and determines all physical or logical ports available for data acquisition, i.e., data acquisition ports, according to the scanning results; collection conflict analysis is performed according to the data acquisition ports, i.e., whether a conflict or congestion will occur due to the competition for system resources such as bus bandwidth, processor time, memory access, etc., when multiple data acquisition ports are started to collect at the same time, resulting in data loss or distortion, and then a port activation time sequence is set for each data acquisition port according to the conflict analysis result to avoid conflicts, wherein the port activation time sequence refers to the order of time activation; then an activation instruction is sent to the corresponding data acquisition ports according to the port activation time sequence, and the response state of the data acquisition ports is continuously monitored to confirm that each data acquisition port is successfully activated and enters a normal working state.

[0025] Preferably, after all data acquisition ports are successfully activated, the multi-dimensional synchronous acquisition of the predetermined transformer outlet, distributed power access point, key node and the like in the power distribution network is started, that is, the voltage, current, power, frequency and other operating parameters of different nodes are collected at the same time point to form a multi-dimensional original operating data set, and the consistency of the operating data in time is ensured; then the multi-dimensional original operating data set collected is subjected to data validity test, including checking whether the operating data is complete, whether there is abnormal value due to interference, whether it is within a reasonable physical range, eliminating invalid or abnormal data and appropriately filling the missing data to finally generate an operating parameter data set, while ensuring the synchronization, reliability and validity of the operating parameters.

[0026] Further, step S140 further includes step S141 of confirming the state of the data acquisition port according to the multi-port activation state to generate a state confirmation result; step S142 of performing operating influence analysis on the power distribution network based on the hardware framework structure to determine the key nodes of the power distribution network; step S143 of performing sampling coordination analysis on the data acquisition port based on the state confirmation result to set a sampling frequency; step S144 of starting the data acquisition port to perform multi-dimensional synchronous acquisition on the key nodes of the power distribution network according to the sampling frequency to obtain a port synchronous data set; step S145 of performing overlap analysis based on the port synchronous data set, performing interference evaluation based on the overlap data, and generating an interference score; and step S146 of integrating the port synchronous data set according to the interference score to construct the multi-dimensional original operating data set.

[0027] Preferably, the state of each data acquisition port is confirmed according to the multi-port activation state to generate a clear state confirmation result, which clearly indicates the normal data acquisition ports and the abnormal data acquisition ports that are ready; then the hardware framework structure is obtained and the operating influence analysis on the power distribution network is performed, that is, the points having a decisive influence on the operating state, stability, power quality and the like of the power distribution network are identified and determined as the key nodes of the power distribution network, such as the distributed power access point, the important load connection point, the network tie switch, the voltage weak point and the like, so as to ensure that the data acquisition target is clear and focused, and the data value is guaranteed; the sampling coordination analysis on the data acquisition port is performed according to the available data acquisition ports in the state confirmation result, that is, the sampling frequency of each data acquisition port is coordinated and distributed according to the monitoring requirements and system resources such as low-frequency acquisition of steady-state data and high-frequency acquisition of transient fault data, so as to ensure the optimization of resources.

[0028] Preferably, all available data acquisition ports are started, and multi-dimensional synchronous acquisition of key nodes of the power distribution network is performed according to a sampling frequency, so as to ensure that different physical quantities such as A-phase voltage, B-phase current, active power and the like from different geographical locations are strictly aligned in time stamps, and then a port synchronous data set is obtained; then, overlap analysis is performed on the port synchronous data set, and power distribution network operation data that exist in time or space correlation or overlap between different data channels is identified, and then interference evaluation is performed based on the overlap data, including quantifying noise, harmonics, electromagnetic coupling interference between different channels and the like existing in the operation data, and finally generating an interference score for reflecting the signal-to-noise ratio and quality level of each part of the data set; finally, data cleaning and weighted integration are performed on the port synchronous data set according to the interference score, and a final multi-dimensional original operation data set is determined, wherein data segments with high interference scores are marked or filtered out, and high-quality data with low interference scores are given higher confidence, so as to ensure the quality and reliability of the operation data.

[0029] Step S200, based on the operation parameter data set, multi-port collaborative analysis is performed in combination with the hardware framework structure, and a topology reconstruction instruction is generated.

[0030] Step S200 further includes the following steps: step S210, based on the operation parameter data set, operation evaluation is performed according to a time dimension, and a first operation state evaluation result is obtained; step S220, based on the operation parameter data set, operation evaluation is performed according to a space dimension, and a second operation state evaluation result is obtained; step S230, according to the first operation state evaluation result and the second operation state evaluation result, operation recognition is performed on the power distribution network, and a plurality of operation regions are divided according to the recognition; step S240, topology analysis is performed by scanning the hardware framework structure, and hardware topology structure data is constructed; step S250, the plurality of operation regions are mapped to the weighted connected graph for multi-port collaborative learning, and a topology optimization target is set; and step S260, according to the topology optimization target, topology reconstruction analysis is performed, and the topology reconstruction instruction is constructed.

[0031] Preferably, the operation parameter dataset is evaluated in the time dimension, i.e. the trend of voltage, current, load and other power distribution network parameters changing over time is analyzed to identify voltage instability, periodic overload of load, fluctuation of distributed power generation, etc., to generate a first operation state evaluation result; the operation parameter dataset is evaluated in the spatial dimension, i.e. the distribution of power distribution network parameters at different geographical location nodes at a certain time section is analyzed to identify local voltage out-of-limit, uneven distribution of line power, concentrated network loss, etc., to generate a second operation state evaluation result; through evaluation in the time and space dimensions, the overall operation health status of the power distribution network is comprehensively mastered. Then, the power distribution network is identified according to the first and second operation state evaluation results, and is divided into multiple operation areas with different characteristics according to the identification results, including "overload area", "voltage out-of-limit area", "power supply bottleneck area", "normal operation area", "high-penetration distributed energy area", etc.

[0032] Preferably, the scanning hardware framework structure is analyzed for topology, including identifying the real-time opening and closing states of all sectional switches and tie switches to determine the physical connection relationship of the current power distribution network and obtain hardware topology structure data; then, graph theory analysis of the power distribution network is performed based on the hardware topology structure data, i.e. using a graph theory model to abstract the power distribution network into a mathematical model, wherein the vertices / nodes represent the busbars, transformers, key load points, etc. of the power distribution network, the edges / branches represent the line, switches and other connecting elements of the power distribution network, and the weights refer to the numerical values assigned to the edges or vertices, representing their electrical properties such as resistance, reactance, or operating states such as transmission capacity limit, current load rate, priority, etc., and then a weighted connected graph is obtained, which can reflect the physical constraints of the real power distribution network.

[0033] Preferably, the multiple operation areas are mapped to the weighted connected graph, i.e. the power distribution network operation and network structure are accurately associated, for example, the lines and switches corresponding to each operation area are accurately marked on the weighted connected graph, then multi-port collaborative learning is performed on the associated graph model, i.e. through multi-objective optimization analysis, how to change the network structure by operating the switches at different location ports to solve the operation problem is analyzed, while multiple topology optimization objectives are set, such as eliminating device overload, maintaining voltage within a qualified range, reducing network loss, improving power supply reliability, etc.; then, topology reconfiguration analysis is performed on the weighted connected graph model guided by the topology optimization objectives, i.e. a set of optimal port switch operation combinations is determined through search calculation to make the topology optimization objectives optimal, and finally the determined optimal switch operation sequence is constructed into executable topology reconfiguration instructions and is issued to the corresponding intelligent switch device for execution, ensuring the realization of automatic optimization decision-making of the intelligent power distribution network operation.

[0034] Further, step S240 further comprises step S241 of identifying the communication links of the hardware components by scanning the device connection relationship of the hardware framework structure to obtain physical connection topology information; step S242 of analyzing the device state of the hardware components based on the physical connection topology information to construct hardware topology structure data, the hardware topology structure data containing device running state data; and step S243 of performing power distribution network graph theory modeling based on the hardware topology structure data.

[0035] S1: mapping the hardware components to graph theory nodes based on the hardware topology structure data;

[0036] S2: mapping the device connection relationship to graph theory branches based on the hardware topology structure data;

[0037] S3: associating the graph theory nodes with the graph theory branches to construct an association matrix, and constructing a power distribution network graph theory framework according to the association matrix; step S244 of constructing node weight factors by calculating based on the device running state data in combination with the graph theory nodes, and constructing branch weight factors by weight calculation based on the device connection relationship in combination with the graph theory branches; and step S245 of mapping the node weight factors and the branch weight factors to the power distribution network graph theory framework to construct the weighted connected graph.

[0038] Preferably, the device connection relationship of the hardware framework structure is scanned and the communication links of the hardware components are identified, that is, how all the hardware components such as intelligent switch controllers, data concentrators, and regional control units in the hardware framework structure are connected to each other through a communication network is identified to obtain physical connection topology information, including the direct connection topology of each hardware component; then the device state of the hardware components is analyzed based on the physical connection topology information, including checking whether the device is online, the CPU / memory utilization rate, and whether the communication is normal, to determine the health status of each device itself as the hardware topology structure data, wherein the hardware topology structure data contains the static connection relationship between devices and the device running state data of each device, such as online / offline, load rate, and communication delay; and then the power distribution network graph theory modeling is performed based on the hardware topology structure data, that is, the hardware components contained in the hardware topology structure data are mapped to graph theory nodes, the device connection relationship is mapped to graph theory branches, and the graph theory nodes and the graph theory branches are associated to construct an association matrix, that is, the association matrix is created to accurately describe the connection relationship between the nodes and the branches, wherein the rows of the matrix usually represent the nodes, the columns of the matrix represent the branches, and each element in the matrix represents the association of the nodes and the branches, such as whether there is a connection and the connection direction, to obtain the power distribution network graph theory framework.

[0039] Preferably, based on the equipment running state data, the calculation is combined with the graph theory node, that is, according to the hardware equipment running state, the corresponding node weight factor of each graph theory node is determined, for example, the higher the communication load rate, the greater the calculation resource margin, the weight, and the node weight factor reflects the importance of the node; based on the equipment connection relationship, the weight calculation is combined with the graph theory branch, that is, according to the communication performance of the communication bandwidth, transmission delay and link reliability of the graph theory branch, the branch weight factor of each graph theory branch is determined, wherein the branch weight factor reflects the cost, efficiency or reliability of the branch in data transmission. Finally, the node weight factor and the branch weight factor are mapped to the power distribution network graph theory framework, and a complete and quantifiable analysis weighted connected graph is finally determined, which not only contains the physical connection topology of the power distribution network, but also integrates the real-time running state, and can solve the topology optimization target through the running shortest path, minimum spanning tree and the like, for example, finding the path with the highest communication efficiency, identifying the most fragile link in the network or re-planning the communication route when some equipment fails.

[0040] Step S300, according to the topology reconstruction instruction, the power distribution network is controlled in a distributed manner, and a topology reconstruction result is generated.

[0041] Preferably, the topology reconstruction instruction is input into the local intelligent control unit or intelligent terminal of different regions of the power distribution network through the local communication network to cooperatively execute the topology reconstruction based on distributed control, thereby avoiding uploading all topology reconstruction instructions to the central platform and then issuing them, ensuring the reliability and response speed of the topology reconstruction, and confirming and feeding back the execution effect after executing the topology reconstruction instruction. Specifically, the local intelligent control unit of each switch uploads its new state such as "closed" and "opened" and related electrical quantities to the monitoring unit after executing the opening / closing operation, and re-scans the connection relationship of the power distribution network according to the new state of all switches to generate a real-time network topology structure as the topology reconstruction result, which clearly indicates the running structure of the power distribution network after switching.

[0042] Step S400, based on the topology reconstruction result, the power distribution network is cooperatively controlled, and a multi-port cooperative control strategy is formulated to optimize the intelligent operation of the power distribution network.

[0043] Step S400 further includes step S410, based on the topology reconstruction result, the real-time operation of the power distribution network is collected to obtain real-time operation parameters; and step S420, the real-time operation parameters are subjected to multi-port AGC automatic control gain:

[0044] S10: Multi-port error detection based on real-time operation parameters, calculation of multi-port control deviation value, multi-port distributed gain adjustment based on the multi-port control deviation value, and setting of multi-port control signal amplification multiple;

[0045] S20: According to the multi-port control signal amplification multiple combined with the multi-port control deviation value, the multi-port of the power distribution network is isolated output, and the initial control signal is generated; step S430, the initial control signal is executed to perform multi-port collaborative control on the power distribution network, and the control effect parameter is generated; step S440, the control effect parameter is synchronized to the feedback network to feedback adjust the initial control signal, and the multi-port collaborative control strategy is formulated.

[0046] Preferably, based on the topology reconstruction result, the real-time operation of the power distribution network is collected, that is, the key operation data such as node voltage, line power and system frequency in the power distribution network after topology reconstruction is collected in real time, the real-time operation parameter is determined, and then the real-time operation parameter is subjected to multi-port AGC automatic control gain, which is used for automatic gain control of multiple dispersed nodes or devices in the power distribution network that can be controlled, that is, the amplification multiple of the control signal is automatically adjusted according to the error size, so that stable control with fast response and without oscillation is realized. Specifically, the collected real-time operation parameter is compared with the respective target set value, the control deviation value of each control port is calculated, that is, the multi-port control deviation value is obtained; and then the multi-port distributed gain adjustment is performed based on the multi-port control deviation value, that is, the control signal amplification multiple of each port is dynamically set according to the size and characteristics of the port deviation value, and then the multi-port control signal amplification multiple is set, wherein when the control deviation is large, a larger gain is used to quickly eliminate the deviation, and when the control deviation is small, a smaller gain is used to prevent system oscillation caused by excessive control.

[0047] Preferably, the multi-port control deviation value is multiplied by its corresponding multi-port control signal amplification multiple, and the multi-port of the power distribution network is isolated output, that is, the control signals independent of each other and electrically isolated are generated for different ports, so as to avoid coupling oscillation between controllers, and finally the initial control signal is generated, that is, the preliminary control instruction is sent to each distributed device. The initial control signal is sent to the multi-port execution device in the power distribution network to perform multi-port collaborative control, and the execution device changes its own operation state according to the instruction, and then the operation parameters of the power distribution network are collected again after the control action is executed, which are used as control effect parameters for evaluating the actual effect of the control action; finally, the control effect parameters are synchronized to the feedback network to feedback adjust the initial control signal, including comparing the control effect parameters with the expected target, if there is a deviation, the control signal amplification multiple or the control instruction itself is adjusted based on the new deviation, and finally the multi-port collaborative control strategy is output, realizing the collaborative control of the power distribution network with fine and decoupled operation state changes.

[0048] Further, step S10 further comprises step S11, performing multi-port operation analysis on the power distribution network, setting an operation parameter target value, the operation parameter target value corresponding to the multi-port of the power distribution network; step S12, performing instantaneous deviation calculation on the real-time operation parameter and the operation parameter target value, obtaining an instantaneous deviation value; step S13, performing cumulative calculation on the instantaneous deviation value according to the operation time, obtaining a cumulative deviation value; step S14, performing multi-port coordination evaluation according to the instantaneous deviation value and the cumulative deviation value, obtaining a multi-port control deviation value; step S15, extracting a deviation characteristic parameter according to the multi-port of the power distribution network based on the multi-port control deviation value; step S16, performing multi-port distributed gain analysis according to the deviation characteristic parameter, obtaining a gain parameter; step S17, performing coupling analysis on the multi-port of the power distribution network, obtaining a multi-port coupling relationship; performing cooperative gain optimization according to the gain parameter, setting a gain parameter upper and lower limit protection value; step S18, constraining the gain parameter according to the gain parameter upper and lower limit protection value, setting the multi-port control signal amplification multiple.

[0049] Preferably, the multi-port operation analysis of the power distribution network sets an expected operation parameter target value for each control port, wherein the operation parameter target value corresponds to the multi-port of the power distribution network, for example, the voltage target of the distributed energy storage port A is 10.5 kV, and the reactive power target of the reactive power compensation device port B is 0 kW. The measured real-time operation parameter is subtracted from the operation parameter target value to obtain an instantaneous deviation value, which reflects the current instantaneous error size. The instantaneous deviation value is integrated and accumulated according to the operation time to determine a cumulative deviation value, which reflects the persistence of the error. Then, the multi-port coordination evaluation is performed according to the instantaneous deviation value and the cumulative deviation value, that is, the instantaneous deviation and the cumulative deviation are combined as the proportional term and the integral term in the PID control, respectively, to form a comprehensive multi-port control deviation value, wherein the instantaneous deviation provides fast response and the cumulative deviation eliminates steady-state error. The change speed, fluctuation frequency and other deviation characteristic parameters of the multi-port control deviation value are analyzed, and the multi-port distributed gain analysis is performed according to the deviation characteristic parameter, that is, the preliminary gain parameter is calculated for each power distribution network port independently, which is used to determine the sensitivity or reaction intensity of the controller to the deviation.

[0050] Preferably, the multi-port coupling of the power distribution network is analyzed to identify and determine the multi-port coupling relationship, i.e. the mutual influence relationship between different control ports, for example, adjusting the energy storage output of port A may affect the voltage of port B. Based on the preliminary gain parameters, the gain of all ports is optimized according to the multi-port coupling relationship to ensure their cooperative work, and the upper and lower limit protection values of the gain parameters are set, i.e. a reasonable allowed range is set for the optimized gain parameters. The lower limit protection prevents the control system from being sluggish due to too small gain, and the upper limit protection prevents the control system from being too aggressive due to too large gain, resulting in oscillation or impact on equipment. The cooperatively optimized gain parameters are limited within the range of the upper and lower limit protection values of the gain parameters, and the gain values after cooperative optimization and safety constraint are finally output as the multi-port control signal amplification multiples for control calculation.

[0051] Further, step S430 further includes step S431 of distributing the initial control signal to the execution terminal of the multi-port of the power distribution network to construct a multi-port control timing coordination matrix; step S432 of performing control logic analysis on the initial control signal according to the multi-port control timing coordination matrix to set a control action dependency relationship; step S433 of performing execution monitoring on the initial control signal based on the control action dependency relationship to obtain real-time execution state information; and step S434 of performing control response analysis according to the real-time execution state information to generate the control effect parameter.

[0052] Preferably, the initial control signal is sent to the execution terminal of the corresponding multi-port of the power distribution network, such as a energy storage converter, an intelligent switch, a controllable load controller, etc., and a multi-port scheduling plan table is constructed as a multi-port control timing coordination matrix for defining the time when each port execution terminal starts to execute an action and the time sequence and delay requirements between different execution terminal actions. Then, the initial control signal is analyzed according to the multi-port control timing coordination matrix, specifically, the multi-port control timing coordination matrix is analyzed in depth to analyze its implicit logical constraints, and then the control action dependency relationship is set, i.e. the logical relationship between the control actions of each port is determined to ensure the logical correctness of the control and prevent oscillation or failure due to incorrect action sequence, for example, only when switch K1 is confirmed to be closed, the subsequent load input operation can be started.

[0053] Preferably, according to the control action dependency relationship and the multi-port control timing matching matrix, initial control signals are sent to each execution terminal and the execution state of each execution terminal is monitored in real time to obtain real-time execution state information, including the instruction receiving state of the execution terminal, the action execution state of the execution terminal, and the opening and closing state of the switch, the real-time power, voltage, current and other process telemetry data of the equipment; then control response analysis is performed according to the real-time execution state information, that is, the monitored real-time execution state information is compared and analyzed with the expected control instructions and timing requirements to determine whether the terminal responds correctly, whether the response is completed on time, and whether the changes in key parameters in the execution process meet the expectations, and finally the control effect parameters are generated, that is, the quality of the control action execution process itself is evaluated, mainly including the response success rate, the actual response time and the action sequence integrity.

[0054] Further, step S440 further comprises step S441 of constructing a multi-level distributed feedback network, wherein the multi-level distributed feedback network comprises a local feedback layer and a global feedback layer; step S442 of synchronizing the control effect parameters to the local feedback layer for control analysis to obtain a first control deviation feature, dynamically modifying and analyzing the initial control signals according to the first control deviation feature to construct a first correction factor; step S443 of synchronizing the control effect parameters to the global feedback layer for control analysis to obtain a second control deviation feature, dynamically modifying and analyzing the initial control signals according to the second control deviation feature to construct a second correction factor; step S444 of performing fuzzy reasoning optimization based on the first correction factor and the second correction factor to determine a control signal adjustment amount; and step S445 of performing feedback adjustment on the initial control signals according to the control signal adjustment amount to formulate the multi-port collaborative control strategy.

[0055] Preferably, the initial control instruction is intelligently corrected through feedback analysis at both local and global levels, so as to formulate an optimal coordinated control strategy that can quickly calm down local fluctuations and also take into account the stable operation of the whole network. Specifically, a multi-level distributed feedback network including a local feedback layer and a global feedback layer is constructed, wherein the local feedback layer is located at the edge side of the intelligent terminal, the regional controller and the like in the control area, and is used to process the rapid and high-frequency changes in the local area and pursue rapid response; the global feedback layer is located at the upper coordination center, and is used to focus on the global stability, economy and coordination between different regions; the control effect parameters are synchronized to the local feedback layer for control analysis, that is, each local controller receives the control effect data within its range, analyzes whether the effect after execution of the control instruction achieves the local target at the local level, such as whether the voltage of the substation is quickly stabilized at the set value, and then generates a first control deviation feature for describing the gap between the control effect of the local area and the local target, for example, voltage overshoot and adjustment time; and then the initial control signal is dynamically corrected and analyzed based on the first control deviation feature, and a first correction factor is constructed to quickly eliminate the local deviation.

[0056] Preferably, the control effect parameters are synchronized to the global feedback layer for control analysis, the control effect is analyzed at the whole power grid level, that is, the influence of the local control action on the global is analyzed, for example, whether the large increase in energy discharge for quickly stabilizing the voltage of M area causes the overload of the main transformer or increases the voltage deviation of N area, and then a second control deviation feature is generated for describing the gap between the current state and the global optimal target, such as the overall network loss and voltage balance degree; and then the initial control signal is dynamically corrected and analyzed according to the second control deviation feature, and a second correction factor is constructed to realize the global optimization. Then, fuzzy reasoning optimization is performed based on the first correction factor and the second correction factor, including adjusting the importance of the first correction factor and the second correction factor according to the preset expert rules, wherein the expert rules can include evaluating the similarity of the two correction factors, if the local voltage deviation is large and the global main transformer load is light, the local correction factor is preferentially adopted, if the two correction factors correspond, the global target is taken as the standard to determine the correction instruction, and then the control signal adjustment amount is determined as the final comprehensive correction instruction.

[0057] Preferably, the initial control signal is feedback adjusted according to the control signal adjustment amount, that is, the control signal adjustment amount is applied to the initial control signal to correct it, a multi-port cooperative control strategy is obtained, and finally the multi-port cooperative control strategy is applied to the real power distribution network for intelligent operation optimization, including overall safety, economy and power quality and other multi-objectives, and coordinating numerous distributed controllable devices, such as automatically adjusting power flow to prevent device overload, dynamically switching reactive power compensation devices to maintain voltage standard allowable range, and realizing fault area isolation and non-fault area recovery power supply when a fault occurs; ensuring that the control actions of all ports are cooperative and consistent, greatly improving the response speed of the power distribution network control and the practicality and flexibility of the control system in complex real environment.

[0058] In the foregoing, reference is made to Figure 1 The multi-port cooperative control method combined with the hardware framework structure according to the embodiment of the application is described in detail. Next, the multi-port cooperative control system combined with the hardware framework structure according to the embodiment of the application will be described with reference to Figure 2 The multi-port cooperative control system combined with the hardware framework structure according to the embodiment of the application is described in detail. Next, the multi-port cooperative control system combined with the hardware framework structure according to the embodiment of the application will be described with reference to

[0059] The multi-port cooperative control system combined with the hardware framework structure according to the embodiment of the application is used to solve the technical problems of slow response, poor fault tolerance, and insufficient flexibility of centralized control in the prior art, and is difficult to cope with high proportion of distributed energy access and complex and variable operating conditions, and achieves the technical effects of realizing real-time and accurate optimization of operating state, improving the response speed and flexibility of power distribution network control, and improving the operating efficiency of the power distribution network. As shown in Figure 2 The multi-port cooperative control system combined with the hardware framework structure includes an operating parameter acquisition module 10, a multi-port cooperative analysis module 20, a topology reconstruction module 30, and a cooperative control module 40.

[0060] The operating parameter acquisition module 10 is used to traverse the hardware framework structure to activate a data acquisition port, and to acquire operating parameter data sets by real-time acquisition of the power distribution network through the data acquisition port. The multi-port cooperative analysis module 20 is used to perform multi-port cooperative analysis based on the operating parameter data sets combined with the hardware framework structure, and to generate a topology reconstruction instruction. The topology reconstruction module 30 is used to perform distributed control of the power distribution network according to the topology reconstruction instruction, and to generate a topology reconstruction result. The cooperative control module 40 is used to perform cooperative control of the power distribution network based on the topology reconstruction result, and to develop a multi-port cooperative control strategy for intelligent operation optimization of the power distribution network.

[0061] The specific configuration of the operation parameter acquisition module 10 will be described in detail below. The operation parameter acquisition module 10 further comprises: performing port configuration scanning by traversing the hardware framework structure through the master control unit, identifying and determining the data acquisition port according to the scanning result; performing acquisition conflict analysis based on the data acquisition port, setting the port activation timing; sending an activation instruction to the data acquisition port according to the port activation timing, performing state monitoring on the data acquisition port, and determining the port activation state; starting the data acquisition port to perform multi-dimensional synchronous acquisition on the key nodes of the power distribution network according to the port activation state, and obtaining a multi-dimensional original operation data set; performing data validity test based on the multi-dimensional original operation data set, and generating the operation parameter data set.

[0062] The specific configuration of the operation parameter acquisition module 10 will be described in detail below. The operation parameter acquisition module 10 further comprises: performing port configuration scanning by traversing the hardware framework structure through the master control unit, identifying and determining the data acquisition port according to the scanning result; performing acquisition conflict analysis based on the data acquisition port, setting the port activation timing; sending an activation instruction to the data acquisition port according to the port activation timing, performing state monitoring on the data acquisition port, and determining the port activation state; starting the data acquisition port to perform multi-dimensional synchronous acquisition on the key nodes of the power distribution network according to the port activation state, and obtaining a multi-dimensional original operation data set; performing data validity test based on the multi-dimensional original operation data set, and generating the operation parameter data set.

[0063] The specific configuration of the multi-port collaborative analysis module 20 will be described in detail below. The multi-port collaborative analysis module 20 further comprises: performing operation evaluation according to the time dimension based on the operation parameter data set, obtaining a first operation state evaluation result; performing operation evaluation according to the space dimension based on the operation parameter data set, obtaining a second operation state evaluation result; performing operation identification on the power distribution network according to the first operation state evaluation result and the second operation state evaluation result, and dividing a plurality of operation regions according to the identification; performing topology analysis by scanning the hardware framework structure, constructing hardware topology structure data, performing power distribution network graph theory analysis based on the hardware topology structure data, and constructing a weighted connected graph; mapping the plurality of operation regions to the weighted connected graph for multi-port collaborative learning, and setting a topology optimization target; performing topology reconstruction analysis according to the topology optimization target, and constructing the topology reconstruction instruction.

[0064] In the following, the specific configuration of the multi-port cooperative analysis module 20 will be described in detail. The multi-port cooperative analysis module 20 further comprises: identifying the communication link of the device connection relationship of the scanning hardware framework structure to obtain the physical connection topology information; performing device state analysis on the hardware components based on the physical connection topology information, constructing hardware topology structure data, and the hardware topology structure data containing device running state data; performing power distribution network graph theory modeling based on the hardware topology structure data: S1: mapping the hardware components to graph theory nodes based on the hardware topology structure data; S2: mapping the device connection relationship to graph theory branches based on the hardware topology structure data; S3: associating the graph theory nodes with the graph theory branches, constructing an association matrix, and constructing a power distribution network graph theory framework according to the association matrix; constructing node weight factors based on the device running state data combined with the graph theory nodes, and constructing branch weight factors based on the device connection relationship combined with the graph theory branches; mapping the node weight factors and the branch weight factors to the power distribution network graph theory framework to construct the weighted connected graph.

[0065] In the following, the specific configuration of the cooperative control module 40 will be described in detail. The cooperative control module 40 further comprises: collecting real-time operation parameters of the power distribution network based on the topology reconstruction result; performing multi-port AGC automatic control gain on the real-time operation parameters: S10: performing multi-port error detection based on real-time operation parameters, calculating multi-port control deviation values, performing multi-port distributed gain adjustment based on the multi-port control deviation values, and setting multi-port control signal amplification multiples; S20: performing multi-channel isolation output on the power distribution network multi-port according to the multi-port control signal amplification multiples combined with the multi-port control deviation values, generating initial control signals; performing multi-port cooperative control on the power distribution network by the initial control signals, generating control effect parameters; synchronizing the control effect parameters to the feedback network to perform feedback adjustment on the initial control signals, and formulating a multi-port cooperative control strategy.

[0066] Below, the specific configuration of the cooperative control module 40 will be described in detail. The cooperative control module 40 further comprises: performing multi-port operation analysis on the power distribution network, setting an operation parameter target value corresponding to the multi-port of the power distribution network; performing instantaneous deviation calculation on the real-time operation parameter and the operation parameter target value to obtain an instantaneous deviation value; performing cumulative calculation on the instantaneous deviation value according to the operation time length to obtain a cumulative deviation value; performing multi-port coordination evaluation according to the instantaneous deviation value and the cumulative deviation value to obtain a multi-port control deviation value; extracting a deviation characteristic parameter according to the multi-port control deviation value based on the multi-port of the power distribution network; performing multi-port distributed gain analysis according to the deviation characteristic parameter to obtain a gain parameter; performing coupling analysis on the multi-port of the power distribution network to obtain a multi-port coupling relationship; performing cooperative gain optimization according to the gain parameter to set upper and lower limit protection values of the gain parameter; and performing constraint on the gain parameter according to the upper and lower limit protection values of the gain parameter to set the amplification multiple of the multi-port control signal.

[0067] Below, the specific configuration of the cooperative control module 40 will be described in detail. The cooperative control module 40 further comprises: distributing the initial control signal to the execution terminal of the multi-port of the power distribution network to construct a multi-port control time sequence coordination matrix; performing control logic analysis on the initial control signal according to the multi-port control time sequence coordination matrix to set a control action dependency relationship; performing execution monitoring on the initial control signal based on the control action dependency relationship to obtain real-time execution state information; performing control response analysis according to the real-time execution state information to generate the control effect parameter.

[0068] Below, the specific configuration of the cooperative control module 40 will be described in detail. The cooperative control module 40 further comprises: constructing a multi-level distributed feedback network, the multi-level distributed feedback network comprising a local feedback layer and a global feedback layer; synchronizing the control effect parameter to the local feedback layer for control analysis to obtain a first control deviation feature, performing dynamic correction analysis on the initial control signal according to the first control deviation feature to construct a first correction factor; synchronizing the control effect parameter to the global feedback layer for control analysis to obtain a second control deviation feature, performing dynamic correction analysis on the initial control signal according to the second control deviation feature to construct a second correction factor; performing fuzzy reasoning optimization based on the first correction factor and the second correction factor to determine a control signal adjustment amount; performing feedback adjustment on the initial control signal according to the control signal adjustment amount to formulate the multi-port cooperative control strategy.

[0069] The multi-port cooperative control system provided by the embodiment of the present application can execute the multi-port cooperative control method provided by any embodiment of the present application, and has the corresponding function modules and beneficial effects.

[0070] The above is only a preferred embodiment of the present application, and does not limit the present application in any form. Although the present application has been disclosed as above with the preferred embodiment, it is not intended to limit the present application. Any person skilled in the art can make some changes or modifications to the above disclosed technical content to obtain equivalent embodiments with equivalent changes, without departing from the technical solution of the present application. Any simple modification, equivalent change and modification made to the above embodiments according to the technical essence of the present application still belongs to the scope of the technical solution of the present application.

Claims

1. A method for multi-port cooperative control in conjunction with a hardware framework structure, characterized in that, The method comprises: Traverse the hardware framework structure to activate the data acquisition port, and collect the power distribution network in real time through the data acquisition port to obtain a running parameter data set; Based on the running parameter data set, a multi-port collaborative analysis is performed in combination with the hardware framework structure to generate a topology reconstruction instruction; According to the topology reconstruction instruction, distributed control is performed on the power distribution network to generate a topology reconstruction result; Based on the topology reconstruction result, collaborative control is performed on the power distribution network, and a multi-port collaborative control strategy is formulated to optimize intelligent operation of the power distribution network; It comprises: Based on the running parameter data set, running evaluation is performed according to the time dimension to obtain a first running state evaluation result; Based on the running parameter data set, running evaluation is performed according to the space dimension to obtain a second running state evaluation result; According to the first running state evaluation result and the second running state evaluation result, the running of the power distribution network is identified, and a plurality of running areas are divided according to the identification; Scan the hardware framework structure to perform topology analysis, construct hardware topology structure data, perform power distribution network graph theory analysis based on the hardware topology structure data, and construct a weighted connected graph; Map the plurality of running areas to the weighted connected graph to perform multi-port collaborative learning, and set a topology optimization target; According to the topology optimization target, topology reconstruction analysis is performed to construct the topology reconstruction instruction; It comprises: Based on the topology reconstruction result, real-time running acquisition is performed on the power distribution network to obtain real-time running parameters; The real-time running parameters are subjected to multi-port AGC automatic control gain: S10: Based on the real-time running parameters, multi-port error detection is performed, a multi-port control deviation value is calculated, multi-port distributed gain adjustment is performed based on the multi-port control deviation value, and a multi-port control signal amplification multiple is set; S20: According to the multi-port control signal amplification multiple in combination with the multi-port control deviation value, multi-port isolation output is performed on the power distribution network, and an initial control signal is generated; The initial control signal is executed to perform multi-port collaborative control on the power distribution network to generate a control effect parameter; The control effect parameter is synchronized to the feedback network to perform feedback adjustment on the initial control signal, and a multi-port collaborative control strategy is formulated.

2. The multi-port collaborative control method combining hardware framework structure as described in claim 1, characterized in that, Traverse the hardware framework structure to activate the data acquisition port, and collect the power distribution network in real time through the data acquisition port to obtain a running parameter data set, the method comprising: Scan the port configuration through the main control unit to traverse the hardware framework structure, and determine the data acquisition port according to the scanning result; Based on the data acquisition port, perform acquisition conflict analysis, and set the port activation time sequence; According to the port activation time sequence, send an activation instruction to the data acquisition port, and perform state monitoring on the data acquisition port to determine the port activation state; According to the port activation state, start the data acquisition port to perform multi-dimensional synchronous acquisition on the key nodes of the power distribution network to obtain a multi-dimensional original running data set; Based on the multi-dimensional original running data set, perform data validity test to generate the running parameter data set.

3. The method of claim 2, wherein the hardware framework structure is a combination of a hardware framework structure and a hardware platform structure. According to the port activation state, start the data acquisition port to perform multi-dimensional synchronous acquisition on the key nodes of the power distribution network to obtain a multi-dimensional original running data set, the method comprising: According to the multi-port activation state, the state of the data acquisition port is confirmed, and a state confirmation result is generated; Based on the hardware framework structure, the operation influence of the power distribution network is analyzed to determine the key nodes of the power distribution network; Based on the state confirmation result, the sampling coordination analysis of the data acquisition port is performed, and the sampling frequency is set; Start the data acquisition port to perform multi-dimensional synchronous acquisition on the key nodes of the power distribution network according to the sampling frequency, and obtain a port synchronization data set; Based on the port synchronization data set, perform overlap analysis, and perform interference evaluation based on the overlap data to generate an interference score; According to the interference score, the port synchronization data set is integrated to construct the multi-dimensional original operation data set.

4. The method of claim 1, wherein the hardware framework structure is a combination of a hardware framework structure and a hardware platform structure. Scan the hardware framework structure to perform topology analysis, construct hardware topology structure data, and perform power distribution network graph theory analysis based on the hardware topology structure data to construct a weighted connected graph, the method comprising: Scan the device connection relationship of the hardware framework structure to identify the communication link of the hardware component and obtain physical connection topology information; Based on the physical connection topology information, the device state analysis of the hardware component is performed to construct hardware topology structure data, which contains device operation state data; Based on the hardware topology structure data, the power distribution network graph theory modeling is performed: S1: Map the hardware component to a graph theory node based on the hardware topology structure data; S2: Map the device connection relationship to a graph theory branch based on the hardware topology structure data; S3: Associate the graph theory node with the graph theory branch to construct an association matrix, and construct a power distribution network graph theory framework according to the association matrix; Based on the device operation state data and the graph theory node, the node weight factor is constructed, and based on the device connection relationship and the graph theory branch, the branch weight factor is constructed; Map the node weight factor and the branch weight factor to the power distribution network graph theory framework to construct the weighted connected graph.

5. The multi-port collaborative control method combining hardware framework structure as described in claim 1, characterized in that, Based on real-time operation parameters, multi-port error detection is performed, the multi-port control deviation value is calculated, and based on the multi-port control deviation value, multi-port distributed gain adjustment is performed, and the multi-port control signal amplification multiple is set, the method comprising: Perform multi-port operation analysis on the power distribution network, set the operation parameter target value, and the operation parameter target value has a corresponding relationship with the power distribution network multi-port; Calculate the instantaneous deviation value by comparing the real-time operation parameter with the operation parameter target value; Cumulative calculation is performed on the instantaneous deviation value according to the operation time to obtain the cumulative deviation value; According to the instantaneous deviation value and the cumulative deviation value, the multi-port coordination evaluation is performed to obtain the multi-port control deviation value; Based on the multi-port control deviation value, the deviation characteristic parameters of the power distribution network multi-port are extracted; According to the gain parameter, the multi-port distributed gain analysis is performed to obtain the gain parameter; Coupling analysis is performed on the power distribution network multi-port to obtain the multi-port coupling relationship, and the cooperative gain optimization is performed according to the gain parameter to set the upper and lower limit protection value of the gain parameter; According to the upper and lower limit protection value of the gain parameter, the gain parameter is constrained, and the multi-port control signal amplification multiple is set.

6. The multi-port collaborative control method combining hardware framework structure as described in claim 1, characterized in that, The initial control signal is executed to perform multi-port collaborative control on the power distribution network to generate a control effect parameter, and the method comprises: The initial control signal is distributed to the execution terminal of the multi-port of the power distribution network to construct a multi-port control time sequence cooperation matrix; The initial control signal is subjected to control logic analysis according to the multi-port control time sequence cooperation matrix, and a control action dependency relationship is set; The initial control signal is executed based on the control action dependency relationship to perform execution monitoring to obtain real-time execution state information; The control effect parameter is obtained by performing control response analysis according to the real-time execution state information.

7. The method of claim 1, wherein the hardware framework structure is a combination of a hardware framework structure and a multi-port cooperative control method. The control effect parameter is synchronized to the feedback network to perform feedback adjustment on the initial control signal, and a multi-port collaborative control strategy is formulated, and the method comprises: A multi-level distributed feedback network is constructed, and the multi-level distributed feedback network comprises a local feedback layer and a global feedback layer; The control effect parameter is synchronized to the local feedback layer to perform control analysis to obtain a first control deviation feature, and the initial control signal is subjected to dynamic correction analysis according to the first control deviation feature to construct a first correction factor; The control effect parameter is synchronized to the global feedback layer to perform control analysis to obtain a second control deviation feature, and the initial control signal is subjected to dynamic correction analysis according to the second control deviation feature to construct a second correction factor; The first correction factor and the second correction factor are subjected to fuzzy reasoning optimization to determine a control signal adjustment amount; The initial control signal is subjected to feedback adjustment according to the control signal adjustment amount to formulate the multi-port collaborative control strategy.

8. A multi-port cooperative control system incorporating a hardware framework structure, characterized by, The system is used to implement the multi-port collaborative control method combined with the hardware framework structure according to any one of claims 1 to 7, and the system comprises: An operating parameter acquisition module is configured to traverse a hardware framework structure activation data acquisition port, acquire real-time data of the power distribution network through the data acquisition port, and obtain an operating parameter data set; A multi-port collaborative analysis module is configured to perform multi-port collaborative analysis based on the operating parameter data set and the hardware framework structure to generate a topology reconstruction instruction; A topology reconstruction module is configured to perform distributed control on the power distribution network according to the topology reconstruction instruction to generate a topology reconstruction result; A collaborative control module is configured to perform collaborative control on the power distribution network based on the topology reconstruction result to formulate a multi-port collaborative control strategy for intelligent operation optimization of the power distribution network.

Images