Low-voltage power distribution network multi-mode collaborative self-healing method, system and equipment based on capacity regulating transformer and medium

Abstract

The invention relates to the technical field of low-voltage power distribution network operation control and fault processing, and discloses a low-voltage power distribution network multi-mode collaborative self-healing method, system and device based on a capacity regulating transformer and a medium. And according to the tap position of the capacity-regulating transformer and the state of the distributed power supply, a system reconstruction mode is decided. And an optimal power supply island is divided by utilizing an improved graph theory algorithm, so that the distributed power supply can be utilized to supply power to the key load to the maximum extent. An optimization model with capacity regulating transformer loss, network loss and switching action times as targets is established for a main network recovery part, and meanwhile, a GA-BFGS hybrid algorithm is adopted for efficient solving. The method effectively solves the problems that a traditional low-voltage power distribution network is insufficient in recovery capability, and the cooperation efficiency of the transformer and the distributed power supply is low, and remarkably improves the power supply reliability and economy.

Description

Technical Field

[0001] This invention relates to the field of low-voltage distribution network operation control and fault handling technology, and in particular to a multi-mode collaborative self-healing method, system, equipment and medium for low-voltage distribution networks based on a capacity-adjustable transformer. Background Technology

[0002] In existing technologies, adjustable-capacity transformers, which automatically adjust their operating capacity according to the load size, are widely used in low-voltage distribution networks with large load fluctuations. These adjustable-capacity transformers can effectively reduce no-load losses in the low-voltage distribution network. However, with the increasing integration of distributed energy sources into low-voltage distribution networks, the penetration rate of distributed power sources such as photovoltaics in low-voltage areas is continuously increasing.

[0003] In this context, how to coordinate the capacity regulation of the regulating transformer and the power supply of distributed generation when a fault occurs becomes a key objective. Solving this objective can achieve rapid and economical self-healing of the low-voltage distribution network. Traditional restoration methods often treat the transformer as a fixed parameter, neglecting the impact of its tap changes. This ignores the impact on system power flow and distributed generation absorption, ultimately leading to poor economic efficiency of the restoration plan, or even making the restoration plan infeasible. Summary of the Invention

[0004] In view of the aforementioned existing problems, the present invention is proposed.

[0005] Therefore, this invention provides a multi-mode collaborative self-healing method, system, equipment, and medium for low-voltage distribution networks based on adjustable-capacity transformers. This can solve the problem that traditional recovery methods do not consider the impact of adjustable-capacity transformer tap changes on system power flow and distributed power absorption, resulting in poor economic efficiency or even infeasibility of the recovery scheme.

[0006] To solve the above-mentioned technical problems, the present invention provides the following technical solution: In a first aspect, the present invention provides a multi-mode collaborative self-healing method for low-voltage distribution networks based on a capacity-adjustable transformer. This method is applicable to low-voltage distribution networks integrating capacity-adjustable transformers and distributed generation, and executes the following steps upon the occurrence of a fault: Based on the current tap position of the regulating transformer, the operating status of the distributed power source, and the location and severity of the fault, determine whether the system should enter islanded operation mode or main grid reconfiguration mode. For non-faulty areas not included in the island, a collaborative reconfiguration optimization model is constructed with the goal of minimizing total system loss and minimizing switching operation cost. The total system loss includes the winding loss of the regulating transformer and the network loss of the distribution network branch. A hybrid strategy combining genetic algorithm and quasi-Newton optimization algorithm is used to solve the cooperative reconstruction optimization model, and the output is a recovery strategy that includes the optimal tap-switching scheme of the capacity-adjusting transformer and the optimal operation sequence of the low-voltage switch. The recovery strategy is implemented, and the load rate of the adjustable transformer and the voltage level of key nodes are monitored in real time to ensure that the system operates safely and stably after reconstruction.

[0007] As a preferred embodiment of the multi-mode collaborative self-healing method for low-voltage distribution networks based on capacity-adjustable transformers described in this invention, it further includes: If the islanded operation mode is entered, the power supply support capacity of the islanded area will be determined based on the importance of the load, the electrical distance, and the current tap of the adjustable transformer. Generate the optimal islanding scheme to maximize the use of distributed power sources to continuously power critical loads.

[0008] As a preferred embodiment of the multi-mode collaborative self-healing method for low-voltage distribution networks based on adjustable-capacity transformers described in this invention, the objective function of the collaborative reconfiguration optimization model includes the following three sub-objectives: the total active power loss of the distribution network, the overall deviation between the voltage of all network nodes and the rated voltage, and the actual number of switches that need to be activated.

[0009] As a preferred embodiment of the multi-mode collaborative self-healing method for low-voltage distribution networks based on adjustable-capacity transformers described in this invention, the total active power loss of the distribution network is composed of the winding loss of the adjustable-capacity transformer at the current operating tap and the line loss caused by the current of each branch. The winding losses of the adjustable transformer are related to its no-load loss, load loss, loss coefficient corresponding to the current tap, actual apparent power, and rated capacity.

[0010] This preferred solution can accurately assess the total active power loss of the distribution network, thus providing more accurate data support for optimizing the operation of the distribution network. By clarifying that the total active power loss consists of the winding loss of the regulating transformer and the line loss, and the correlation between winding loss and multiple factors, effective loss reduction measures can be taken for different situations in actual operation.

[0011] As a preferred embodiment of the multi-mode collaborative self-healing method for low-voltage distribution networks based on adjustable-capacity transformers described in this invention, the optimization basis of the islanding scheme includes a power supply capacity coordination factor. The power supply capacity coordination factor is used to determine whether the sum of the total output of the distributed power source in the island and the power supply capacity that the adjustable transformer can provide at the current tap level is sufficient to meet the total demand of all candidate loads in the island. The candidate islanding scheme is only allowed to form when the power supply capacity meets the load demand.

[0012] As a preferred embodiment of the multi-mode collaborative self-healing method for low-voltage distribution networks based on adjustable-capacity transformers described in this invention, the method of solving the collaborative reconfiguration optimization model using a hybrid strategy combining genetic algorithms and quasi-Newtonian optimization algorithms includes two stages: In the first stage, a genetic algorithm is used to perform a global search on the switching state combinations and discrete taps of the adjustable transformer. The objective function is used as the fitness evaluation criterion to obtain a set of preliminary feasible recovery schemes. In the second stage, the transformer tap position in the optimal solution obtained in the first stage is fixed, the switch state variables are relaxed to continuous variables, and a quasi-Newton optimization algorithm is used for local fine-grained search to finally determine the precise switch operation sequence.

[0013] As a preferred embodiment of the multi-mode collaborative self-healing method for low-voltage distribution networks based on adjustable-capacity transformers described in this invention, the power flow constraints of the distribution network are modeled in a second-order conical convex relaxation form when constructing the collaborative reconfiguration optimization model to ensure the solvability and computational efficiency of the optimization problem.

[0014] Secondly, the present invention provides a multi-mode collaborative self-healing system for low-voltage distribution networks based on a capacity-adjustable transformer, comprising: The judgment module is used to determine whether the system should enter islanded operation mode or main grid reconfiguration mode based on the current tap position of the regulating transformer, the operating status of the distributed power source, and the location and severity of the fault. The optimization model building module is used to build a collaborative reconfiguration optimization model for non-faulty areas not included in the island, with the goal of minimizing the total system loss and the minimum switching operation cost. The total system loss includes the winding loss of the regulating transformer and the network loss of the distribution network branches. The solution module is used to solve the collaborative reconstruction optimization model using a hybrid strategy that combines genetic algorithms and quasi-Newton optimization algorithms, and outputs a recovery strategy that includes the optimal tap-switching scheme of the capacity-adjusting transformer and the optimal operation sequence of the low-voltage switch. The execution module is used to execute the recovery strategy and monitor the load rate of the adjustable transformer and the voltage level of key nodes in real time to ensure that the system operates safely and stably after reconstruction.

[0015] Thirdly, the present invention provides an electronic device including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the method described above.

[0016] Fourthly, the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method described above.

[0017] Compared with existing technologies, the beneficial effects of this invention are that it proposes a multi-mode collaborative self-healing method for low-voltage distribution networks based on adjustable-capacity transformers. For low-voltage distribution networks integrating adjustable-capacity transformers and distributed generation, the system reconfiguration mode is determined based on the tap position of the adjustable-capacity transformer and the state of the distributed generation when a fault occurs. An improved graph theory algorithm is then used to partition optimal power supply islands, thereby maximizing the utilization of distributed generation to power critical loads. For the main grid recovery part, an optimization model is established with adjustable-capacity transformer losses, network losses, and the number of switching operations as objectives, and a GA-BFGS hybrid algorithm is used for efficient solution. This invention effectively solves the problems of insufficient recovery capability and low collaborative efficiency between transformers and distributed generation in traditional low-voltage distribution networks, significantly improving power supply reliability and economy. Attached Figure Description

[0018] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1 This is a flowchart of a multi-mode collaborative self-healing method for low-voltage distribution networks based on a capacity-adjustable transformer, provided as an embodiment of the present invention.

[0020] Figure 2 This invention provides a multi-mode collaborative self-healing method for low-voltage distribution networks based on adjustable-capacity transformers, as shown in the IEEE 33-node distribution network topology diagram.

[0021] Figure 3 This is a topology diagram of the network after reconstruction following a fault 1 (a permanent fault occurs in branch 27) in a low-voltage distribution network based on a multi-mode collaborative self-healing method using a variable-capacity transformer, as provided in an embodiment of the present invention.

[0022] Figure 4 This is a topology diagram of the network after reconstruction following fault 2 (when branch 9 and branch 22 simultaneously experience permanent faults) in a low-voltage distribution network multi-mode collaborative self-healing method based on a variable-capacity transformer, as provided in an embodiment of the present invention.

[0023] Figure 5 This is a comparison diagram of node voltages before and after reconstruction for fault 1, provided as an embodiment of the present invention, for a multi-mode collaborative self-healing method for low-voltage distribution networks based on a variable-capacity transformer.

[0024] Figure 6This is a comparison diagram of node voltages before and after reconstruction for fault 2, provided as an embodiment of the present invention, for a multi-mode collaborative self-healing method for low-voltage distribution networks based on a variable-capacity transformer. Detailed Implementation

[0025] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of the present invention.

[0026] It should be noted in advance that the system mentioned in the embodiments as the subject of real-time operation refers to any system configured with this method.

[0027] Example 1, referring to Figure 1 This is the first embodiment of the present invention, which provides a multi-mode collaborative self-healing method for low-voltage distribution networks based on a capacity-adjustable transformer, comprising: This invention provides a method that can effectively solve the problems mentioned above. The following will describe in detail how to implement the multi-mode collaborative self-healing method for low-voltage distribution networks based on capacity-adjustable transformers using multiple embodiments. Figure 1 A flowchart of a multi-mode collaborative self-healing method for low-voltage distribution networks based on adjustable-capacity transformers is shown. The method is applicable to low-voltage distribution networks integrating adjustable-capacity transformers and distributed generation, and is executed after a fault occurs. S101: Based on the current tap position of the regulating transformer, the operating status of the distributed power source, and the location and severity of the fault, determine whether the system should enter islanded operation mode or main grid reconfiguration mode. In one optional implementation, the operating capacity and loss characteristics of the adjustable transformer can be determined based on its current tap position. For example, when the adjustable transformer is in the large capacity tap position, its rated capacity is 400kVA, with high no-load and load losses but strong power supply capacity. When it is in the small capacity tap position, its rated capacity is 100kVA, with lower losses but limited power supply capacity. At the same time, the real-time operating status of the distributed power source can be obtained, such as whether the photovoltaic inverter is online, whether the active power output is stable, and whether it has islanding operation control capability. Then, the power loss area range can be determined by combining the location of the fault. For example, when the fault occurs in branch 27, it causes the nodes 28 to 33 to lose power. When the fault occurs in branch 9 and branch 22, it causes multiple branch areas to lose power simultaneously. And the severity of the fault can be used to distinguish whether it is a permanent fault. If it is a permanent fault, the network needs to be reconstructed or islanded. If it is a transient fault, it can wait for automatic reclosing to restore power.

[0028] In an optional implementation, the system can comprehensively determine whether the islanding operation conditions are met, such as the total power of critical loads in the islanding candidate area not exceeding the sum of the output of the distributed power source and the capacity that the regulating transformer can provide at the current tap, and the electrical distance is relatively short and the voltage support capability is sufficient.

[0029] If the conditions are met, the system will enter islanded operation mode and initiate the islanding process; otherwise, it will enter main network reconstruction mode to reconstruct the network for non-faulty power loss areas.

[0030] Furthermore, the final output of the corresponding operating mode decision result is used for subsequent recovery strategy generation.

[0031] Here, tap position refers to the winding connection point inside the adjustable transformer used to switch between high and low capacity operating states; operating state of distributed power source refers to whether it has continuous power supply capability and islanding operation function; fault location refers to the specific branch number in the distribution network where the fault occurred; and fault severity refers to whether the fault is a permanent short circuit or open circuit, or other non-recoverable type.

[0032] It should be noted that this step S101 determines the overall recovery strategy of the system, which can ensure that critical loads are continuously powered and find the most suitable recovery plan for non-critical loads.

[0033] S102: For non-faulty areas not included in the island, construct a collaborative reconfiguration optimization model with the goal of minimizing total system loss and minimizing switching operation cost. The total system loss includes the winding loss of the regulating transformer and the network loss of the distribution network branches. In this embodiment of the invention, the objective function of the collaborative reconfiguration optimization model includes the following three sub-objectives: the total active power loss of the distribution network, the overall deviation between the voltage of all network nodes and the rated voltage, and the actual number of switches that need to be activated.

[0034] In this embodiment of the invention, the total active power loss of the distribution network is composed of the winding loss of the regulating transformer at the current operating tap and the line loss caused by the current of each branch. The winding losses of a variable-capacity transformer are related to its no-load loss, load loss, loss coefficient corresponding to the current tap, actual apparent power, and rated capacity.

[0035] In an alternative implementation, the objective function of the collaborative reconfiguration optimization model can be constructed as a weighted sum of three sub-objectives: the total active power loss of the distribution network reflects the system operating efficiency, the overall deviation of the voltage of all network nodes from the rated voltage characterizes the power supply quality, and the number of switches that actually need to be activated reflects the operational complexity and the impact on equipment lifespan.

[0036] In one optional implementation, the total active power loss is composed of the winding loss of the adjustable transformer at the current operating tap and the line loss caused by the current flowing through the line impedance of each branch. For example, when the adjustable transformer is at the 400kVA tap, its no-load loss is 420W and its load loss is 3800W, while at the 100kVA tap, the no-load loss drops to 180W and the load loss is 950W. The winding loss needs to be corrected by combining the loss coefficient corresponding to the current tap and the ratio of the actual apparent power flowing through the transformer to the rated capacity of the tap when calculating the winding loss.

[0037] In an alternative implementation, the line loss can be obtained by summing the product of the resistance of each branch and the square of the branch current segment by segment.

[0038] In an alternative implementation, the degree of voltage deviation across the entire network nodes can be quantified by calculating the sum or root mean square value of the absolute values ​​of the differences between the voltage amplitudes of all load nodes and the nominal voltage (e.g., 400V).

[0039] In one alternative implementation, the number of switching actions can be counted as the total number of switches whose states changed before and after the reconfiguration. For example, switches that were initially closed but became open after optimization are counted as actions. During the optimization process, these three sub-objectives participate in the fitness evaluation simultaneously to ensure that the resulting recovery scheme achieves a synergistic balance between reducing losses, increasing voltage levels, and reducing the amount of operation.

[0040] Among them, the total active power loss refers to the sum of all active power losses in the distribution network under the current operating state; the winding loss refers to the sum of the no-load loss generated by the core of the adjustable transformer and the load loss caused by the winding resistance; the loss coefficient refers to the correction ratio of no-load and load losses relative to the reference value under different taps; the overall voltage deviation degree refers to the comprehensive measure of the deviation of the voltage of all nodes from the rated value; and the number of switching actions refers to the number of switches that actually undergo state switching during the reconfiguration process.

[0041] Specifically, the objective function of the collaborative reconstruction model for: In the formula, , , They are respectively , , The baseline value is used for normalization. , , These are the weighting coefficients, and .

[0042] objective function The three sub-objectives , , They are defined as follows: 1) System network loss : In the formula, branch road The resistance, branch road The square value of the current, It is the set of all branches.

[0043] 2) Voltage deviation : In the formula, For nodes voltage, Rated voltage reference value, This represents the total number of nodes.

[0044] 3) Number of switch operations : In the formula, The target state of the switch after reconstruction. This represents the initial state of the switch before reconfiguration. This is the set of all switches.

[0045] In one optional implementation, the total system loss The calculation formula is: In the formula, To adjust the winding losses of the transformer, The total active power loss of the network branch.

[0046] In one alternative implementation, the winding loss of the regulating transformer... The calculation formula is: In the formula, For transformer no-load loss, This refers to the load loss of the transformer at its rated capacity. The loss factor is related to the current tap position. This represents the current apparent power of the transformer. This refers to the rated capacity of the transformer.

[0047] In an optional implementation, the power flow constraints of the cooperative reconfiguration model adopt a second-order conical convex relaxation form, and the relaxed branch power constraints are as follows: In the formula, , For flow through branch road Active and reactive power, branch road The square of the current, For nodes The square of the voltage.

[0048] In this embodiment of the invention, when constructing the collaborative reconfiguration optimization model, the power flow constraints of the distribution network are also modeled in a second-order conical convex relaxation form to ensure the solvability and computational efficiency of the optimization problem.

[0049] It should be noted that step S102, based on the judgment results of the first step, constructs an optimization model for non-faulty areas not included in the island, which helps to determine the best recovery path, reduce energy loss in power transmission, and lower operating costs.

[0050] S103: A hybrid strategy combining genetic algorithm and quasi-Newton optimization algorithm is used to solve the collaborative reconstruction optimization model and output a recovery strategy that includes the optimal tap-switching scheme of the capacity-adjusting transformer and the optimal operation sequence of the low-voltage switch. In this embodiment of the invention, a hybrid strategy combining genetic algorithms and quasi-Newtonian optimization algorithms is employed to solve the collaborative reconstruction optimization model, which includes two stages: In the first stage, a genetic algorithm is used to perform a global search on the switch state combination and the discrete taps of the adjustable transformer. The objective function is used as the fitness evaluation criterion to obtain a set of preliminary feasible recovery schemes. In the second stage, the transformer tap position in the optimal solution obtained in the first stage is fixed, the switch state variables are relaxed to continuous variables, and a quasi-Newton optimization algorithm is used for local fine-grained search to finally determine the precise switch operation sequence.

[0051] Specifically, the hybrid optimization solution process includes: A genetic algorithm is used for global search, with decision variables including switch state combinations and discrete transformer tap positions, and the objective function is... To determine the fitness, a preliminary solution is obtained through the search; The transformer tap positions in the obtained optimal solution are fixed, and relaxation is performed with the switch state as a continuous variable. The BFGS algorithm is used for local fine search to obtain the final switch operation sequence.

[0052] In one alternative implementation, a genetic algorithm can be used to perform a global search of the mixed discrete variable space, which includes combinations of low-voltage switch states and discrete tap positions of the transformer. For example, each individual can be encoded as a chromosome consisting of switch opening and closing states and transformer tap positions. Multiple recovery schemes can be generated iteratively through selection, crossover, and mutation operations. The objective function value of the collaborative reconstruction optimization model can be used as the fitness for evaluation, and several feasible solutions with lower network losses, smaller voltage deviations, and fewer switching operations can be selected.

[0053] Furthermore, in the second stage, the tap position of the adjustable transformer corresponding to the individual with the best fitness in the first stage is fixed, and only the switch state is retained as the optimization variable. It is relaxed from a discrete 0-1 variable to a continuous variable between 0 and 1. For example, the binary value that originally represented the switch being open or closed is converted into a differentiable variable for gradient calculation.

[0054] In an alternative implementation, based on this, a quasi-Newton optimization algorithm such as the BFGS algorithm can be used to perform a local fine-grained search on the relaxed continuous optimization problem. The gradient information of the objective function is used to quickly converge to the neighborhood optimum. The final continuous switching variable values ​​are then restored to the actual switching operation sequence through threshold determination, thereby outputting a complete recovery strategy that includes the optimal tap position switching command of the regulating transformer and the precise action combination of the low-voltage switch.

[0055] Here, the switch state combination refers to the opening and closing configuration of all operable tie switches and sectional switches in the distribution network; the discrete taps of the regulating transformer refer to its switchable finite capacity taps, such as 100kVA or 400kVA; the objective function is a comprehensive optimization index composed of the total active power loss of the system, the voltage deviation of all nodes in the network, and the number of switch operations; and the quasi-Newton optimization algorithm is an unconstrained optimization method that achieves efficient local search through an approximate Hessian matrix.

[0056] It should be noted that this hybrid strategy in step S103 combines the advantages of global search capability and local fine-tuning, enabling it to efficiently find the optimal or near-optimal solution in a vast solution space.

[0057] S104: Execute the recovery strategy and monitor the load rate of the regulating transformer and the voltage level of key nodes in real time to ensure the safe and stable operation of the system after reconstruction.

[0058] In an optional implementation, after determining the final recovery strategy, the tap switching command of the regulating transformer and the precise operation sequence of the low-voltage switch can be executed sequentially. For example, the regulating transformer can be switched from the current 100kVA tap to the 400kVA tap to increase the power supply capacity. Then, according to the optimization results, the tie switch S17 can be closed and the sectionalizing switch S23 can be opened to complete the network topology adjustment.

[0059] In one optional implementation, during and after the strategy is executed, the current and voltage data of the high-voltage and low-voltage sides of the transformer can be collected in real time to calculate its load rate. For example, the load rate value can be obtained by dividing the real-time apparent power by the rated capacity of the current tap. If the value exceeds a preset threshold such as 85%, an overload warning is triggered.

[0060] In an optional implementation, the voltage amplitude of key load nodes such as node 31, node 45 and hospital-dedicated nodes can be monitored synchronously, for example by uploading the effective voltage value every second through the power distribution terminal unit to determine whether it is maintained within the allowable deviation range of 380V to 418V. If monitoring detects that the load rate is consistently higher than 90% or the voltage of any critical node is lower than 370V, the secondary adjustment mechanism will be activated immediately, including reconnecting the backup distributed power supply or fine-tuning the switch status to transfer the load. It should be noted that the entire monitoring process lasts at least 15 minutes to ensure that the system has no transient oscillations, no equipment overloads, and no voltage overruns after reconstruction, thereby ensuring the safe and stable operation of the distribution network.

[0061] Here, the load factor refers to the ratio of the actual apparent power of the adjustable transformer to its rated capacity at the current tap. The critical node refers to the important load connection point that has special requirements for power supply continuity and voltage quality. The voltage level refers to the degree of deviation of the effective value of the node voltage from the nominal voltage of 400V in real time.

[0062] In this embodiment of the invention, if the islanded operation mode is entered, the power supply support capacity of the islanded area is determined based on the importance of the load, the electrical distance, and the current tap of the regulating transformer. Generate the optimal islanding scheme to maximize the use of distributed power sources to continuously power critical loads.

[0063] In an alternative implementation, an optimal islanding scheme can be generated based on an improved minimum spanning tree algorithm, using load priority, electrical distance, and the supporting capacity of the adjustable transformer at the current tap as comprehensive indicators. In this embodiment of the invention, the optimization basis for the islanding scheme includes a power supply capacity coordination factor; The power supply capacity coordination factor is used to determine whether the sum of the total output of the distributed power source in the island and the power supply capacity that the adjustable transformer can provide at the current tap level is sufficient to meet the total demand of all candidate loads in the island. The candidate islanding scheme is only allowed to form when the power supply capacity meets the load demand.

[0064] Specifically, the optimization metrics for adaptive island partitioning The calculation formula is: In the formula, For load Importance weight, Its active power, From distributed power source to load The sum of the impedance magnitudes of all branches along the path, This represents the total number of load nodes considered within the isolated island. This is a power supply capacity coordination factor.

[0065] Power supply capacity coordination factor The calculation formula is: In the formula, Distributed power supply within the island Those who have made contributions This refers to the apparent capacity of the adjustable transformer at the current tap position. For transformer power factor, This is the sum of the active power of all loads within the candidate island. This factor is set to 1 when the total power supply capacity is greater than or equal to the total load demand; otherwise, it is 0.

[0066] In an alternative implementation, connectivity modeling of load nodes and distributed power sources within the island candidate area can be performed based on an improved minimum spanning tree algorithm. For example, each load node and distributed power source can be regarded as a vertex in the graph, and branches can be regarded as weighted edges. The weight of the edge is composed of load priority, electrical distance, and the supporting capacity of the transformer at the current tap. The load priority is set according to the user type. For example, critical loads such as hospitals and communication base stations are given high priority weights, while residential loads are given low priority weights.

[0067] In one alternative implementation, the electrical distance is defined as the sum of the magnitudes of the impedances of all branches along the path from the distributed power source to the load node; the smaller the impedance, the closer the electrical connection. The capacity of the tuned transformer at the current tap is quantified by the degree of matching between its current available capacity and the total load demand within the island.

[0068] In one optional implementation, when constructing the spanning tree, priority is given to retaining branches with high-priority loads and short electrical distances, and load nodes that would cause the power supply capacity coordination factor to be less than 1 are removed. That is, when the sum of the output of the distributed power source and the power supply capacity of the current tap of the regulating transformer in the island is insufficient to cover the total load demand, the load is not included in the island. Finally, a minimum spanning tree with the distributed power source as the root node, covering the largest critical load and satisfying the power supply capacity constraint is generated as the optimal island partitioning scheme.

[0069] Here, load priority refers to the numerical weight assigned based on the importance of the load; electrical distance refers to the cumulative impedance value along the network path from the power source to the load; support capacity refers to the maximum active power that the adjustable transformer can provide to the island at the current tap level; and power supply capacity coordination factor refers to the ratio of the total power supply capacity of the island to the total load demand. When this factor is less than 1, the islanding scheme is not feasible.

[0070] The invention will now be described in detail using a typical low-voltage section of an integrated adjustable-capacity transformer as an example.

[0071] Monitor the current operation of the capacity-adjustable transformer (such as the SZ13-M-400 / 100 type) at the high-capacity level (400kVA), the status of the low-voltage side feeder switch, and the real-time output of the photovoltaic inverters at each node.

[0072] When a main feeder fails, the non-faulty areas of the system decision-making section become isolated by photovoltaic power sources. During this partitioning, a power supply capacity coordination factor is calculated. This ensures that the total load on the island matches the transformer's support capacity for the island at the current tap level.

[0073] For the remaining mainnet, establish a system based on... An optimization model for the objective, where network loss Voltage deviation and switch action The calculations are based on formulas related to the objective function and satisfy second-order cone power flow constraints. Using the GA-BFGS algorithm, the results may indicate that switching the transformer to a smaller capacity setting (100kVA) and operating the two tie switches is the optimal strategy.

[0074] Implement the above strategy and monitor whether the transformer load rate is within a safe range, and verify that the voltage of all low-voltage users meets the requirements. Requirements.

[0075] Example 2, by Figure 2 , 3As shown in sections 4, 5, and 6, under the premise of setting two types of faults, the voltage fluctuation results before and after using the invented method are significantly different. After using the GA-BFGS method, the node voltage fluctuation is smaller. According to the node voltage distribution curves shown in the figure below, the proposed reconfiguration method has a significant improvement effect on the distribution network voltage level. After reconfiguration, the voltage values ​​of all nodes in the entire network are effectively improved, and the overall voltage curve is more stable and closer to the rated voltage level than before reconfiguration. In particular, it solves the problem of low voltage at some nodes before reconfiguration (e.g., the lowest voltage is below 0.97 pu). This indicates that the proposed method not only optimizes the network power flow distribution and reduces system network losses, but also effectively suppresses the risk of voltage exceeding limits, comprehensively improving the power quality and operational safety of the distribution network.

[0076] Table 1 Comparison of results of this method before and after the failure

[0077] As shown in Table 1, simulation results demonstrate that the proposed GA-BFGS algorithm effectively improves system performance under various fault scenarios. Compared to the pre-fault state, the optimized system achieves a stable or slightly improved minimum voltage, while active power loss is significantly reduced by 10.6% to 32.1%. Furthermore, all reconfiguration schemes require only the operation of two switches, effectively improving the economic efficiency and safety of power grid operation while maintaining low operating costs.

[0078] Therefore, this invention employs the aforementioned multi-mode collaborative self-healing method for low-voltage distribution networks based on adjustable-capacity transformers. This method targets low-voltage distribution networks integrating adjustable-capacity transformers and distributed generation. Upon a fault, it first determines the system reconfiguration mode based on the tap position of the adjustable-capacity transformer and the state of the distributed generation. Subsequently, an improved graph theory algorithm is used to divide the network into optimal power supply islands, maximizing the utilization of distributed generation to power critical loads. For the main grid recovery portion, an optimization model is established with adjustable-capacity transformer losses, network losses, and the number of switching operations as objectives, and a GA-BFGS hybrid algorithm is used for efficient solution. This invention effectively solves the problems of insufficient recovery capability and low collaborative efficiency between transformers and distributed generation in traditional low-voltage distribution networks, significantly improving power supply reliability and economy.

[0079] Example 3, this example also provides a multi-mode collaborative self-healing system for low-voltage distribution networks based on a capacity-adjustable transformer, including: The judgment module is used to determine whether the system should enter islanded operation mode or main grid reconfiguration mode based on the current tap position of the regulating transformer, the operating status of the distributed power source, and the location and severity of the fault. The optimization model building module is used to build a collaborative reconfiguration optimization model for non-faulty areas not included in the island, with the goal of minimizing total system loss and minimizing switching operation cost. The total system loss includes the winding loss of the regulating transformer and the network loss of the distribution network branches. The solution module is used to solve the collaborative reconstruction optimization model using a hybrid strategy that combines genetic algorithms and quasi-Newton optimization algorithms, and outputs a recovery strategy that includes the optimal tap-switching scheme for the capacity-adjusting transformer and the optimal operating sequence for the low-voltage switch. The execution module is used to implement the recovery strategy and monitor the load rate of the regulating transformer and the voltage level of key nodes in real time to ensure the safe and stable operation of the system after reconstruction.

[0080] The above-mentioned unit modules can be embedded in the processor of the electronic device in hardware form or independent of it, or they can be stored in the memory of the electronic device in software form, so that the processor can call and execute the corresponding operations of the above modules.

[0081] This embodiment also provides an electronic device, which can be a terminal. The electronic device includes a processor, memory, communication interface, display screen, and input device connected via a system bus. The processor provides computing and control capabilities. The memory includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, carrier networks, NFC (Near Field Communication), or other technologies. When the computer program is executed by the processor, it implements a multi-mode collaborative self-healing method for low-voltage distribution networks based on a variable-capacity transformer. The display screen can be a liquid crystal display (LCD) or an e-ink display. The input device can be a touch layer covering the display screen, buttons, a trackball, or a touchpad mounted on the device's casing, or an external keyboard, touchpad, or mouse.

[0082] This embodiment also provides a computer-readable storage medium on which a computer program is stored, and when the computer program is executed by a processor, it performs the following steps: The method is applicable to low-voltage distribution networks that integrate regulating transformers and distributed generation, and is executed upon the occurrence of a fault: Based on the current tap position of the regulating transformer, the operating status of the distributed power source, and the location and severity of the fault, determine whether the system should enter islanded operation mode or main grid reconfiguration mode. For non-faulty areas not included in the island, a collaborative reconfiguration optimization model is constructed with the goal of minimizing total system loss and minimizing switching operation cost. The total system loss includes the winding loss of the regulating transformer and the network loss of the distribution network branches. A hybrid strategy combining genetic algorithm and quasi-Newton optimization algorithm is adopted to solve the collaborative reconstruction optimization model, and the output is a recovery strategy including the optimal tap position switching scheme of the capacity regulating transformer and the optimal operation sequence of the low-voltage switch. Implement recovery strategies and monitor the load rate of the adjustable transformer and the voltage level of key nodes in real time to ensure the safe and stable operation of the system after reconstruction.

[0083] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

[0084] Although preferred embodiments of the invention have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of the invention.

[0085] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A multi-mode collaborative self-healing method for low-voltage distribution networks based on adjustable-capacity transformers, characterized in that, The method is applicable to low-voltage distribution networks that integrate adjustable-capacity transformers and distributed power sources, and is executed after a fault occurs: Based on the current tap position of the regulating transformer, the operating status of the distributed power source, and the location and severity of the fault, determine whether the system should enter islanded operation mode or main grid reconfiguration mode. For non-faulty areas not included in the island, a collaborative reconfiguration optimization model is constructed with the goal of minimizing total system loss and minimizing switching operation cost. The total system loss includes the winding loss of the regulating transformer and the network loss of the distribution network branch. A hybrid strategy combining genetic algorithm and quasi-Newton optimization algorithm is used to solve the cooperative reconstruction optimization model, and the output is a recovery strategy that includes the optimal tap-switching scheme of the capacity-adjusting transformer and the optimal operation sequence of the low-voltage switch. The recovery strategy is implemented, and the load rate of the adjustable transformer and the voltage level of key nodes are monitored in real time to ensure that the system operates safely and stably after reconstruction.

2. The multi-mode collaborative self-healing method for low-voltage distribution networks based on a capacity-adjustable transformer as described in claim 1, characterized in that, Also includes: If the islanded operation mode is entered, the power supply support capacity of the islanded area will be determined based on the importance of the load, the electrical distance, and the current tap of the adjustable transformer. Generate the optimal islanding scheme to maximize the use of distributed power sources to continuously power critical loads.

3. The multi-mode collaborative self-healing method for low-voltage distribution networks based on a capacity-adjustable transformer as described in claim 2, characterized in that, The objective function of the collaborative reconfiguration optimization model includes the following three sub-objectives: the total active power loss of the distribution network, the overall deviation between the voltage of all network nodes and the rated voltage, and the actual number of switches that need to be activated.

4. The multi-mode collaborative self-healing method for low-voltage distribution networks based on a capacity-adjustable transformer as described in claim 3, characterized in that, The total active power loss of the distribution network is composed of the winding loss of the regulating transformer at the current operating tap and the line loss caused by the current of each branch. The winding losses of the adjustable transformer are related to its no-load loss, load loss, loss coefficient corresponding to the current tap, actual apparent power, and rated capacity.

5. The multi-mode collaborative self-healing method for low-voltage distribution networks based on a capacity-adjustable transformer as described in claim 4, characterized in that, The optimization basis of the islanding scheme includes a power supply capacity coordination factor; The power supply capacity coordination factor is used to determine whether the sum of the total output of the distributed power source in the island and the power supply capacity that the adjustable transformer can provide at the current tap level is sufficient to meet the total demand of all candidate loads in the island. The candidate islanding scheme is only allowed to form when the power supply capacity meets the load demand.

6. The multi-mode collaborative self-healing method for low-voltage distribution networks based on a capacity-adjustable transformer as described in claim 5, characterized in that, The hybrid strategy combining genetic algorithms and quasi-Newtonian optimization algorithms to solve the collaborative reconstruction optimization model includes two stages: In the first stage, a genetic algorithm is used to perform a global search on the switching state combinations and discrete taps of the adjustable transformer. The objective function is used as the fitness evaluation criterion to obtain a set of preliminary feasible recovery schemes. In the second stage, the transformer tap position in the optimal solution obtained in the first stage is fixed, the switch state variables are relaxed to continuous variables, and a quasi-Newton optimization algorithm is used for local fine-grained search to finally determine the precise switch operation sequence.

7. The multi-mode collaborative self-healing method for low-voltage distribution networks based on a capacity-adjustable transformer as described in claim 6, characterized in that, When constructing the collaborative reconfiguration optimization model, the power flow constraints of the distribution network are modeled in a second-order conical convex relaxation form to ensure the solvability and computational efficiency of the optimization problem.

8. A multi-mode collaborative self-healing system for low-voltage distribution networks based on a capacity-adjustable transformer, employing the method described in any one of claims 1 to 7, characterized in that, include: The judgment module is used to determine whether the system should enter islanded operation mode or main grid reconfiguration mode based on the current tap position of the regulating transformer, the operating status of the distributed power source, and the location and severity of the fault. The optimization model building module is used to build a collaborative reconfiguration optimization model for non-faulty areas not included in the island, with the goal of minimizing the total system loss and the minimum switching operation cost. The total system loss includes the winding loss of the regulating transformer and the network loss of the distribution network branches. The solution module is used to solve the collaborative reconstruction optimization model using a hybrid strategy that combines genetic algorithms and quasi-Newton optimization algorithms, and outputs a recovery strategy that includes the optimal tap-switching scheme of the capacity-adjusting transformer and the optimal operation sequence of the low-voltage switch. The execution module is used to execute the recovery strategy and monitor the load rate of the adjustable transformer and the voltage level of key nodes in real time to ensure that the system operates safely and stably after reconstruction.

9. An electronic device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the multi-mode collaborative self-healing method for low-voltage distribution networks based on a variable-capacity transformer, as described in any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the multi-mode collaborative self-healing method for low-voltage distribution networks based on a variable-capacity transformer, as described in any one of claims 1 to 7.

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