Power distribution network dynamic cell reconfiguration method based on generalized energy flow entropy

Abstract

The application discloses a power distribution network dynamic cell reconstruction method based on generalized energy flow entropy, and relates to the technical field of power distribution network operation control and network reconstruction. The method collects operation data of key nodes of sources, loads and storages of a county power distribution network, constructs a generalized energy flow entropy evaluation model containing power balance entropy, voltage distribution entropy and carbon flow consumption entropy, and quantifies system operation disorder degree; based on power distribution network topology, a full-energy flow potential field is generated to identify topological evolution anchor points such as source core, load core and fault core; further, cell fusion, fission or maintenance strategies are generated according to the minimum entropy generation principle, and flexible interconnection, power transfer and fault isolation are realized through intelligent soft switches, tie switches and fast circuit breakers. The application can improve the local consumption rate of distributed energy, reduce voltage out-of-limit and main network exchange power, and enhance the low-carbon semi-autonomous operation capability of the county power distribution network.

Description

Technical Field

[0001] This invention relates to the field of distribution network operation control and network reconfiguration technology, specifically to a dynamic cell reconfiguration method for distribution networks based on generalized energy flow entropy, applicable to dynamic zoning, flexible interconnection, topology reconfiguration, and low-carbon semi-autonomous operation control of county-level distribution networks under conditions of high proportion of distributed power source access. Background Technology

[0002] With the continuous advancement of the construction of new power systems, county-level distribution networks are evolving from traditional passive radial networks to complex active networks with high penetration of distributed power sources. Distributed energy sources such as photovoltaic and wind power are characterized by randomness, intermittency, and spatiotemporal fluctuations, leading to frequent changes in the power flow direction, node voltage, and source-load balance of the distribution network. The traditional "source follows load" dispatching mode is no longer able to maintain the dynamic balance of the system.

[0003] Existing distribution network zoning and reconfiguration methods largely rely on fixed physical boundaries or optimize single indicators such as minimum network loss and voltage qualification rate. While these methods can alleviate local voltage exceedance, backflow of power flow, and network losses to some extent, they still suffer from rigid topological boundaries, delayed response to source-load fluctuations, and insufficient inter-regional energy exchange capacity in scenarios with a high proportion of distributed energy integration. Furthermore, existing methods lack a unified quantitative characterization of power balance, voltage stability, clean energy absorption, and operational orderliness, making it difficult to support the low-carbon, efficient, and resilient operation of county-level distribution networks. Therefore, there is an urgent need to propose a dynamic cell reconfiguration method that can adaptively evolve based on energy flow status to improve the distribution network's ability to absorb distributed energy locally and its semi-autonomous operation capabilities. Summary of the Invention

[0004] To address the technical challenges of existing fixed-zone strategies for distribution networks being unable to adapt to the spatiotemporal fluctuations of high-proportion distributed generation, the single objective of traditional network reconfiguration lacking comprehensive quantification of system orderliness and low-carbon characteristics, and the inability of regional boundaries to actively evolve with energy flow states, this invention proposes a dynamic cell reconfiguration method for distribution networks based on generalized energy flow entropy.

[0005] This invention introduces thermodynamic entropy theory to uniformly map the power balance state, voltage distribution state, and carbon flow absorption state in the distribution network into a generalized energy flow entropy index, quantifying the disorder of distribution network operation. Furthermore, it constructs a global energy flow entropy potential field based on graph theory to identify high-entropy accumulation areas, low-entropy convergence areas, and key entropy-increasing nodes. On this basis, it introduces the principle of minimum entropy generation to generate dynamic cell fission, fusion, or maintenance strategies, and achieves flexible boundary control and physical topology reconfiguration through intelligent soft switches, interconnection switches, and fast circuit breakers. Finally, through reconfiguration effect verification and rolling feedback correction, it realizes the adaptive evolution and low-carbon semi-autonomous operation of the county-level distribution network under complex source-load fluctuation conditions.

[0006] To meet the above requirements, the present invention proposes the following technical solution: A dynamic cell reconfiguration method for distribution networks based on generalized energy flow entropy, the method comprising the following steps: 1) Construct a multi-dimensional generalized energy flow entropy evaluation system and state perception model: Collect real-time operation data of key nodes of power source, load and storage in the county distribution network, normalize the multi-source heterogeneous information such as active power, reactive power, node voltage amplitude, distributed power carbon emission factor and energy storage charge state, and construct a generalized energy flow entropy evaluation model composed of power balance entropy, voltage distribution entropy and carbon flow absorption entropy; 2) Mapping the global energy flow entropy potential field and identifying key entropy-increasing nodes: Construct a weighted directed graph model based on the topological connection relationship of the distribution network, map the generalized energy flow entropy value of each node to the topological node, form a global energy flow entropy potential field, and identify high-entropy accumulation areas, low-entropy convergence areas and key entropy-increasing nodes through entropy flow density calculation; 3) Generate dynamic cavity fission and fusion strategies based on the principle of minimum entropy generation: Using key entropy increase nodes as topological evolution anchors, calculate the total entropy production rate of the system under different cavity combinations, determine the fusion gain and safety constraint state between adjacent cavities, and generate reconstruction strategies for cavity fusion, cavity fission, or maintaining the status quo. 4) Implement flexible boundary intelligent soft switch collaborative control and real-time topology reconfiguration: Based on the dynamic cavity reconfiguration strategy, collaborative control is performed on intelligent soft switches, tie switches and fast circuit breakers deployed on tie lines and key segment points, so that the cavity partitioning results of the logic layer are transformed into flexible interconnection, power transfer or isolation blocking actions of the physical layer. 5) Conduct multi-dimensional verification of the reconfiguration effect and optimize the semi-autonomous closed loop of county energy: monitor the grid operation data after reconfiguration in real time, calculate the posterior generalized energy flow entropy value, node voltage over-limit rate, local absorption rate of distributed power sources, network loss and main grid switching power, and determine whether to maintain the current topology or trigger a new round of reconfiguration cycle based on the semi-autonomous constraints.

[0007] Furthermore, in step 1), the generalized energy flow entropy evaluation model includes power balance entropy, voltage distribution entropy, and carbon flow absorption entropy. The power balance entropy is used to characterize the dispersion of source-load supply and demand matching, the voltage distribution entropy is used to quantify the fluctuation level of node voltage deviating from the rated value, and the carbon flow absorption entropy is used to characterize the risk of clean energy blockage and curtailment. The weights of each sub-entropy are adaptively determined by the entropy weight method according to the real-time variation of the indicators.

[0008] Furthermore, in step 2), the entropy density is calculated based on the difference in generalized energy flow entropy between the nodes at both ends of the branch and the line impedance, and compared with a preset safety threshold. When the entropy density exceeds the threshold, the corresponding region is determined to be a high-entropy potential energy accumulation region or a high-entropy convergence region, and the nodes are further divided into source cores, charge cores, fault cores or ordinary nodes, as a spatial guide for the evolution of the cell boundary.

[0009] Furthermore, in step 3), the objective function is to minimize the generalized energy flow entropy production rate of the entire network. The power imbalance, voltage deviation, carbon emission entropy production, and switching action penalty term inside the cavity are incorporated into the unified optimization model. When the fusion of adjacent regions can reduce the total entropy production and meet the safety constraints, a cavity fusion strategy is generated. When a fault, blockage, or increase in entropy production rate after fusion is detected, a cavity fission or isolation strategy is generated.

[0010] Furthermore, in step 4), for the cell boundaries that need to be merged, the intelligent soft switch is controlled to operate in constant DC voltage control mode, power reactive power dispatch mode or voltage frequency support mode to achieve accurate transfer of active power and reactive voltage support across regions; for the cell boundaries that need to be split or isolated from faults, the fast circuit breaker is controlled to perform the opening action, or a blocking command is issued to the intelligent soft switch, thereby completing the real-time reconfiguration of the distribution network topology.

[0011] Furthermore, in step 5), when the reconstructed posterior generalized energy flow entropy value is lower than the pre-reconstruction generalized energy flow entropy value, and the exchange power between the county power grid and the upper-level main grid is less than the set semi-autonomous power threshold, the reconstruction strategy is deemed effective and the current topology is maintained; when the verification index fails to meet the standard or a new entropy increase anomaly occurs, a deviation feedback signal is generated to trigger the next round of closed-loop optimization of weight correction, anchor point identification, and topology reconstruction.

[0012] Compared with existing technologies, the technical effects of this invention are as follows: By constructing a generalized energy flow entropy evaluation system, this invention maps multi-dimensional operating states such as power balance, voltage stability, and clean energy consumption into measurable system orderliness indicators, overcoming the shortcomings of traditional network reconfiguration with a single objective; through the energy flow entropy potential field and key entropy-increasing node identification mechanism, it achieves rapid location of disordered sources in the distribution network; through a dynamic cavity fission and fusion strategy based on the principle of minimum entropy generation, it breaks through the limitation of fixed physical boundaries on regional energy mutual assistance, enabling the distribution network to perform adaptive "breathing" reconfiguration according to source-load fluctuations; and with the flexible boundary collaborative control of intelligent soft switches and fast circuit breakers, it significantly improves voltage stability, local clean energy consumption rate, fault isolation capability, and county-level energy semi-autonomy level under conditions of high proportion of distributed power source access. Attached Figure Description

[0013] To more clearly present the relevant technical solutions of the present invention, the accompanying drawings used in the description of the implementation methods are briefly introduced below. These drawings represent only part of the content of the present invention, and not all of it.

[0014] in: Figure 1 This is a flowchart of the global energy flow entropy potential field mapping and anchor point identification in an embodiment of the present invention; Figure 2 This is a flowchart illustrating the dynamic cell cavity fission and fusion decision-making process in an embodiment of the present invention. Figure 3 This is a flowchart of the flexible boundary collaborative control logic in an embodiment of the present invention; Detailed Implementation

[0015] The following description, in conjunction with the accompanying drawings and specific embodiments, provides further details. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of the invention.

[0016] This embodiment selects a typical high-penetration distributed photovoltaic (PV) county-level distribution network demonstration area in southern my country as the implementation object. This area includes three 35kV substations and 45 key nodes, with a PV penetration rate of approximately 60%. The source-load distribution exhibits significant imbalances, with some village and town transformer areas facing reverse power flow and voltage exceeding limits during peak PV generation periods at midday, and some industrial load areas experiencing power supply pressure and localized power flow congestion during peak load periods. To verify the applicability of this invention, intelligent soft switches, high-precision synchronous phasor measurement units, and intelligent measurement terminals are deployed at key feeder connections, transformer interconnection points, and common connection points in the distribution network, forming a dynamic monitoring and control system covering source, load, storage, and flexible boundary equipment.

[0017] Step 1: Construction of a multidimensional generalized energy flow entropy evaluation system and establishment of a state-aware model First, relying on synchronous phasor measurement units and intelligent measurement terminals, real-time status data is collected for all key nodes in the county's power distribution network. The data collection targets include distributed photovoltaic nodes, typical load nodes, energy storage nodes, points of common coupling (PCC), and feeder interconnection nodes; the collected data includes the active power of each node. P i reactive power Q i Node voltage amplitude U i Voltage phase angle Carbon emission factors of distributed power sources and the state of charge (SOC) of energy storage i The sampling period for active power, reactive power, voltage amplitude, and voltage phase angle is set to 10ms, and the sampling period for energy storage state of charge is set to 1min, in order to balance the real-time nature of dynamic reconfiguration with the slow-variable characteristics of energy storage regulation.

[0018] To eliminate the differences in dimensions, orders of magnitude, and range of variation among multi-source heterogeneous data, the range standardization method is used to normalize the original state variables, forming a normalized state matrix. R For the first j The normalized expression for the class state index is:

[0019] In the formula, r ij For nodes i In the j Normalized values ​​under class indicators x ij For the corresponding original measurement value, and The first j The maximum and minimum values ​​of the category indicator within the current time window.

[0020] Based on this, we construct the generalized energy flow entropy. As a comprehensive scalar function for measuring the disorder and unbalanced state of distribution network operation, its expression is:

[0021] in, Power balance entropy is used to characterize the degree of dispersion in the supply and demand matching of source loads; Voltage distribution entropy is used to quantify the level of voltage fluctuation at all network nodes that deviates from the rated value. The carbon flow absorption entropy is used to characterize the risks of clean energy blockage, backfeeding, and curtailment. ω p , ω u , ω c These are the adaptive weighting coefficients for power balance entropy, voltage distribution entropy, and carbon flow absorption entropy, respectively. Their values ​​are dynamically determined by the entropy weighting method based on the degree of variation of each index within the current time window.

[0022] The power balance entropy can be expressed as:

[0023] Voltage distribution entropy can be expressed as:

[0024] In the formula, N is the total number of monitoring nodes. P i ( t ) is a node i At any moment t active power, This refers to the total active power of the entire network. U i ( t ) is a node i voltage amplitude, The rated voltage is used. Through the above model, the complex physical state, operational state, and low-carbon consumption state of the distribution network are uniformly mapped to a single measurable generalized energy flow entropy value. When When a sudden increase occurs, it indicates a decrease in the system's operational order, requiring further entropy potential field mapping and identification of key entropy-increasing nodes.

[0025] Step 2: Mapping the global energy flow entropy potential field and identifying key entropy increase nodes Based on the node state data and generalized energy flow entropy calculation results obtained in step one, this step abstracts the physical distribution network into a weighted directed topology model. G ( V , E ),in V Represents a set of nodes. E This represents the set of branches. Projecting the generalized energy flow entropy value corresponding to each node onto the topological nodes forms a global energy flow entropy potential field, such as... Figure 1 As shown, this potential field can reflect the degree of operational disorder and energy imbalance trend in different areas of the distribution network. Areas with higher entropy values ​​usually correspond to photovoltaic backfeeding over-limit points, load overload points, or fault disturbance points, while areas with lower entropy values ​​usually correspond to areas with absorption capacity or relatively stable operation.

[0026] To further quantify the spatial propagation characteristics of system disorder in the network, branch entropy flow density is introduced. J ij Its expression is:

[0027] In the formula, J ij ( t ) is a connection node i With nodes j branch entropy flow density, S i ( t )and S j ( t ) are nodes i and nodes j At any moment t The generalized energy flux entropy value, Z ij For nodes i With nodes j The impedance modulus between the lines. J ij ( tThe larger the absolute value of ), the more obvious the difference in operating status between the two ends of the branch, and the stronger the tendency of unbalanced energy to spread or converge along the branch.

[0028] The system compares the entropy current density of each branch with a preset safety threshold. By comparison, when the following equation is satisfied, it is determined that a significant entropy potential gradient exists in the corresponding region:

[0029] If the region exhibits excess photovoltaic output, reverse power flow, or voltage rise, it is defined as a high-entropy accumulation region, and the central node is marked as the source core. If the region exhibits concentrated load, low voltage, or power deficit, it is defined as a high-entropy convergence region, and the central node is marked as the load core. If the region has branch faults, power outages, or isolated entropy increases, it is defined as a fault core. Regions with low entropy values, stable entropy flow density changes, and do not exceed the safety threshold are marked as ordinary follower nodes.

[0030] Through the above identification process, the system can obtain the set of topological evolution anchor points required for dynamic cavity reconstruction. .in, This represents the source nucleus set, primarily used to drive the outward expansion of the cell lumen boundary to promote the on-site consumption of clean energy. This represents a set of charge cores, primarily used to guide neighboring regions to provide cross-regional power support; This represents the set of faulty nuclei, primarily used to drive the contraction or locking of cell cavity boundaries to achieve fault isolation and local autonomy. The above anchor point identification results provide spatial guidance and a decision-making basis for subsequent generation of dynamic cell cavity fission and fusion strategies based on the principle of minimum entropy.

[0031] Step 3: Generation of Dynamic Cellular Fission and Fusion Strategies Based on the Minimum Entropy Generation Principle Based on the topological evolution anchors such as source nuclei, charge nuclei, and faulty nuclei identified in step two, this embodiment further introduces the minimum entropy generation principle from non-equilibrium thermodynamics to construct a dynamic cell cavity fission and fusion decision model, such as... Figure 2 As shown. The dynamic cavity is not a fixed physical partition, but a logical operating unit that adaptively evolves with changes in source load power, node voltage state, and carbon flow absorption state. The system takes the key entropy-increasing node as the center, performs a tentative search to its electrical neighborhood, calculates the total entropy production rate of the system under different cavity combinations, and uses the minimum total entropy production rate as the optimization objective for cavity partitioning at the current moment.

[0032] In this embodiment, the objective function for minimizing the total entropy productivity of the system is expressed as:

[0033] In the formula, The total entropy yield of the system;M This represents the current number of cavities. For the first k Power imbalance within each cell cavity; For the first k The voltage deviation within each cell cavity; For the first k The carbon flow absorption entropy production term corresponding to each cell cavity; α , β , γ These are the weighting coefficients for the corresponding evaluation items; The number of switchgear devices involved in the reconfiguration; This is the penalty coefficient for the switching action; S n ( t )and S n ( t -1) are respectively the first n The on / off state of a boundary device at the current moment and the previous moment.

[0034] To determine whether adjacent cavities have fusion value, the entropy fusion gain between adjacent cavities is calculated, and its expression is as follows:

[0035] In the formula, For cavity fusion gain; The total entropy production rate after the fusion of adjacent cell cavities; , Pre-fusion cavities i and cell cavity j Entropy production rate; This refers to the cost of the switching actions involved in this integration. When the power flow, voltage, and equipment capacity after fusion meet safety constraints, the system generates a cavity fusion strategy to drive boundary expansion, thereby achieving energy mutual assistance between the photovoltaic surplus area and the adjacent load center. When a branch fault is detected, the entropy production rate increases after fusion, or the safety constraints are not met, the system generates a cavity fission or isolation strategy to shrink the cavity boundary and form local autonomous units. By traversing the feasible reconfiguration space through an improved dynamic programming algorithm, the optimal cavity partitioning scheme is finally output. And the corresponding flexible boundary execution set.

[0036] Step 4: Cooperative Control of Flexible Boundary Intelligent Soft Switching and Real-time Topology Reconfiguration Based on the optimal cell partitioning scheme output in step three This embodiment constructs a flexible boundary control architecture for cloud-edge collaboration, transforming the cellular fusion, fission, or maintenance strategies of the logic layer into action sequences for physical layer devices, such as... Figure 3As shown. The physical layer execution devices include intelligent soft switches, tie switches, and fast circuit breakers deployed at feeder connection points, critical segment points, and transformer interconnection points.

[0037] When the system determines that adjacent cavities need to be merged, the intelligent soft switch deployed at the boundary is activated. The intelligent soft switch employs a back-to-back voltage source converter structure, where the main converter operates in a constant DC voltage control mode to maintain DC bus energy balance; the slave converters operate in an active-reactive power dispatch mode or a voltage-frequency support mode based on the mutual assistance power setpoint given by the reconfiguration strategy, achieving precise transfer of active power across regions and active support of reactive voltage. At this time, the boundary mutual assistance power setpoint can be expressed as:

[0038] In the formula, This is the active power setting value for the intelligent soft switch. For the cavity i into the cell cavity j The power transfer required for energy exchange. Through the above control methods, flexible interconnection and continuous power flow regulation between regions can be achieved without altering the basic structure of the traditional distribution network.

[0039] When the system determines that cavity fission or fault isolation operations are required, it controls the fast circuit breaker to perform an opening action, or issues a pulse blocking command to the intelligent soft switch, thereby isolating the fault area, high-entropy congestion area, or core power supply area that needs to operate independently from the external network. Through the synergistic effect of the flexible adjustment of the intelligent soft switch and the physical isolation of the fast circuit breaker, the distribution network topology is transformed from a traditional fixed boundary to a dynamic boundary that can evolve in real time according to the operating status, thereby effectively suppressing the inrush current, voltage fluctuations, and fault propagation during mode switching.

[0040] Step 5: Multi-dimensional verification of reconstruction results and closed-loop optimization of semi-autonomous energy management in counties. After the topology reconfiguration operation is completed, this embodiment immediately initiates a multi-dimensional verification mechanism for the reconfiguration effect. The system re-collects global operational data after reconfiguration and calculates the posterior generalized energy flow entropy value. Simultaneously monitor node voltage over-limit rate, distributed photovoltaic local consumption rate, average network loss, and exchange power between the county power grid's public connection point and the upstream main grid. This embodiment uses semi-autonomous operation constraints as an important criterion for closed-loop optimization, and its expression is:

[0041] In the formula, This refers to the real-time power exchange between the county-level power distribution network and the upstream main grid. A preset semi-autonomous power constraint threshold is used. The current round of cell cavity reconstruction is considered valid when the following equation is simultaneously satisfied:

[0042] In the formula, The generalized energy flow entropy value before reconstruction. This represents the reconstructed posterior generalized energy flow entropy value. If the system's orderliness is improved, node voltage exceedances are eliminated, local photovoltaic power consumption is increased, and the main grid switching power is within a set threshold after reconstruction, then the current dynamic cavity topology is maintained and the system enters steady-state cruise mode.

[0043] If the verification result shows If the voltage is not reduced, or the main network switching power exceeds the semi-autonomous threshold, or new voltage exceedances, light curtailment, or abnormal fault entropy increases are detected, the system generates a deviation feedback signal:

[0044] In the formula, This is the closed-loop feedback deviation signal. The node voltage over-limit rate, For the local consumption rate of distributed photovoltaic power, This represents network loss. The deviation signal is fed back to the state-aware model in step one, triggering a new round of entropy weight correction, entropy potential field mapping, anchor point identification, and topology reconstruction loop. Through the above-mentioned rolling closed-loop mechanism, this invention can achieve low-carbon semi-autonomous operation of the county power distribution network under complex operating conditions such as random fluctuations in source load, rapid changes in photovoltaic output, and fault disturbances, with "internal balance as the main focus and external mutual assistance as a supplement."

Claims

1. A method for dynamic cell reconfiguration of a distribution network based on generalized energy flow entropy, characterized in that, comprising the following steps: 1) Construct a multi-dimensional generalized energy flow entropy evaluation system and state perception model: collect real-time operation data of key nodes of the source, load and storage in the county-level distribution network, perform normalization processing on multi-source heterogeneous data, and construct a generalized energy flow entropy evaluation model including power balance entropy, voltage distribution entropy and carbon flow consumption entropy; 2) Perform global energy flow entropy potential field mapping and key entropy increase node identification: based on the topology structure of the distribution network, map the generalized energy flow entropy value of each node to the network nodes to form a global energy flow entropy potential field, and identify high entropy accumulation areas, low entropy convergence areas and key entropy increase nodes through entropy flow density; 3) Generate a dynamic cell fission and fusion strategy based on the principle of minimum entropy production: take the key entropy increase nodes as the anchor points for topology evolution, calculate the total entropy production rate of the system under different cell division schemes, and generate cell fusion, fission or maintenance strategies that reduce the generalized energy flow entropy of the entire network; 4) Perform cooperative control of flexible boundary intelligent soft switches and real-time topology reconfiguration: according to the cell reconfiguration strategy, cooperatively control the intelligent soft switches, tie switches and fast circuit breakers to realize flexible interconnection, power transfer or physical isolation of cell boundaries; 5) Perform multi-dimensional verification of reconfiguration effect and semi-autonomous closed-loop optimization of county-level energy: monitor the operation state after reconfiguration in real time, calculate the posterior generalized energy flow entropy value and the exchange power between the county-level power grid and the superior main grid, and maintain the current topology or trigger a new round of reconfiguration optimization according to the verification result.

2. The method for dynamic cell reconfiguration of distribution networks based on generalized energy flow entropy according to claim 1, characterized in that, Step 1) specifically includes: collecting active power, reactive power, node voltage amplitude, distributed generation carbon emission factor, and energy storage state of charge data; forming a normalized state matrix using the range standardization method; and constructing a generalized energy flow entropy function. S sys = ω p S balance + ω u S voltage + ω c S carbon ,in S balance For power balance entropy, S voltage For voltage distribution entropy, S carbon To eliminate entropy in the carbon flow, ω p , ω u , ω c These are the adaptive weighting coefficients determined by the entropy weighting method.

3. The method for dynamic cell reconfiguration of distribution networks based on generalized energy flow entropy according to claim 1, characterized in that, Step 2) specifically includes: abstracting the distribution network into a weighted directed topology model. G ( V , E Projecting the generalized energy flow entropy values ​​of the nodes onto the topological nodes forms an energy flow entropy potential field; calculating the connection nodes i With nodes j branch entropy flow density J ij =( S i - S j ) / Z ij ,in S i , S j They are nodes i , j The generalized energy flux entropy value, Z ij The line impedance magnitude; J ij By comparing with a preset safety threshold, source cores, charge cores, faulty cores and ordinary nodes are identified, and source cores, charge cores and faulty cores are used as topological evolution anchors for dynamic cavity reconstruction.

4. The method for dynamic cell reconfiguration of distribution networks based on generalized energy flow entropy according to claim 1, characterized in that, The step 3) specifically comprises: taking the minimum total entropy production rate of the system as the objective, establishing an objective function including the power imbalance inside the cell, voltage deviation, carbon emission entropy production and switch action penalty term; calculating the entropy fusion gain between adjacent cells, and generating a cell fusion strategy when the entropy fusion gain is less than a set threshold and safety constraints are satisfied; generating a cell fission or isolation strategy when a fault is detected or the total entropy production rate increases after fusion; solving through dynamic programming to output the optimal cell division scheme and the flexible boundary execution set.

5. The method for dynamic cell reconfiguration of distribution networks based on generalized energy flow entropy according to claim 1, characterized in that, The step 4) specifically comprises: for the cell boundaries that need to be fused, controlling the intelligent soft switch to operate in the constant DC voltage control mode, power and reactive power scheduling mode or voltage and frequency support mode, so as to realize the accurate transfer of cross-regional active power and reactive power voltage support; for the cell boundaries that need fission or fault isolation, controlling the fast circuit breaker to perform the opening action, or issuing a locking instruction to the intelligent soft switch, so as to complete the real-time reconfiguration of the distribution network topology at the physical level.

6. The method for dynamic cell reconfiguration of distribution networks based on generalized energy flow entropy according to claim 1, characterized in that, The step 5) specifically comprises: collecting global operation data after reconfiguration is completed, calculating the posterior generalized energy flow entropy value S′sys, node voltage over-limit rate, local consumption rate of distributed power sources, network loss and main grid exchange power; when S′sys<Ssys is satisfied and the main grid exchange power is less than the semi-autonomous power constraint threshold, maintaining the current cell topology; when the verification index fails to meet the standard or a new entropy increase abnormality occurs, generating a deviation feedback signal, feeding it back to the state perception model, and triggering a new round of weight correction, anchor point identification and topology reconfiguration cycle.

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