Self-adaptive intelligent distribution network distribution automation system for distribution network

Abstract

The invention discloses a self-adaptive intelligent distribution network distribution automation system for a distribution network, and relates to the technical field of power grid distribution systems, a calculation module introduces a block-level risk confidence scoring model to calculate a risk confidence score of each physical node, and the physical nodes are graded and sorted according to scoring results; a continuous section with a score higher than a preset safety threshold value is screened out to serve as a priority disposal target area, the whole network is divided into a plurality of control execution units according to feeder line sections and control domains, a particle size operation lock is applied for each control execution unit containing the priority disposal target area, and a level difference isolation module carries out level difference isolation and protection logic operation. And all switching-on and switching-off instructions and protection parameter adjustment are synchronized to related terminals in real time through a 5G network. According to the system, the fault handling speed and accuracy of the power distribution network in a complex operation environment are effectively improved, and the maloperation and refusal operation risks are reduced.

Description

Technical Field

[0001] This invention relates to the field of power grid distribution system technology, and specifically to an adaptive intelligent distribution network automation system for distribution networks. Background Technology

[0002] In power supply systems such as 750 kV and above AC transmission, large-scale power grid security and defense systems, and intelligent dispatching systems, timely detection and accurate handling of faults are directly related to power supply reliability and continuous power supply to important loads. However, distribution networks are complex in structure, diverse in topology, and have varying communication conditions. Moreover, their operating conditions are often accompanied by multi-source disturbances and mode switching. Traditional distribution automation systems struggle to perceive the overall network operation status in real time, accurately identify the consistency between the physical state of equipment and control intentions, and effectively assess potential risks and identify priority handling sections. This leads to problems such as incomplete isolation, conflicting recovery paths, false operation or refusal to operate in complex fault scenarios, prolonging power outage time and expanding the scope of impact.

[0003] Given the urgent need for high reliability, high efficiency, and intelligent handling capabilities in modern power distribution networks, especially when facing harsh conditions such as mixed ring and radial networks, fluctuating communication quality, and multiple concurrent faults, it is necessary to propose an adaptive intelligent power distribution automation system and method that can integrate full-domain situational awareness, dual-domain mapping comparison, risk feature extraction, and differential isolation control. This will enable closed-loop targeted management and control from risk identification to precise execution, significantly improving the accuracy, real-time performance, and adaptability of fault handling. Summary of the Invention

[0004] The purpose of this invention is to provide an adaptive intelligent distribution network automation system for distribution networks, so as to solve the problems in the background technology.

[0005] To achieve the above objectives, the present invention provides the following technical solution: an adaptive intelligent distribution network automation system for distribution networks, comprising a construction module, a comparison module, a calculation module, and a differential isolation module; Construction module: Obtain a real-time operational status data pool in the target distribution network operating environment. The operational status data pool includes a physical mapping set and a control intent release set. The physical mapping set and the control intent release set are sent to the comparison module. The comparison module performs a cross-comparison between the physical mapping set and the control intention release set item by item to construct a two-way state comparison matrix, which is then sent to the calculation module. Calculation module: Introduces a block-level risk confidence scoring model to calculate the risk confidence score of each physical node. Based on the scoring results, the physical nodes are classified and sorted. Continuous segments with scores higher than the preset safety threshold are selected as priority handling target areas. The entire network is divided into several control execution units according to feeder segments and control domains. A granular operation lock is applied for for each control execution unit containing priority handling target areas. The granular operation lock is sent to the differential isolation module. Differential isolation module: When a micro-level operation lock is acquired and a fault occurs, differential isolation and protection logic are performed, and all opening and closing commands and protection parameter adjustments are synchronized to relevant terminals in real time via the 5G network.

[0006] Preferably, the calculation module introduces a block-level risk confidence scoring model to calculate the risk confidence score for each physical node: Based on telemetry and teleindication data, feature quantities reflecting abnormal equipment and line conditions are extracted and transformed into risk factors. Various risk factors are aggregated according to node affiliation in the block-level risk confidence scoring model to form the original risk feature vector of the node; After obtaining the original risk feature vector, the block-level risk confidence scoring model assigns an influence weight to each risk factor; The count or magnitude of each risk factor is normalized and mapped to a uniform score range; The scores of each factor after normalization are summed according to their weights to obtain the initial risk confidence score of the node. An environmental correction factor is introduced to adjust the initial score up or down, forming a block-level risk confidence score.

[0007] Preferably, the calculation module sorts the physical nodes according to the scoring results and selects continuous segments with scores higher than a preset safety threshold as priority target areas for treatment: After completing the risk confidence score of all physical nodes in the network, the nodes are sorted according to the score results to form a node risk sequence from high to low. The sorting results are used for hierarchical management. The nodes are divided into safe zones, warning zones, and high-risk zones according to preset safety thresholds; Scan the distribution of high-risk nodes in the topology and merge several high-risk nodes that are electrically connected and continuously distributed into priority treatment target areas.

[0008] Preferably, the calculation module divides the entire network into several control execution units according to feeder segments and control domains, and applies for a fine-grained operation lock for each control execution unit containing a priority treatment target area: After completing the identification of priority target areas, the entire network is divided into several control execution units based on the established feeder segment division and control domain boundary of the distribution network. Each unit contains several feeder segments and corresponding switches, protection devices and distribution terminals. For control execution units containing priority target areas, a granular operation lock request is initiated to the scheduling and control system, including the right to pre-emptively reserve switches, the right to temporarily modify protection settings, and the right to temporarily disable feeder automation logic. The request logic is as follows: Identify a list of all devices within the target area that require status changes or parameter adjustments during subsequent strategy execution, and verify their current control status; For devices that are already occupied by other processes or are in an uncontrollable state, mark the conflict and return the application failure message, and re-initiate after the conflict is resolved; For conflict-free devices, generate a lock request message that clearly specifies the type of resource to be locked, the target device number, the lock validity period and the unlocking conditions, and attach the target area identifier and risk confidence score summary calculated in this case. After security authentication, the lock request is sent to the corresponding device control service or area controller, which then completes the lock registration and provides confirmation.

[0009] Preferably, the logic for generating risk factors is as follows: For each physical node, its voltage sampling sequence is collected within a set statistical period to detect whether a sudden drop event occurs where the amplitude is lower than the normal operating limit and the duration exceeds a set threshold. Each time this occurs, the corresponding voltage drop risk count is accumulated. The current sampling sequence is subjected to abrupt change detection to identify sudden increase or decrease events with a change rate exceeding a set slope and a duration, and the current abrupt change risk count is accumulated. The number of opening and closing actions of the sectionalizing switches and tie switches associated with the statistical nodes within the cycle is used to determine whether the operation is abnormally frequent by combining the action interval and the cause code, and the risk count of high-frequency operation of the switches is accumulated. By combining the protection trigger records, the number of abnormal starts of the protection relays is counted.

[0010] Preferably, the comparison module performs a cross-comparison of the physical mapping set and the control intent release set item by item to construct a bidirectional state comparison matrix: Traverse each node record in the physical mapping set, and based on the node number and acquisition time, search the control intent release set for all instruction records that are the same as the target node and whose issuance time is earlier than or equal to the acquisition time, forming a candidate instruction set. For each instruction in the candidate instruction set, determine whether its execution feedback time is before the acquisition time, in order to determine whether the instruction has already had an execution effect before the acquisition time; If the state of a node in the physical mapping set is consistent with the expected execution state of the corresponding instruction in the control intention release set, and the execution feedback has been confirmed, then the state is considered consistent. If the state is inconsistent or there is a lack of execution feedback, it is marked as a state deviation; A two-way state comparison matrix is ​​constructed. The row dimension of the two-way state comparison matrix corresponds to the spatial nodes in the physical mapping set, and the column dimension corresponds to the intention commands in the control intention release set. The matrix element values ​​are obtained by quantifying the degree of matching between nodes and commands and related operational quality parameters through comprehensive indicators.

[0011] Preferably, the generation of the bidirectional state alignment matrix elements includes the following steps: Check whether the actual state of the node at the time of data collection matches the expected execution state of the corresponding instruction. If they match, assign a high matching degree flag. If they partially match or do not match, reduce the matching degree level according to the type of deviation. Analyze whether there is an interruption or timeout in the link from the issuance of the command to the execution feedback. If there is packet loss in the communication link resulting in missing feedback or the device is unable to respond to the command due to the locked state, it is determined to be unreachable or partially reachable, and the reachability level is recorded in the element value. Retrieve the communication quality indicators of the power distribution terminal to which the node belongs within the current instruction cycle, including link delay, packet loss rate and retransmission count. If the delay exceeds the preset threshold or the packet loss rate is higher than the allowable upper limit, a communication reliability degradation factor is introduced into the element value. The system queries the historical execution records of nodes and similar instructions, calculates the proportion of successful executions, incorporates the historical success rate as a weighting coefficient into the element values, and combines matching degree, reachability, communication reliability, and historical success rate into a comprehensive score according to preset priority rules to obtain matrix element values.

[0012] Preferably, the physical mapping set is established based on the actual spatial layout and electrical coupling relationship of the field equipment, including the installation location coordinates of sectionalizing switches, tie switches and protective relays, the feeder segment number to which they belong, and the identification of upstream and downstream electrical connection nodes; The control intent release set originates from the historical and currently effective fault handling contingency plan library and the sequence of operation mode switching instructions, including remote control / remote adjustment instructions issued by the master station centralized control, autonomous control actions triggered by local feeder automation logic, and inter-node negotiation instructions when the intelligent distributed collaborative control strategy is started.

[0013] Preferably, the differential isolation module performs differential isolation and protection logic: When a fine-grained operation lock is acquired and a fault occurs, the sectionalizing switch A closest to the power supply side of the fault point is tripped, while the sectionalizing switch B closest to the load side remains uncharged and does not trip. After a set delay, attempt to close section switch A. If a permanent fault is detected, accelerate the tripping and forward blocking. At the same time, section switch B trips after detecting a short-term power supply. The interconnection switch is closed to enable reverse power supply, and other section switches are closed in sequence. In the mixed scenario of ring network / radial network, differential protection or overcurrent protection logic is dynamically selected based on the state estimation and power flow calculation results to avoid false operation or failure to operate.

[0014] Preferably, the differential isolation module retrieves the operation lock list to confirm that the pre-occupancy right of the tie switch has been authorized, and checks that the voltage status on both sides of it meets the synchronization or quasi-synchronization conditions. If the conditions are met, a closing command is issued to enable the healthy power supply side to supply power to the load side of the original fault section. The other section switches are closed sequentially according to the feeder section and control domain level to gradually restore the power supply range of the healthy section.

[0015] The technical effects and advantages provided by the present invention in the above technical solution are as follows: 1. This application introduces a block-level risk confidence scoring model to quantify abnormal characteristics such as voltage drops, current surges, and frequent switching actions into risk factors. Combined with the marking of frequently occurring abnormal event sections, the risk confidence score of each physical node is calculated. Based on the scoring results, the nodes are graded and sorted, and high-risk continuous sections are selected as priority handling target areas. This allows limited control resources to be focused on the most critical parts, significantly improving handling efficiency. At the same time, by dividing the control execution units and applying granular operation locks to units containing priority handling target areas, conflicts between multiple sources of commands and inconsistencies in state are avoided, ensuring the uniqueness and security of subsequent control processes.

[0016] 2. After obtaining the operation lock and a fault occurs, this application can execute isolation and protection logic according to the differential strategy, and synchronize the opening and closing commands and protection parameters to the relevant terminals in real time through the 5G network, so as to ensure the accuracy of multi-terminal coordination and reduce the dependence on optical fiber, and realize the rapid isolation of faults and the safe recovery of non-faulty sections.

[0017] 3. This application acquires a situational data pool covering the physical mapping set and the control intent release set in real time in the target distribution network operating environment, providing a complete and synchronous basis for the actual status of equipment and operation and maintenance decision-making for subsequent analysis, enabling the system to have a global cognitive ability on the physical wiring structure and control strategy invocation.

[0018] 4. This application constructs a bidirectional state comparison matrix by cross-comparing the physical mapping set and the control intention release set item by item. This matrix can intuitively reveal the differences and potential conflicts between the actual state of the device and the expected control intention in terms of matching degree, communication reachability and historical success rate. It can effectively identify risk points that may cause malfunctions or command competition, and provide a reliable basis for accurately locating problems. Attached Figure Description

[0019] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this invention. For those skilled in the art, other drawings can be obtained based on these drawings.

[0020] Figure 1 This is a system framework diagram of the automation system of the present invention.

[0021] Figure 2 This is a flowchart of the automation system of the present invention.

[0022] Figure 3 This is a flowchart of the risk confidence scoring model of the present invention.

[0023] Figure 4 This is a schematic diagram of scanning high-risk nodes and merging target areas in the topology structure of the present invention.

[0024] Figure 5 This is a schematic diagram of the power distribution network fault point response of the present invention. Detailed Implementation

[0025] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0026] Example: This example provides an adaptive intelligent distribution network automation system for distribution networks. Please refer to [link / reference]. Figures 1-2 As shown, it includes a construction module, a comparison module, a calculation module, and a differential isolation module; The construction module acquires real-time sampled values ​​of electrical quantities such as voltage and current in the target distribution network operating environment, captures discrete status signals of sectionalizing switches, tie switches, and protection devices, and receives communication quality indicators and topology connection information from each distribution terminal, forming a real-time operational status data pool covering the entire network. The operational status data pool includes: The physical mapping set consists of the actual locations and electrical connections of sectionalizing switches, tie switches, and protective relays, reflecting the current physical wiring and power supply path of the network. The control intent release set originates from historical and current fault handling plans and mode switching instruction sequences (such as centralized control issuance from the master station, activation of local feeder automation logic, and intelligent distributed collaborative startup), reflecting the invocation and release of defined control strategies by operation and maintenance decisions.

[0027] Using the central processing unit as the aggregation node, the physical mapping set and the control intent release set are synchronized and organized to ensure that the two sets correspond in terms of spatial nodes (switch numbers, feeder segment numbers) and time sequences (instruction issuance time, execution feedback time). The physical mapping set and the control intent release set are then sent to the comparison module.

[0028] The comparison module performs a cross-comparison of the physical mapping set and the control intent release set item by item to identify differences and potential conflicts between the two in terms of state consistency, instruction reachability and communication reliability. It constructs a bidirectional state comparison matrix, in which the rows and columns correspond to physical nodes and intent instructions, respectively, and the element values ​​reflect comprehensive indicators such as matching degree, communication latency, and historical success rate. The bidirectional state comparison matrix is ​​sent to the calculation module.

[0029] The calculation module introduces a block-level risk confidence scoring model, quantifying features such as voltage sag amplitude, current surge, and switching operation frequency in telemetry / telecommunications data into risk factors, and calculating the risk confidence score for each physical node. Based on the scoring results, physical nodes are ranked and selected, with continuous sections whose scores exceed a preset safety threshold designated as priority handling target areas. The entire network is divided into several control execution units according to feeder segments and control domains. For each control execution unit containing a priority handling target area, a granular operation lock (such as switch pre-emption right, temporary modification right of protection settings) is requested, and the granular operation lock is sent to the differential isolation module.

[0030] Differential isolation module: When a micro-level operation lock is acquired and a fault occurs, the sectionalizing switch A closest to the fault point on the power supply side trips, while the sectionalizing switch B closest to the load side remains untripped due to undervoltage. After a set delay, an attempt is made to close sectionalizing switch A. If a permanent fault is detected, the tripping is accelerated and forward blocking is implemented. Simultaneously, sectionalizing switch B trips after detecting a short-term power restoration. The tie switch closes to achieve reverse power supply, and other sectionalizing switches close sequentially. In mixed ring / radial network scenarios, differential protection or overcurrent protection logic is dynamically selected based on state estimation and power flow calculation results to avoid false tripping or failure to trip. All tripping and closing commands and protection parameter adjustments are synchronized to relevant terminals in real time via the 5G network to ensure coordination accuracy and reduce fiber optic dependence.

[0031] The following is a detailed description of each functional module in this embodiment: In the specific implementation process, multiple types of data acquisition units are configured in the distribution network operation environment, including high-precision real-time sampling devices for analog electrical quantities such as voltage and current, as well as status monitoring interfaces for sectionalizing switches, tie switches and protection devices, which are used to capture discrete status signals such as the opening and closing positions, action events and protection trigger information of the above-mentioned devices.

[0032] Communication quality monitoring functions are deployed at each power distribution terminal to periodically collect communication performance indicators such as link latency, packet loss rate, and retransmission count. Combined with topology identification results reported by the terminals, topology connection information is constructed, including node connectivity, power supply path redundancy, and device adjacency relationships. This multi-source heterogeneous data, after being calibrated with a unified timescale, is fed into the central processor, which acts as the aggregation node to perform data fusion and standardization processing, forming a real-time operational status data pool covering the entire network.

[0033] The real-time operational status data pool is logically divided into two core subsets: one is the physical mapping set, and the other is the control intent release set.

[0034] The physical mapping set is established based on the actual spatial layout and electrical coupling relationships of primary equipment on site. Specifically, it consists of elements such as the installation coordinates of sectionalizing switches, tie switches, and protective relays, their respective feeder segment numbers, and upstream and downstream electrical connection node identifiers. A graph structure modeling method is used to map equipment entities as nodes and electrical connections as edges, thus fully reflecting the physical wiring configuration and dynamic power supply path of the current network. For example, when a tie switch is closed, its corresponding edge weight in the graph structure is marked as a valid path; otherwise, it is marked as open, thereby achieving real-time visual representation of the power supply path.

[0035] The control intent release set originates from historical and currently effective fault handling contingency plans and operating mode switching instruction sequences. It covers remote control / remote adjustment instructions issued by the master station's centralized control, autonomous control actions triggered by local feeder automation logic, and inter-node negotiation instructions when intelligent distributed collaborative control strategies are initiated. This set records the specific invocation and release process of predefined control strategies by analyzing attributes such as instruction source, target device number, instruction type, issuance time, expected execution sequence, and actual execution feedback time. For example, in a scenario where a feeder section experiences a fault, if the master station issues a sequence of instructions to isolate the faulty section and restore power to the non-faulty sections, the control intent release set needs to record the master station's instruction issuance timestamp, the target status change instructions for each section switch and tie switch, whether the local FA logic is bypassed or activated, and the actual timestamp and status confirmation results of each device's execution feedback.

[0036] After aggregating the two types of sets mentioned above, the central processing unit performs a synchronization and sorting operation: A unified spatiotemporal indexing framework is established, with switch number and feeder segment number as the primary keys of spatial nodes, and instruction issuance time and execution feedback time as the primary keys of time series. Each spatial node in the physical mapping set is traversed, and all time events associated with it in the control intent release set are retrieved. The physical state of the same node and the execution trajectory of the control instruction are aligned on the time axis by a timestamp matching algorithm.

[0037] The processing logic of this timestamp matching algorithm is as follows: For a given spatial node, extract its latest confirmed electrical state and acquisition time in the physical mapping set, then search for all instruction records corresponding to the node in the control intent release set, filter out the record whose issuance time is no later than the acquisition time and whose execution feedback time is closest to the acquisition time, and bind this record with the physical state as a set of spatiotemporal correspondence entries; if there are multiple instruction records whose time intervals cover the acquisition time, then select the latest issued instruction record according to the priority rule.

[0038] In practice, after receiving the physical mapping set and control intent release set from the central processing unit, the comparison module performs structured parsing on the two sets of data and establishes a unified cross-comparison benchmark.

[0039] The physical mapping set is centered on spatial nodes, recording the actual electrical state of each section switch, tie switch and protection relay at the time of acquisition and the feeder segment number where it is located. The control intent release set is based on a time series, recording information such as the source, target node, instruction type, issuance time, expected execution status, execution feedback time, and actual execution result of each control instruction.

[0040] The alignment process first performs alignment matching between spatial nodes and time series data: Traverse each node record in the physical mapping set, and based on the node number and acquisition time, search the control intent release set for all instruction records that are the same as the target node and whose issuance time is earlier than or equal to the acquisition time, forming a candidate instruction set. For each instruction in the candidate instruction set, it is further determined whether its execution feedback time is before the acquisition time, so as to determine whether the instruction has produced an observable execution effect before the acquisition time.

[0041] If the state of a node in the physical mapping set is consistent with the expected execution state of the corresponding instruction in the control intent release set, and the execution feedback has been confirmed, then it is determined to be consistent in state; if the state is inconsistent or there is a lack of execution feedback, it is marked as a state deviation.

[0042] Based on this, the comparison module constructs a bidirectional state comparison matrix. The row dimension of this matrix corresponds to spatial nodes in the physical mapping set, and the column dimension corresponds to intent commands in the control intent release set. Matrix element values ​​are obtained by quantifying the degree of matching between nodes and commands, as well as related operational quality parameters, through comprehensive indicators. The generation and processing logic of matrix elements includes the following steps: Check whether the actual state of the node at the time of data collection matches the expected execution state of the corresponding instruction. If they match completely, assign a high matching degree flag. If they match partially or do not match, reduce the matching degree level according to the type of deviation.

[0043] Analyze whether there is an interruption or timeout in the complete link from the issuance of the command to the execution feedback. If there is packet loss in the communication link resulting in missing feedback or the device is unable to respond to the command due to the locked state, it is determined to be unreachable or partially reachable, and the reachability level is recorded in the element value.

[0044] Retrieve the communication quality indicators of the power distribution terminal to which the node belongs within the current instruction cycle, including link delay, packet loss rate and retransmission count. If the delay exceeds the preset threshold or the packet loss rate is higher than the allowable upper limit, a communication reliability degradation factor is introduced into the element value.

[0045] The system queries the historical execution records of this node and similar instructions, calculates the success rate, and incorporates the historical success rate as a weighting coefficient into the element values ​​to reflect the reference significance of long-term stability for the current comparison. Following preset priority rules, the four sub-indicators—matching degree, reachability, communication reliability, and historical success rate—are combined into a single comprehensive score. The scoring method involves first quantifying each sub-indicator by level, then applying different weights based on the node's importance and the instruction's criticality, ultimately yielding the matrix element values.

[0046] In one embodiment of this application, the calculation logic of the matrix element comprehensive score, after sequentially completing the matching degree determination, accessibility assessment, communication reliability measurement, and historical success rate weighting, merges the four types of sub-indicators into a single comprehensive score according to a preset priority rule. Each sub-indicator is first graded and quantified. The matching degree is divided into three levels: complete matching is the highest level with 3 points, partial matching is the intermediate level with 2 points, and non-matching is the low level with 1 point. Accessibility is divided into accessible with 3 points, partially accessible with 2 points, and unaccessible with 1 point. Communication reliability is quantified as follows: normal range with 3 points, slight degradation with 2 points, and severe degradation with 1 point. Historical success rate is quantified by interval: 95% and above with 3 points, between 80% and 95% with 2 points, and below 80% with 1 point.

[0047] Weights are applied based on the importance of nodes and the criticality of instructions. Node importance is categorized by power supply reliability requirements for the feeder segment it belongs to: high weight coefficient 1.2, medium weight coefficient 1.0, and low weight coefficient 0.8. Instruction criticality is categorized by its involvement in fault isolation or power restoration: high weight coefficient 1.2, medium weight coefficient 1.0, and low weight coefficient 0.8. The comprehensive scoring logic involves multiplying the four quantitative scores by their corresponding weights, summing the results, and then dividing by the total weighted score to obtain the normalized score.

[0048] Taking a certain communication switch node as an example, its matching degree is 3 points for perfect match, node importance is 1.2, and the product is 3.6; reachability is 3 points for reachability, instruction criticality is 1.2, and the product is 3.6; communication reliability is within the normal range and it gets 3 points, the weight of which is the average of node importance 1.2 and instruction criticality 1.2 multiplied by 3, which is 3.6; historical success rate is 95% and it gets 3 points, also multiplied by the average weight 1.2, which is 3.6. Summing these up gives 14.4, the total weighted sum is 4.8, and the normalized comprehensive score is 14.4 divided by 4.8 equals 3. This score reflects that the node and the corresponding instruction are in optimal matching in terms of status, link, communication, and historical execution. If the matching degree of another node is mismatched, it gets 1 point multiplied by the node importance of 1.0, which equals 1. If the reachability is unreachable, it gets 1 point multiplied by the instruction criticality of 1.2, which equals 1.2. If the communication reliability is severely degraded, it gets 1 point multiplied by the average weight of 1.1, which equals 1.1. If the historical success rate is less than 80%, it gets 1 point multiplied by the average weight of 1.1, which equals 1.1. The sum of these values ​​is 4.4. The total weight is 4.4. The normalized comprehensive score is equal to 1, indicating that there is a significant deviation and risk between the node and the instruction.

[0049] In practical implementation, after receiving the bidirectional state comparison matrix and related deviation analysis results, the calculation module introduces a block-level risk confidence scoring model to quantitatively assess the operational risk of each physical node in the distribution network. For example... Figure 3 As shown, this model, based on telemetry and teleindication data, extracts key features reflecting abnormal equipment and line conditions, including voltage sag magnitude, current surge, switching frequency, protection triggering frequency, and communication anomaly duration. These features are then transformed into comparable risk factors according to predefined mapping rules. The logic for generating risk factors is as follows: For each physical node, its voltage sampling sequence is collected within a set statistical period to detect whether there is a sudden drop event where the amplitude is lower than the normal operating limit and the duration exceeds the set threshold. Each time this occurs, the corresponding voltage drop risk count is accumulated. The current sampling sequence is subjected to abrupt change detection to identify sudden increase or decrease events where the rate of change exceeds the set slope and lasts for a certain period of time, and the current abrupt change risk count is accumulated. The system counts the number of opening and closing operations of the sectionalizing switches and tie switches associated with the node within a cycle, and uses the operation interval and cause code to determine whether the operation is abnormally frequent, accumulating a risk count of high-frequency switch operations. It also uses protection trigger records provided by the comparison module to count the number of abnormal starts of protection relays, and introduces the duration of communication quality degradation as a supplementary risk factor. All risk factors are aggregated in the model according to node affiliation to form the original risk feature vector of that node.

[0050] After obtaining the original risk feature vector, the block-level risk confidence scoring model performs the scoring calculation logic as follows: Each risk factor is assigned an impact weight, and the weight value is determined based on the statistical results of the factor's contribution to the occurrence or amplification of failures in historical failure cases. The count or magnitude of each factor is normalized and mapped to a uniform score range to avoid evaluation bias caused by different units. The scores of each factor after normalization are summed according to their weights to obtain the initial risk confidence score for that node. An environmental correction factor is introduced, taking into account factors such as whether the feeder segment where the node is located has recently undergone maintenance, whether it is under heavy load operation, and the communication reliability degradation identified by the comparison module. This factor is used to adjust the initial score upwards or downwards, resulting in a final block-level risk confidence score. This score uses a scalar value to characterize the overall confidence level of the probability of a node's failure and the severity of its consequences under the current operating environment.

[0051] In one embodiment disclosed in this application, the risk factors include voltage drop risk count, current surge risk count, high-frequency switch operation risk count, number of protection abnormal activations, and duration of communication quality degradation, denoted as V_d, I_t, S_f, P_e, and C_q, respectively. The weights are determined based on historical fault statistics and are set as w_V, w_I, w_S, w_P, and w_C. Normalization maps the counts of each factor to a range of zero to ten. The mapping method is to use the maximum count across the entire network within the statistical period as a benchmark, divide the count value of a node by this maximum value, multiply by ten, and round down. The initial score R_0 is the sum of each normalized score multiplied by its corresponding weight, i.e., R_0 = V_n × w_V + I_n × w_I + S_n × w_S + P_n × w_P + C_n × w_C.

[0052] The environmental correction factor K_env is set to a range of 0.8-1.2 based on whether the feeder section has recently undergone maintenance, is under heavy load, and has experienced communication reliability degradation. A value of 1.2 is used for maintenance, heavy load, and communication degradation; 1.1 is used for maintenance, heavy load, or communication degradation; and 0.8 is used for all other cases. The final score R_f = R_0 × K_env. Taking a certain segment switch node as an example, the maximum count of voltage drop within the statistical period is 10, and this node has occurred twice, resulting in a normalized score of 2 points; the maximum count of current surge is 8, and this node has occurred four times, resulting in a normalized score of 5 points; the maximum count of switch action is 20, and this node has experienced six abnormal actions, resulting in a normalized score of 3 points; the maximum count of protection abnormal activation is 5, and this node has experienced one, resulting in a normalized score of 2 points; the maximum duration of communication degradation is 100 minutes, and this node has experienced 30 minutes of degradation, resulting in a normalized score of 3 points.

[0053] The weights are set to w_V=1.2, w_I=1.3, w_S=1.1, w_P=1.4, and w_C=1.0, respectively. The initial score R_0=2×1.2+5×1.3+3×1.1+2×1.4+3×1.0=2.4+6.5+3.3+2.8+3.0=18.0. The feeder segment where this node is located has recently undergone maintenance and is operating under heavy load, resulting in a degradation in communication reliability. K_env is set to 1.2, and the final score R_f=18.0×1.2=21.6. This scalar value represents the comprehensive confidence level of the node's high probability of failure and the severity of its consequences under the current operating environment.

[0054] After completing the risk confidence score for all physical nodes in the network, the calculation module sorts the nodes according to the score results, forming a node risk sequence from high to low. The sorting results are used for hierarchical management, dividing nodes into safe zones, warning zones, and high-risk zones according to preset security thresholds. Specifically, nodes in the safe zone have scores below the threshold, nodes in the warning zone have scores close to or slightly above the threshold, and nodes in the high-risk zone have scores significantly above the threshold.

[0055] Furthermore, the computing module scans the distribution of high-risk nodes in the topology and merges several high-risk nodes that are electrically connected and continuously distributed into priority treatment target areas.

[0056] After identifying the priority target area, the entire network is divided into several control execution units based on the established feeder segment division and control domain boundary of the distribution network. Each unit contains several feeder segments and corresponding switches, protection devices and distribution terminals.

[0057] For control execution units containing priority target areas, a granular operation lock application is initiated to the scheduling and control system. The type and scope of this operation lock are determined based on the controllable attributes of the equipment within the target area, including switch pre-emption rights, temporary modification rights of protection settings, and short-term deactivation rights of feeder automation logic. The application logic for the granular operation lock is as follows: like Figure 4 As shown, a list of all devices within the target area that require state changes or parameter adjustments during subsequent policy execution is determined, and their current control status is verified. For devices already occupied by other processes or in an uncontrollable state, a conflict is marked and an application failure message is returned. The request is re-initiated after the conflict is resolved. For devices without conflicts, a lock request message is generated, specifying the type of resource to be locked, the target device number, the lock validity period, and the unlocking conditions, and attaching the target area identifier and risk score summary calculated in this instance. After security authentication, the lock request is sent to the corresponding device control service or area controller, which completes the lock registration and provides feedback confirmation.

[0058] In one embodiment of this application, after completing the risk confidence score of all physical nodes in the network, the calculation module generates a node risk sequence from high to low according to the score value, and classifies them according to a preset safety threshold TH_sec. Nodes with scores less than TH_sec are classified into the safe zone, those with scores in the interval [TH_sec, TH_sec+Δ] are classified into the warning zone, and those with scores greater than TH_sec+Δ are classified into the high-risk zone, where Δ is the threshold fluctuation tolerance.

[0059] The distribution of high-risk nodes in the topology is scanned, and several electrically connected and consecutively adjacent high-risk nodes are grouped into priority target areas. The identification logic is to check whether adjacent nodes belong to the same high-risk area and are directly connected along the feeder segment. If so, they are merged into the same target area set. The entire network is divided into several control execution units according to the predetermined feeder segments and control domain boundaries. Each unit contains several feeder segments and auxiliary switches, protection devices, and distribution terminals.

[0060] For control execution units containing priority target zones, the calculation module executes granular operation lock requests. This includes generating a list of devices within the target zone that require status changes or parameter adjustments, verifying their control status, and marking a conflict and returning a failure if the device is already occupied or uncontrollable. Otherwise, a lock request message is generated, specifying the resource type, target device number, lock validity period T_lock, unlocking conditions, and additional target zone identifier ID_zone and risk score summary Score_sum. After security authentication, the lock request is sent to the device control service or zone controller for registration and confirmation.

[0061] Let TH_sec be 20.0 and Δ be 5.0. After ranking the nodes across the network, the high-risk area is defined as those with a score greater than 25.0. Within a certain feeder segment, the scores of nodes X, Y, and Z are 26.5, 27.2, and 25.8 respectively, and they are electrically connected. Therefore, they are merged into a target area ID_zone=FX12, and Score_sum is the sum of the three node scores, 79.5. The control execution unit of this target area includes switches S1 and S2, and protection R1. If the equipment control rights check result is that all are idle, a lock request is generated. The resource type includes switch pre-occupancy rights and temporary modification rights of protection settings. T_lock is set to 300 seconds. The unlocking condition is that the target area risk score drops below the threshold or the strategy is completed. After the message is authenticated and confirmed, the lock is completed.

[0062] In practice, the differential isolation module enters the fault response state after acquiring the micro-level operation lock. Its overall logic follows the differential control principle of "first quickly isolating the nearest fault, then verifying the nature of the fault, and then restoring power supply according to the network structure differences" to achieve effective isolation of the faulty section and continuous power supply to the healthy area.

[0063] After receiving the priority target area information and operation lock list from the calculation module, the module first locates the fault point in the distribution network topology and determines the relative position of that point on the power supply side and load side of the power supply path. The nearest sectionalizing switch on the power supply side is denoted as switch A, and the nearest sectionalizing switch on the load side is denoted as switch B. The design is as follows: After the fault is detected and confirmed, a trip command is immediately issued to disconnect switch A. At this time, switch B remains in its original undervoltage state and does not trip, so as to avoid the healthy section on the load side being accidentally disconnected.

[0064] After acquiring the micro-level operation lock, the differential isolation module enters the fault response state. Its control process follows the principle of first isolating the faulty section nearby, then identifying the nature of the fault, and finally performing differentiated power supply restoration according to the network structure, so as to achieve the removal of the faulty section and the continuous power supply of the healthy section.

[0065] like Figure 5 As shown, the fault location is determined based on the distribution network topology, and the nearest sectionalizing switch A on the power supply side and the nearest sectionalizing switch B on the load side are identified along the power supply path. After fault detection and confirmation, a tripping command is issued to switch A to disconnect it, while switch B remains in its original undervoltage state and is not tripped.

[0066] After switch A is opened, a pre-set delay period is entered. The value of this delay period is determined by the network structure characteristics and the coordination settings of the protection and switch operations. During the delay period, the closing probe logic is activated to perform a closing operation on switch A. Real-time sampling and monitoring of voltage and current are configured at both ends of switch A and adjacent nodes to capture changes in electrical quantities after closing.

[0067] After the closing command is issued, monitor the current amplitude and voltage recovery level of the downstream line of switch A. If the current rises to the fault characteristic level within the set short time window and the voltage still drops significantly, it is determined to be a permanent fault. If both the current and voltage recover to near the normal load level, it is determined to be a transient fault.

[0068] When a permanent fault is determined, the accelerated tripping logic is executed, causing switch A to open again and triggering the positive interlocking mechanism. Automatic reclosing of the switch is also prohibited to prevent reverse reclosing from exacerbating equipment damage or inducing secondary accidents. If a short-term power signal is detected during the closing test of switch B (i.e., a situation where the voltage momentarily recovers and then disappears), the switch will immediately trip.

[0069] After switch A and switch B complete their isolation operations, the tie switch closing logic is initiated according to the distribution network topology to establish a reverse power supply path. The timing and sequence of tie switch closing must ensure that they do not conflict with existing lockout states. The operation lock list is retrieved to confirm that the tie switch's pre-emptive rights have been authorized, and the voltage states on both sides of the tie switch are checked to ensure that they meet the synchronization or quasi-synchronization conditions. If the conditions are met, a closing command is issued to supply power from the healthy power supply side to the load side of the original faulty section. The remaining section switches are closed sequentially according to the feeder section and control domain level to gradually restore the power supply range of the healthy section.

[0070] In scenarios where ring and radial networks operate in combination, to prevent maloperation or failure to operate due to protection logic mismatch, state estimation and power flow calculation results are introduced to participate in the dynamic selection of protection strategies. Specifically, the process is as follows: The system calls upon the node voltage magnitude and phase angle, branch power flow direction, and other data output from the state estimation module, and combines this with the short-circuit current distribution characteristics obtained from power flow calculations to determine whether the current network structure is closer to ring network operation or radial network operation. When the state estimation results indicate the presence of multiple power sources in parallel and significant bidirectional power flow, differential protection is selected. When power flow calculations show that the network is unidirectionally radial and the short-circuit current direction remains stable, overcurrent protection is selected to balance the requirements of operating speed and selectivity.

[0071] The switching of protection logic and the adjustment of related parameters are initiated by the module and synchronized in real time to the relevant power distribution terminals and protection devices through the 5G network, ensuring that each node works together to complete protection and control actions under unified criteria.

[0072] In one embodiment disclosed in this application, the differential isolation module enters a fault response state after acquiring a micro-level operation lock. Its control logic performs isolation and restoration operations according to the relative positions of the power supply side sectionalizing switch A and the load side sectionalizing switch B, and dynamically selects the protection strategy based on the state estimation and power flow calculation results. After fault location, switches A and B are determined on the power supply path. After fault detection confirmation, switch A is immediately tripped, while switch B remains under voltage and does not trip.

[0073] After switch A is opened, a set delay T_delay is entered. This delay is set according to the network structure and protection coordination and is used to distinguish between transient and permanent faults. During the delay, the closing test logic is activated, switch A is closed and its downstream current amplitude I_down and voltage amplitude U_down are monitored. If I_down≥I_fault_th and U_down≤U_fault_th are satisfied within a short time window T_win, it is determined to be a permanent fault. If I_down≤I_norm_th and U_down≥U_norm_th, it is determined to be a transient fault, where I_fault_th is the fault characteristic current threshold, U_fault_th is the fault voltage drop threshold, and I_norm_th and U_norm_th are the normal load current and voltage lower limits.

[0074] Once a permanent fault is determined, the accelerated tripping command is executed to disconnect switch A again and trigger positive interlocking. The interlocking period T_block prohibits automatic reclosing. If switch B detects a short-term power supply during the closing attempt, i.e., U_B momentarily recovers to above U_rec_th and falls back to zero within T_rec, it will immediately trip.

[0075] After isolation is completed, the operation lock list is retrieved to confirm that the pre-emption right of the tie switch has been authorized, and it is checked that the voltages U_side1 and U_side2 on both sides of it meet the synchronization conditions |U_side1-U_side2|≤ΔU_sync and the phase angle difference |θ_side1-θ_side2|≤Δθ_sync. Then the tie switch is closed to establish a reverse power supply path, and other section switches are closed in sequence according to the feeder segment and control domain level.

[0076] In a mixed ring and radial network scenario, the state estimation outputs the node voltage amplitude U_i, phase angle θ_i, and branch power flow direction P_ij. Combined with power flow calculation, the short-circuit current amplitude I_sc_ij is determined. If the proportion of ∑(P_ij bidirectional absolute value) to ∑|P_ij| exceeds K_loop and the number of multi-source nodes N_source ≥ 2, it is determined to be a ring network operation and differential protection logic is selected. If the proportion of unidirectional power flow is higher than 1-K_loop and N_source = 1, and the direction of I_sc_ij is stable, it is determined to be a radial network operation and overcurrent protection logic is selected, where K_loop is set to 0.6. Protection logic switching commands and parameters are synchronized to the terminal in real time via the 5G network.

[0077] To illustrate with specific values, T_delay is set to 1.5 seconds, T_win to 0.2 seconds, I_fault_th = 1.5 times the rated current, U_fault_th = 0.5 times the rated voltage, I_norm_th = 1.1 times the rated current, and U_norm_th = 0.9 times the rated voltage. During the closing test, I_down = 1.6In and U_down = 0.45Un, meeting the permanent fault condition. Switch A accelerates the opening and blocks for T_block = 30 seconds. During the closing test of switch B, U_B instantaneously rises from 0.92Un to 0.94Un and then drops to 0, with T_rec = 0.1 seconds, following the opening. The phase differences on both sides of the interconnecting switch are U_side1 = 0.98Un and U_side2 = 0.96Un. The |difference| = 0.02Un < ΔU_sync = 0.05Un, and the phase angle difference = 4° < Δθ_sync = 10°, indicating successful closing. The state estimation shows that P_ij has a bidirectional ratio of 0.68 > K_loop = 0.6 and N_source = 2. The ring network is determined to be in operation, and the differential protection logic is enabled. The protection parameters are synchronized to the relevant devices via the 5G network to achieve coordinated action under unified criteria.

[0078] In the description of this specification, references to terms such as "an embodiment," "example," "specific example," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0079] The preferred embodiments of the present invention disclosed above are merely illustrative of the invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the invention to any specific implementation. Clearly, many modifications and variations can be made based on the content of this specification. This specification selects and specifically describes these embodiments to better explain the principles and practical applications of the invention, thereby enabling those skilled in the art to better understand and utilize the invention. The invention is limited only by the claims and their full scope and equivalents.

Claims

1. An adaptive intelligent distribution network automation system for distribution networks, characterized in that: It includes a construction module, a comparison module, a calculation module, and a differential isolation module; The module constructs a real-time operational status data pool in the target distribution network operating environment. The operational status data pool includes a physical mapping set and a control intent release set. The physical mapping set includes the installation location coordinates of sectional switches, tie switches, and protection relays, their respective feeder segment numbers, and upstream and downstream electrical connection node identifiers. The control intent release set includes remote control / remote adjustment commands issued by the master station's centralized control, autonomous control actions triggered by local feeder automation logic, and inter-node negotiation commands when the intelligent distributed collaborative control strategy is activated. Comparison module: Performs item-by-item cross-comparison between the physical mapping set and the control intent release set to construct a two-way state comparison matrix; Calculation module: Introduces a block-level risk confidence scoring model to calculate the risk confidence score of each physical node, sorts the physical nodes according to the scoring results, selects continuous segments with scores higher than the preset safety threshold as priority handling target areas, divides the entire network into several control execution units according to feeder segments and control domains, and applies for granular operation locks for each control execution unit containing priority handling target areas. Differential isolation module: When a micro-level operation lock is acquired and a fault occurs, differential isolation and protection logic are performed, and all opening and closing commands and protection parameter adjustments are synchronized to relevant terminals in real time via the 5G network.

2. The adaptive intelligent distribution network automation system for distribution networks according to claim 1, characterized in that: The calculation module introduces a block-level risk confidence scoring model to calculate the risk confidence score for each physical node: Based on telemetry and teleindication data, feature quantities reflecting abnormal equipment and line conditions are extracted and transformed into risk factors. Various risk factors are aggregated according to node affiliation in the block-level risk confidence scoring model to form the original risk feature vector of the node; After obtaining the original risk feature vector, the block-level risk confidence scoring model assigns an influence weight to each risk factor; The count or magnitude of each risk factor is normalized and mapped to a uniform score range; The scores of each factor after normalization are summed according to their weights to obtain the initial risk confidence score of the node. An environmental correction factor is introduced to adjust the initial score up or down, forming a block-level risk confidence score.

3. The adaptive intelligent distribution network automation system for distribution networks according to claim 2, characterized in that: The calculation module ranks and sorts physical nodes based on the scoring results, and selects continuous segments with scores higher than a preset safety threshold as priority target areas for treatment. After completing the risk confidence score of all physical nodes in the network, the nodes are sorted according to the score results to form a node risk sequence from high to low. The sorting results are used for hierarchical management. The nodes are divided into safe zones, warning zones, and high-risk zones according to preset safety thresholds; Scan the distribution of high-risk nodes in the topology and merge several high-risk nodes that are electrically connected and continuously distributed into priority treatment target areas.

4. The adaptive intelligent distribution network automation system for distribution networks according to claim 3, characterized in that: The calculation module divides the entire network into several control execution units based on feeder segments and control domains, and applies for granular operation locks for each control execution unit containing a priority target area: After completing the identification of priority target areas, the entire network is divided into several control execution units based on the established feeder segment division and control domain boundary of the distribution network. Each unit contains several feeder segments and corresponding switches, protection devices and distribution terminals. For control execution units containing priority target areas, a granular operation lock request is initiated to the scheduling and control system, including the right to pre-emptively reserve switches, the right to temporarily modify protection settings, and the right to temporarily disable feeder automation logic. The request logic is as follows: Identify a list of all devices within the target area that require status changes or parameter adjustments during subsequent strategy execution, and verify their current control status; For devices that are already occupied by other processes or are in an uncontrollable state, mark the conflict and return the application failure message, and re-initiate after the conflict is resolved; For conflict-free devices, generate a lock request message that clearly specifies the type of resource to be locked, the target device number, the lock validity period and the unlocking conditions, and attach the target area identifier and risk confidence score summary calculated in this case. After security authentication, the lock request is sent to the corresponding device control service or area controller, which then completes the lock registration and provides confirmation.

5. The adaptive intelligent distribution network automation system for distribution networks according to claim 2, characterized in that: The generation logic of the risk factor is as follows: For each physical node, its voltage sampling sequence is collected within a set statistical period to detect whether a sudden drop event occurs where the amplitude is lower than the normal operating limit and the duration exceeds a set threshold. Each time this occurs, the corresponding voltage drop risk count is accumulated. The current sampling sequence is subjected to abrupt change detection to identify sudden increase or decrease events with a change rate exceeding a set slope and a duration, and the current abrupt change risk count is accumulated. The number of opening and closing operations of the sectionalizing switches and tie switches associated with the statistical nodes within the cycle is used to determine whether the operation is abnormally frequent by combining the operation interval and cause code, and the risk count of high-frequency operation of the switches is accumulated. By combining the protection trigger records, the number of abnormal starts of the protection relays is counted.

6. The adaptive intelligent distribution network automation system for distribution networks according to claim 1, characterized in that: The comparison module performs a cross-comparison of the physical mapping set and the control intent release set item by item to construct a two-way state comparison matrix: Traverse each node record in the physical mapping set, and based on the node number and acquisition time, search the control intent release set for all instruction records that are the same as the target node and whose issuance time is earlier than or equal to the acquisition time, forming a candidate instruction set. For each instruction in the candidate instruction set, determine whether its execution feedback time is before the acquisition time, in order to determine whether the instruction has already had an execution effect before the acquisition time; If the state of a node in the physical mapping set is consistent with the expected execution state of the corresponding instruction in the control intention release set, and the execution feedback has been confirmed, then the state is considered consistent. If the state is inconsistent or there is a lack of execution feedback, it is marked as a state deviation; A bidirectional state comparison matrix is ​​constructed. The row dimension of the bidirectional state comparison matrix corresponds to the spatial nodes in the physical mapping set, and the column dimension corresponds to the intention commands in the control intention release set. The matrix element values ​​are obtained by quantifying the degree of matching between nodes and commands and related operational quality parameters through comprehensive indicators.

7. The adaptive intelligent distribution network automation system for distribution networks according to claim 6, characterized in that: The generation of elements of the bidirectional state comparison matrix includes the following steps: Check whether the actual state of the node at the time of data collection matches the expected execution state of the corresponding instruction. If they match, assign a high matching degree flag. If they partially match or do not match, reduce the matching degree level according to the type of deviation. Analyze whether there is an interruption or timeout in the link from the issuance of the command to the execution feedback. If there is packet loss in the communication link resulting in missing feedback or the device is unable to respond to the command due to the locked state, it is determined to be unreachable or partially reachable, and the reachability level is recorded in the element value. Retrieve the communication quality indicators of the power distribution terminal to which the node belongs within the current instruction cycle, including link delay, packet loss rate and retransmission count. If the delay exceeds the preset threshold or the packet loss rate is higher than the allowable upper limit, a communication reliability degradation factor is introduced into the element value. The system queries the historical execution records of nodes and similar instructions, calculates the proportion of successful executions, incorporates the historical success rate as a weighting coefficient into the element values, and combines matching degree, reachability, communication reliability, and historical success rate into a comprehensive score according to preset priority rules to obtain matrix element values.

8. The adaptive intelligent distribution network automation system for distribution networks according to claim 1, characterized in that: The physical mapping set is established based on the actual spatial layout and electrical coupling relationship of the field equipment; The control intent release set is derived from the historical and currently effective fault handling contingency plan library and the sequence of operation mode switching instructions.

9. The adaptive intelligent distribution network automation system for distribution networks according to claim 1, characterized in that: The differential isolation module performs differential isolation and protection logic: When a fine-grained operation lock is acquired and a fault occurs, the sectionalizing switch A closest to the power supply side of the fault point is tripped, while the sectionalizing switch B closest to the load side remains uncharged and does not trip. After a set delay, attempt to close section switch A. If a permanent fault is detected, accelerate the tripping and forward blocking. At the same time, section switch B trips after detecting a short-term power supply. The interconnection switch is closed to enable reverse power supply, and other section switches are closed in sequence. In the mixed scenario of ring network / radial network, differential protection or overcurrent protection logic is dynamically selected based on the state estimation and power flow calculation results to avoid false operation or failure to operate.

10. The adaptive intelligent distribution network automation system for distribution networks according to claim 9, characterized in that: The differential isolation module retrieves the operation lock list to confirm that the pre-occupancy right of the tie switch has been authorized, and checks whether the voltage status on both sides of it meets the synchronization or quasi-synchronization conditions. If the conditions are met, a closing command is issued to enable the healthy power supply side to supply power to the load side of the original fault section. The other section switches are closed sequentially according to the feeder section and control domain level to gradually restore the power supply range of the healthy section.

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