Ring network switch cabinet dynamic capacity expansion collaborative recovery control method and system based on digital twinning

Abstract

The present application belongs to the technical field of power distribution network ring network power supply control, and particularly relates to a ring network switch cabinet dynamic capacity increasing collaborative recovery control method and system based on digital twinning, which first establishes a digital twinning model of the ring network switch cabinet system; when a fault occurs, fault positioning and isolation are completed through the model and the topological structure is updated; then, a short-term emergency capacity increasing threshold is calculated according to real-time operating parameters of the remaining non-fault switch cabinets; a double-layer optimization model is constructed, the outer layer optimizes topological reconstruction with the minimum number of switch operations and the maximum estimated operating margin as the target, and the inner layer optimizes capacity increasing load distribution with the minimum network loss and the maximum actual operating margin as the target; the digital twinning model enters the capacity increasing operating state according to the optimal topological reconstruction and load distribution scheme. The present application can break through the static rated value limit under fault conditions, and realize global optimization of topological reconstruction and load distribution through the double-layer optimization model, thereby maximizing power supply recovery under the premise of ensuring equipment thermal safety.

Description

Technical Field

[0001] This invention belongs to the field of power supply control technology for distribution network ring networks, specifically relating to a dynamic capacity expansion and collaborative recovery control method and system for ring network switchgear based on digital twins. Background Technology

[0002] Currently, ring mains switchgear systems commonly employ feeder automation (FA) and standby power transfer (BZT) devices to achieve load transfer after a fault. When a switchgear within the ring mains fails and goes out of service, the system transfers the lost load to an adjacent, non-faulty switchgear through topology reconfiguration. The transfer is contingent on the current of the receiving equipment not exceeding its rated current. If it does, only a portion of the secondary load can be disconnected via a circuit breaker.

[0003] The existing technology has the following main problems:

[0004] (1) Limited load transfer capacity. Existing ring network switchgear systems use feeder automation strategies to perform only simple topology reconfiguration, transferring the load of the faulty section to the tie switch side. However, due to the lack of assessment capabilities for equipment capacity expansion in traditional solutions, when the current of the healthy switchgear exceeds its rated value after load transfer, only some secondary loads can be disconnected, resulting in unnecessary power outage losses and failing to restore power supply to the maximum extent while ensuring equipment safety.

[0005] (2) The modeling and prediction capabilities for the thermal dynamic processes of ring network switchgear are still imperfect. The actual current-carrying capacity of switchgear under short-term overload conditions is affected by multiple factors such as ambient temperature, initial temperature, heat dissipation conditions, and thermal aging characteristics of insulation materials, which is a typical nonlinear time-varying process. Since the existing technology is not yet perfect in real-time modeling and prediction capabilities for the above-mentioned thermal dynamic processes, it is difficult to accurately assess the safe operating boundary of healthy switchgear under emergency conditions, which reduces the power supply reliability of switchgear.

[0006] (3) Insufficient multi-device collaborative control capability. When multiple switch cabinets in a ring network switch cabinet need to collaboratively take over the transferred load, the existing technology lacks consideration of the differences in the capacity expansion capabilities of each device, the balance of load distribution, and the optimization of the number of operations, making it difficult to achieve system-level global optimal control.

[0007] (4) Fixed protection settings of ring network switchgear. The overcurrent protection settings of traditional ring network switchgear are fixed based on the rated current, without taking into account dynamic factors such as the actual ambient temperature, load change trend, and equipment heat accumulation status of the ring network switchgear, which makes it impossible for the protection device to dynamically adapt to the actual operating conditions. Summary of the Invention

[0008] The purpose of this invention is to address the aforementioned problems in the existing technology by providing a dynamic capacity expansion and collaborative recovery control method and system for ring network switchgear based on digital twins. This system overcomes the limitations of static rated capacity by rapidly isolating faults and dynamically assessing capacity expansion thresholds through digital twins, while simultaneously employing a two-layer optimization model to achieve global optimization of topology reconstruction and load distribution. This system maximizes power supply recovery while ensuring equipment thermal safety.

[0009] To achieve the above objectives, the technical solution of the present invention is as follows:

[0010] In a first aspect, the present invention provides a dynamic capacity expansion and collaborative recovery control method for ring network switchgear based on digital twins, the dynamic capacity expansion and collaborative recovery control method for ring network switchgear comprising:

[0011] Establish a digital twin model of the ring network switchgear system;

[0012] Using a digital twin model, fault location and isolation are performed based on the operating parameters of the ring network switchgear system when a switchgear failure occurs, and the topology of the digital twin model after fault isolation is updated.

[0013] After the topology is updated, obtain the current operating parameters of each non-faulty switchgear and calculate the short-term emergency capacity expansion threshold of each non-faulty switchgear based on the current operating parameters of each non-faulty switchgear.

[0014] A two-layer optimization model is constructed. The outer layer model is a topology reconfiguration model with the optimization objectives of minimizing the total number of switching operations and maximizing the estimated system operating margin. The inner layer model is a capacity expansion load allocation model with the objectives of minimizing system active power loss and maximizing system actual operating margin. The estimated system operating margin and the actual system operating margin are calculated based on short-term emergency capacity expansion thresholds. Solving the two-layer optimization model yields the optimal topology reconfiguration scheme and the optimal capacity expansion load allocation scheme.

[0015] Based on the optimal topology reconfiguration scheme and the optimal capacity expansion load allocation scheme, the digital twin model enters the capacity expansion operation state until the faulty switchgear is repaired and then exits the capacity expansion operation state.

[0016] The calculation of the short-term emergency capacity expansion threshold for each non-faulty switchgear based on its current operating parameters includes:

[0017] Preset safe operation constraints for non-faulty switchgear. Calculate the maximum allowable current that satisfies the safe operation constraints for each non-faulty switchgear based on its current operating parameters. Use the maximum allowable current as the short-term emergency capacity expansion threshold for that non-faulty switchgear. The safe operation constraints include conductor maximum allowable temperature constraints, temperature rise rate constraints, and heat accumulation constraints.

[0018] The objective function of the topology reconstruction model is:

[0019] ;

[0020] ;

[0021] ;

[0022] ;

[0023] In the above formula, The objective function for the topology reconstruction model; , All are outer layer weight coefficients; Indicates the total number of switch operations; This indicates the system's estimated operational margin; For the first A non-faulty switchgear in the topology reconfiguration scheme The following state; For the first time after fault isolation The initial state of a non-faulty switchgear; In the topology reconfiguration scheme When the total power loss load is evenly distributed according to the rated capacity of each cabinet, the first... Estimated current for a non-faulty switchgear; This represents the total number of non-faulty switchgear. This is the threshold for short-term emergency capacity expansion; The first time before the failure occurred The initial load current of a non-faulty switchgear; For the first Rated capacity of a non-faulty switchgear; For the first Rated capacity of a non-faulty switchgear; This is the total power-off operating current;

[0024] The constraints of the topology reconstruction model include: topology connectivity constraints, radial operation constraints, fault isolation maintenance constraints, and outer layer current prediction constraints. The expression for the outer layer current prediction constraints is as follows:

[0025] ;

[0026] In the above formula, This is the margin coefficient.

[0027] The objective function of the capacity expansion load allocation model is:

[0028] ;

[0029] ;

[0030] ;

[0031] ;

[0032] In the above formula, The objective function for the capacity expansion load allocation model; , All are inner layer weighting coefficients; For system active power loss; This represents the actual operational margin of the system. This represents the optimal topology reconstruction scheme obtained from the topology reconstruction model. A defined set of closed branches; , Branch roads Active power and reactive power flowing upstream; branch road The resistance; For nodes The voltage amplitude; This is the threshold for short-term emergency capacity expansion; Indicates the first Each non-faulty switchgear is included in the load distribution scheme for capacity expansion. The actual operating current under these conditions; This represents the total number of non-faulty switchgear. The first time before the failure occurred The initial load current of a non-faulty switchgear; For load allocation variables, it represents the load allocation scheme under capacity expansion. The Middle The power failure load branch is assigned to the first The active power of a non-faulty switchgear; The system's nominal line voltage; The load power factor angle;

[0033] The constraints of the capacity expansion load allocation model include: short-term emergency capacity expansion threshold constraint for non-faulty switchgear, node power balance constraint, branch power flow equation constraint, node voltage constraint, full load transfer constraint, and load allocation non-negativity constraint. The expression for the short-term emergency capacity expansion threshold constraint for non-faulty switchgear is as follows:

[0034] .

[0035] The dynamic capacity expansion and coordinated recovery control method for ring network switchgear also includes:

[0036] After entering the capacity expansion operation state, the digital twin model continuously monitors the operating parameters of each non-faulty switchgear and determines whether an early warning is triggered. If an early warning is triggered, dynamic adjustments are made step by step from the first priority measure to the third priority measure until the early warning disappears.

[0037] First priority measure: Use a consensus algorithm to redistribute the load of the capacity expansion load allocation scheme to average the load rate of each non-faulty switchgear;

[0038] Second priority measures: If the warning is still triggered after the first priority measures are adopted, the digital twin will start orderly load reduction according to the preset load reduction sequence, which is preset according to the importance of the load.

[0039] Third priority measure: If the warning is still triggered after the second priority measure is adopted, the non-faulty switchgear that triggered the warning by the digital twin control will be forced to exit the capacity expansion mode and the transferred load undertaken by the non-faulty switchgear will be cut off.

[0040] The digital twin model includes a geometric model, a behavioral model, a rule model, and a thermal circuit model. The geometric model is a scaled simulation model established for the ring network switchgear system. The behavioral model describes the operating characteristics, timing relationships, and dynamic response processes of each switchgear. The rule model sets the insulation material temperature rating, contact safety temperature threshold, temperature rise rate limit, and emergency capacity expansion operation time limit for each switchgear. The thermal circuit model identifies the equivalent heat capacity and equivalent thermal conductivity of each switchgear. The expression of the thermal circuit model is:

[0041] ;

[0042] In the above formula, Equivalent heat capacity; The current conductor temperature; This represents the current power loss. Equivalent thermal conductivity; This represents the current ambient temperature.

[0043] After entering the capacity expansion operation state, the digital twin model will temporarily raise the overcurrent protection setting value of the switchgear allocated to the capacity expansion load from the rated current value to the adjusted value;

[0044] In the third priority measure, the digital twin issues an overcurrent protection setting value restoration command to the switchgear allocated to the increased capacity load, so that its overcurrent protection setting value is restored from the adjustment value when the increased capacity operation state to the current rated value.

[0045] The bi-layer optimization model is solved using the particle swarm optimization algorithm and the original-dual interior point method. The solution steps include:

[0046] First, initialize the particle swarm. The position vector of each particle in the particle swarm corresponds to a set of topology reconstruction schemes. Based on the topology reconstruction scheme, use the inner layer original dual interior point method to solve the capacity expansion load allocation model and output the optimal load allocation scheme.

[0047] The total number of switching operations under the topology reconstruction scheme corresponding to the current particle is weighted and calculated by combining the actual operating margin of the system and the active power loss of the system under the optimal load allocation scheme obtained based on the topology reconstruction scheme. The current comprehensive fitness of the particle is compared with the historical best comprehensive fitness of the particle. If the current comprehensive fitness is better, the individual optimal position vector is updated based on the current comprehensive fitness. The current comprehensive fitness is compared with the historical global best comprehensive fitness. If the current comprehensive fitness is better, the global optimal position vector is updated based on the current comprehensive fitness.

[0048] After completing the comprehensive fitness calculation of all particles at the current iteration number, update the velocity vector and position vector of each particle; repeat the above steps until the preset maximum iteration number is reached, output the topology reconstruction scheme corresponding to the globally optimal particle as the optimal topology reconstruction scheme, and output the optimal load allocation scheme corresponding to the optimal topology reconstruction scheme.

[0049] The particle swarm algorithm is an improved particle swarm algorithm, which includes: performing a particle repair operation after initializing the particle swarm and updating the particle positions. The particle repair operation refers to adjusting the particle position vector so that the topology reconstruction scheme corresponding to the particle satisfies the radial running constraint and the fault isolation maintenance constraint.

[0050] Secondly, this invention provides a dynamic capacity expansion and collaborative recovery control system for ring network switchgear based on digital twins.

[0051] The dynamic capacity expansion and collaborative recovery control system for the ring network switchgear includes:

[0052] The model building module is used to create a digital twin model of the ring network switchgear system;

[0053] The fault isolation module is used to locate and isolate faults based on the operating parameters of the ring network switchgear system when a switchgear fault occurs, using a digital twin model, and to update the topology of the digital twin model after fault isolation.

[0054] The capacity expansion threshold calculation module is used to obtain the current operating parameters of each non-faulty switchgear after the topology is updated, and to calculate the short-term emergency capacity expansion threshold of each non-faulty switchgear based on the current operating parameters of each non-faulty switchgear.

[0055] The topology reconfiguration and load allocation module is used to construct a two-layer optimization model. The outer layer model is a topology reconfiguration model with the optimization objectives of minimizing the total number of switching operations and maximizing the estimated system operating margin. The inner layer model is a capacity expansion load allocation model with the objectives of minimizing system active power loss and maximizing system actual operating margin. The estimated system operating margin and the actual system operating margin are calculated based on short-term emergency capacity expansion thresholds. Solving the two-layer optimization model yields the optimal topology reconfiguration scheme and the optimal capacity expansion load allocation scheme.

[0056] The capacity expansion operation control module is used to enter the capacity expansion operation state based on the optimal topology reconstruction scheme and the optimal capacity expansion load allocation scheme using a digital twin model, until the faulty switchgear is repaired and the capacity expansion operation state is exited.

[0057] The capacity expansion threshold calculation module is used to calculate the short-term emergency capacity expansion threshold for each non-faulty switchgear according to the following steps:

[0058] Preset safe operation constraints for non-faulty switchgear. Calculate the maximum allowable current that satisfies the safe operation constraints for each non-faulty switchgear based on its current operating parameters. Use the maximum allowable current as the short-term emergency capacity expansion threshold for that non-faulty switchgear. The safe operation constraints include conductor maximum allowable temperature constraints, temperature rise rate constraints, and heat accumulation constraints.

[0059] The objective function of the topology reconstruction model is:

[0060] ;

[0061] ;

[0062] ;

[0063] ;

[0064] In the above formula, The objective function for the topology reconstruction model; , All are outer layer weight coefficients; Indicates the total number of switch operations; This indicates the system's estimated operational margin; For the first A non-faulty switchgear in the topology reconfiguration scheme The following state; For the first time after fault isolation The initial state of a non-faulty switchgear; In the topology reconfiguration scheme When the total power loss load is evenly distributed according to the rated capacity of each cabinet, the first... Estimated current for a non-faulty switchgear; This represents the total number of non-faulty switchgear. This is the threshold for short-term emergency capacity expansion; The first time before the failure occurred The initial load current of a non-faulty switchgear; For the first Rated capacity of a non-faulty switchgear; For the first Rated capacity of a non-faulty switchgear; This is the total power-off operating current;

[0065] The constraints of the topology reconstruction model include: topology connectivity constraints, radial operation constraints, fault isolation maintenance constraints, and outer layer current prediction constraints. The expression for the outer layer current prediction constraints is as follows:

[0066] ;

[0067] In the above formula, This is the margin coefficient.

[0068] The objective function of the capacity expansion load allocation model is:

[0069] ;

[0070] ;

[0071] ;

[0072] ;

[0073] In the above formula, The objective function for the capacity expansion load allocation model; , All are inner layer weighting coefficients; For system active power loss; This represents the actual operational margin of the system. This represents the optimal topology reconstruction scheme obtained from the topology reconstruction model. A defined set of closed branches; , Branch roads Active power and reactive power flowing upstream; for; For nodes The voltage amplitude; This is the threshold for short-term emergency capacity expansion; Indicates the first Each non-faulty switchgear is included in the load distribution scheme for capacity expansion. The actual operating current under these conditions; This represents the total number of non-faulty switchgear. The first time before the failure occurred The initial load current of a non-faulty switchgear; For load allocation variables, it represents the load allocation scheme under capacity expansion. The Middle The power failure load branch is assigned to the first The active power of a non-faulty switchgear; for; for;

[0074] The constraints of the capacity expansion load allocation model include: short-term emergency capacity expansion threshold constraint for non-faulty switchgear, node power balance constraint, branch power flow equation constraint, node voltage constraint, full load transfer constraint, and load allocation non-negativity constraint. The expression for the short-term emergency capacity expansion threshold constraint for non-faulty switchgear is as follows:

[0075] .

[0076] The dynamic capacity expansion and coordinated recovery control method for ring network switchgear also includes:

[0077] The capacity expansion operation control module is also used to continuously monitor the operating parameters of each non-faulty switchgear after entering the capacity expansion operation state through a digital twin model and determine whether an early warning is triggered. If an early warning is triggered, dynamic adjustments are made step by step from the first priority measure to the third priority measure until the early warning disappears.

[0078] First priority measure: Use a consensus algorithm to redistribute the load of the capacity expansion load allocation scheme to average the load rate of each non-faulty switchgear;

[0079] Second priority measures: If the warning is still triggered after the first priority measures are adopted, the digital twin will start orderly load reduction according to the preset load reduction sequence, which is preset according to the importance of the load.

[0080] Third priority measure: If the warning is still triggered after the second priority measure is adopted, the non-faulty switch cabinet that triggered the warning by the digital twin control will be forced to exit the capacity expansion mode and the transferred load undertaken by the non-faulty switch cabinet will be cut off.

[0081] The digital twin model includes a geometric model, a behavioral model, a rule model, and a thermal circuit model. The geometric model is a scaled simulation model established for the ring network switchgear system. The behavioral model describes the operating characteristics, timing relationships, and dynamic response processes of each switchgear. The rule model sets the insulation material temperature rating, contact safety temperature threshold, temperature rise rate limit, and emergency capacity expansion operation time limit for each switchgear. The thermal circuit model identifies the equivalent heat capacity and equivalent thermal conductivity of each switchgear. The expression of the thermal circuit model is:

[0082] ;

[0083] In the above formula, Equivalent heat capacity; The current conductor temperature; This represents the current power loss. Equivalent thermal conductivity; This represents the current ambient temperature.

[0084] The capacity expansion operation control module is also used to temporarily raise the overcurrent protection setting value of the switchgear allocated to the capacity expansion load from the rated current value to the adjusted value after entering the capacity expansion operation state through a digital twin model.

[0085] The capacity expansion operation control module is also used in the third priority measure to issue an overcurrent protection setting value restoration command to the switchgear allocated to the capacity expansion load through a digital twin, so that its overcurrent protection setting value is restored from the adjustment value when the capacity expansion operation is in the state to the rated current value.

[0086] The topology reconstruction and load allocation module is also used to solve the bi-layer optimization model using the particle swarm optimization algorithm and the original-dual interior point method according to the following steps:

[0087] First, initialize the particle swarm. The position vector of each particle in the particle swarm corresponds to a set of topology reconstruction schemes. Based on the topology reconstruction scheme, use the inner layer original dual interior point method to solve the capacity expansion load allocation model and output the optimal load allocation scheme.

[0088] The total number of switching operations under the topology reconstruction scheme corresponding to the current particle is weighted and calculated by combining the actual operating margin of the system and the active power loss of the system under the optimal load allocation scheme obtained based on the topology reconstruction scheme. The current comprehensive fitness of the particle is compared with the historical best comprehensive fitness of the particle. If the current comprehensive fitness is better, the individual optimal position vector is updated based on the current comprehensive fitness. The current comprehensive fitness is compared with the historical global best comprehensive fitness. If the current comprehensive fitness is better, the global optimal position vector is updated based on the current comprehensive fitness.

[0089] After completing the comprehensive fitness calculation of all particles at the current iteration number, update the velocity vector and position vector of each particle; repeat the above steps until the preset maximum iteration number is reached, output the topology reconstruction scheme corresponding to the globally optimal particle as the optimal topology reconstruction scheme, and output the optimal load allocation scheme corresponding to the optimal topology reconstruction scheme.

[0090] The particle swarm algorithm is an improved particle swarm algorithm, which includes: performing a particle repair operation after initializing the particle swarm and updating the particle positions. The particle repair operation refers to adjusting the particle position vector so that the topology reconstruction scheme corresponding to the particle satisfies the radial running constraint and the fault isolation maintenance constraint.

[0091] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0092] 1. The method described in this invention, on the one hand, uses a digital twin model to map the entire state of the ring network switchgear system in real time, and dynamically calculates the short-term emergency capacity expansion threshold based on the real-time operating parameters of non-faulty switchgear and the thermal circuit model. This breaks through the equipment capacity boundary from a fixed current rating to a dynamic short-term emergency capacity expansion threshold, which can fully explore the potential current-carrying capacity of non-faulty switchgear while ensuring the thermal safety of the equipment. On the other hand, it constructs a two-layer optimization model of outer topology reconstruction and inner load allocation. The outer layer aims to quickly determine the feasible topology structure with the goal of minimizing the number of switching operations and maximizing the estimated operating margin of the system. The inner layer aims to finely allocate the capacity expansion load with the goal of minimizing network loss and maximizing the actual operating margin. This achieves the global optimization of fault recovery speed, economy and safety, and can significantly improve the power supply recovery rate and distribution network reliability after a fault.

[0093] 2. The method described in this invention dynamically calculates the short-term emergency capacity expansion threshold based on the real-time operating parameters of non-faulty switchgear. It can accurately match the thermal dynamic characteristics of the switchgear with multiple factors coupled together, avoid the risk of fault expansion caused by the inability of traditional fixed protection settings to adapt to actual working conditions and inaccurate overload assessment, and ensure that the capacity expansion operation is within the safety boundary throughout the entire process.

[0094] 3. The method described in this invention, after triggering an early warning when a non-faulty switchgear enters the capacity expansion operation state, adopts a three-level priority measure for dynamic adjustment. First, a consensus algorithm is used to balance the load rate to ensure power supply continuity. Then, the load is reduced in an orderly manner according to the importance of the load. Finally, the capacity expansion protection equipment is forcibly deactivated to form a safety closed loop. The entire process is monitored and warned in real time by a digital twin, taking into account both power supply restoration and equipment safety, and improving system robustness. Attached Figure Description

[0095] Figure 1 This is a flowchart of the method described in this invention.

[0096] Figure 2 This is a schematic diagram illustrating the principle of the method described in this invention.

[0097] Figure 3 The equivalent circuit diagram for constructing the thermal circuit model using the method described in this invention.

[0098] Figure 4 This is a schematic diagram of the Pareto front obtained from performance verification.

[0099] Figure 5 The graph showing the relationship between the operating time and the operating current of the RMU-3 inverse time overcurrent protection, obtained for performance verification.

[0100] Figure 6 This is a structural block diagram of the system described in this invention. Detailed Implementation

[0101] The present invention will now be described in further detail with reference to specific embodiments and accompanying drawings.

[0102] Example 1:

[0103] See Figure 1 , Figure 2 A dynamic capacity expansion and collaborative recovery control method for ring network switchgear based on digital twins is implemented in the following steps:

[0104] S1. Establish a digital twin model of the ring network switchgear system.

[0105] Specifically, the digital twin model includes a geometric model, a behavioral model, a rule model, and a thermal circuit model. A data mapping module enables real-time data synchronization between the physical entity of the ring network switchgear system and the digital twin model, including uplink mapping of sensor data and downlink mapping of control commands. The geometric model is a scaled simulation model established for the ring network switchgear system. The behavioral model accurately describes the operational characteristics, timing relationships, and dynamic response processes of each switchgear in the ring network switchgear system, serving as the foundation for the digital twin to simulate switch operation behavior and assess mechanical health. The rule model sets the temperature rating of the ring network switchgear insulation material, contact safety temperature thresholds, temperature rise rate limits, and capacity expansion operation time limits, providing accurate and quantifiable thermal safety boundaries for subsequent calculations of short-term emergency capacity expansion thresholds. The thermal circuit model identifies parameters based on historical operating data of the ring network switchgear system; the equivalent circuit diagram of the thermal circuit model is shown below. Figure 3 As shown, its mathematical expression is:

[0106] ;

[0107] In the above formula, Equivalent heat capacity; The current conductor temperature; This represents the current power loss. Equivalent thermal conductivity; Given the current ambient temperature; the equivalent heat capacity can be identified through the thermal circuit model. Equivalent thermal conductivity Parameters are set to enable millisecond-level simulation of the temperature rise trajectory of the switchgear conductors under current surges. In the thermal circuit model... This is the equivalent resistance.

[0108] Multiple types of sensor groups are deployed in the ring network switchgear system to form a comprehensive state awareness network. The sensor groups include: (1) Current transformers: installed on the incoming, outgoing and bus sides of the switchgear, used to collect the three-phase current, zero-sequence current and harmonic components of the equipment, with a sampling frequency of not less than 10kHz and a measurement accuracy of ±0.5%, used to monitor load changes and fault current characteristics in real time; (2) Fiber optic temperature sensors: installed on key heat-generating parts such as main circuit conductor connectors, stationary contacts, moving contacts and cable terminals in the switchgear, used to collect the real-time temperature of these parts of the switchgear, with a temperature range of -20℃ to 200℃, an accuracy of ±1℃, and a response time of not more than 1 second; (3) Ambient temperature and humidity sensors: installed inside and outside the switchgear, used to collect the temperature and humidity of the switchgear. Ambient temperature and ambient humidity, with accuracies of ±0.5℃ and ±3%RH respectively, are used for thermal circuit models; (4) Switch status sensor: installed at the operating mechanism of the switch cabinet circuit breaker, load switch, grounding switch and disconnecting switch, used to collect the opening and closing status of the switch and the number of operations, with a status change detection time of no more than 1ms; (5) Vibration sensor: installed at the switch cabinet housing and operating mechanism, used to collect mechanical vibration signals, with a sampling frequency of no less than 5kHz, used to assist in judging mechanical faults and the status of the operating mechanism; (6) Ultrasonic partial discharge sensor: installed near the cable terminal and insulation parts, its principle is ultrasonic detection technology, used for early warning of insulation degradation trends. The above various types of sensor groups are connected to the edge computing node through RS485 bus.

[0109] Edge computing nodes are integrated into the power distribution terminal units of the ring network switchgear system to perform real-time preprocessing of raw data collected by sensors. Specifically, the preprocessing includes: first, data cleaning to remove obviously abnormal sensor data, and using a moving average filtering algorithm to suppress high-frequency noise interference, with a window length of 5 sampling points; then, data alignment to synchronize the time-series data collected by each sensor according to a unified timestamp to ensure the spatiotemporal consistency of multi-source data; and finally, feature extraction to extract fault characteristics such as steady-state current RMS value, transient current peak value, zero-sequence current amplitude, and current change rate from the raw current waveform, and to extract conductor real-time temperature, temperature rise rate, and temperature change trend from the temperature data.

[0110] After receiving the preprocessed data, the digital twin platform synchronizes the real-time status of the physical entities of the ring network switchgear system to the virtual model through the data mapping module. Specifically, the data mapping includes: writing the real-time electrical parameters such as current, voltage, and power of each switchgear into the electrical attribute fields of the geometric model to update the load distribution and power flow status in the geometric model; writing the thermal parameters such as conductor temperature, ambient temperature, and temperature rise rate into the thermal attribute fields of the thermal circuit model to update the boundary conditions and initial state of the thermal circuit model; writing the switch opening and closing status, number of operations, vibration characteristics, etc. into the state fields of the behavior model to update the action characteristics and remaining mechanical life of the actuator; and combining the rule model to perform real-time verification of the mapped status to determine whether there are abnormal operating conditions such as over-limit operation or abnormal temperature rise.

[0111] S2. Using the digital twin model, fault location and isolation are performed based on the operating parameters of the ring network switchgear system when a switchgear failure occurs, and the topology of the digital twin model after fault isolation is updated.

[0112] Specifically, firstly, a comprehensive fault criterion matrix integrating current mutation characteristics, switch state changes, partial discharge signals, and vibration characteristics is constructed, and a corresponding typical fault comprehensive criterion matrix is ​​preset for each fault type. When a switchgear fault occurs in the ring network switchgear system, the fault type is initially classified based on the real-time collected comprehensive fault criterion matrix combined with the typical fault comprehensive criterion matrix. Subsequently, using the ring network topology information combined with multi-sensor data, Kirchhoff's current law and the temperature distribution cloud map of the fiber optic temperature sensor array are used to achieve precise location of the fault point.

[0113] After the fault location is completed, the digital twin model starts the isolation simulation module. Based on the current ring network topology and the location of the fault point, it generates an isolation scheme through simulation and deduction. The digital twin model topology is updated according to the isolation scheme to achieve fault isolation of the faulty switchgear. The status of the faulty switchgear is updated to "out of operation", and the status of the incoming circuit breaker of the faulty switchgear is updated to "open".

[0114] S3. Obtain the current operating parameters of each non-faulty switchgear after updating the topology, and calculate the short-term emergency capacity expansion threshold of each non-faulty switchgear based on the current operating parameters of each non-faulty switchgear.

[0115] Specifically, after updating the digital twin model topology according to the isolation scheme, the digital twin model records real-time operating parameters such as the current operating current, conductor temperature, ambient temperature, and ambient humidity of the remaining non-faulty switchgear, and performs a comprehensive health assessment on each non-faulty switchgear to obtain a comprehensive health assessment score. The comprehensive health assessment includes: first calculating the current overload rate of each non-faulty switchgear. Thermal safety margin Current overload rate The calculation formula is ,in Current operating current, thermal safety margin The calculation formula is ,in The current conductor temperature is given; then the comprehensive health assessment score is calculated according to the following formula. : ,in The mechanical health index is calculated based on the number of operations and historical failure rate. , , These are the weighting coefficients for overload rate, thermal safety margin, and mechanical health index, respectively. A higher comprehensive health assessment score indicates a better health status for the corresponding non-faulty switchgear. The remaining non-faulty switchgear are then ranked according to their comprehensive health assessment scores to obtain the health status ranking results.

[0116] Specifically, the calculation of the short-term emergency capacity expansion threshold for each non-faulty switchgear based on its current operating parameters includes: pre-setting safe operation constraints for the non-faulty switchgear; and, based on the current operating parameters of each non-faulty switchgear, obtaining the threshold during the emergency capacity expansion operation time (e.g., the pre-set emergency capacity expansion operation time) by solving the following thermal circuit differential equation. The maximum permissible current that meets the safety operation constraints of the non-faulty switchgear within 30 minutes:

[0117] ;

[0118] ;

[0119] In the above formula, This is the threshold for short-term emergency capacity expansion; Resistance of a conductor; Ambient temperature; Initial time The conductor temperature; The conductor temperature recorded by the digital twin model after updating the digital twin model topology according to the isolation scheme; The AC resistance of the conductor at 20℃;

[0120] The safety operation constraints include the maximum allowable temperature of the conductor, the temperature rise rate constraint, and the heat accumulation constraint.

[0121] The maximum allowable temperature constraint for the conductor is: ;

[0122] The temperature rise rate constraint is: ;

[0123] The heat accumulation constraint is: ; The rated operating temperature of the conductor is 500℃. This is the maximum permissible cumulative over-temperature based on the thermal aging characteristics of the insulation material. This maximum permissible cumulative over-temperature can be adjusted according to the actual heat resistance level and expected life of the insulation material.

[0124] The above thermal differential equation is solved using the fourth-order Runge-Kutta method, with the time step set to... Set to a time interval of 1 second, the maximum allowable current is obtained through a bisection iterative search. This maximum allowable current is then used as the short-term emergency capacity expansion threshold for the non-faulty switchgear. For ease of real-time application, a two-dimensional lookup table matrix for the maximum allowable current can be pre-constructed offline within an initial temperature range of 30℃-80℃ and an ambient temperature range of 20℃-40℃. During online operation, the maximum allowable current is quickly obtained through bilinear interpolation. The overload rate is then calculated based on the ratio of the short-term emergency capacity expansion threshold to the rated current.

[0125] S4. Construct a two-layer optimization model. The outer layer of the two-layer optimization model is a topology reconfiguration model with the optimization objectives of minimizing the total number of switching operations and maximizing the estimated system operating margin. The topology reconfiguration model aims to find a topology from all feasible ring network switch state combinations that can both quickly restore power supply and reserve sufficient safety space for subsequent load loss allocation. The optimization variables of the topology reconfiguration model are the switch state variables. , used to represent nodes With nodes Branch roads between Switch status, This indicates that the switch is closed, and the branch circuit... Put into operation; This indicates that the switch is open, and the branch circuit... Exit operation; the inner model is a capacity expansion load allocation model that minimizes system active power loss and maximizes the system's actual operating margin. The optimization variable of the capacity expansion load allocation model is the load allocation variable. , indicating the first The power failure load branch is assigned to the first The active power of each non-faulty switchgear can be arbitrarily distributed among the non-faulty switchgears, and the power failure load can be distributed among the non-faulty switchgears.

[0126] Specifically, this invention assumes that the total power outage load is evenly distributed according to the rated capacity ratio of each non-faulty switchgear, thereby quickly estimating the predicted current of each non-faulty switchgear under a certain topology. Using the estimated current Rapidly evaluating the safety margin of a topology avoids performing precise power flow calculations for each topology, thus reducing computational load and quickly assessing the safety potential of a topology. The objective function of the topology reconstruction model is:

[0127] ;

[0128] ;

[0129] ;

[0130] ;

[0131] In the above formula, The objective function for the topology reconstruction model; , All of these are outer layer weight coefficients; in this embodiment... This is to highlight the priority of ensuring the safety margin of capacity expansion operations; Indicates the total number of switch operations; This indicates the system's estimated operational margin; For the first A non-faulty switchgear in the topology reconfiguration scheme The following state; For the first time after fault isolation The initial state of a non-faulty switchgear; In the topology reconfiguration scheme When the total power loss load is evenly distributed according to the rated capacity of each cabinet, the first... Estimated current for a non-faulty switchgear; This represents the total number of non-faulty switchgear. For the first The short-term emergency capacity expansion threshold for a non-faulty switchgear; The first time before the failure occurred The initial load current of a non-faulty switchgear; For the first Rated capacity of a non-faulty switchgear; For the first The rated current of each non-faulty switchgear; This is the total power-off operating current;

[0132] The constraints of the topology reconfiguration model include: topology connectivity constraints, radial operation constraints, fault isolation maintenance constraints, and outer layer current prediction constraints. The topology connectivity constraints ensure that there is a connected path between all non-faulty load nodes and at least one power source in the topology, and can be described using graph theory language as follows: for a given set of nodes... Sum of edges Undirected graph For any non-faulty load node, there exists at least one path connecting it to a power supply node. The radial operating constraint is used to restrict open-loop operation of the ring network, i.e., it satisfies... ,in , , These represent the number of branches, the total number of nodes, and the number of power supply nodes, respectively. The fault isolation holding constraint restricts the direct connection of branch switches to the faulty switchgear from closing simultaneously if the incoming circuit breakers on both sides of the faulty switchgear have tripped, to prevent reconnection of the fault point to the system. , , These represent the branch switch states directly connected to both sides of the faulty switchgear. The outer current-carrying prediction constraint is used to limit the predicted current of each non-faulty switchgear from exceeding the short-term emergency capacity expansion threshold, ensuring that the generated topology has feasible space for subsequent power outage load allocation, i.e., satisfying: ,in In this embodiment, the margin coefficient is used. Take 0.9.

[0133] Specifically, the objective function of the capacity expansion load allocation model is:

[0134] ;

[0135] ;

[0136] ;

[0137] ;

[0138] In the above formula, The objective function for the capacity expansion load allocation model; , All of these are inner layer weighting coefficients; in this embodiment... To focus on ensuring the safe operation of the equipment; The system's active power loss is represented by a smaller value, indicating that the system operates more economically. This represents the actual operational margin of the system; the lower the value, the better the safety of the system during capacity expansion. This represents the optimal topology reconstruction scheme obtained from the topology reconstruction model. A defined set of closed branches; , Branch roads Active power and reactive power flowing upstream; branch road The resistance; For nodes The voltage amplitude; Indicates the first Each non-faulty switchgear is included in the load distribution scheme for capacity expansion. The actual operating current during the capacity expansion operation; This represents the total number of non-faulty switchgear. The first time before the failure occurred The initial load current of a non-faulty switchgear; For load allocation variables, it represents the load allocation scheme under capacity expansion. The Middle The power failure load branch is assigned to the first The active power of a non-faulty switchgear; The nominal line voltage of the system is taken as 10kV in this embodiment; The load power factor angle;

[0139] The constraints of the capacity expansion load allocation model include: node power balance constraints, branch power flow equation constraints, short-term emergency capacity expansion threshold constraints for non-faulty switchgear, node voltage constraints, full load transfer constraints, and non-negativity constraints for load allocation. Node power balance constraints are applied to any node. The injected power is limited to the sum of the outflow power and the load consumption. , ,in , Representing nodes respectively Net injected active power, net injected reactive power; , Branch roads Active power and reactive power flowing upstream; The number of nodes; branch power flow equation constraints are used to ensure that branch power flow satisfies the AC power flow equation; short-term emergency capacity expansion threshold constraints for non-faulty switchgear are used to limit the actual operating current of each non-faulty switchgear from exceeding its short-term emergency capacity expansion threshold. Node voltage constraints are used to limit the voltage amplitude at each node to a safe range. The load transfer constraint is used to restrict all lost-power loads to be fully taken over and not to be disconnected. ,in For the first The total active power of the de-energized branches. The load allocation non-negativity constraint is used to restrict the load allocation variable from being negative: .

[0140] Specifically, the topology reconfiguration model solves for the branch switch states based on the initial capacity expansion load allocation scheme to determine the optimal topology reconfiguration scheme. Then, using this optimal topology reconfiguration scheme as the input to the capacity expansion load allocation model, it solves for the capacity expansion load that each non-faulty switchgear should bear, thus determining the optimal capacity expansion load allocation scheme. Since the two-layer optimization model is a mixed-integer nonlinear programming problem with discrete variables in the outer layer and continuous variables in the inner layer, this invention designs a nested solution framework that combines the improved particle swarm optimization algorithm (IBPSO) in the outer layer with the original dual interior point method (PDIPM) in the inner layer.

[0141] The improved particle swarm optimization (IBPSO) algorithm includes:

[0142] 1. The position vector of each particle in the particle swarm corresponds to a set of binary codes for switch states. Traditional binary particle swarm optimization (PSO) algorithms are suitable for discrete space searches, but when directly applied to the topology reconstruction of ring network switchgear, the randomly generated combinations of switch states are prone to violating radial operation constraints and fault isolation maintenance constraints. Therefore, it is necessary to perform a particle repair operation on particles that do not satisfy these two constraints, adjusting the switch states to meet these constraints. This repair operation ensures that each particle in the initial population and during the iteration process corresponds to an engineering-feasible topology, avoiding the problem of infeasible solutions flooding the traditional PSO algorithm due to blind searching in discrete space.

[0143] 2. Adaptive decreasing inertia weight and Sigmoid mapping. The particle velocity update formula is:

[0144] ;

[0145] In the above formula, , The first The second iteration and the first The second iteration The particle in the first Speed ​​in dimensions; For the first The second iteration The particle in the first Position on the dimension; For individual learning factors; As a social learning factor; , All are independent uniform random numbers within the interval [0-1]. For the first The inertia weight of the next iteration; For the first The particle in the first The optimal position of an individual in the historical dimension; For the entire particle swarm in the th The historical global optimal position on the dimension;

[0146] The updated particle velocity values ​​are mapped using the Sigmoid function: If random number Less than Then the particle position ,otherwise After updating particle positions, a repair operation is performed again to ensure the population remains within the feasible region. The particle inertia weights are adjusted. The value is linearly reduced from 0.9 to 0.4 to balance global exploration with local development.

[0147] The solution steps of the original dual interior point method (PDIPM) include:

[0148] 1. Introduce slack variables to transform inequality constraints into equality constraints, construct a logarithmic barrier function for the slack variables and incorporate it into the objective function of the capacity expansion load allocation model;

[0149] 2. Derive the KKT conditions for the Lagrange function to obtain the nonlinear equation system;

[0150] 3. The Newton-Raphson method is used to iteratively solve the corrected equations of the nonlinear equation system, and the barrier parameter in the logarithmic barrier function is gradually reduced along the central path until convergence.

[0151] The outer layer first decodes the optimal particle position vector obtained by the IBPSO method into specific switching state combinations, forming a candidate topology. This topology is then passed as a fixed parameter to the capacity expansion load allocation model. The capacity expansion load allocation model calls the PDIPM method to solve the power outage load allocation subproblem, outputting the optimal load allocation scheme under this topology and the corresponding actual system operating margin. The actual system operating margin and system active power loss obtained from the inner layer are combined with the total number of switching operations obtained from the outer layer, and weighted according to preset weight coefficients to obtain the comprehensive fitness value of the particle. The current comprehensive fitness is compared with the historical best comprehensive fitness value of the particle. If the current comprehensive fitness is better, the individual optimal position is updated based on the current comprehensive fitness. The current comprehensive fitness is also compared with the historical global best comprehensive fitness. If the current comprehensive fitness is better, the global optimal position is updated based on the current comprehensive fitness. After evaluating all particles in the current iteration, the inertia weight value is updated according to the inertia weight decreasing formula, and then the velocity vector and position vector of each particle are updated. The velocity vector update is determined by the particle's historical best position, global best position, and random perturbation term. The position vector update is determined by mapping the velocity value to the probability of a value between 0 and 1 using the Sigmoid function and then determining it through random sampling. Feasibility checks and repairs are performed again on the newly generated particle positions to ensure that the next generation of the population remains within constraints. This process is repeated until the preset maximum number of iterations is reached. After the algorithm terminates, the switching state corresponding to the globally optimal particle is output as the optimal topology reconstruction scheme, along with the optimal load allocation scheme obtained by the interior-point method under this topology. To enhance decision-making flexibility, the algorithm synchronously records all non-dominated solutions to form a Pareto front during the iteration process, as shown in the following figure. Figure 4 As shown, the overall satisfaction level of each non-dominated solution is calculated, and the solution with the highest overall satisfaction level is selected as the final solution. Specifically, the overall satisfaction level of the non-dominated solutions is calculated according to the following formula:

[0152] ; ;

[0153] In the above formula, For the first The overall satisfaction level of each solution; For preference weights; For the first The solution is at the th solution. Satisfaction with each objective function; For the first The maximum value of each objective function; For the first Minimum value on each objective function; For the first The solution is at the th solution. The objective function value, The time represents the actual operating margin of the system obtained from the inner layer. The time represents the system active power loss obtained from the inner layer. The time indicates the total number of switch operations obtained from the outer layer.

[0154] S5. The digital twin model enters the capacity expansion operation state based on the optimal topology reconfiguration scheme and the optimal capacity expansion load allocation scheme, and exits the capacity expansion operation state after the faulty switchgear is repaired. After entering the capacity expansion operation state, the digital twin model temporarily raises the overcurrent protection setting value of the switchgear allocated to the capacity expansion load to ensure sufficient delay in the event of overcurrent and avoid false tripping due to short-term fluctuations.

[0155] Specifically, after entering the capacity expansion operation state, the digital twin model collects the operating parameters of each non-faulty switchgear at a period of 10ms and determines whether an early warning is triggered. If the operating parameters reach a preset threshold, an early warning is triggered (for example, the conductor temperature reaches the preset early warning threshold of 88°C). After the early warning is triggered, dynamic adjustments are made step by step from the first priority measure to the third priority measure until the early warning disappears.

[0156] First priority measure: Use a consensus algorithm to redistribute the load of the capacity expansion load allocation scheme to average the overload rate of each non-faulty switchgear;

[0157] Second priority measures: If the warning is still triggered after the first priority measures are adopted, the digital twin will start orderly load shedding according to the preset load shedding sequence. The load shedding sequence is preset according to the importance of the load, and interruptible loads and secondary industrial and commercial loads are cut off first to ensure that the power supply of important users is not affected.

[0158] Third priority measure: If the warning is still triggered after the second priority measure is adopted, the non-faulty switchgear that triggered the warning by the digital twin control will be forced to exit the capacity expansion mode, the transferred load undertaken by the non-faulty switchgear will be cut off and the protection setting value restoration command will be issued, so that the overcurrent protection setting value of the non-faulty switchgear will be restored from the temporary raised value during the capacity expansion operation state to the rated current value, so as to prevent damage to the healthy equipment of the ring network switchgear system.

[0159] Performance verification:

[0160] Taking a 10kV distribution network ring switchgear system of model KYN28-12 as an example, this ring network consists of five ring switchgear units with a rated current of 1250A connected in a daisy-chain configuration. The method described in this invention is used for capacity expansion and coordinated recovery control.

[0161] (1) Construct a digital twin model for each ring network switchgear. In the geometric model, the cabinet dimensions are set to 1000×1800×2350mm, the main busbar dimensions are 2×125×10mm, and the internal busbar dimensions are 2×125×10mm; in the thermal model, the equivalent heat capacity is identified. It is 52000 J / K, with an equivalent thermal conductivity of 1000 J / K. The temperature coefficient of conductor resistance is 82 W / K. It is 0.00393K. -1 In the behavioral model, the timing characteristics of circuit breaker opening and closing actions are set as follows: the total closing time is approximately 45ms, and the total opening time is approximately 35ms; the torque characteristics of the electric operating mechanism are: the total closing time is approximately 80ms, and the total opening time is approximately 30ms; the timing characteristics of auxiliary contact actions are: during the closing process, the closing auxiliary contact closes approximately 3ms after the main contact contacts; approximately 2ms after the main contact reaches its position, the opening auxiliary contact opens; during the opening process, approximately 5ms after the main contact begins to move, the opening auxiliary contact opens; approximately 2ms after the main contact reaches its opening position, the closing auxiliary contact opens; the operating characteristics of load switch and grounding switch are: the closing time of the load switch is approximately 60ms, and the opening time is approximately 50ms. The grounding switch closing time is approximately 100ms, and the opening time is approximately 80ms. The operating torque is approximately 60%-70% of that of the circuit breaker. The cumulative number of operations for each operating mechanism is recorded. The rated mechanical life of the circuit breaker is approximately 10,000 operations, the load switch approximately 5,000 operations, and the grounding switch approximately 2,000 operations. In the rule model, the insulation material temperature rating is set to F, the contact safety temperature threshold is 90℃, and the temperature rise rate limit is 0.5℃ / min. The switchgear is rated at 1250A. The real-time operating parameters of each switchgear during initial operation are shown in Table 1.

[0162] Table 1 Real-time operating parameters of each switchgear during initial operation

[0163] RMU-1 640 51.2% 28 52 RMU-2 760 60.8% 28 58 RMU-3 580 46.4% 28 49 RMU-4 820 65.6% 28 62 RMU-5 700 56.0% 28 55

[0164] (2) Assume that at time T0, RMU-2 experiences an insulation breakdown fault. The digital twin performs fault detection and location based on the real-time mapped physical state. At the moment the fault occurs, the digital twin detects the following fault characteristics: the A-phase current of RMU-2 suddenly drops from 760A to 0A, with a current change of 760A. Although this does not exceed the set value of 6250A, the switch status sensor detects that the RMU-2 incoming circuit breaker is abnormally tripped (the switch status changes from closed to open without corresponding operation instructions). At the same time, the RMU-2 partial discharge sensor detects that the amplitude of the ultra-high frequency signal exceeds the warning threshold (20dB) and the duration exceeds 5 power frequency cycles (100ms). This fault characteristic is characterized by an enhanced partial discharge signal accompanied by an abnormal temperature increase (the conductor temperature of RMU-2 before the fault was 58℃, slightly higher than other switch cabinets), and there is no overcurrent phenomenon, which is consistent with the typical characteristics of an insulation fault. Therefore, it is determined that an insulation fault has occurred. By comparing the measured values ​​of the current transformers upstream and downstream of RMU-2, the current on the outgoing side of upstream RMU-1 and the incoming side of downstream RMU-3 were normal, while the current of RMU-2 itself suddenly dropped to zero. Kirchhoff's current law was used to determine that the fault section was located inside RMU-2. At the same time, the temperature distribution cloud map of the fiber optic temperature sensor array was analyzed, and an abnormal temperature point was identified at the location of the insulation component inside RMU-2, further confirming that the fault point was located in the main insulation part of RMU-2. The entire fault detection and location process was completed within 50ms.

[0165] (3) After completing fault detection and fault location, the digital twin generates an isolation scheme: trip the sectionalizing switches of the upstream and downstream adjacent switchgear (RMU-1 and RMU-3) of RMU-2 to isolate the faulty section, but this will cause some non-faulty loads to lose power. Update the status of RMU-2 to out of operation, update the status of the incoming circuit breaker of RMU-2 to open, and identify and mark the remaining four non-faulty switchgear (RMU-1, RMU-3, RMU-4, RMU-5). Record their current operating current, conductor temperature, environmental parameters and other operating parameters, and conduct a health status assessment for each non-faulty switchgear to obtain a comprehensive health score. According to the calculation, RMU-3 has the highest comprehensive health score and RMU-4 has the lowest. The digital twin outputs the following information for maintenance personnel to refer to during inspections and for subsequent steps: the updated ring network topology diagram (RMU-2 is out of operation, and its incoming circuit breaker is tripped); a list of non-faulty switchgear and corresponding real-time operating parameters and comprehensive health scores; the total power loss load is 760A (i.e., the total incoming current before the RMU-2 fault); the fault type and location result (RMU-2 internal insulation breakdown, the fault point is located in the main insulation part).

[0166] (4) The digital twin model reads the current operating parameters of the four non-faulty switchgear and calculates the short-term emergency capacity expansion threshold. RMU-1: Current operating current 640A, conductor temperature 52℃, ambient temperature 28℃, ambient humidity 55℃; after identification, the equivalent heat capacity is 52000J / K, the equivalent thermal conductivity is 82W / K, and the conductor resistance temperature coefficient is 0.00393K. -1 At 20℃, the conductor's AC resistance is 0.00012 Ω / m. The maximum allowable current satisfying all safe operating constraints was calculated. The results show that when the maximum allowable current is 560A, the conductor temperature reaches 94.8℃ at the end of 30 minutes of operation, with the peak temperature rise rate occurring at the 5th minute at 0.48℃ / min, all within the safety constraints. However, when the current continues to increase to 1570A, the temperature reaches 95.3℃ at the end of 30 minutes, exceeding the safety threshold. Therefore, the short-term emergency capacity expansion threshold for RMU-1 is determined to be 1560A, with an overload rate of 124.8%. The calculated short-term emergency capacity expansion thresholds for the four non-faulty switchgear are shown in Table 2.

[0167] Table 2 Short-term emergency capacity expansion thresholds for four non-faulty switchgear cabinets

[0168]

[0169] (5) The model was solved using Table 2 as the constraint input for the multi-objective optimization model. IBPSO settings: particle population size 50, maximum number of iterations 200, initial inertia weight 0.9 which decreased linearly to 0.4 during iteration, and learning factor 2.0. The optimal topology reconstruction scheme was obtained as follows: The sectionalizing switch between RMU-1 and RMU-2 remains open to isolate the fault. The sectionalizing switch between RMU-2 and RMU-3 is closed, forming a load transfer path; The interconnection switch between RMU-3 and RMU-4 remains open to prevent circulating current. The sectionalizing switch between RMU-4 and RMU-5 remains closed; the incoming circuit breaker of RMU-2 remains open for fault isolation.

[0170] For the For each non-faulty switchgear, its capacity expansion margin Defined as ,in During the capacity expansion operation period The actual operating current of a non-faulty switchgear For the first Short-term emergency capacity expansion threshold for each non-faulty switchgear; overload rate Defined as , For the first The current rating of each non-faulty switchgear. Capacity expansion margin. The overload rate reflects the safety margin relative to the short-term emergency capacity expansion threshold, while the overload rate reflects the load level relative to a fixed current rating; both together describe the operating status of the switchgear. When... When the switchgear is operating within its rated range, it is considered normal operating condition. This indicates that the switchgear is in an increased capacity operation state, exceeding the rated value but still within the thermal safety boundary. This indicates that the switchgear is overloaded and exceeds the safety threshold, and should be prohibited.

[0171] The optimal load allocation scheme for capacity expansion is as follows: the 760A power outage load is jointly handled by RMU-3 and RMU-5. RMU-3 handles 320A of the power outage load, resulting in a final operating current of 580 + 320 = 900A, an overload rate of 900 / 1250 = 72.0%, and a capacity expansion margin of 1 - 900 / 1600 = 43.8%. RMU-5 handles 440A of the power outage load, resulting in a final operating current of 700 + 440 = 1140A, an overload rate of 1140 / 1250 = 91.2%, and a capacity expansion margin of 1 - 1140 / 1580 = 27.8%. RMU-1 and RMU-4 do not participate in the capacity expansion and maintain their original operating status (RMU-1 operating current is 640A, and RMU-4 operating current is 820A).

[0172] Feasibility Verification: The final operating current of RMU-3 (900A) is less than the corresponding short-term emergency capacity expansion threshold of 1600A, and the final operating current of RMU-5 (1140A) is less than the corresponding short-term emergency capacity expansion threshold of 1580A, thus meeting the constraints of the short-term emergency capacity expansion threshold for non-faulty switchgear. Power flow calculations show that after reconfiguration, the voltage of each node remains within 0.97 pu-1.02 pu, meeting the power supply voltage quality requirements. After reconfiguration, the network exhibits a radial structure. The power supply on the RMU-1 side independently powers RMU-1; the power supply on the RMU-5 side powers RMU-3, RMU-4, and RMU-5 via a tie path. All non-faulty loads have been restored to power, with no islanding or loops, meeting the topology connectivity constraints. Although RMU-3 and RMU-5 exceed their rated current of 1250A, they operate safely within the 30-minute emergency capacity expansion time window, and their real-time temperatures do not exceed the 95℃ limit. It is evident that the obtained capacity expansion load allocation scheme minimizes the comprehensive objective function while strictly meeting the constraints, taking into account the speed of fault recovery, the safety of equipment operation, and the economy of system operation.

[0173] (6) The digital twin performs switching operations based on the optimal topology reconfiguration scheme. After confirming the successful topology reconfiguration, it issues an expansion operation command according to the expansion load allocation scheme. The digital twin monitoring data shows that the actual operating current of RMU-3 is 905A and the actual operating current of RMU-5 is 902A, both within their respective expansion threshold ranges. All power-loss loads have been restored to power supply, and the system enters the expansion operation state.

[0174] The digital twin also temporarily raises the overcurrent protection setting of RMU-3 from the rated value of 1250A to 1472A and the overcurrent protection setting of RMU-5 from the rated value of 1250A to 1454A, and adjusts the inverse-time overcurrent protection time multiplier setting of both to 0.15. RMU-1 and RMU-4 do not participate in capacity expansion operation, so their protection settings remain unchanged, ensuring sufficient delay in overcurrent protection to avoid false tripping due to short-term fluctuations. The overcurrent protection setting is a protection action threshold between the rated current and the short-term emergency capacity expansion threshold, and is temporarily raised during capacity expansion operation. When the actual operating current continuously rises and reaches this overcurrent protection setting, the protection device trips according to the inverse-time characteristic delay, thus forming a three-layer progressive protection system from real-time monitoring, thermal safety boundary to fault protection.

[0175] Temporary raising of overcurrent protection settings should follow the relationship curve between the inverse-time overcurrent protection operating time and the operating current: ; For the switchgear operation time, This is the temporarily adjusted overcurrent protection setting value. The time multiplier setting for inverse time overcurrent protection is 0.1-0.3. , These are all curve coefficients, taken as 2 and 0.14 respectively. The operating current is shown in the graph below. Taking RMU-3 as an example, the relationship between its inverse time overcurrent protection operating time and operating current is shown in the graph below. Figure 5 As shown, when the operating current reaches 1472A, it does not operate at the set value; when the operating current reaches 1600A, the protection operation time is about 1.2 seconds, ensuring sufficient time for adjustment; when the operating current further increases to 2000A, the protection operation time is shortened to about 0.3 seconds, ensuring rapid fault clearing.

[0176] During the capacity expansion operation, the digital twin detected an overload rate of 46.6% for RMU-3 and 57.1% for RMU-5. A consensus algorithm was used to gradually converge RMU-3 and RMU-5 to maintain equilibrium through iterative communication.

[0177] (7) The digital twin collects the operating parameters of each non-faulty switchgear in the ring network switchgear system at a period of 10ms. When the ring network switchgear system reaches the 15th minute of capacity expansion operation, the digital twin detects that the ambient temperature gradually rises from the initial 28℃ to 32℃. Based on the identified thermal circuit model, the digital twin makes a rolling prediction of the temperature change trajectory of RMU-5 in the next 5 minutes. The prediction results show that under the current operating current of 1140A and ambient temperature of 32℃, the conductor temperature of RMU-5 will continue to rise at a rate of 0.5℃ / min, and is expected to reach 92℃ after 20 minutes (i.e., the 35th minute of capacity expansion operation), approaching the safety threshold of 95℃. When the predicted temperature reaches the preset warning threshold of 88℃, the digital twin immediately triggers the warning signal and then makes dynamic adjustments according to the first priority to the third priority measures.

[0178] This example demonstrates that the rapid temperature rise problem can be resolved through load redistribution using the first priority measure, eliminating the need for second or third priority measures. In the first priority measure, the digital twin retrieves the real-time operating parameters of each non-faulty switchgear in the ring network system: RMU-3 operating current 900A, conductor temperature 61℃, capacity expansion threshold 1600A; RMU-4 operating current 820A, conductor temperature 65℃, capacity expansion threshold 1470A; RMU-5 operating current 1140A, conductor temperature 79℃, capacity expansion threshold 1580A. The local controllers of each switchgear exchange overload rate information via the communication network: RMU-3 56.3%, RMU-4 55.8%, and RMU-5 72.2%. Calculations show that RMU-4 still has 650A of remaining capacity expansion, capable of handling some of the transferred load. Using a consensus algorithm and iterative calculations, a load adjustment scheme is determined: 80A of load from RMU-5 is transferred to RMU-4. The digital twin performed the following operations according to the load adjustment plan: appropriately reducing the outgoing load of RMU-5 while correspondingly increasing the outgoing load of RMU-4. After the adjustment, the current of RMU-5 dropped to 1060A, the overload rate was 67.1%, and the temperature rise rate dropped to 0.3℃ / min; the current of RMU-4 rose to 900A, the overload rate was 61.2%, still within the capacity increase threshold of 1470A. The overload rates of each cabinet tended to be balanced, and the margin was restored.

[0179] (8) After the maintenance personnel complete the fault repair work on RMU-2 and it is ready for reoperation, the digital twin detects through the switch status sensor that the RMU-2 incoming circuit breaker has been manually closed and the equipment status has returned to normal, and confirms the newly available capacity (RMU-2 rated capacity 1250A, currently unloaded). At this time, the digital twin automatically executes the capacity expansion mode exit procedure. The specific steps of the capacity expansion mode exit procedure are as follows:

[0180] The first step involves the digital twin, with the current topology (RMU-2 out of service, RMU-3 and RMU-5 operating with increased capacity), issuing a control command: disconnect the sectionalizing switch between RMU-2 and RMU-3, and switch the 760A load originally belonging to RMU-2 back to RMU-2 for power supply. This operation requires only one switching action, minimizing the impact on the system.

[0181] Step 2: After the load is switched back to the faulty switchgear, the digital twin sends a protection setting value restoration command to RMU-3 and RMU-5, restoring the overcurrent protection setting value from the temporarily raised adjustment value (1472A and 1454A) to the rated value of 1250A, and the inverse time multiplier setting value. Restored to the normal operating value of 0.1;

[0182] The third step involves the digital twin monitoring system detecting that the operating current of RMU-2 is normal, the current of RMU-3 has recovered to 580A, and the current of RMU-5 has recovered to 700A. All switch cabinets are operating below the rated current, the system status is marked as normal operation, and the capacity expansion mode is completely exited.

[0183] The beneficial effects of the method described in this invention include:

[0184] 1. By using the short-term emergency capacity increase threshold to evaluate the dynamic current carrying capacity of non-faulty switchgear in real time, the limitation of traditional ring main units relying solely on rated current for protection is broken. This enables the potential of remaining equipment to be tapped under fault conditions, allowing loads that could not be transferred due to insufficient current carrying capacity to be taken over by non-faulty switchgear. This significantly improves the power supply recovery rate and the reliability of the distribution network, and reduces the scope of unplanned power outages under fault conditions.

[0185] 2. To address the complexities of multi-physics coupling calculations in ring network switchgear, a digital twin model is employed to accurately model the thermal dynamics of the switchgear. By combining a thermal circuit model with online parameter identification, the calculation model is simplified while maintaining accuracy in engineering calculations. The digital twin model provides an accurate simulation and preview environment, enabling the prediction of heat accumulation after capacity expansion. This ensures that capacity expansion operations are performed within the equipment's safety boundaries, avoiding the risk of escalating faults due to overload.

[0186] 3. The constructed multi-objective optimization model comprehensively considers multiple optimization indicators such as network loss, number of operations, and operational margin, achieving globally optimal control while ensuring thermal safety. Furthermore, a hybrid solution strategy combining an improved particle swarm optimization algorithm and the primal-dual interior-point method is adopted to solve the problem of traditional optimization algorithms easily getting trapped in local optima when optimizing mixed discrete and continuous variables. Finally, a consensus algorithm is used to achieve distributed coordination among the switchgear, improving the system's scalability and robustness.

[0187] 4. During the collaborative control execution process, the organic combination of timing control, collaborative mechanisms, and dynamic coordination of protection settings enables rapid load recovery and safe capacity expansion operation after a fault. After the faulty equipment is repaired, it can automatically and smoothly exit the capacity expansion mode without affecting normal power supply. The digital twin records data throughout the entire capacity expansion operation process, providing data support for subsequent equipment status assessment, remaining life prediction, and maintenance strategy optimization.

[0188] Example 2:

[0189] See Figure 6 A dynamic capacity expansion and collaborative recovery control system for ring network switchgear based on digital twins includes a model building module, a fault isolation module, a capacity expansion threshold calculation module, a topology reconstruction and load distribution module, and a capacity expansion operation control module. The model building module is used to establish a digital twin model of the ring network switchgear system; specifically, the model building module executes S1 in Example 1, which will not be elaborated here. The fault isolation module is used to locate and isolate faults based on the operating parameters of the ring network switchgear system when a switchgear fault occurs, using the digital twin model, and updates the topology of the digital twin model after fault isolation; specifically, the fault isolation module executes S2 in Example 1, which will not be elaborated here. The capacity expansion threshold calculation module is used to obtain the current operating parameters of each non-faulty switchgear after the topology is updated, and calculate the short-term emergency capacity expansion threshold of each non-faulty switchgear based on the current operating parameters of each non-faulty switchgear; specifically, the capacity expansion threshold calculation module executes S2 in Example 1. 3. (Details omitted here) The topology reconstruction and load allocation module is used to construct a two-layer optimization model. The outer layer model of the two-layer optimization model is a topology reconstruction model with the optimization objectives of minimizing the total number of switch operations and maximizing the estimated system operating margin. The inner layer model is a capacity expansion load allocation model with the objectives of minimizing system active power loss and maximizing system actual operating margin. The estimated system operating margin and the actual system operating margin are calculated based on the short-term emergency capacity expansion threshold. Solving the two-layer optimization model yields the optimal topology reconstruction scheme and the optimal capacity expansion load allocation scheme. Specifically, the topology reconstruction and load allocation module is used to execute S4 in Example 1, which will not be detailed here. The capacity expansion operation control module is used to enter the capacity expansion operation state based on the optimal topology reconstruction scheme and the optimal capacity expansion load allocation scheme using a digital twin model, until the faulty switchgear is repaired and the capacity expansion operation state is exited. Specifically, the capacity expansion operation control module is used to execute S5 in Example 1, which will not be detailed here.

[0190] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program goods. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program goods embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0191] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, as well as combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0192] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0193] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0194] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A dynamic capacity expansion and collaborative recovery control method for ring network switchgear based on digital twins, characterized in that: The dynamic capacity expansion and coordinated recovery control method for ring network switchgear includes: Establish a digital twin model of the ring network switchgear system; Using a digital twin model, fault location and isolation are performed based on the operating parameters of the ring network switchgear system when a switchgear failure occurs, and the topology of the digital twin model after fault isolation is updated. After the topology is updated, obtain the current operating parameters of each non-faulty switchgear and calculate the short-term emergency capacity expansion threshold of each non-faulty switchgear based on the current operating parameters of each non-faulty switchgear. A two-layer optimization model is constructed. The outer layer model is a topology reconfiguration model with the optimization objectives of minimizing the total number of switching operations and maximizing the estimated system operating margin. The inner layer model is a capacity expansion load allocation model with the objectives of minimizing system active power loss and maximizing system actual operating margin. The estimated system operating margin and the actual system operating margin are calculated based on short-term emergency capacity expansion thresholds. Solving the two-layer optimization model yields the optimal topology reconfiguration scheme and the optimal capacity expansion load allocation scheme. The digital twin model enters the capacity expansion operation state based on the optimal topology reconstruction scheme and the optimal capacity expansion load allocation scheme, until the faulty switchgear is repaired and the capacity expansion operation state is exited.

2. The dynamic capacity expansion and collaborative recovery control method for ring network switchgear based on digital twins according to claim 1, characterized in that: The calculation of the short-term emergency capacity expansion threshold for each non-faulty switchgear based on its current operating parameters includes: Preset safe operation constraints for non-faulty switchgear. Calculate the maximum allowable current that satisfies the safe operation constraints for each non-faulty switchgear based on its current operating parameters. Use the maximum allowable current as the short-term emergency capacity expansion threshold for that non-faulty switchgear. The safe operation constraints include conductor maximum allowable temperature constraints, temperature rise rate constraints, and heat accumulation constraints.

3. The dynamic capacity expansion and collaborative recovery control method for ring network switchgear based on digital twins according to claim 1 or 2, characterized in that: The objective function of the topology reconstruction model is: ； ； ； ； In the above formula, The objective function for the topology reconstruction model; , All are outer layer weight coefficients; Indicates the total number of switch operations; This indicates the system's estimated operational margin; For the first A non-faulty switchgear in the topology reconfiguration scheme The following state; For the first time after fault isolation The initial state of a non-faulty switchgear; In the topology reconfiguration scheme When the total power loss load is evenly distributed according to the rated capacity of each cabinet, the first... Estimated current for a non-faulty switchgear; This represents the total number of non-faulty switchgear. This is the threshold for short-term emergency capacity expansion; The first time before the failure occurred The initial load current of a non-faulty switchgear; For the first Rated capacity of a non-faulty switchgear; For the first Rated capacity of a non-faulty switchgear; This is the total power-off operating current; The constraints of the topology reconstruction model include outer current-carrying prediction constraints, the expression of which is: ； In the above formula, This is the margin coefficient.

4. The dynamic capacity expansion and collaborative recovery control method for ring network switchgear based on digital twins according to claim 1 or 2, characterized in that: The objective function of the capacity expansion load allocation model is: ； ； ； ； In the above formula, The objective function for the capacity expansion load allocation model; , All are inner layer weighting coefficients; For system active power loss; This represents the actual operational margin of the system. This represents the optimal topology reconstruction scheme obtained from the topology reconstruction model. A defined set of closed branches; , Branch roads Active power and reactive power flowing upstream; branch road The resistance; For nodes The voltage amplitude; This is the threshold for short-term emergency capacity expansion; Indicates the first Each non-faulty switchgear is included in the load distribution scheme for capacity expansion. The actual operating current under these conditions; This represents the total number of non-faulty switchgear. The first time before the failure occurred The initial load current of a non-faulty switchgear; For load allocation variables, it represents the load allocation scheme under capacity expansion. The Middle The power failure load branch is assigned to the first The active power of a non-faulty switchgear; The system's nominal line voltage; The load power factor angle; The constraints of the capacity expansion load allocation model include a short-term emergency capacity expansion threshold constraint for non-faulty switchgear, and the expression for the short-term emergency capacity expansion threshold constraint for non-faulty switchgear is as follows: 。 5. The dynamic capacity expansion and collaborative recovery control method for ring network switchgear based on digital twins according to claim 1 or 2, characterized in that: The dynamic capacity expansion and coordinated recovery control method for ring network switchgear also includes: After entering the capacity expansion operation state, the digital twin model continuously monitors the operating parameters of each non-faulty switchgear and determines whether an early warning is triggered. If an early warning is triggered, dynamic adjustments are made step by step from the first priority measure to the third priority measure until the early warning disappears. First priority measure: Use a consensus algorithm to redistribute the load of the capacity expansion load allocation scheme to average the load rate of each non-faulty switchgear; Second priority measures: If the warning is still triggered after the first priority measures are adopted, the digital twin will start orderly load reduction according to the preset load reduction sequence, which is preset according to the importance of the load. Third priority measure: If the warning is still triggered after the second priority measure is adopted, the non-faulty switch cabinet that triggered the warning by the digital twin control will be forced to exit the capacity expansion mode and the transferred load undertaken by the non-faulty switch cabinet will be cut off.

6. The dynamic capacity expansion and collaborative recovery control method for ring network switchgear based on digital twins according to claim 1 or 2, characterized in that: The digital twin model includes a geometric model, a behavioral model, a rule model, and a thermal circuit model. The geometric model is a scaled simulation model established for the ring network switchgear system. The behavioral model describes the operating characteristics, timing relationships, and dynamic response processes of each switchgear. The rule model sets the insulation material temperature rating, contact safety temperature threshold, temperature rise rate limit, and emergency capacity expansion operation time limit for each switchgear. The thermal circuit model identifies the equivalent heat capacity and equivalent thermal conductivity of each switchgear. The expression of the thermal circuit model is: ； In the above formula, Equivalent heat capacity; The current conductor temperature; This represents the current power loss. Equivalent thermal conductivity; This represents the current ambient temperature.

7. The dynamic capacity expansion and collaborative recovery control method for ring network switchgear based on digital twins according to claim 1 or 2, characterized in that: After entering the capacity expansion operation state, the digital twin model will temporarily raise the overcurrent protection setting value of the switchgear allocated to the capacity expansion load from the rated current value to the adjusted value; In the third priority measure, the digital twin issues an overcurrent protection setting value restoration command to the switchgear allocated to the increased capacity load, so that its overcurrent protection setting value is restored from the adjustment value when the increased capacity operation state to the current rated value.

8. The dynamic capacity expansion and collaborative recovery control method for ring network switchgear based on digital twins according to claim 1 or 2, characterized in that: The bi-layer optimization model is solved using the particle swarm optimization algorithm and the original-dual interior point method. The solution steps include: First, initialize the particle swarm. The position vector of each particle in the particle swarm corresponds to a set of topology reconstruction schemes. Based on the topology reconstruction scheme, use the inner layer original dual interior point method to solve the capacity expansion load allocation model and output the optimal load allocation scheme. The total number of switching operations under the topology reconstruction scheme corresponding to the current particle is weighted and calculated by combining the actual operating margin of the system and the active power loss of the system under the optimal load allocation scheme obtained based on the topology reconstruction scheme. The current comprehensive fitness of the particle is compared with the historical best comprehensive fitness of the particle. If the current comprehensive fitness is better, the individual optimal position vector is updated based on the current comprehensive fitness. The current comprehensive fitness is compared with the historical global best comprehensive fitness. If the current comprehensive fitness is better, the global optimal position vector is updated based on the current comprehensive fitness. After completing the comprehensive fitness calculation of all particles at the current iteration number, update the velocity vector and position vector of each particle; repeat the above steps until the preset maximum iteration number is reached, output the topology reconstruction scheme corresponding to the globally optimal particle as the optimal topology reconstruction scheme, and output the optimal load allocation scheme corresponding to the optimal topology reconstruction scheme.

9. The dynamic capacity expansion and collaborative recovery control method for ring network switchgear based on digital twins according to claim 8, characterized in that: The particle swarm algorithm is an improved particle swarm algorithm, which includes: performing a particle repair operation after initializing the particle swarm and updating the particle positions. The particle repair operation refers to adjusting the particle position vector so that the topology reconstruction scheme corresponding to the particle satisfies the radial running constraint and the fault isolation maintenance constraint.

10. A dynamic capacity expansion and collaborative recovery control system for ring network switchgear based on digital twins, characterized in that: The dynamic capacity expansion and collaborative recovery control system for the ring network switchgear includes: The model building module is used to create a digital twin model of the ring network switchgear system; The fault isolation module is used to locate and isolate faults based on the operating parameters of the ring network switchgear system when a switchgear fault occurs, using a digital twin model, and to update the topology of the digital twin model after fault isolation. The capacity expansion threshold calculation module is used to obtain the current operating parameters of each non-faulty switchgear after the topology is updated, and to calculate the short-term emergency capacity expansion threshold of each non-faulty switchgear based on the current operating parameters of each non-faulty switchgear. The topology reconfiguration and load allocation module is used to construct a two-layer optimization model. The outer layer model is a topology reconfiguration model with the optimization objectives of minimizing the total number of switching operations and maximizing the estimated system operating margin. The inner layer model is a capacity expansion load allocation model with the objectives of minimizing system active power loss and maximizing system actual operating margin. The estimated system operating margin and the actual system operating margin are calculated based on short-term emergency capacity expansion thresholds. Solving the two-layer optimization model yields the optimal topology reconfiguration scheme and the optimal capacity expansion load allocation scheme. The capacity expansion operation control module is used to enter the capacity expansion operation state based on the optimal topology reconstruction scheme and the optimal capacity expansion load allocation scheme using a digital twin model, until the faulty switchgear is repaired and the capacity expansion operation state is exited.

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