Primary and secondary fusion ring network box nitrogen insulation integrated control method, system and device

Abstract

The present application relates to the technical field of integrated control, and particularly relates to a primary and secondary fusion ring network box nitrogen insulation integrated control method, system and device. The method comprises the following steps: constructing a multi-interval reference insulation model by reading primary wiring and gas charging parameters; synchronously collecting multi-dimensional state parameters to form a reliable state vector after preprocessing; calculating real-time insulation margin index and instability trend value, generating a control label and a control permission domain according to five-prevention logic and network operation state; solving an optimal segmentation, tie-in and transfer action sequence in the permission domain, and performing time sequence simulation and interlocking check; after execution, the measured state is collected again and compared with simulation estimation, and the insulation margin threshold and control parameters are self-adjusted. The present application solves the technical problem that the existing pure nitrogen insulation ring network cabinet is split into insulation state monitoring and segmentation tie-in and transfer operation, which leads to misoperation and refusal to operate, and realizes safe closed-loop linkage of multi-interval gas insulation equipment state evaluation and power supply control.

Description

Technical Field

[0001] This invention relates to the field of integrated control technology, and in particular to an integrated control method, system and device for nitrogen insulation of a primary and secondary fusion ring network box. Background Technology

[0002] Integrated primary and secondary ring main units (RMUs) are key equipment in urban power distribution networks, industrial parks, and data center power supply systems. By structurally integrating primary switching equipment with secondary measurement and control protection equipment, they achieve compact and intelligent deployment of power distribution networks. With increasingly stringent environmental protection requirements, pure nitrogen-insulated ring main units, which use nitrogen as the insulating medium instead of traditional sulfur hexafluoride gas, are widely used. Their internal multiple compartments are electrically connected via busbars, and the insulation status of each compartment directly determines the operational safety and power supply reliability of the power distribution network.

[0003] In existing technologies, research on pure nitrogen-insulated ring main units mainly focuses on optimizing the cabinet sealing structure, improving mechanical transmission, and configuring single-point nitrogen pressure monitoring devices. A multi-dimensional insulation assessment system integrating nitrogen pressure, cavity temperature and humidity, partial discharge, contact temperature rise, and mechanical movement characteristics has not yet been established. Publicly available primary and secondary integration technologies primarily focus on the physical integration of primary and secondary equipment, communication interface compatibility, and standardized design. A closed-loop coupling mechanism is lacking between secondary control strategies and the real-time insulation status of primary equipment. Existing status monitoring schemes mostly remain at the level of single-variable threshold over-limit alarms or remote transmission and display of status data. The heterogeneous data collected by various sensors lacks time-stamp alignment and reliable fusion processing, making it difficult to form a high-quality status vector supporting precise control decisions. Existing network reconfiguration control methods for distribution network segmentation, interconnection, and transfer primarily generate control permission domains based on five-prevention logic, power flow calculation, or fault isolation requirements. They do not incorporate the real-time insulation margin of each bay as a dynamic constraint into control decisions. This results in the system potentially performing switching and load transfer operations according to conventional logic even when the insulation margin of a certain bay decreases but a hard threshold alarm has not yet been triggered. As a result, a disconnect occurs between control decisions and insulation status, making it easy to apply additional electrical stress to weak insulation gaps, causing malfunctions, failures to operate, or a decrease in equipment reliability, making it difficult to meet the dual requirements of safe operation and power supply continuity for multi-gap gas-insulated equipment under complex working conditions.

[0004] Therefore, there is an urgent need for a method that deeply integrates nitrogen insulation state self-verification with multi-bay collaborative control, so that insulation assessment and network control can form a closed-loop linkage. Summary of the Invention

[0005] This invention provides an integrated control method, system, and device for nitrogen insulation of primary and secondary integrated ring network boxes, which solves the technical problems of disconnect between nitrogen insulation status monitoring and network control strategy and lack of multi-bay collaborative insulation constraints in the prior art.

[0006] In a first aspect, the present invention provides an integrated control method for nitrogen insulation of a primary and secondary fusion ring main unit, the method comprising: S1. Read the primary wiring topology, rated insulation level, initial nitrogen charging pressure, cabinet volume, sensor layout table and secondary port mapping of each bay, and construct a multi-bay reference insulation model. S2. Synchronously collect nitrogen pressure, cavity temperature and humidity, partial discharge, contact temperature rise, mechanism action characteristics and line electrical parameters of each interval, perform preprocessing, and bind them with the corresponding interval in the multi-interval reference insulation model to form a reliable state vector. S3. Calculate the real-time insulation margin index and instability trend value of each interval based on the trusted state vector, and generate executable, restricted or prohibited control tags; generate control permission domains based on the control tags, the five-proof logic and the network operation status. S4. Within the control permission domain, with the minimum power outage load, minimum number of switch actions, and maximum insulation safety margin as optimization objectives, solve for the optimal segmentation, connection, and transfer action sequence; perform timing simulation on the action sequence using the multi-interval reference insulation model, and verify the mechanical interlock, door lock status, grounding switch relationship, and post-transfer current carrying constraints, and output the verified action sequence. S5. Execute the verified action sequence, re-collect the measured state vector, calculate the insulation margin threshold in the tag generation process based on the deviation between the estimated state vector and the measured state vector, and correct the action priority parameters of each interval and the corresponding control parameter library.

[0007] Secondly, the present invention provides an integrated control system for nitrogen insulation of a primary and secondary fusion ring main unit, used to implement the above-mentioned method, the system comprising: The model building unit is used to read the primary wiring topology, rated insulation level, initial nitrogen charging pressure, cabinet volume, sensor layout table and secondary port mapping of each bay, and build a multi-bay reference insulation model. The data acquisition unit is used to synchronously acquire nitrogen pressure, cavity temperature and humidity, partial discharge, contact temperature rise, mechanism action characteristics and line electrical parameters of each interval, perform preprocessing, and bind with the corresponding interval in the multi-interval reference insulation model to form a reliable state vector. The control decision unit is used to calculate the real-time insulation margin index and instability trend value of each interval based on the trusted state vector, and generate executable, restricted or prohibited control tags; and generate control permission domains based on the control tags, the five-prevention logic and the network operation status. The solution verification unit is used to solve the optimal segmentation, connection and transfer action sequence within the control permission domain, with the minimum power outage load, minimum number of switch actions and maximum insulation safety margin as optimization objectives; the action sequence is simulated in time sequence through the multi-interval reference insulation model, and the mechanical interlock, door lock status, grounding switch relationship and post-transfer current carrying constraints are verified, and the action sequence that passes the verification is output. The feedback tuning unit is used to execute the action sequence that has passed the verification, collect the measured state vector, calculate the insulation margin threshold in the self-tuning tag generation process based on the deviation between the estimated state vector and the measured state vector and the insulation margin index, and correct the action priority parameters of each interval and the corresponding control parameter library.

[0008] Thirdly, the present invention provides an integrated control device for nitrogen insulation of a primary and secondary fusion ring network box, the device comprising: a memory and at least one processor, wherein the memory stores instructions; The at least one processor invokes the instructions in the memory to cause the primary and secondary fusion ring network box nitrogen insulation integrated control device to execute the above method.

[0009] The beneficial effects of this invention are as follows: This invention provides a unified reference point for insulation capability in subsequent real-time assessment by establishing a four-layer digital object and a baseline insulation margin matrix. Through defect elimination and cross-source time synchronization, multi-dimensional heterogeneous sensor data is transformed into a reliable state vector, ensuring the data quality and accuracy of the assessment input. Based on the reliable state vector, the real-time insulation margin index and instability trend value are calculated, integrating multi-dimensional parameters such as nitrogen pressure, temperature and humidity, partial discharge, and contact temperature rise into intuitive control labels. Furthermore, these labels are coupled with the five-proof logic and network operation status to generate a control permission domain, enabling insulation status monitoring to leap from passive monitoring to advanced monitoring. To proactively control constraints, the optimal sequence of segmentation, interconnection, and transfer actions is solved within the control permissible domain, with progressive objectives of minimum outage load, minimum number of switching actions, and maximum insulation safety margin. Timing simulation and interlock verification are used to proactively expose risks of mechanical conflicts, current exceeding limits, and insulation margin drops, ensuring the execution scheme holds true within the triple boundaries of topological feasibility, electrical safety, and insulation reliability. By retrieving measured conditions and comparing them with model-predicted closed-loop values, the insulation margin threshold is adaptively adjusted, and the adjustment is transmitted to the bus coupling interval, updating the control parameter library and enabling the system to continuously evolve with equipment aging and environmental changes. Through the synergy of these technical solutions, the safety, power supply continuity, and long-term reliability of the distribution network under complex operating conditions are significantly improved. Attached Figure Description

[0010] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0011] Figure 1 This is a flowchart of the nitrogen insulation integrated control method for the primary and secondary fusion ring network box in the embodiment; Figure 2 This is a comparison chart of the measured and estimated insulation margin index in the example; Figure 3 The diagram shows the structure of the nitrogen-insulated integrated control system for the primary and secondary fusion ring network box in this embodiment. Detailed Implementation

[0012] This invention provides an integrated control method, system, and apparatus for nitrogen insulation of a primary and secondary fusion ring main unit. The terms "first," "second," "third," "fourth," etc. (if applicable) in the specification, claims, and accompanying drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments described herein can be implemented in a sequence other than that illustrated or described herein. Furthermore, the terms "comprising" or "having" and any variations thereof are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0013] For ease of understanding, the specific process of the embodiments of the present invention will be described below, such as... Figure 1 As shown in the figure, an integrated control method for nitrogen insulation of a primary and secondary fusion ring main unit according to an embodiment of the present invention includes: S1. Read the primary wiring topology, rated insulation level, initial nitrogen charging pressure, cabinet volume, sensor layout table, and secondary port mapping for each bay, and construct a multi-bay reference insulation model; specifically including: The integrated controller reads the primary wiring topology, rated insulation level, initial nitrogen charging pressure, cabinet volume, sensor layout table and secondary port mapping of each bay, and establishes the correspondence between primary equipment identifiers and secondary measuring point identifiers; Based on the primary wiring topology and the corresponding relationship, the multi-bay reference insulation model is constructed. The multi-bay reference insulation model includes four layers of digital objects: bay, busbar, load branch, and insulation cavity. Among them, the insulation cavity layer calculates the nitrogen density under standard operating conditions based on the initial nitrogen filling pressure and the cabinet volume, and generates the reference insulation strength value of each bay according to the nonlinear mapping relationship between nitrogen density and insulation strength. The busbar layer generates busbar connection state constraints based on the primary wiring topology. Based on the bus connection status constraints and the reference insulation strength values ​​of each bay, combined with the rated insulation level, a reference insulation margin matrix is ​​output. The reference insulation margin matrix records the insulation capacity margin of each bay relative to the rated insulation level.

[0014] Specifically, in a primary and secondary integrated ring main unit, the nitrogen insulation status of each bay is the fundamental basis for subsequent collaborative control decisions. Therefore, it is necessary to first construct a benchmark model that can accurately reflect the physical attributes and electrical topology of each bay. In this embodiment, a communication connection is established with the intelligent terminals deployed in each bay through the primary and secondary integrated bus. The primary wiring topology is obtained from the primary equipment terminal of each bay, clarifying the busbar assignment and electrical connection method of each bay in the ring main unit, such as single busbar segmented wiring or double busbar wiring. At the same time, the rated insulation level of each bay is read. The rated insulation level is determined according to the voltage level of the ring main unit. The primary equipment terminal refers to the high-voltage side equipment body and its integrated intelligent terminal interface that carries the main power transmission in the bay, including circuit breakers, disconnect switches, contacts, busbars and nitrogen insulation cavities, as well as the status sensing devices integrated on the equipment body. The initial nitrogen filling pressure is read from the gas chamber monitoring terminal. This pressure value is the standard nitrogen pressure filled into the insulation cavity of each bay after factory or maintenance. Force values ​​are typically recorded in megapascals (MPA). Cabinet volume, the physical gas chamber volume of each insulating cavity, is typically recorded in liters (L). This volume, along with the initial nitrogen filling pressure, determines the initial gas density within the cavity. The sensor layout table details the physical installation locations and device numbers of sensors used to monitor pressure, temperature, and humidity within each bay. The secondary port mapping records the specific electrical channels and interface numbers for each sensor signal transmission to the integrated controller, ensuring that subsequently acquired physical quantities can be accurately traced back to their corresponding primary equipment locations. The secondary terminals corresponding to these secondary ports refer to the low-voltage side equipment within the bay used for status monitoring, control, protection, and communication, including sensors, door lock position switches, and measurement and control communication modules. Based on these readings, the integrated controller establishes a correspondence between primary equipment identifiers and secondary measurement point identifiers. This involves binding the physical identifiers of each primary side bay and insulating cavity to the measurement point identifiers of each secondary side sensor, forming a one-to-one index relationship.

[0015] After acquiring the aforementioned basic data and establishing the corresponding relationships, the integrated controller constructs a multi-bay reference insulation model. This multi-bay reference insulation model adopts an object-oriented layered modeling approach, specifically including four layers of digital objects: bay, busbar, load branch, and insulation cavity. Specifically, the bay layer corresponds to each independent gas-insulated switchgear unit within the ring main unit, with each bay object encapsulating its primary wiring attributes, rated insulation level, and associated busbar identifier. The busbar layer corresponds to the common electrical nodes connecting each bay, with each busbar object recording the busbar affiliation and the on / off status of busbar segments or connections based on the primary wiring topology. The load branch layer corresponds to the power supply output paths connecting each bay's outgoing terminals to the user terminals, with each branch object recording its power supply load type and load priority. The insulation cavity layer corresponds to the enclosed nitrogen insulation space within each bay, with each cavity object encapsulating the initial nitrogen filling pressure, cabinet volume, and subsequently calculated reference insulation strength value for that bay. The four layers of objects reference each other through electrical topology associations. Specifically, the bay objects are associated with the bus objects through the bus identifier, the load branch objects through the outgoing line identifier, and the insulation cavity objects through the cavity number, thus forming a complete multi-level digital mirror that reflects the integrated status of the primary and secondary ring main unit.

[0016] For the insulation cavity layer, the integrated controller calculates the nitrogen density under standard operating conditions based on the initial nitrogen filling pressure of each compartment and the cabinet volume. Standard operating conditions refer to a preset reference temperature used to standardize the insulation evaluation of multiple compartments. Specifically, let the standard operating temperature be T. s The initial nitrogen charging pressure is p0, the cabinet volume is V, the ideal gas constant is R, and the molar mass of nitrogen is M. The integrated controller is based on the ideal gas law. The described relationship between gas pressure, volume, temperature, and amount of substance includes the initial nitrogen filling pressure p0, cabinet volume V, and standard operating temperature T for each interval. s Substituting into the equation, we can find the amount of nitrogen gas in that interval. Then, the total mass of nitrogen in the cavity can be calculated. Then divide the total mass m by the cabinet volume V to obtain the nitrogen density value ρ under standard operating conditions. s = m / V = ​​p0M / (RT s ), where p0 is the initial nitrogen filling pressure; V is the cabinet volume; T s R is the standard operating temperature; M is the ideal gas constant; ρ is the molar mass of nitrogen; s The nitrogen density is given by the standard operating condition. If the initial nitrogen charging temperature in each interval is different from the standard operating condition temperature, the initial charging pressure is converted to the equivalent pressure p at the standard operating condition temperature according to the ideal gas law. s ,Right now Where T0 is the initial inflation temperature, with dimensions in K; then the equivalent pressure p after conversion is... s Substituting p0 into the density formula above to ensure the mass conservation relationship holds, we obtain the nitrogen density value ρ under a unified temperature standard. s This eliminates the density incomparability caused by differences in initial inflation temperature. After obtaining the nitrogen density under standard operating conditions, the integrated controller generates a reference insulation strength value for each bay based on a pre-set nonlinear mapping relationship between nitrogen density and insulation strength. This nonlinear mapping relationship is calibrated based on the physical properties of nitrogen insulation medium. Specifically, nitrogen insulation strength increases with increasing gas density, but exhibits a saturation trend in the high-density region. Therefore, a nonlinear function or lookup table is used to characterize the correspondence between density and insulation strength, ensuring that the calculated reference insulation strength value accurately reflects the theoretical insulation capacity of each bay in the initial stage of commissioning.

[0017] For the busbar layer, the integrated controller generates busbar connection state constraints based on the primary wiring topology. Specifically, the controller parses the configuration information of busbar sectionalizing switches and tie switches in the primary wiring topology, encoding the busbar affiliation, busbar sectionalizing position, and busbar tie path of each bay into topology constraints. For example, it records the electrical coupling relationships between bays via buses, as well as the electrical isolation boundaries of each bay under busbar sectionalizing conditions, in the form of adjacency matrices or association matrices. These busbar connection state constraints not only describe static connection relationships but also carry dynamic boundary conditions when the busbar operating mode changes, ensuring that subsequent control permission domain generation and action sequence optimization strictly adhere to the safety boundaries of the actual electrical topology.

[0018] After obtaining the baseline insulation strength value for each bay, the integrated controller, based on the bus connection status constraints and the baseline insulation strength value of each bay, combined with the rated insulation level of each bay, outputs a baseline insulation margin matrix. Specifically, the ratio of the baseline insulation strength value of each bay to the rated insulation level (i.e., the rated insulation strength value) is used as the insulation capacity margin of each bay relative to the rated insulation requirement under initial commissioning conditions. This margin is quantified as a percentage or relative ratio. The rows of the matrix correspond to the bay number, the columns correspond to the insulation assessment dimensions, and the matrix elements are the insulation capacity margins of each bay. Finally, the baseline insulation margin matrix records the insulation capacity margin of each bay relative to the rated insulation level, i.e., the baseline insulation margin index, which serves as the benchmark reference for subsequent real-time insulation margin index calculations. When the real-time status parameters are processed and compared with the benchmark value, the degree of insulation performance degradation of each bay can be accurately assessed.

[0019] The above technical solution not only establishes a reference benchmark for bay status assessment, but also ensures that primary and secondary data are from the same source through the correspondence between primary equipment identifiers and secondary measurement point identifiers, and ensures the accurate characterization of the electrical coupling relationship of multiple bays through bus connection status constraints, thus providing a precise and reliable model foundation for subsequent work.

[0020] S2. Synchronously collect nitrogen pressure, cavity temperature and humidity, partial discharge, contact temperature rise, mechanism action characteristics and line electrical parameters of each interval, perform preprocessing, and bind them with the corresponding interval in the multi-interval reference insulation model to form a reliable state vector. In S2, the credible state vector is formed, including: Based on the secondary port mapping, establish the node index relationship between each sensor acquisition channel and the corresponding interval in the multi-interval reference insulation model, and synchronously acquire the nitrogen pressure, cavity temperature and humidity, partial discharge pulse, contact temperature rise, opening and closing time, current and voltage and door lock status of each interval, and use them as interval status parameters. For the interval state parameters, perform bad point elimination and cross-source time synchronization processing, bind the verified multidimensional state quantities to the corresponding intervals in the multi-interval reference insulation model according to the node index relationship, and output the reliable state vector.

[0021] Specifically, in the primary and secondary integrated ring network box, the nitrogen insulation state of each bay dynamically evolves with changes in the operating environment and load. In order to achieve coordinated control among multiple bays, it is necessary to first establish a spatial binding relationship between the collected data and the benchmark model.

[0022] Based on the aforementioned secondary port mapping, the corresponding information of each sensor signal channel and primary equipment identifier recorded in the integrated controller is read, and a node index relationship is established between each sensor acquisition channel and the corresponding interval in the aforementioned multi-interval reference insulation model. This node index relationship uses the digital object of each interval in the model as the root node, and attaches nitrogen pressure, temperature and humidity, partial discharge, contact temperature, and electrical measurement points to the corresponding interval and insulation cavity sub-nodes, respectively. This ensures that each dimension's original sampled value carries a clear interval attribution identifier at the acquisition source, ensuring that the data for subsequent state assessment and model calculation are from the same source.

[0023] After the node index relationship is established, the nitrogen pressure, cavity temperature and humidity, partial discharge pulse, contact temperature rise, opening and closing time, current and voltage and door lock status of each interval are synchronously acquired under the same hard trigger time base and used as interval status parameters. Specifically, pressure sensors are installed on the walls of the nitrogen insulation chambers in each bay to collect real-time nitrogen pressure and reflect changes in gas density. Temperature and humidity sensors are placed inside the chambers or at the inlet and outlet to simultaneously acquire chamber temperature and humidity data to assess the impact of environmental conditions on nitrogen insulation performance. High-frequency pulse sensors are placed on the surface of the insulation chambers or at busbar connections to capture partial discharge pulses and detect the presence of partial discharge within the insulation. Infrared thermometers are installed near the circuit breaker contacts to monitor contact temperature rise and reflect the electrical connection status and thermal aging degree of the switch contacts. Displacement or time encoders are installed on the circuit breaker operating mechanism to record opening and closing times to assess the mechanical performance of the mechanism. Current and voltage are acquired through the secondary side of the current transformers integrated at the outgoing terminals of each bay to reflect the current electrical load. Position sensors are installed at the cabinet doors and chamber covers of each bay to monitor door lock status and confirm whether the equipment is in a safe locked state. The acquisition of all the above parameters is synchronously initiated by the integrated controller through the primary and secondary fusion bus under the same hard trigger signal, ensuring that the multi-dimensional state quantities of each bay have a unified time reference.

[0024] The aforementioned interval state parameters undergo defect removal and cross-source time synchronization processing. Defect removal is based on the physical rationality boundaries of each parameter, which are derived from the initial nitrogen charging parameters and rated operating conditions of the corresponding interval. For example, data points that clearly violate physical laws, such as a sudden drop in nitrogen pressure to zero or exceeding the pressure limit of the gas chamber, or a contact temperature rise exceeding the melting point of the material, will be directly removed and interpolated using the valid values ​​from the previous moment. Cross-source time synchronization processing addresses the issue of inconsistent timestamps from different acquisition devices by aligning the data streams from each sensor with the unified clock of the integrated controller as the reference. The aforementioned multidimensional state variables, after defect removal and cross-source time synchronization processing, are directly written into the state attribute domains of the corresponding interval and insulation cavity nodes in the aforementioned multi-interval reference insulation model through the aforementioned node index relationship, achieving deep binding between the state variables and the model digital objects, and finally outputting the aforementioned reliable state vector.

[0025] The above technical solution ensures that primary and secondary data are from the same source through node indexing in the primary and secondary integrated ring network box scenario, and ensures data quality and timing consistency through bad point elimination and cross-source time synchronization processing, thereby providing a reliable data foundation for the subsequent calculation of the real-time insulation margin index.

[0026] S3. Calculate the real-time insulation margin index and instability trend value of each interval based on the trusted state vector, and generate executable, restricted or prohibited control tags; generate control permission domains based on the control tags, the five-proof logic and the network operation status. In S3, executable, restricted, or prohibited control tags are generated, including: Extract from the reliable state vector and calculate the pressure-temperature conversion value, humidity degradation coefficient, partial discharge activity and contact heating influence coefficient based on the nitrogen pressure, cavity temperature and humidity, partial discharge pulse and contact temperature rise of each interval. Based on the reference insulation strength value of each interval in the multi-interval reference insulation model, calculate the real-time insulation margin index of each interval. The instability trend value is calculated based on the historical sequence of the insulation margin index corresponding to each interval and the rate of change of current and voltage of the corresponding interval in the reliable state vector. Based on the real-time insulation margin index, the instability trend value, and the bus connection status constraints, generate executable, restricted, or prohibited control tags.

[0027] Specifically, existing monitoring solutions mostly rely on single-parameter limit-exceeding alarms, failing to comprehensively reflect the true insulation status of primary and secondary integrated ring main units under complex operating conditions, and even more so, making it difficult to predict the accelerating trend of insulation degradation. Therefore, the technical problem addressed in this step is how to transform multi-dimensional reliable state vectors into quantitative indicators that comprehensively characterize the insulation status of the bay, predict insulation degradation trends, and generate control tags directly mapped to operating permissions. This allows control decisions to be based on multi-dimensional integrated assessment and dynamic trend prediction, rather than isolated threshold comparisons of single parameters.

[0028] The integrated controller extracts nitrogen pressure, cavity temperature and humidity, partial discharge pulse, and contact temperature rise for each interval from the reliable state vector, serving as the raw data source for calculating the insulation degradation factor. For nitrogen pressure and cavity temperature, the integrated controller calculates the density conversion value. This conversion process is based on the coupling relationship between gas density and pressure / temperature described by the ideal gas law. Since the insulation strength of nitrogen is essentially determined by gas density, and gas density decreases as pressure decreases or temperature increases, directly using the measured pressure for insulation assessment would introduce misjudgments due to temperature fluctuations. Therefore, it is necessary to calculate the actual nitrogen density under the current operating conditions based on the measured nitrogen pressure and measured cavity temperature, and then calculate the ratio of the difference between the theoretical density under standard operating conditions and this actual density to the theoretical density under standard operating conditions, obtaining a dimensionless density conversion value.

[0029] Let the measured nitrogen pressure be p, the measured chamber temperature be T, the molar mass of nitrogen be M, and the ideal gas constant be R. The integrated controller calculates the actual nitrogen density ρ = pM / (RT) under the current operating conditions based on the density expression of the ideal gas equation of state. Let the theoretical density under standard operating conditions be ρ0, which is determined by the standard operating temperature T. sThe initial nitrogen charging pressure p0 for this interval is determined according to the aforementioned ideal gas law. The integrated controller calculates the density conversion value δρ = (ρ0 - ρ) / ρ0, where ρ0 is the theoretical density under standard operating conditions; ρ is the actual density under current operating conditions; and δρ is the density conversion value. This conversion value is zero to indicate that the current gas density is completely consistent with the standard operating conditions, and a positive value indicates that the insulation capacity is deteriorated due to the decrease in gas density. The larger the value, the more severe the deterioration.

[0030] For cavity temperature and humidity, the integrated controller calculates a humidity degradation coefficient. This coefficient characterizes the degree of degradation of nitrogen insulation performance when the cavity ambient humidity exceeds the safe range. Specifically, the integrated controller extracts the measured cavity humidity value from the reliable state vector and compares it with a preset standard operating condition humidity threshold. If the measured humidity is lower than the threshold, the humidity is considered to be within the normal range, and the humidity degradation coefficient is set to zero. If the measured humidity is higher than the threshold, the ratio of the humidity excess to the maximum allowable humidity excess is calculated, and this ratio is used as the humidity degradation coefficient δh. This coefficient ranges from zero to one; a larger value indicates a more severe impact of humidity on insulation degradation. The physical meaning of this coefficient lies in converting the absolute humidity value into a relative degradation ratio relative to the insulation safety boundary, achieving dimensional unification.

[0031] For partial discharge (PD) pulses, the integrated controller calculates the PD activity level. PD activity level is a comprehensive indicator characterizing the activity level of internal insulation defects, based on the frequency and amplitude of PD pulses within a unit time window. Specifically, the integrated controller sets a fixed-length time window, counts the number of PD pulses captured within this window, and weights and accumulates the amplitude of each pulse. Pulses with larger amplitudes pose a higher potential risk of insulation damage and are therefore assigned a higher weight. The weighted accumulation result is used as the raw value of the PD activity level. Subsequently, the integrated controller compares this raw value with a preset historical baseline activity level, normalizes the result to the range of 0 to 1, and obtains the dimensionless PD activity level α.

[0032] Regarding contact temperature rise, the integrated controller calculates the contact heating effect coefficient. This coefficient reflects the degree of thermal degradation of the surrounding nitrogen insulating gas when the switch contact operating temperature exceeds the normal range. Specifically, the contact temperature rise value is extracted from the reliable state vector and compared with the preset upper limit of normal temperature rise. If the contact temperature rise does not exceed the upper limit, the contact heating effect coefficient is zero, indicating that the contact is within the normal heating range and has no additional degradation effect on the insulation. If the contact temperature rise exceeds the upper limit, the ratio of the excess amount to the difference between the rated temperature rise limit and the upper limit of normal temperature rise is calculated. This ratio is used as the contact heating effect coefficient δT, which ranges from 0 to 1. The larger the value, the more significant the thermal degradation effect of contact overheating on the insulation.

[0033] After obtaining the above four degradation factors, the integrated controller performs a weighted fusion calculation. Since the density conversion value δρ, humidity degradation coefficient δh, partial discharge activity α, and contact heating influence coefficient δT have all been converted into dimensionless quantities with values ​​ranging from 0 to 1 through the above normalization process, weighting coefficients are set according to the degree of influence of each degradation factor on the nitrogen insulation performance. The weighting coefficients were set by comprehensively considering the physical characteristics of nitrogen insulation and engineering practice experience: the density conversion value directly reflects the fundamental influence of gas density on insulation strength. Since the benchmark insulation strength value is generated based on the nonlinear mapping from nitrogen density to insulation strength, the local rate of change of this mapping in the standard operating condition neighborhood characterizes the sensitivity of density fluctuations to insulation strength. Therefore, the weighting coefficient of the density conversion value comprehensively carries the local sensitivity of this nonlinear mapping, making the weighted relative density decrease rate equivalent to the relative degradation contribution of insulation strength, and assigning it the highest weight; the partial discharge activity directly characterizes whether there are partial discharge defects inside the insulation. The damage of defects to insulation is direct and irreversible, so it is assigned the second highest weight; the contact heating influence coefficient reflects the gradual degradation of insulation materials by thermal effects. Its influence rate is relatively slow but accumulates continuously, so it is assigned a medium weight; the humidity degradation coefficient reflects the auxiliary influence of environmental intrusion on insulation. In a well-sealed ring main unit, humidity fluctuations are relatively limited, so it is assigned a low weight. Let the weight of density conversion value be w1, the weight of partial discharge activity be w2, the weight of contact heating influence coefficient be w3, and the weight of humidity degradation coefficient be w4. The sum of each weight coefficient is 1, i.e., w1 + w2 + w3 + w4 = 1. And they are sorted in descending order of influence. The specific values ​​are obtained by calibration from equipment type test data and insulation aging statistical laws, and are pre-stored in the parameter library of the integrated controller.

[0034] The integrated controller calculates the real-time insulation margin index for each bay based on the baseline insulation strength values ​​of each bay in the multi-bay reference insulation model, combined with the normalized degradation factors and their weighting coefficients. This index is a percentage indicator representing the bay's insulation capacity relative to the rated insulation level margin under current operating conditions, and its calculation starts from the baseline insulation strength value. Specifically, the integrated controller first calculates the overall degradation degree. The overall degradation degree D is a dimensionless quantity, representing the proportion of insulation performance degradation under current operating conditions relative to standard operating conditions. Subsequently, the integrated controller calculates the equivalent insulation strength value under the current operating conditions based on the reference insulation strength value E0. Where E0 is the baseline insulation strength value, in kV; and E is the equivalent insulation strength value, also in kV. This equivalent insulation strength value characterizes the actual insulation capability of the current space after degradation correction. Finally, the integrated controller divides the equivalent insulation strength value E by the rated insulation level. Multiply by 100% to obtain the real-time insulation margin index. The real-time insulation margin index η represents the percentage of the current insulation capacity relative to the rated insulation level. A higher value indicates a larger insulation margin, while a lower value indicates that the insulation capacity is close to or below the rated safety boundary. When all degradation factors are zero, the overall degradation degree D is zero, and the equivalent insulation strength value E is equal to the reference insulation strength value E0. At this time, the real-time insulation margin index η is consistent with the initial insulation capacity margin recorded in the reference insulation margin matrix, ensuring the consistency of data between the reference model and the real-time assessment.

[0035] After obtaining the real-time insulation margin index, the integrated controller further calculates the instability trend value. Specifically, it retrieves the real-time insulation margin index from the data buffer at each sampling moment within a preset time window, arranging them chronologically to form a historical sequence. A first-order difference operation is performed on this historical sequence to obtain the rate of change sequence of the margin index between adjacent sampling moments. This rate of change sequence reflects the speed and direction of increase and decrease in insulation margin within different time segments. Since the recent rate of change has a stronger predictive effect on the current state than the long-term rate of change, the integrated controller performs a weighted average operation on this rate of change sequence, assigning a larger weight to the recent rate of change and a smaller weight to the long-term rate of change. Each weight is allocated according to the exponential decay law and sums to one, thus obtaining the insulation margin decay rate r. A negative value indicates a decrease in margin, and a positive value indicates a recovery in margin.

[0036] Simultaneously, the integrated controller extracts the current and voltage change rates of the corresponding interval from the reliable state vector. These change rates reflect the dynamic disturbance degree of load fluctuations on contact temperature rise and gas pressure. Using the rated current and rated voltage of the load branch where the interval is located as a benchmark, the ratio of current change to rated current and the ratio of voltage change to rated voltage are calculated respectively. Then, the current ratio and voltage ratio are fused according to preset weights and normalized to obtain the dimensionless load disturbance coefficient change rate β. This change rate has zero as the equilibrium point; a positive value indicates a sudden increase in load, and a negative value indicates a decrease in load. The larger the absolute value, the higher the disturbance intensity.

[0037] The integrated controller integrates the insulation margin decay rate *r* with the load disturbance coefficient change rate *β* for risk trend analysis. The specific integration logic is as follows: when the insulation margin decay rate *r* is negative, it indicates a decrease in the margin, and its risk contribution is the absolute value of the decay rate, |r|⁻, where |r|⁻ represents the absolute value when *r* is negative; the risk contribution is zero when *r* is positive. When the load disturbance coefficient change rate *β* is positive, it indicates a sudden increase in load that significantly burdens the insulation; its risk contribution is β⁺, where β⁺ represents the value when *β* is positive; the risk contribution is zero when *β* is negative. Let the risk weight of the insulation margin decay rate be *w5*, and the risk weight of the load disturbance be *w6*, with *w5* + *w6* = 1. The instability trend value *S* is the weighted sum of the risk contributions of the two factors: *S* = *w5|r|⁻* + *w6β⁺*. This instability trend value *S* is a non-negative dimensionless quantity; a larger value indicates a higher risk of insulation instability, while a value of zero indicates that the current insulation situation is stable or improving.

[0038] The integrated controller generates executable, restricted, or prohibited control tags based on the real-time insulation margin index, instability trend value, and bus connection status constraints. This generation process first determines the initial tag for each bay based on the real-time insulation margin index and instability trend value: when the real-time insulation margin index η of a bay is higher than the first threshold η1 and the instability trend value S is lower than the instability trend threshold S0, it indicates that the current insulation capacity of the bay is sufficient and the degradation trend is gradual, thus generating an executable tag; when the real-time insulation margin index η is lower than the second threshold η2, or the instability trend value S is higher than the instability trend threshold S0 and the margin index η is lower than the first threshold η1, it indicates that the insulation state of the bay is at a dangerous boundary or the risk of instability is imminent, thus generating a prohibited tag; otherwise, a restricted tag is generated. After determining the initial labels, the integrated controller calls the bus connection state constraints in the multi-bay reference insulation model to perform topology correlation verification: if a bay is marked with a prohibited label, then based on the electrical connectivity relationship between that bay and adjacent bays in the bus connection state constraints, the initial labels of adjacent bays directly coupled via the bus are forcibly adjusted to restricted labels to prevent single-bay insulation faults from affecting other bays through bus electrical coupling; if the original initial label of the adjacent bay is already prohibited, then the prohibited label remains unchanged. After topology correlation verification and adjustment, the final executable, restricted, or prohibited control labels are output.

[0039] The above technical solution solves the technical problems in the existing technology where single parameter alarms cannot reflect the overall insulation status, lack trend prediction capabilities, and are disconnected from the control decision and insulation status. It enables the control operation of the primary and secondary integrated ring network box to be based on a reliable foundation of multi-dimensional integrated evaluation and dynamic trend prediction, which significantly reduces the risk of false operation or failure to operate due to insufficient insulation margin or failure to detect instability trends, and improves the safety and reliability of equipment operation under complex working conditions.

[0040] Furthermore, in S3, the control permission domain is generated, including: Read the control tags of each bay, and parse the bus topology association and load power supply path of each bay according to the multi-bay reference insulation model. Combined with the network operation status, couple and map the five-prevention logic with the control tags to establish a bay operation permission matrix. Based on the interval operation permission matrix, with the control tag as a hard constraint, the five-prevention logic as a security constraint, and the network operation status as a topology and timing constraint, a set of opening and closing sequences that satisfy all constraint conditions is generated by traversing the network. Redundancy removal and conflict resolution are performed on the set of opening and closing sequences, and the control permission field is output. The control permission field records each permitted action sequence and its corresponding lower limit of insulation safety margin and load power outage range.

[0041] Specifically, since the aforementioned labels only reflect the insulation status of the bay itself and have not yet been integrated with the network topology, electrical safety rules, and real-time operating status, directly executing control actions based on single-bay labels may still lead to erroneous operations due to neglecting bus coupling relationships, five-proof safety constraints, or network topology limitations. Therefore, based on the control labels of each bay, the bus topology associations and load power supply paths of each bay are analyzed using a multi-bay reference insulation model. The bus layer of the multi-bay reference insulation model records the connection relationships between each bay and the bus and the bus connection status constraints in the form of an adjacency matrix. The load branch layer records the power supply affiliation of the downstream load nodes of each bay and the rated current carrying parameters of the branches. The integrated controller calls upon the bus connection status constraints in this model to analyze the bus grouping to which each bay belongs and the electrical coupling paths formed through the buses, clarifying the energy transfer channels between bays; simultaneously, it traces the power supply range of the loads connected to each bay along the load branch layer, establishing the power supply mapping relationship between bays and load nodes, providing a topological basis for subsequent outage range statistics.

[0042] Based on the network operating status, the integrated controller couples and maps the five-prevention logic with control tags to establish a bay operation permission matrix. The network operating status includes the on / off status of interconnecting switches in each bay, current load current, bus energization status, grounding switch position status, and the action sequence records of each switching device. These statuses are uploaded in real-time and stored in the integrated controller's status buffer. The five-prevention logic is a safety rule system for preventing misoperation in the power system, including five basic constraints: preventing accidental opening and closing of circuit breakers, preventing opening and closing disconnecting switches under load, preventing grounding wires from being connected while energized, preventing closing switches with grounding wires connected, and preventing accidental entry into energized bays. These constraints are pre-installed in the integrated controller as a rule base. Coupling and mapping refers to cross-conditionally binding control tags with the five-prevention logic, allowing insulation status constraints and electrical safety constraints to jointly affect operation permission determination. Specifically, the mapping process involves constructing a two-dimensional matrix structure with each bay as a row and operation type as a column; this matrix is ​​the bay operation permission matrix. For each bay, if its control tag is prohibited, all operation types corresponding to that bay are marked as prohibited from execution. This marking constitutes a hard constraint unaffected by other conditions in the five-prevention logic. If the control tag is restricted, only necessary operation types that do not affect insulation safety are allowed in that bay, such as tripping to disconnect power, while closing to load or transferring power is prohibited. If the control tag is executable, the operation is further verified item by item according to the five-prevention logic to ensure it meets electrical safety conditions. For example, it is verified whether the circuit breaker has been tripped before the disconnecting switch is operated, and whether the line has been de-energized before the grounding switch is closed. Only when all five-prevention logic passes is the operation marked as allowed to execute. Thus, each element of the bay operation permission matrix comprehensively carries the dual constraint information of insulation status and electrical safety, realizing the coupled mapping between control tags and the five-prevention logic.

[0043] After establishing the interval operation permission matrix, the integrated controller, based on this matrix, uses control tags as hard constraints, five-prevention logic as safety constraints, and network operating status as topology and timing constraints to generate a set of opening and closing sequences that satisfy all constraints. Specifically, the current position status of each switch, the energized status of the bus, and the on / off status of tie switches in the network operating status constitute topology constraints, limiting the operation sequence to unfold within the current physical connectivity structure. The timing constraints, along with the operation sequence rules in the five-prevention logic, define the temporal order of opening and closing actions, ensuring the timing feasibility of the operation sequence. The integrated controller uses the current network operating status as the initial state, selects candidate operations from the intervals and operation types marked as allowed in the interval operation permission matrix, and combines them according to the operation order specified by the five-prevention logic to form a candidate action sequence. For each candidate action sequence, the integrated controller simulates the state transition step by step: executing the first operation based on the current topology status, verifying whether the step satisfies the control tag constraints and five-prevention logic constraints, updating the topology status, and then executing the next operation, iterating until the sequence is complete. If any step in the sequence violates the above constraints, the sequence is removed; if all steps satisfy the constraints, the sequence is included in the set of opening and closing sequences. Thus, the traversal process does not exhaustively search all possible combinations, but rather performs a bounded search under the dual guidance of the permission matrix and state transition simulation, ensuring that the generated sequence set satisfies both the hard constraints of insulation safety and the operational boundaries of electrical safety and topological feasibility.

[0044] For the set of opening and closing sequences that have passed the triple constraint verification, the integrated controller further performs redundancy removal and conflict resolution. Redundancy removal refers to identifying and removing functionally equivalent duplicate sequences in the set. Specifically, if two action sequences ultimately lead to the same network topology, have the same outage load range, and similar lower limits of insulation safety margin, but differ in the number of intermediate operation steps, then only the sequence with fewer switching action steps is retained, and the sequences with redundant operation steps are removed to reduce mechanical wear and operational risks in subsequent execution stages. Conflict resolution refers to identifying and removing mutually exclusive candidate sequences in the set. Specifically, if two action sequences require operating the same switchgear to different target states within the same execution period, or require transferring conflicting load combinations through the same busbar, then a resource mutual exclusion conflict exists. The integrated controller sorts the conflicting sequences according to load priority and lower limit of insulation safety margin, prioritizing the retention of sequences involving important loads and with higher insulation safety margins, and removing low-priority sequences from the set of opening and closing sequences. This ensures that there is no resource competition among the permitted action sequences within the control permission domain, and that each sequence has the feasibility of independent execution.

[0045] After redundancy removal and conflict resolution, the integrated controller outputs a control permission domain. The control permission domain is a data structure that records the allowed sequence of actions and their constraint boundaries. It stores all validated action sequences in a set, and attaches an insulation safety margin lower limit and load outage range attribute to each action sequence. The process for determining the insulation safety margin lower limit is as follows: the integrated controller extracts all intervals involved in the action sequence, retrieves the current margin index values ​​of these intervals from the real-time insulation margin index output in step S3, and takes the minimum value as the insulation safety margin lower limit of the action sequence. This lower limit represents the lowest boundary of the insulation state in the intervals involved in the sequence. If this lower limit is lower than a preset threshold, the sequence is not included in the control permission domain. The process of determining the load outage range is as follows: Based on the load branch layer information in the multi-bay reference insulation model, the integrated controller traces the load branches cut off by the tripping operation in the action sequence, and counts the set of load nodes connected to these branches, which is the load outage range of the action sequence. This range is recorded in the control permission domain in the form of a load node identification list, providing a data basis for minimizing the outage load when solving the optimal action sequence in the future.

[0046] The above technical solution solves the technical problems in the prior art where control decisions are disconnected from insulation status and the five-prevention logic is not integrated into insulation margin constraints, which leads to the risk of malfunction. It enables subsequent control actions to be carried out within the permissible boundaries of the triple constraints of insulation safety, electrical safety and topology feasibility, which significantly improves the safety and reliability of control decisions and ensures the safe operation of the primary and secondary integrated ring network box under complex working conditions.

[0047] S4. Within the control permission domain, with the minimum power outage load, minimum number of switch actions, and maximum insulation safety margin as optimization objectives, solve for the optimal segmentation, connection, and transfer action sequence; perform timing simulation on the action sequence using the multi-interval reference insulation model, and verify the mechanical interlock, door lock status, grounding switch relationship, and post-transfer current carrying constraints, and output the verified action sequence. In S4, solving for the optimal segmentation, communication, and transfer action sequence includes: Within the control permission domain, based on the control tags of each bay and the load power supply path in the multi-bay reference insulation model, the segment combination is traversed with the minimum power outage load as the target. The loads carried by the prohibited tag bays and the restricted tag bays coupled with their busbars are included in the power outage range, and the segment scheme with the minimum power outage load is selected. Based on the segmentation scheme and the status of the tie switches in the control permission domain, the tie path is determined with the goal of minimizing the number of switch actions, and a backup electrical path is established between each power supply area after segmentation. The real-time insulation margin index of each interval on the tie path is verified to be lower than the lower limit of the insulation safety margin. Based on the segmentation scheme and the connection path, the transfer combination is optimized with the goal of maximizing the insulation safety margin. The impact of the load current change of each interval on the contact temperature rise after the transfer is estimated according to the multi-interval reference insulation model. The real-time insulation margin index after the transfer is calculated, and the optimal segmentation, connection and transfer action sequence is output.

[0048] Specifically, the aforementioned control permission domain records the permissible action sequences that satisfy the triple constraints of insulation safety, electrical safety, and topology feasibility, along with their lower limit of insulation safety margin and load outage range. However, the control permission domain typically contains multiple feasible action sequences. Directly selecting and executing these sequences randomly may still lead to excessive outage load, too many switching operations, or insufficient insulation safety margin after power transfer, failing to balance power supply reliability and equipment safety. Therefore, within the control permission domain, based on the control tags of each bay and the load power supply path in the multi-bay reference insulation model, the segment combinations are traversed with the minimum outage load as the target. The load branch layer of the multi-bay reference insulation model has been constructed in step S1, recording the power supply attribution relationship of downstream load nodes in each bay and the rated current carrying parameters of the branches. The segment attributes, i.e., tag attributes, of each permissible action sequence in the control permission domain are called to identify the bays involved in the tripping operation in each sequence, and the power supply range of the loads carried by these bays is traced based on the load branch layer. For each candidate segmentation scheme, the integrated controller performs outage range statistics: First, all loads carried by bays with the control tag "prohibited" are included in the outage range. This inclusion is based on the hard constraint attribute of the control tag, which states that bays with the "prohibited" tag must not be energized under any circumstances. Second, based on the bus connection status constraint analysis and the restricted tag bays that are directly electrically coupled to the prohibited tag bays via the bus, the loads carried by these restricted tag bays coupled to the bus are also included in the outage range. This inclusion is based on the safety redundancy principle of bus topology association to prevent insulation faults in prohibited bays from being conducted to coupled restricted bays via the bus. Finally, the loads of the load branches cut off by the executable tag bays that are actively tripped in the segmentation scheme are included in the outage range. This inclusion reflects the power interruption caused by the segmentation operation itself. The integrated controller sums and compares the outage loads of each candidate segmentation scheme, using the minimum total outage load as the screening criterion to determine the segmentation scheme with the minimum outage load. For example, assuming the ring main unit includes bays one through four, bay two is marked as prohibited due to decreased nitrogen density and active partial discharge, bay three is marked as restricted due to direct coupling with the busbar of bay two, and bays one and four are marked as executable. The integrated controller retrieves candidate segmentation schemes from the control permission domain: Scheme one only trips bay two, and Scheme two trips the main incoming circuit breaker of bay one to isolate the entire busbar. Based on the load branch layer in the multi-bay reference insulation model, the outage range of Scheme one includes the load of bay two itself and the load of bay three coupled to the busbar, while bays one and four remain powered; the outage range of Scheme two covers all the loads of bays one through four. After comparing the total outage load, Scheme one only involves the power outage of two bays, and its outage load is significantly less than the total busbar power loss of Scheme two. Therefore, the integrated controller selects Scheme one as the segmentation scheme with the minimum outage load.

[0049] After determining the segmentation scheme, the integrated controller, based on the segmentation scheme and the status of tie switches in the control permission domain, determines the tie path with the goal of minimizing the number of switch actions. The control permission domain records the tie switches involved in each permitted action sequence and their current on / off status. The integrated controller analyzes the power supply area division formed after the segmentation scheme is executed, identifies whether there is electrical isolation between the power supply areas, and for areas with isolation, extracts the set of operable tie switches from the control permission domain, and evaluates the number of switch action steps required to establish a backup electrical path by closing these tie switches one by one. For example, after the ring main unit is segmented, it forms two independent power supply areas. Bus A remains energized, while bus B is de-energized. A backup electrical path can be established between the two through tie switch bay three. The integrated controller extracts candidate tie paths from the control permission domain: Path one only requires a single-step operation of closing tie switch bay three to connect the two areas; Path two requires sequential operation of bus segment bay one, load transfer bay four, and the end tie switch, involving multiple opening and closing actions. The number of switch actions includes the closing operation of the tie switch itself and the operation of the preceding disconnecting switch required to establish the path. Based on the bus topology association in the multi-bay reference insulation model, the integrated controller calculates the total number of switch action steps for each candidate tie path and selects the path with the fewest action steps as the tie path. After determining the tie path, the integrated controller verifies whether the real-time insulation margin index of each bay on the tie path is not lower than the lower limit of the insulation safety margin recorded in the action sequence in the control permission domain. The verification process is as follows: extract all the intervals traversed by the connection path, retrieve the current margin index values ​​of these intervals from the real-time insulation margin index output in step S3, compare the minimum value with the lower limit of the insulation safety margin, if the minimum value is not lower than the lower limit, the verification passes, indicating that the connection path has a safe basis for carrying the transferred load in terms of insulation status; if the minimum value is lower than the lower limit, the verification fails, the integrated controller returns to the connection path selection stage, selects the candidate connection path with the second-best number of action steps and re-verifies, until a connection path that meets the lower limit of the insulation safety margin is found.

[0050] After determining the segmentation scheme and connection paths, the integrated controller optimizes the load transfer combination based on both, aiming for the maximum insulation safety margin. A load transfer combination refers to a load allocation scheme that transfers part or all of the load from the segmented power-loss area to other power supply areas via connection paths. Based on the power supply area divisions formed by the segmentation scheme and the backup electrical paths established by the connection paths, the integrated controller enumerates all feasible load allocation schemes for transferring the load from the power-loss area to other power supply areas, forming a candidate load transfer combination set; each candidate load transfer combination specifies the specific load branch to be transferred and its target power supply area.

[0051] The integrated controller estimates the impact of load current changes on contact temperature rise in each bay after the transfer of power based on a multi-bay reference insulation model, and then calculates the real-time insulation margin index after the transfer. The specific estimation process is as follows: the rated current-carrying parameters of each transferred load branch are extracted from the load branch layer in the multi-bay reference insulation model, and the measured load current in the current line electrical parameters are combined to calculate the new load current value of each receiving bay after the transfer; based on the Joule thermal coupling relationship between contact temperature rise and load current, the expected temperature rise value of each receiving bay contact is estimated from the new load current value. This estimation is based on the balance between heat generation power and heat dissipation represented by the product of current square and resistance. The expected temperature rise value is substituted into the contact heating influence coefficient calculation method in step S3 to obtain the contact heating influence coefficient after the transfer; the contact heating influence coefficient after the transfer, together with the unchanged density conversion value, humidity degradation coefficient and partial discharge activity, are substituted into the real-time insulation margin index calculation formula in step S3 to recalculate the real-time insulation margin index of each receiving bay under the transfer condition. The integrated controller iterates through all candidate transfer combinations, calculates the real-time insulation margin index of all relevant intervals under each combination, takes the minimum value as the insulation safety margin of the transfer combination, and selects the transfer combination that maximizes the minimum insulation margin of the system after transfer with the goal of maximizing this margin. Finally, the integrated controller integrates the determined segmentation scheme, connection path, and transfer combination into the optimal segmentation, connection, and transfer action sequence and outputs it.

[0052] The above technical solution solves the technical problems of excessive power outage range, redundant operation steps, and insufficient insulation margin after power transfer caused by the indiscriminate execution of multiple sequences within the control permission domain in the existing technology. It enables the integrated primary and secondary ring network box to minimize the impact on power supply and maximize the insulation margin of equipment while ensuring safety when facing insulation degradation or load adjustment requirements, which significantly improves the economy and reliability of distribution network operation.

[0053] Furthermore, in S4, the output of the validated action sequence includes: Based on the five-prevention logic and the bus connection state constraints in the multi-bay reference insulation model, the action sequence is subjected to timing simulation. The mechanical interlocks, door lock states and grounding switch relationships of each step are verified in the order of operation, and the real-time insulation margin index changes of each bay after each step is executed are deduced. For the action sequence simulated by time sequence, based on the rated current of the load branch layer in the multi-interval reference insulation model and the line electrical parameters, the current carrying constraints of each interval after the transfer are verified, and based on the real-time insulation margin index after the deduction, the insulation status of each interval after the transfer is verified to meet the operation permission level corresponding to the control tag. The action sequences that pass the current-carrying constraint verification and the insulation state verification are output as verified action sequences; for action sequences that fail the verification, a backoff instruction is generated and fed back to the control permission domain to trigger the re-solution of the optimal segmentation, connection and transfer action sequences within the control permission domain.

[0054] Specifically, the optimal sequence of segmentation, interconnection, and transfer actions has been solved using the integrated controller. This sequence is formed under the objectives of minimizing power outage load, minimizing the number of switch actions, and maximizing insulation safety margin. However, this sequence is only optimized based on static constraints within the control permission domain and has not yet undergone dynamic verification at the operation execution level. If the action timing is inappropriate or the operating conditions change abruptly after transfer, mechanical interlock conflicts, current exceeding limits, or insulation margin falling below the safety boundary may still be triggered. Therefore, timing simulation is performed on the action sequence based on the five-prevention logic and the bus connection state constraints in the multi-bay reference insulation model. Timing simulation is a state deduction method based on discrete event progression. It takes the current network operating state as the initial state, and each step operation in the action sequence as a discrete event, driving state transition step by step according to the operation sequence, and verifying constraint satisfaction before each transition. The integrated controller reads the bus connection state constraints in the multi-bay reference insulation model to clarify the electrical connectivity between bays and the bus segmentation state, as the topological boundary conditions for timing simulation; at the same time, it reads the pre-set five-prevention logic rule library as the safety rule boundary for timing simulation.

[0055] The integrated controller executes step-by-step simulations sequentially according to the operation order. For each operation, the safety execution conditions are first verified: based on the interlocking relationship between circuit breakers and disconnectors in the five-prevention logic, it verifies whether the upstream associated switch of the switch to be operated is in the target state that allows this step to be operated. For example, it verifies whether the corresponding circuit breaker is in the open state before the disconnector is opened. If the prerequisite is not met, the mechanical interlock verification is deemed to have failed. The door lock sensor signal of the compartment to be operated is read to confirm that the equipment cabinet door is in the locked state to prevent personnel from accidentally entering the energized compartment during operation. Based on the interlocking rules between grounding switches, circuit breakers, and disconnectors in the five-prevention logic, it verifies whether the position status of the switch to be operated and the grounding switch meet the constraints such as the grounding switch being open before energized closing and the line being de-energized before grounding closing. If any safety verification fails, the timing simulation of this action sequence is terminated, and the timing simulation is deemed to have failed.

[0056] After each operation's safety verification is passed, the integrated controller executes that operation and extrapolates the real-time insulation margin index changes of the relevant bays after each step. This extrapolation process is based on the real-time insulation margin index calculation model in step S3, using the updated load current distribution after each operation as new input conditions to recalculate the contact heating influence coefficient for bays directly involved in or affected by that operation. Density conversion values, humidity degradation coefficients, and partial discharge activity are considered constant within the short-time operation scale of opening and closing. Specifically, when an opening operation disconnects the load of a bay, the load current of that bay drops to zero, and the expected contact temperature rise decreases accordingly. The integrated controller recalculates the contact heating influence coefficient based on the updated contact temperature rise value. When a closing operation introduces new load into a bay through the connection path, the load current of that bay increases, and the expected contact temperature rise rises. The integrated controller recalculates the contact heating influence coefficient accordingly. For bays not directly involved in the current operation step and not affected by load transfer, their contact heating influence coefficient remains unchanged. The integrated controller combines the updated contact heating influence coefficient with the unchanged density conversion value, humidity degradation coefficient, and partial discharge activity into the real-time insulation margin index calculation formula in step S3, and recalculates the inferred real-time insulation margin index of the relevant interval after the operation. Thus, the inferred margin index sequence after each step is obtained in the order of operation. This sequence reflects the dynamic evolution trajectory of the insulation state of each interval during the execution of the action sequence.

[0057] For the action sequences that pass the timing simulation, the integrated controller further verifies the current-carrying constraints of each bay after the power transfer. Based on the rated current parameters of the load branch layer in the multi-bay reference insulation model, the integrated controller extracts the rated current upper limit value of each load branch. At the same time, it reads the actual load current value of each bay after the power transfer obtained from the timing simulation and compares the actual load current value with the rated current upper limit value of the corresponding branch. If the actual load current of all bays does not exceed the rated current upper limit, the current-carrying constraint verification is passed; if the actual load current of any bay exceeds the rated upper limit, it is determined that the action sequence will cause line overload, and the current-carrying constraint verification fails.

[0058] Based on the successful verification of current-carrying constraints, the integrated controller verifies whether the insulation status of each bay after the transfer meets the operation permission level corresponding to the control tag, according to the deduced real-time insulation margin index. Specifically, the integrated controller retrieves the final margin index of each bay obtained from the timing simulation and compares it with the first and second thresholds used in step S3 to generate the control tag: if the deduced margin index is higher than the first threshold, it is determined that the operation permission level corresponding to the executable tag is met; if it is between the first and second thresholds, it is determined that the operation permission level corresponding to the restricted tag is met; if it is lower than the second threshold, it is determined that no operation permission level is met, and the insulation status verification fails. Only when the deduced margin indices of all bays meet the minimum operation permission level required by the action sequence in which they are located, is the insulation status verification of that action sequence passed.

[0059] The action sequences that pass the current-carrying constraint verification and insulation status verification are output as verified action sequences, which then proceed to the next execution stage. For action sequences that fail verification, the integrated controller generates a backoff instruction and feeds it back to the control permission domain to trigger a re-solution of the optimal segmentation, interconnection, and transfer action sequences within the control permission domain. The backoff instruction is a control signal that marks the action sequence as invalid and requests re-optimization. It carries the specific constraint type that failed verification and the identifier of the involved interval. After being fed back to the control permission domain, the integrated controller removes the action sequence from the current permission domain based on the backoff information, or adjusts the lower limit of the insulation safety margin and action priority parameters of the intervals involved in the sequence, narrows the search space, and restarts the solution process in step S5 until all verified action sequences are obtained.

[0060] The above technical solution establishes a complete virtual verification environment before the execution of the action sequence through the timing simulation and multi-constraint verification process. By gradually simulating the network state evolution after the operation, potential risks such as mechanical interlock conflicts, current exceeding limits, and insulation margin drops are exposed in advance, avoiding equipment damage or safety accidents that may result from directly executing unverified action sequences. The rollback and re-solution mechanism enables the system to have adaptive correction capabilities. When the static optimization result does not match the dynamic execution conditions, it can automatically roll back to the control permission domain and adjust the constraint boundaries to re-optimize, ensuring that the final output action sequence meets the nitrogen insulation integrated control requirements of the primary and secondary fused ring network box in terms of insulation safety, electrical safety, mechanical safety, and topology feasibility. This significantly improves the reliability and executability of control decisions under complex operating conditions.

[0061] S5. Execute the verified action sequence, re-sample the measured state vector, calculate the insulation margin threshold during tag generation based on the deviation between the estimated and measured state vectors and the insulation margin index, and correct the action priority parameters and corresponding control parameter libraries for each bay; specifically including: Execute the verified action sequence, and collect the actual opening and closing status of each bay, line electrical parameters, nitrogen pressure, cavity temperature and humidity, partial discharge pulse, and contact temperature rise according to the node index relationship to form a measured state vector. The measured state vector is compared with the estimated state vector derived from the multi-segment reference insulation model before the execution of the action sequence. The estimated deviation of the insulation margin index corresponding to each segment is calculated. The insulation margin threshold of each segment in the multi-segment reference insulation model is corrected according to the estimated deviation. The correction amount is then transferred to the bus coupling segment according to the bus connection state constraint. The action priority parameters of each interval are updated according to the corrected insulation margin threshold, and a control parameter library is output. The control parameter library is used to update the boundary conditions of the control permission domain and the solution weights of the optimal segmentation, connection and transfer action sequence.

[0062] Specifically, the integrated controller has output a validated action sequence, but this sequence is based on preset model parameters. If equipment aging or environmental changes cause the model to deviate from reality, long-term lack of correction will lead to misjudgment of control labels and distortion of control permissible domain boundaries. Therefore, by comparing the measured state vector with the estimated state vector, specifically calculating the estimated load current distribution of each interval according to the power transfer combination scheme, estimating the estimated contact temperature rise using the Joule heating formula, and estimating the estimated nitrogen pressure using the gas heating effect and the ideal gas law, the cavity temperature and humidity and partial discharge pulse are considered constant within the short-time operation scale of opening and closing, thus forming the above-mentioned estimated state vector. The integrated controller compares each dimension of the two vectors item by item. Differences in opening and closing are used to verify execution effectiveness, and differences in line electrical parameters are recorded as load fluctuations. Based on the real-time insulation margin index calculation method in step S3, using nitrogen pressure, temperature and humidity, partial discharge, and contact temperature rise, the measured and estimated real-time insulation margin indices are calculated separately. The difference between the two is used as the estimated deviation of the insulation margin index for each bay. This deviation, expressed as a percentage, represents the direction and degree of deviation between the measured margin and the estimated margin. A positive value indicates that the measured margin is higher than the estimated margin, and a negative value indicates that the measured margin is lower than the estimated margin. Figure 2 As shown, the deviation distribution between the measured state and the estimated state is illustrated, providing a basis for the self-tuning insulation margin threshold.

[0063] The integrated controller adjusts the insulation margin threshold based on the estimated deviation. This threshold refers to the first threshold used in step S3 to distinguish between executable and restricted states, and the second threshold used to distinguish between restricted and prohibited states. The integrated controller compares the absolute value of the estimated deviation with the model's confidence tolerance, which is determined based on the root mean square error of the historical estimated error statistical distribution. If the absolute value does not exceed the tolerance, the first and second thresholds are deemed reliable, and no correction is made. If they exceed the tolerance, the correction amount is calculated based on the sign and magnitude of the deviation. When the deviation is negative, it indicates that the measured margin is lower than the estimate, and the actual degradation is higher than expected. In this case, the first and second thresholds are simultaneously lowered by an amount equal to the absolute value of the deviation multiplied by a correction ratio coefficient. This coefficient is determined by the regression relationship between the deviation and the threshold adjustment effect in historical feedback data, and its value range is 0 to 1, so that the control tag triggers the restricted or prohibited state earlier. When the deviation is positive, it indicates that the estimate is conservative, so the thresholds are simultaneously raised by an amount equal to the absolute value multiplied by the correction ratio coefficient, avoiding unnecessary overprotection that leads to overly strict operational restrictions.

[0064] After correcting the threshold values ​​for each bay, the integrated controller transmits the correction amount to the bus-coupled bays based on the bus connection state constraints. The bus connection state constraints record the electrical connectivity relationships of each bay using an adjacency matrix. The integrated controller reads adjacent bays that have a valid connection with the bay being corrected, identifies the bus-coupled bays, and allocates the correction amount based on the coupling strength coefficient. The coupling strength coefficient is determined by the connection impedance and electrical distance in the bus topology; bays directly connected via the bus and with a shorter electrical distance receive higher values, ranging from zero to one. The bus-coupled bays receive the allocated correction amount and add it to the original threshold value, achieving coordinated correction of multiple bay thresholds and ensuring that the control boundaries of bus-related equipment synchronously adapt to changes in operating conditions.

[0065] Based on the corrected thresholds, the integrated controller updates the action priority parameters for each bay. These parameters are used for weighting the generation of the control permitting domain and solving the action sequence, and are updated based on the proximity of the corrected thresholds to the current real-time insulation margin index. The integrated controller calculates the difference between the current margin index of each bay and the corrected first and second thresholds. Smaller differences indicate proximity to the degradation boundary, and the priority parameter is correspondingly lowered, reducing the control permitting domain's reliance on active operation of that bay. Bays with larger differences maintain or appropriately increase their priority. After updating the priority parameters for each bay, the integrated controller integrates them with the corrected thresholds, coupling strength coefficients, and historical execution records into a control parameter library. This library is used in subsequent control loops to update the boundary conditions of the control permitting domain and the weight allocation for solving the optimal action sequence, ensuring that control decisions continuously adapt to the evolution of the actual insulation characteristics of the equipment.

[0066] The aforementioned execution feedback self-tuning process quantifies model prediction deviation and adaptively corrects thresholds through closed-loop comparison of measured and predicted state vectors, ensuring that the control label generation boundary always aligns with the actual insulation characteristics of the equipment. The correction quantity propagation driven by bus connection state constraints enables the co-evolution of multi-interval thresholds, avoiding the fragmentation of bus-related equipment control boundaries caused by single-interval corrections. Dynamic updates to the control parameter library allow the generation of the control permission domain and the solution of action sequences to continuously absorb execution feedback, solving the problem of fixed control model parameters and inability to adapt to equipment aging and environmental changes in existing technologies. This gives the nitrogen insulation integrated control method for primary and secondary integrated ring main units self-checking and self-evolving capabilities, significantly improving the accuracy and reliability of control decisions under long-term operating conditions.

[0067] This invention also provides an integrated control system for nitrogen insulation of a primary and secondary fusion ring main unit, used to implement the above-mentioned method, such as... Figure 3 As shown, the system includes: The model building unit is used to read the primary wiring topology, rated insulation level, initial nitrogen charging pressure, cabinet volume, sensor layout table and secondary port mapping of each bay, and build a multi-bay reference insulation model. The data acquisition unit is used to synchronously acquire nitrogen pressure, cavity temperature and humidity, partial discharge, contact temperature rise, mechanism action characteristics and line electrical parameters of each interval, perform preprocessing, and bind with the corresponding interval in the multi-interval reference insulation model to form a reliable state vector. The control decision unit is used to calculate the real-time insulation margin index and instability trend value of each interval based on the trusted state vector, and generate executable, restricted or prohibited control tags; and generate control permission domains based on the control tags, the five-prevention logic and the network operation status. The solution verification unit is used to solve the optimal segmentation, connection and transfer action sequence within the control permission domain, with the minimum power outage load, minimum number of switch actions and maximum insulation safety margin as optimization objectives; the action sequence is simulated in time sequence through the multi-interval reference insulation model, and the mechanical interlock, door lock status, grounding switch relationship and post-transfer current carrying constraints are verified, and the action sequence that passes the verification is output. The feedback tuning unit is used to execute the action sequence that has passed the verification, collect the measured state vector, calculate the insulation margin threshold in the self-tuning tag generation process based on the deviation between the estimated state vector and the measured state vector and the insulation margin index, and correct the action priority parameters of each interval and the corresponding control parameter library.

[0068] The present invention also provides an integrated control device for nitrogen insulation of a primary and secondary fusion ring network box, the device comprising: a memory and at least one processor, wherein the memory stores instructions; the at least one processor invokes the instructions in the memory to cause the integrated control device for nitrogen insulation of the primary and secondary fusion ring network box to execute the above-described method.

[0069] In summary, this invention provides a unified reference point for insulation capability in subsequent real-time assessments by establishing a four-layer digital object and a baseline insulation margin matrix. Through defect elimination and cross-source time synchronization, multi-dimensional heterogeneous sensor data is transformed into reliable state vectors, ensuring the quality and accuracy of the assessment input data. Based on these reliable state vectors, the real-time insulation margin index and instability trend value are calculated, integrating multi-dimensional parameters such as nitrogen pressure, temperature and humidity, partial discharge, and contact temperature rise into intuitive control labels. Furthermore, these labels are coupled with the five-prevention logic and network operation status to generate a control permission domain, enabling the insulation status to transition from passive monitoring to dynamic monitoring. The system is upgraded to an active control constraint. Within the control permissible domain, the optimal sequence of segmentation, interconnection, and transfer actions is solved with progressive objectives of minimum outage load, minimum number of switching actions, and maximum insulation safety margin. Timing simulation and interlock verification are used to proactively expose risks of mechanical conflicts, current exceeding limits, and insulation margin drops, ensuring the execution scheme holds true within the triple boundaries of topological feasibility, electrical safety, and insulation reliability. By retrieving measured conditions and comparing them with model-predicted closed-loop values, the insulation margin threshold is adaptively adjusted and the adjustment is transmitted to the bus coupling interval, updating the control parameter library and enabling the system to continuously evolve with equipment aging and environmental changes. Through the synergy of these technical solutions, the safety, power supply continuity, and long-term reliability of the distribution network under complex operating conditions are significantly improved.

[0070] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0071] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0072] The above-described embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for integrated control of nitrogen insulation in a primary and secondary fusion ring main unit, characterized in that, The method includes: S1. Read the primary wiring topology, rated insulation level, initial nitrogen charging pressure, cabinet volume, sensor layout table and secondary port mapping of each bay, and construct a multi-bay reference insulation model. S2. Synchronously collect nitrogen pressure, cavity temperature and humidity, partial discharge, contact temperature rise, mechanism action characteristics and line electrical parameters of each interval, perform preprocessing, and bind them with the corresponding interval in the multi-interval reference insulation model to form a reliable state vector. S3. Calculate the real-time insulation margin index and instability trend value of each interval based on the trusted state vector, and generate executable, restricted or prohibited control tags; generate control permission domains based on the control tags, the five-proof logic and the network operation status. S4. Within the control permission domain, with the minimum power outage load, minimum number of switch actions, and maximum insulation safety margin as optimization objectives, solve for the optimal segmentation, connection, and transfer action sequence; perform timing simulation on the action sequence using the multi-interval reference insulation model, and verify the mechanical interlock, door lock status, grounding switch relationship, and post-transfer current carrying constraints, and output the verified action sequence. S5. Execute the verified action sequence, re-collect the measured state vector, calculate the insulation margin threshold in the tag generation process based on the deviation between the estimated state vector and the measured state vector, and correct the action priority parameters of each interval and the corresponding control parameter library.

2. The method according to claim 1, characterized in that, In S1, a multi-interval reference insulation model is constructed, including: Read the primary wiring topology, rated insulation level, initial nitrogen charging pressure, cabinet volume, sensor layout table and secondary port mapping for each interval, and establish the correspondence between primary equipment identifiers and secondary measuring point identifiers; Based on the primary wiring topology and the corresponding relationship, the multi-bay reference insulation model is constructed. The multi-bay reference insulation model includes four layers of digital objects: bay, busbar, load branch, and insulation cavity. Among them, the insulation cavity layer calculates the nitrogen density under standard operating conditions based on the initial nitrogen filling pressure and the cabinet volume, and generates the reference insulation strength value of each bay according to the nonlinear mapping relationship between nitrogen density and insulation strength. The busbar layer generates busbar connection state constraints based on the primary wiring topology. Based on the bus connection status constraints and the reference insulation strength values ​​of each bay, combined with the rated insulation level, a reference insulation margin matrix is ​​output. The reference insulation margin matrix records the insulation capacity margin of each bay relative to the rated insulation level.

3. The method according to claim 1, characterized in that, In S2, a reliable state vector is formed, including: Based on the secondary port mapping, a node index relationship is established between each sensor acquisition channel and the corresponding interval in the multi-interval reference insulation model. The nitrogen pressure, cavity temperature and humidity, partial discharge pulse, contact temperature rise, opening and closing time, current and voltage, and door lock status of each interval are acquired synchronously and used as interval state parameters. For the interval state parameters, bad point elimination and cross-source time synchronization processing are performed. The verified multidimensional state quantities are bound to the corresponding interval in the multi-interval reference insulation model according to the node index relationship, and the reliable state vector is output.

4. The method according to claim 1, characterized in that, In S3, executable, restricted, or prohibited control tags are generated, including: The controller extracts from the trusted state vector and calculates the pressure-temperature conversion value, humidity degradation coefficient, partial discharge activity and contact heating influence coefficient based on the nitrogen pressure, cavity temperature and humidity, partial discharge pulse and contact temperature rise of each interval. It also calculates the real-time insulation margin index of each interval based on the reference insulation strength value of each interval in the multi-interval reference insulation model. The instability trend value is calculated based on the historical sequence of the insulation margin index corresponding to each interval and the rate of change of current and voltage of the corresponding interval in the reliable state vector. Based on the real-time insulation margin index, the instability trend value, and the bus connection status constraints, generate executable, restricted, or prohibited control tags.

5. The method according to claim 1, characterized in that, In S3, control permission domains are generated, including: Read the control tags of each bay, and parse the bus topology association and load power supply path of each bay according to the multi-bay reference insulation model. Combined with the network operation status, couple and map the five-prevention logic with the control tags to establish a bay operation permission matrix. Based on the interval operation permission matrix, with the control tag as a hard constraint, the five-prevention logic as a security constraint, and the network operation status as a topology and timing constraint, a set of opening and closing sequences that satisfy all constraint conditions is generated by traversing the network. Redundancy removal and conflict resolution are performed on the set of opening and closing sequences, and the control permission field is output. The control permission field records each permitted action sequence and its corresponding lower limit of insulation safety margin and load power outage range.

6. The method according to claim 1, characterized in that, In S4, solving for the optimal segmentation, communication, and transfer action sequence includes: Within the control permission domain, based on the control tags of each bay and the load power supply path in the multi-bay reference insulation model, the segment combination is traversed with the minimum power outage load as the target. The loads carried by the prohibited tag bays and the restricted tag bays coupled with their busbars are included in the power outage range, and the segment scheme with the minimum power outage load is selected. Based on the segmentation scheme and the status of the tie switches in the control permission domain, the tie path is determined with the goal of minimizing the number of switch actions, and a backup electrical path is established between each power supply area after segmentation. The real-time insulation margin index of each interval on the tie path is verified to be lower than the lower limit of the insulation safety margin. Based on the segmentation scheme and the connection path, the transfer combination is optimized with the goal of maximizing the insulation safety margin. The impact of the load current change of each interval on the contact temperature rise after the transfer is estimated according to the multi-interval reference insulation model. The real-time insulation margin index after the transfer is calculated, and the optimal segmentation, connection and transfer action sequence is output.

7. The method according to claim 1, characterized in that, In S5, the output of the validated action sequence includes: Based on the five-prevention logic and the bus connection state constraints in the multi-bay reference insulation model, the action sequence is subjected to timing simulation. The mechanical interlocks, door lock states and grounding switch relationships of each step are verified in the order of operation, and the changes in the real-time insulation margin index of each bay after each step is executed are deduced. For the action sequence simulated by time sequence, based on the rated current of the load branch layer in the multi-interval reference insulation model and the line electrical parameters, the current carrying constraints of each interval after the transfer are verified, and based on the real-time insulation margin index after the deduction, the insulation status of each interval after the transfer is verified to meet the operation permission level corresponding to the control tag. The action sequence that passes the current-carrying constraint verification and the insulation state verification is output as the verified action sequence; for the action sequence that fails the verification, a backoff instruction is generated and fed back to the control permission domain to trigger the re-solution of the optimal segmentation, connection and transfer action sequence in the control permission domain.

8. The method according to claim 1, characterized in that, In S5, the self-tuning insulation margin threshold and control parameters include: Execute the verified action sequence, and collect the actual opening and closing status of each bay, line electrical parameters, nitrogen pressure, cavity temperature and humidity, partial discharge pulse, and contact temperature rise according to the node index relationship to form a measured state vector. The measured state vector and the estimated state vector derived from the multi-interval reference insulation model before the execution of the action sequence are used to calculate the estimated deviation of each interval. The insulation margin threshold in the process of generating control tags is corrected according to the estimated deviation, and the correction amount is transmitted to the bus coupling interval according to the bus connection state constraint. The action priority parameters of each interval are updated according to the corrected insulation margin threshold, and a control parameter library is output. The control parameter library is used to update the boundary conditions of the control permission domain and the solution weights of the optimal segmentation, connection and transfer action sequence.

9. A primary and secondary integrated ring main unit nitrogen insulation integrated control system, used to implement the method as described in any one of claims 1-8, characterized in that, The system includes: The model building unit is used to read the primary wiring topology, rated insulation level, initial nitrogen charging pressure, cabinet volume, sensor layout table and secondary port mapping of each bay, and build a multi-bay reference insulation model. The data acquisition unit is used to synchronously acquire nitrogen pressure, cavity temperature and humidity, partial discharge, contact temperature rise, mechanism action characteristics and line electrical parameters of each interval, perform preprocessing, and bind with the corresponding interval in the multi-interval reference insulation model to form a reliable state vector. The control decision unit is used to calculate the real-time insulation margin index and instability trend value of each interval based on the trusted state vector, and generate executable, restricted or prohibited control tags; and generate control permission domains based on the control tags, the five-prevention logic and the network operation status. The solution verification unit is used to solve the optimal segmentation, connection and transfer action sequence within the control permission domain, with the minimum power outage load, minimum number of switch actions and maximum insulation safety margin as optimization objectives; the action sequence is simulated in time sequence through the multi-interval reference insulation model, and the mechanical interlock, door lock status, grounding switch relationship and post-transfer current carrying constraints are verified, and the action sequence that passes the verification is output. The feedback tuning unit is used to execute the action sequence that has passed the verification, collect the measured state vector, calculate the insulation margin threshold in the self-tuning tag generation process based on the deviation between the estimated state vector and the measured state vector and the insulation margin index, and correct the action priority parameters of each interval and the corresponding control parameter library.

10. A primary and secondary integrated ring main unit nitrogen insulation integrated control device, characterized in that, The device includes: a memory and at least one processor, wherein the memory stores instructions; The at least one processor invokes the instructions in the memory to cause the primary and secondary fusion ring network box nitrogen insulation integrated control device to perform the method as described in any one of claims 1-8.

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