A power distribution network load restoration system based on failure restoration state assessment

Abstract

The application discloses a power distribution network load recovery system based on fault recovery state evaluation and belongs to the field of power systems, in particular to adaptive operation control after power distribution system failure. The system comprises a data acquisition module for acquiring power recovery data; a state evaluation module for synchronism evaluation on the power recovery data to obtain a line recovery risk level; and a control module for determining recovery control parameters according to the line recovery risk level and a preset parameter mapping table when the line recovery risk level is greater than a preset risk threshold, adjusting the current recovery control parameters based on the recovery control parameters, and controlling the power distribution network to recover loads through power distribution network control equipment according to the recovery control parameters. Therefore, through implementation of the application, dynamic power balance and voltage stability collaborative control in the load recovery process after power distribution network fault removal can be realized, and stable recovery of the power distribution network load and safe and stable operation of the power distribution network can be realized.

Description

Technical Field

[0001] This invention relates to the field of power control systems, and more particularly to a distribution network load restoration system based on fault recovery status assessment. Background Technology

[0002] As a crucial link in power distribution, the reliability of power supply in the power distribution network is of paramount importance. During operation, distribution lines may experience transient faults due to lightning strikes, external damage, or other reasons, such as phase-to-phase short-circuit arcs. After the fault is cleared, the arc extinguishes itself, and the system enters the post-fault load recovery process. The core objective of this process is to quickly and safely restore power supply to users, and the quality of its control strategy directly affects the continuity of power supply and the stability of the system.

[0003] In existing technologies, load restoration control after a distribution network fault largely relies on preset fixed logic. For example, reclosing devices are used to connect lines in a fixed delay sequence, or operations are performed according to a pre-defined load restoration sequence. However, existing restoration systems lack the adaptive capability to control restoration strategies based on real-time operating conditions. When a large amount of load is rapidly connected, it instantly draws enormous active and reactive power from the grid, causing severe fluctuations in system power flow. Previously restored lines may trip again, leading to load restoration failure. Furthermore, the impact of load connection can cause node voltage drops. In the initial, vulnerable operating state of system restoration, this may exceed the system's voltage regulation capacity, potentially leading to local voltage instability or even voltage collapse, thus causing load restoration failure. Summary of the Invention

[0004] This invention provides a distribution network load restoration system based on fault recovery status assessment, which can solve the problem of how to achieve dynamic power balance and voltage stability coordinated control during the load restoration process after the distribution network fault is cleared, so as to avoid secondary power oscillation or voltage collapse caused by load input timing and rate mismatch.

[0005] This invention provides a power distribution network load restoration system based on fault recovery status assessment, comprising:

[0006] The data acquisition module is used to acquire, in real time, the power recovery data of each recovery node within the recovery time interval under the current recovery control parameters during the load recovery process after the fault is cleared in the distribution network. The recovery time interval refers to the time interval from the time the fault was cleared to the current time.

[0007] The status assessment module is connected to the data acquisition module. It performs a synchronization assessment on the power restoration data using the correlation coefficient method to obtain an assessment value that characterizes the stability of the distribution network line restoration. If the assessment value is greater than or equal to a preset stability threshold, the line restoration risk level corresponding to the current restoration control parameter is determined based on the power restoration data and the restoration time interval.

[0008] A control module, connected to the status assessment module, is used to determine recovery control parameters based on the line recovery risk level and a preset parameter mapping table when the line recovery risk level is greater than a preset risk threshold. The module then adjusts the current recovery control parameters based on these parameters and controls the distribution network to perform load recovery via distribution network control equipment. The recovery control parameters are used to generate a load recovery sequence and a load recovery rate, including an upper capacity limit for restricting single load input capacity to smooth power oscillations and a time interval for controlling load input intervals to maintain node voltage stability.

[0009] This invention, through a data acquisition module, enables real-time sensing of key electrical parameters (such as recovery rate, insulation strength, and line damping) during fault recovery, providing a data foundation for adaptive control. A state assessment module evaluates synchronicity, determining whether the system has moved out of transient mode and entered a stable recovery phase, providing a prerequisite for reliable subsequent risk assessment. Furthermore, the state assessment module can determine the risk level, enabling quantitative diagnosis of the effectiveness of the currently executed recovery control strategy and identifying whether the strategy matches the actual system state. The control module enables online dynamic optimization of recovery control parameters (load input sequence and rate). By setting a capacity upper limit, active power surges are directly suppressed to prevent system oscillations; by setting time intervals, the reactive load input rhythm is controlled to maintain voltage stability. Finally, through collaborative optimization of the sequence and rate, the load recovery process is transformed from "open-loop execution" to "closed-loop adaptive optimization," maximizing recovery speed while ensuring system stability.

[0010] Furthermore, the control module also includes:

[0011] The load distribution balancing unit is used to obtain the real-time transmission power of each line in the distribution network, calculate the load rate of each line by calculating the real-time transmission power and the corresponding rated transmission capacity, and calculate the standard deviation of each load rate to obtain the load distribution balance degree.

[0012] When the load distribution balance is less than a preset balance threshold, identify high-load lines with a load rate greater than the average load rate and low-load lines with a load rate less than or equal to the average load rate.

[0013] Based on the load recovery sequence, a load transfer sequence is determined in the high-load line, and a load receiving sequence is determined in the low-load line. Based on the load transfer sequence and the load receiving sequence, a load transfer mapping relationship between the high-load line and the low-load line is determined.

[0014] The time and capacity of each load transfer are determined based on the load transfer mapping relationship, the load rates, the capacity limit, and the time interval. The load transfer operation is repeated until the load distribution balance is greater than or equal to the preset balance threshold, at which point the load transfer stops.

[0015] By adding load distribution balancing units to achieve load transfer between feeders, the power flow distribution can be optimized and the recovery capacity can be improved. Specifically, by calculating the load distribution balance online and executing load transfer, the load between each feeder is dynamically balanced, preventing local line overload caused by the recovery process. This extends the local stability control effect to the entire power distribution network, improving the overall recovery capability and safety margin of the system.

[0016] Furthermore, the real-time acquisition of power recovery data, recovery rate data, insulation strength data, and line damping data for each recovery node within the recovery time interval under the current recovery control parameters specifically includes:

[0017] The distribution network topology is obtained, and the line corresponding to the recovery node is determined based on the distribution network topology. After the current of the line recovers from zero to the target value, the current recovery time of the recovery node is determined in real time, and the current recovery rate sequence is calculated for each current recovery time. The output power difference of the recovery node is determined based on each current recovery time to obtain the power transfer sequence.

[0018] The load recovery rate time series is obtained by multiplying the current recovery rate sequence and the power transfer sequence and dividing by the rated power. The load recovery rate time series is then subjected to time-domain difference operation, and the root mean square value of the load recovery rate time series within a preset sliding time window is calculated to obtain the recovery rate data.

[0019] Obtain the sequence of instantaneous insulation resistance values ​​within the recovery time interval, and compare each instantaneous insulation resistance value with the rated insulation resistance value to obtain the insulation strength data;

[0020] The output power oscillation waveform data within the recovery time interval is obtained, and the output power oscillation waveform is fitted with an exponential decay function to obtain a sequence of instantaneous damping ratio values. The absolute value of each instantaneous damping ratio value is obtained by subtracting it from the damping ratio reference value.

[0021] By employing specific methods such as calculating the current recovery rate, extracting power transfer, and applying an exponential fitting damping ratio, the data input to the state assessment module is ensured to accurately reflect the system's true dynamics, thereby directly enhancing the accuracy and reliability of state assessment and risk identification. Furthermore, the synchronization assessment of the power recovery data using the correlation coefficient method specifically involves:

[0022] The Pearson correlation coefficient between the insulation strength data and the line damping data is calculated using a sliding time window method. The absolute value of the Pearson correlation coefficient is used as the synchronicity index, and a percentage linear mapping is performed on the synchronicity index to obtain the evaluation value.

[0023] By specifying the use of the Pearson correlation coefficient method with a sliding time window to calculate the synchronicity index, a standard and quantifiable implementation path for the "correlation coefficient method" is provided. Transforming the abstract judgment of "assessing synchronicity" into a precise mathematical operation makes the assessment of system stability recovery more objective and consistent, avoids subjective errors, and strengthens the decision-making foundation for closed-loop control.

[0024] Furthermore, the distribution network load restoration system based on fault recovery status assessment also includes: a monitoring module, which is connected to the data acquisition module and the control module respectively, for continuously monitoring the current load restoration rate and the current voltage stability during the execution of load restoration control commands according to the restoration control parameters, wherein the current voltage stability is determined by the percentage of the difference between the actual voltage and the rated voltage to the rated voltage;

[0025] If the current load recovery rate is found to be less than a preset rate threshold and the duration is greater than a preset time threshold, and the voltage stability is less than a preset voltage threshold, then the capacity limit value is increased and the time interval value is extended.

[0026] If the difference between the current load recovery rate and the preset rate threshold is greater than a first preset difference threshold, and the difference between the current voltage stability and the preset voltage threshold is greater than a second preset difference threshold, then the upper limit of capacity is reduced and the time interval is shortened.

[0027] By adding a monitoring module and setting parameter adjustment rules based on real-time feedback, a fast and precise secondary feedback adjustment loop is added on top of the macroscopic closed-loop control. Fine-tuning is performed based on the immediate effects of parameter execution (voltage stability, recovery rate), which greatly enhances the dynamic response and adaptability of the control system, enabling the recovery process to better cope with unexpected situations.

[0028] Furthermore, before obtaining the sequence of instantaneous insulation resistance values ​​within the recovery time interval, the method further includes:

[0029] The raw monitoring data of insulation resistance is obtained, and outliers in the raw monitoring data are identified and removed using the 3σ criterion. The raw monitoring data after removal is then smoothed using the moving average filtering method to obtain the instantaneous value sequence of insulation resistance.

[0030] By employing the 3σ criterion and moving average filtering for data preprocessing before acquisition, data quality is improved from the data source. This effectively eliminates the interference of monitoring noise and outliers on subsequent evaluations, providing more stable and reliable data input for the entire closed-loop control system and fundamentally enhancing the robustness of the entire system.

[0031] Furthermore, determining the line restoration risk level based on the power restoration data and the restoration time interval specifically involves:

[0032] The risk of line restoration is divided into three levels: low risk, medium risk, and high risk.

[0033] The risk assessment coefficient is obtained by calculating the ratio of the mean of the recovery rate data to the recovery time interval.

[0034] If the risk assessment coefficient is less than the first risk threshold, it is determined to be low risk; if the risk assessment coefficient is greater than or equal to the first risk threshold and less than the second risk threshold, it is determined to be medium risk; if the risk assessment coefficient is greater than or equal to the second risk threshold, it is determined to be high risk, wherein the second risk threshold is greater than the first risk threshold.

[0035] By classifying risk levels based on risk assessment coefficients and clear thresholds, the key step of "determining the risk level of line restoration" is standardized, making the risk assessment decision-making process clear, repeatable, and operable, and reducing uncertainty.

[0036] Furthermore, the distribution network load restoration system based on fault recovery status assessment also includes: a data storage and analysis module, used to store recovery rate data, insulation strength data, line damping data, recovery control parameters, and actual recovery data after the distribution network is restored using the recovery control parameters during each fault recovery process;

[0037] Cluster analysis is performed on the stored data based on a preset period to identify typical recovery characteristics corresponding to each fault type;

[0038] The preset parameter mapping table and the preset stability threshold are optimized according to the typical recovery characteristics, and the load recovery of the distribution network is controlled according to the optimized preset parameter mapping table and the preset stability threshold.

[0039] By adding data storage and analysis modules and utilizing cluster analysis for self-learning, the intelligent closed-loop control system acquires the ability to continuously optimize. Through analyzing historical data, it can automatically optimize the "preset parameter mapping table" and "preset stability threshold," allowing the system to accumulate experience and become increasingly intelligent over time, thereby continuously improving the adaptability and effectiveness of the control strategy in the long term.

[0040] Another embodiment of the present invention provides a distribution network load restoration method based on fault recovery status assessment, comprising:

[0041] During the load restoration process after the distribution network fault is cleared, the power restoration data of each restoration node within the restoration time interval is acquired in real time under the current restoration control parameters. The restoration time interval refers to the time interval from the time the fault was cleared to the current time.

[0042] The power restoration data is evaluated for synchronicity using the correlation coefficient method to obtain an evaluation value characterizing the stability of the distribution network line restoration. If the evaluation value is greater than or equal to a preset stability threshold, the line restoration risk level corresponding to the current restoration control parameter is determined based on the power restoration data and the restoration time interval.

[0043] When the risk level of line restoration is greater than a preset risk threshold, restoration control parameters are determined according to the line restoration risk level and a preset parameter mapping table. The current restoration control parameters are adjusted based on the restoration control parameters, and the distribution network is controlled by the distribution network control equipment to restore the load according to the restoration control parameters.

[0044] This invention enables real-time sensing of key electrical parameters (recovery rate, insulation strength, and line damping) during fault recovery through a data acquisition module, providing a data foundation for adaptive control.

[0045] The state assessment module evaluates synchronicity, enabling the determination of whether the system has exited the transient state and entered the stable recovery phase, providing a prerequisite guarantee for the reliability of subsequent risk assessments. Furthermore, the state assessment module can determine the risk level, enabling quantitative diagnosis of the effectiveness of the currently implemented recovery control strategy and identifying whether the strategy matches the actual system state. The control module achieves online dynamic optimization of recovery control parameters (load input sequence and rate). By setting a capacity upper limit, active power surges are directly suppressed to prevent system oscillations; by setting time intervals, the reactive load input rhythm is controlled to maintain voltage stability. Finally, through collaborative optimization of the sequence and rate, the load recovery process is transformed from "open-loop execution" to "closed-loop adaptive optimization," maximizing recovery speed while ensuring system stability.

[0046] Furthermore, the distribution network load restoration method based on fault recovery status assessment also includes:

[0047] The real-time transmission power of each line in the distribution network is obtained. The load factor of each line is obtained by calculating the real-time transmission power and the corresponding rated transmission capacity. The standard deviation of each load factor is calculated to obtain the load distribution balance.

[0048] When the load distribution balance is less than a preset balance threshold, identify high-load lines with a load rate greater than the average load rate and low-load lines with a load rate less than or equal to the average load rate.

[0049] Based on the load recovery sequence, a load transfer sequence is determined in the high-load line, and a load receiving sequence is determined in the low-load line. Based on the load transfer sequence and the load receiving sequence, a load transfer mapping relationship between the high-load line and the low-load line is determined.

[0050] The time and capacity of each load transfer are determined based on the load transfer mapping relationship, the load rates, the capacity limit, and the time interval. The load transfer operation is repeated until the load distribution balance is greater than or equal to the preset balance threshold, at which point the load transfer stops.

[0051] By adding load distribution balancing units to achieve load transfer between feeders, the power flow distribution can be optimized and the recovery capacity can be improved. Specifically, by calculating the load distribution balance online and executing load transfer, the load between each feeder is dynamically balanced, preventing local line overload caused by the recovery process. This extends the local stability control effect to the entire power distribution network, improving the overall recovery capability and safety margin of the system. Attached Figure Description

[0052] To more clearly illustrate the technical solution of this application, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0053] Figure 1 This is a schematic diagram of a power distribution network load restoration system based on fault recovery status assessment provided in an embodiment of the present invention;

[0054] Figure 2 This is a schematic diagram of another distribution network load restoration system based on fault recovery status assessment provided in an embodiment of the present invention;

[0055] Figure 3This is a schematic diagram of another distribution network load restoration system based on fault recovery status assessment provided in an embodiment of the present invention;

[0056] Figure 4 This is a flowchart illustrating a distribution network load restoration method based on fault recovery status assessment provided in an embodiment of the present invention. Detailed Implementation

[0057] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions of this application will be clearly and completely described below with reference to the accompanying drawings of the embodiments. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0058] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains; the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the application; the terms “comprising” and “having”, and any variations thereof, in the specification, claims, and foregoing description of the drawings are intended to cover non-exclusive inclusion.

[0059] In the description of the embodiments of this application, technical terms such as "first" and "second" are used only to distinguish different objects and should not be construed as indicating or implying relative importance or implicitly specifying the number, specific order, or primary and secondary relationship of the indicated technical features. In the description of the embodiments of this application, "multiple" means two or more, unless otherwise explicitly defined.

[0060] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0061] In the description of the embodiments in this application, the term "and / or" is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, and B existing alone. Additionally, the character " / " in this document generally indicates that the preceding and following related objects have an "or" relationship.

[0062] In the description of the embodiments of this application, the term "multiple" refers to two or more (including two), similarly, "multiple sets" refers to two or more (including two sets), and "multiple pieces" refers to two or more (including two pieces).

[0063] In the description of the embodiments of this application, unless otherwise expressly specified and limited, technical terms such as "installation," "connection," "joining," and "fixing" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral part; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; they can refer to the internal communication of two components or the interaction between two components. For those skilled in the art, the specific meaning of the above terms in the embodiments of this application can be understood according to the specific circumstances.

[0064] See Figure 1 To address the problems of existing distribution network load restoration systems based on fault recovery status assessment, an embodiment of the present invention provides a distribution network load restoration system 100 based on fault recovery status assessment, comprising:

[0065] The data acquisition module 110 is used to acquire, in real time, the power restoration data of each restoration node within the restoration time interval under the current restoration control parameters during the load restoration process after the fault is cleared in the distribution network. The restoration time interval refers to the time interval from the time the fault was cleared to the current time.

[0066] In this embodiment, the data acquisition module first synchronizes its clock with the distribution network dispatch master station, using the fault clearance time as the zero point. While monitoring the recovery nodes with cleared faults according to the current recovery control parameters, it continuously records time until the current moment, thus dynamically defining the recovery time interval. Through intelligent monitoring units (such as fault recorders, smart meters, and dedicated sensors) deployed at each recovery node in the distribution network, the module acquires and stores the data monitored by the monitoring units in real time at a preset frequency or period within the recovery time interval, thus obtaining power recovery data.

[0067] As an example of an embodiment of the present invention, the real-time acquisition of power recovery data for each recovery node within the recovery time interval under the current recovery control parameters, wherein the power recovery data includes recovery rate data, insulation strength data, and line damping data, specifically involves: acquiring the distribution network topology; determining the line corresponding to the recovery node based on the distribution network topology; determining the current recovery time of the recovery node in real time after the current of the line recovers from zero to the target value; calculating the current recovery rate sequence for each current recovery time; and determining the output power difference of the recovery node based on each current recovery time to obtain a power transfer sequence; and comparing the current recovery rate sequence with the... The load recovery rate time series is obtained by multiplying the power transfer sequence and dividing by the rated power. A time-domain difference operation is performed on the load recovery rate time series, and the root mean square value of the load recovery rate time series within a preset sliding time window is calculated to obtain the recovery rate data. The instantaneous insulation resistance value sequence within the recovery time interval is obtained, and each instantaneous insulation resistance value is compared with the rated insulation resistance value to obtain the insulation strength data. The output power oscillation waveform data within the recovery time interval is obtained, and the output power oscillation waveform is fitted with an exponential decay function to obtain the damping ratio instantaneous value sequence. The absolute value of the difference between each instantaneous damping ratio value and the damping ratio reference value is taken to obtain the line damping data.

[0068] In this embodiment, the system first calls a pre-stored distribution network topology database to determine the power supply line to which the node to be restored belongs. When the current of the line recovers from zero after the fault is cleared and reaches a preset target value (e.g., 80% of the steady-state current), the data acquisition process is initiated. The current recovery time of each restored node is recorded using a high-precision clock, and the rate of change between adjacent time points is calculated using the differential method to form a current recovery rate sequence. Simultaneously, a power transfer sequence is generated by comparing the difference in output power of the nodes before and after the fault. Subsequently, the current recovery rate sequence and the power transfer sequence are multiplied by a dot product and normalized by dividing by the system's rated power to obtain a load recovery rate time sequence. To further extract dynamic features, time-domain differential operations are performed on this sequence, and the root mean square value of the data within the window is calculated using a sliding time window (e.g., a window length of 100 milliseconds). The final output is recovery rate data characterizing the severity of the recovery. For acquiring insulation strength data, the system continuously collects instantaneous insulation resistance values ​​within the recovery time interval using online insulation monitoring devices. After forming a time series, the resistance value at each sampling point is compared with the rated insulation resistance value to obtain a percentage sequence reflecting the degree of insulation recovery, which serves as the insulation strength data. For acquiring line damping data, the system monitors the output power oscillation waveform of the monitoring nodes. The least squares method is used to fit an exponential function to the oscillation decay segment, extracting the instantaneous damping ratio to form a sequence. This sequence is then compared with the damping ratio benchmark value established during normal system operation, and the absolute value is taken to obtain the line damping data characterizing the dynamic stability recovery state of the system. All data acquisition processes are timestamped synchronously with the fault clearance time to ensure data consistency and comparability.

[0069] As an example of an embodiment of the present invention, the method of evaluating the synchronicity of the power restoration data by means of correlation coefficient method is as follows: using the sliding time window method, the Pearson correlation coefficient between the insulation strength data and the line damping data is calculated, the absolute value of the Pearson correlation coefficient is used as the synchronicity index, and the synchronicity index is linearly mapped as a percentage to obtain the evaluation value.

[0070] In this embodiment, the system employs a sliding time window method to analyze the synchronization of insulation strength data sequences and line damping data sequences. First, a fixed-length time window (e.g., 200 milliseconds) is set, sliding within the recovery time interval with a preset step size (e.g., 10 milliseconds). At each window position, aligned insulation strength and line damping data sub-sequences within that time period are extracted. Then, the linear correlation between the two sub-sequences is calculated based on the Pearson correlation coefficient formula. This calculation is performed by calculating the covariance of the two sequences and dividing it by the product of their respective standard deviations. The absolute value of the calculated Pearson correlation coefficient is taken to eliminate the influence of positive and negative correlation directions, resulting in a pure numerical value between 0 and 1, which serves as the synchronization index within that time window. Finally, this synchronization index is standardized using a percentage linear mapping function, i.e., multiplying the value by 100% to convert it into an evaluation value within the range of 0% to 100%. This evaluation value intuitively reflects the degree of synchronization between the insulation recovery state and the damping recovery state within a specific time period.

[0071] As an example of an embodiment of the present invention, before obtaining the sequence of instantaneous insulation resistance values ​​within the recovery time interval, the method further includes: obtaining the original monitoring data of insulation resistance, identifying and removing outliers in the original monitoring data using the 3σ criterion, and smoothing the removed original monitoring data using a moving average filtering method to obtain the sequence of instantaneous insulation resistance values.

[0072] In this embodiment, the system first acquires the raw insulation resistance monitoring data stream from distributed insulation monitoring sensors. For this raw data, outlier identification and removal are performed using the 3σ criterion based on normal distribution: the mean (μ) and standard deviation (σ) of the data within the current sliding window are calculated, and data points falling outside the interval [μ-3σ, μ+3σ] are marked as outliers and removed. For missing data points resulting from outlier removal, linear interpolation is used to fill them in to ensure data continuity. Subsequently, a moving average filtering algorithm is applied to the processed data sequence, using a rectangular window of fixed length N (e.g., N=5), and the arithmetic mean of the data within the window is calculated as the filtered output value at that moment. The window slides sequentially according to the sampling order, traversing the entire data sequence, and finally outputs a sequence of instantaneous insulation resistance values ​​after outlier removal and smoothing, providing a stable and reliable data foundation for subsequent insulation strength assessment.

[0073] The status assessment module 120 is connected to the data acquisition module 110. It performs a synchronization assessment on the power restoration data using the correlation coefficient method to obtain an assessment value that characterizes the stability of the power distribution network line restoration. If the assessment value is greater than or equal to a preset stability threshold, the line restoration risk level corresponding to the current restoration control parameter is determined based on the power restoration data and the restoration time interval.

[0074] As an example of an embodiment of the present invention, determining the line restoration risk level based on the power restoration data and the restoration time interval specifically involves: dividing the line restoration risk into three levels: low risk, medium risk, and high risk; calculating the ratio of the mean of the restoration rate data to the restoration time interval to obtain a risk assessment coefficient; if the risk assessment coefficient is less than a first risk threshold, it is determined to be low risk; if the risk assessment coefficient is greater than or equal to the first risk threshold and less than a second risk threshold, it is determined to be medium risk; if the risk assessment coefficient is greater than or equal to the second risk threshold, it is determined to be high risk, wherein the second risk threshold is greater than the first risk threshold.

[0075] In this embodiment, the module first receives insulation strength data sequences and line damping data with synchronization timestamps from the data acquisition module 110. A sliding time window technique is used to analyze the synchronicity of the two sequences. The window length is set to cover the main dynamic response period of the system (e.g., 200 milliseconds), and the window slides through the entire recovery time interval with a fixed step size. Within each window, the linear correlation between the two data sequences is calculated using the Pearson correlation coefficient algorithm. Specifically, this is achieved by calculating the ratio of the product of the covariance of the two sequences to their respective standard deviations, and the absolute value of the calculation result is taken as the synchronicity index for that period. This index is converted into an evaluation value in the range of 0-100% using a linear mapping function. When this value exceeds a preset stability threshold (e.g., 75%), the system is determined to have entered a stable recovery state. Subsequently, a risk level assessment process is initiated: the recovery rate data sequence is extracted from the power recovery data, and its dynamic characteristic parameters within the recovery time interval are calculated, including calculating the average recovery efficiency from the sequence mean and obtaining the sequence standard deviation to characterize recovery volatility. The characteristic parameters are combined with the time interval length to generate a risk assessment coefficient, for example, using a quantitative model of "volatility / (efficiency × time)". The coefficient is compared with a preset risk threshold matrix. When the coefficient is lower than the first threshold, it is marked as low risk (strategy effective). When it is between the first and second thresholds, it is marked as medium risk (strategy needs optimization). When it exceeds the second threshold, it is marked as high risk (strategy ineffective). Finally, the result of the line recovery risk level judgment corresponding to the current recovery control parameter is output.

[0076] The control module 130, connected to the status assessment module 120, is used to determine the recovery control parameters according to the line recovery risk level and the preset parameter mapping table when the line recovery risk level is greater than the preset risk threshold, adjust the current recovery control parameters based on the recovery control parameters, and control the distribution network to perform load recovery through the distribution network control equipment according to the recovery control parameters. The recovery control parameters are used to generate the load recovery sequence and the load recovery rate, including an upper limit value for limiting the single load input capacity to smooth out power oscillations and a time interval value for controlling the load input interval to maintain node voltage stability.

[0077] In this embodiment, the module continuously receives line recovery risk level signals sent by the status assessment module 120. When the risk level exceeds a preset risk threshold, a control strategy adjustment mechanism is immediately triggered. The module's built-in preset parameter mapping table is stored in the form of a two-dimensional matrix, where the row index corresponds to low, medium, and high risk levels, and the column index contains optimized configuration combinations of capacity upper limit values ​​and time interval values. Recovery control parameters matching the current risk level are obtained through table lookup operations. The capacity upper limit is subject to gradient limitation according to the risk level (e.g., high risk corresponds to 30% of the rated capacity, medium risk to 50%, and low risk to 80%), while the time interval value is adjusted inversely according to the risk level (e.g., high risk is extended to 200 milliseconds, medium risk is set to 100 milliseconds, and low risk is shortened to 50 milliseconds).

[0078] The parameter adjustment process employs a progressive replacement algorithm, reordering the load restoration sequence in the current recovery control parameters according to the principle of prioritizing important loads, while replacing the original parameters with newly acquired capacity upper limit values ​​and time interval values. The adjusted control parameters are transmitted to distribution network control equipment, including actuators such as smart circuit breakers and load switches, via the IEC 61850 communication protocol. Based on the new load restoration sequence and recovery rate parameters, these devices control the closing sequence of circuit breakers to achieve batch load restoration. Each closing operation is strictly controlled within the capacity upper limit, and a preset time interval is maintained between consecutive operations, thereby achieving precise control of the distribution network load restoration process.

[0079] As an example of an embodiment of the present invention, such as Figure 2As shown, the distribution network load restoration system 200 based on fault recovery status assessment further includes: a monitoring module 140, connected to the data acquisition module and the control module respectively, used to continuously monitor the current load restoration rate and the current voltage stability during the execution of load restoration control commands according to the restoration control parameters. The current voltage stability is determined by the percentage of the difference between the actual voltage and the rated voltage relative to the rated voltage. If the current load restoration rate is detected to be less than a preset rate threshold and the duration is greater than a preset time threshold, and the voltage stability is less than a preset voltage threshold, then the capacity upper limit is increased and the time interval is extended. If the difference between the current load restoration rate and the preset rate threshold is detected to be greater than a first preset difference threshold, and the difference between the current voltage stability and the preset voltage threshold is greater than a second preset difference threshold, then the capacity upper limit is decreased and the time interval is shortened.

[0080] In this embodiment, the monitoring module 140 first acquires the operating data corresponding to the current recovery control parameters (including the current capacity limit and time interval) from the data acquisition module 110 in real time, based on its connection with the data acquisition module 110. This includes real-time load data (such as load current and active power) and node voltage data of the recovery node. Next, the monitoring module 140 performs calculations on the acquired real-time data: the current load recovery rate is determined by the ratio of the increase in load active power per unit time to the time interval (for example, by taking active power data from five consecutive monitoring cycles, calculating the power difference between adjacent cycles and averaging it to obtain the current load recovery rate; this calculation process is associated with the time dimension of the recovery time interval to ensure the timeliness of the rate calculation). The current voltage stability is strictly calculated according to the formula "(actual node voltage - rated node voltage) / rated node voltage × 100%" to obtain a voltage stability value expressed as a percentage. Subsequently, the monitoring module 140... The calculated current load recovery rate and current voltage stability are compared with preset thresholds. If the current load recovery rate is less than the preset rate threshold (e.g., the preset rate threshold is set to 0.6 kW / s, which is based on the design range of the recovery control parameters), and the duration of this low-rate state exceeds the preset time threshold (e.g., 5 seconds), while the current voltage stability is less than the preset voltage threshold (e.g., ±1.5%, which meets the distribution network voltage quality standard), then it is determined that the recovery efficiency under the current recovery control parameters is too low and the voltage stability is insufficient. At this time, the monitoring module 140 sends a parameter adjustment request to the control module 130, requesting that the upper limit of capacity be increased by 10%-20% based on the current value (e.g., if the current upper limit of capacity is 500 kW, it is adjusted to 550-600 kW, and is greater than or equal to the maximum design capacity of the recovery control parameters), and the time interval be extended from the current value by 20%-30% (e.g., if the current interval is 8 seconds, it is extended to 9.6-10.4 seconds). If the difference between the current load recovery rate and the preset rate threshold is greater than the first preset difference threshold (e.g., 0.4kW / s, meaning the rate far exceeds the preset value), and the difference between the current voltage stability and the preset voltage threshold is greater than the second preset difference threshold (e.g., ±1%, meaning the voltage stability far exceeds the safe range), then it is determined that the recovery speed under the current recovery control parameters is too fast and there is an overload risk. At this time, the monitoring module 140 sends a parameter adjustment request to the control module 130, requesting that the upper limit of capacity be reduced by 15%-25% from the current value (e.g., from 500kW to 375-425kW), and the time interval value be shortened by 15%-25% from the current value (e.g., from 8 seconds to 6-6 seconds).(8 seconds); Finally, the monitoring module 140 continuously tracks the execution effect of the adjusted recovery control parameters. Through real-time updated load recovery rate and voltage stability data, it verifies whether the parameter adjustments meet the recovery control objective of "smoothing power oscillations and maintaining node voltage stability," ensuring the safety and stability of the distribution network load recovery process.

[0081] As an example of an embodiment of the present invention, such as Figure 2 As shown, the control module 130 further includes: a load distribution balancing unit 131, used to acquire the real-time transmission power of each line in the distribution network, calculate the load rate corresponding to each line by calculating the real-time transmission power and the corresponding rated transmission capacity, and calculate the standard deviation of each load rate to obtain the load distribution balancing degree; when the load distribution balancing degree is less than a preset balancing degree threshold, identify high-load lines with load rates greater than the average load rate and low-load lines with load rates less than or equal to the average load rate; determine a load transfer sequence in the high-load lines and a load receiving sequence in the low-load lines according to the load recovery sequence, determine the load transfer mapping relationship between the high-load lines and the low-load lines according to the load transfer sequence and the load receiving sequence; determine the time and capacity of each load transfer according to the load transfer mapping relationship, each load rate, the upper limit of capacity and the time interval value, and repeat the load transfer operation until the load distribution balancing degree is greater than or equal to the preset balancing threshold, and stop the load transfer.

[0082] In this embodiment, firstly, the load distribution balancing unit 131 needs to establish an association with the core control logic of the data acquisition module 110 and the control module 130: on the one hand, it obtains the real-time transmission power data of each line of the distribution network (including the line where the fault recovery node is located and the associated feeder) through the data acquisition module 110, and at the same time calls the rated transmission capacity of each line pre-stored in the system; on the other hand, it receives the current load recovery sequence (i.e., the sequence result corresponding to the "recovery control parameters used to generate the load recovery sequence") issued by the control module 130, and clarifies the division of high-priority loads and low-priority loads, providing a basis for the sequence sorting of load transfer.

[0083] Specifically, the load distribution balancing unit 131 first divides the real-time transmission power of each line by its corresponding rated transmission capacity to obtain the load rate of each line. Then, it calculates the standard deviation of the load rates of all lines using statistical methods (the smaller the standard deviation, the more balanced the load distribution across lines), and defines this standard deviation as the load distribution balancing degree. Next, it compares the load distribution balancing degree with a preset balancing degree threshold: if the load distribution balancing degree is less than the preset balancing degree threshold, it first calculates the arithmetic mean of the load rates of all lines (i.e., the average load rate, such as 65% for 10 lines), and then uses this as a benchmark to identify high-load lines with load rates greater than the average load rate (such as lines with load rates of 85% or 90%), and low-load lines with load rates less than or equal to the average load rate (such as lines with load rates of 50% or 60%).

[0084] Subsequently, the load distribution balancing unit 131, in conjunction with the load recovery sequence generated by the control module 130 (e.g., sequence priority is "hospital load > residential load > industrial general load"), determines the load transfer sequence in high-load lines: prioritizing the lowest priority loads (e.g., industrial general loads) on high-load lines to be included in the transfer sequence, avoiding the transfer of high-priority loads from affecting core power supply (consistent with the goal of "ensuring the rationality of the load recovery sequence"); simultaneously, it determines the load receiving sequence in low-load lines: calculating the remaining capacity of each low-load line by "line rated transmission capacity - real-time transmission power", and sorting them from largest to smallest remaining capacity to form a receiving sequence, ensuring that low-load lines still meet the requirement of "maintaining stable node voltage" after receiving load (avoiding voltage drop caused by receiving excessive load).

[0085] Subsequently, based on the total amount of load to be transferred in the load transfer sequence (e.g., 150kW of load to be transferred from a high-load line) and the remaining capacity in the load receiving sequence (e.g., 200kW remaining in low-load line A and 100kW remaining in line B), a load transfer mapping relationship between high-load lines and low-load lines is established (e.g., mapping 100kW of load from a high-load line to line A and 50kW of load to line B). During this process, the recovery control parameters must be strictly correlated: the capacity of each load transfer must not exceed the "capacity limit for limiting the single load input capacity" (e.g., if the capacity limit is 80kW, then the single transfer capacity is set to 80kW, and 150kW of load is transferred in two separate transfers). The time interval between each transfer must be consistent with the "time interval for controlling the load input interval" (e.g., if the time interval is 10 seconds, then the interval between two transfer operations is 10 seconds to avoid power oscillation caused by superimposing with the load recovery operation). At the same time, the transfer parameters should be dynamically adjusted in conjunction with the real-time load rate of each line—if the load rate of a high-load line is much higher than the average load rate (e.g., more than 15%), the single transfer capacity can be appropriately increased (but not exceeding the capacity limit). If the remaining capacity of a low-load line is close to the threshold (e.g., the remaining capacity is only 30kW), then the single transfer capacity must be reduced to below 30kW.

[0086] Finally, the load distribution balancing unit 131 sends load transfer instructions to the distribution network control equipment (such as tie switches and load switches) through the control module 130. After each transfer operation, it obtains the updated real-time transmission power of each line through the data acquisition module 110 and recalculates the load factor and load distribution balance. The above transfer process is repeated until the load distribution balance is greater than or equal to the preset balance threshold, at which point the load transfer stops, ensuring that the distribution network achieves balanced load distribution under the premise of meeting the goal of "smoothing power oscillations and maintaining voltage stability".

[0087] As an example of an embodiment of the present invention, such as Figure 3 As shown, the distribution network load restoration system 300 based on fault recovery status assessment further includes: a data storage and analysis module 150, used to store recovery rate data, insulation strength data, line damping data, recovery control parameters, and actual recovery data after the distribution network is restored using the recovery control parameters during each fault recovery process; to perform cluster analysis on the stored data based on a preset period to identify typical recovery characteristics corresponding to each fault type; to optimize the preset parameter mapping table and the preset stability threshold according to each typical recovery characteristic; and to control the load restoration of the distribution network according to the optimized preset parameter mapping table and the preset stability threshold.

[0088] In this embodiment, the module uses a database system to completely record data for each fault recovery process, including the initial recovery control parameters at the time of the fault and their corresponding power recovery data, the assessment values ​​and risk levels output by the status assessment module, the recovery control parameters adjusted by the control module, and the actual power recovery data of the system after parameter adjustment. The stored data structure includes complete waveform data of the time-series recovery rate, insulation strength, and line damping, as well as timestamps of key control events.

[0089] The module initiates a data analysis process based on a preset cycle (e.g., quarterly), employing the K-means clustering algorithm to perform multi-dimensional feature mining on historical fault data. Using fault type (phase-to-phase short circuit, single-phase grounding, etc.), recovery control parameter adjustment mode, and final recovery effect as cluster feature vectors, it automatically identifies fault recovery patterns with similar dynamic characteristics through Euclidean distance calculation. For each cluster, typical recovery features are extracted, including the optimal insulation-damping synchronicity threshold range for that type of fault, the most effective combination of capacity upper limit and time interval values, and the corresponding risk assessment coefficient boundary values.

[0090] Based on cluster analysis results, the module achieves system self-optimization in the following ways: First, it establishes a mapping relationship between fault types and optimal control parameters, updating the capacity upper limit and time interval configuration in the preset parameter mapping table; second, it dynamically adjusts the preset stability threshold according to the synchronicity characteristics distribution of each type of fault; finally, the optimized parameters are distributed online to the state assessment module and control module after security verification. The system continuously compares recovery indicators (such as average recovery time and voltage stability pass rate) before and after optimization to verify the optimization effect and establish an iterative optimization mechanism, achieving self-evolution and performance improvement of distribution network load recovery control parameters.

[0091] like Figure 4 As shown, based on the above system item embodiments, corresponding method item embodiments are provided;

[0092] An embodiment of the present invention provides a distribution network load restoration method based on fault recovery status assessment, comprising:

[0093] Step S401: During the load restoration process after the distribution network fault is cleared, the power restoration data of each restoration node within the restoration time interval under the current restoration control parameters is acquired in real time. The restoration time interval refers to the time interval from the fault clearing time to the current time.

[0094] Step S402: The power restoration data is evaluated for synchronicity using the correlation coefficient method to obtain an evaluation value that characterizes the stability of the distribution network line restoration. If the evaluation value is greater than or equal to a preset stability threshold, the line restoration risk level corresponding to the current restoration control parameter is determined based on the power restoration data and the restoration time interval.

[0095] Step S403: When the line restoration risk level is greater than the preset risk threshold, determine the restoration control parameters according to the line restoration risk level and the preset parameter mapping table, adjust the current restoration control parameters based on the restoration control parameters, and control the distribution network to restore the load through the distribution network control equipment according to the restoration control parameters. The restoration control parameters are used to generate the load restoration sequence and the load restoration rate, including the upper limit value of the capacity used to limit the single load input capacity to smooth out power oscillations, and the time interval value used to control the load input interval to maintain the node voltage stability.

[0096] It should be noted that the system embodiments described above are merely illustrative, and some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Furthermore, in the accompanying drawings of the system embodiments provided by this invention, the connection relationships between modules indicate that they have communication connections, which can be specifically implemented as one or more communication buses or signal lines. Those skilled in the art can understand and implement this without any creative effort.

[0097] For ease of description and brevity, the embodiments of the method of the present invention include all the implementation methods in the above embodiments of the distribution network load restoration system based on fault recovery status assessment, and will not be repeated here.

[0098] The modules / units integrated in the device / terminal equipment, if implemented as software functional units and sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the above embodiments of the present invention can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a portable hard drive, a magnetic disk, an optical disk, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium, etc.

[0099] The above description represents the preferred embodiments of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of the present invention, and these improvements and modifications are also considered to be within the scope of protection of the present invention.

Claims

1. A power distribution network load restoration system based on failure restoration state assessment, characterized by, The application relates to a power distribution network recovery control method and device. The data acquisition module is used for acquiring power recovery data of each recovery node in a recovery time interval under current recovery control parameters during a load recovery process after fault removal of a power distribution network; the recovery time interval refers to a time interval from the moment of fault removal to the current moment; The state evaluation module is connected with the data acquisition module and evaluates the power recovery data by a correlation coefficient method to obtain an evaluation value for representing line recovery stability of the power distribution network; if the evaluation value is greater than or equal to a preset stability threshold value, the line recovery risk level corresponding to the current recovery control parameters is determined according to the power recovery data and the recovery time interval; The control module is connected with the state evaluation module and is used for determining recovery control parameters according to the line recovery risk level and a preset parameter mapping table when the line recovery risk level is greater than a preset risk threshold value, adjusting the current recovery control parameters based on the recovery control parameters, and controlling the power distribution network to recover the load according to the recovery control parameters through a power distribution network control device; the recovery control parameters are used for generating a load recovery sequence and a load recovery rate, including a capacity upper limit value for limiting single load input capacity to suppress power oscillation and a time interval value for controlling load input interval to maintain node voltage stability.

2. The fault restoration status assessment based distribution network load restoration system, as recited in claim 1, wherein, The control module further comprises: The load distribution balancing unit is used for acquiring real-time transmission power of each line in the power distribution network, obtaining a load rate corresponding to each line by calculating each real-time transmission power and a corresponding rated transmission capacity, and calculating a standard deviation of each load rate to obtain a load distribution balancing degree; When the load distribution balancing degree is less than a preset balancing degree threshold value, a high-load line with a load rate greater than an average load rate and a low-load line with a load rate less than or equal to the average load rate are identified; According to the load recovery sequence, a load transfer sequence is determined in the high-load line, a load receiving sequence is determined in the low-load line, and a load transfer mapping relationship between the high-load line and the low-load line is determined according to the load transfer sequence and the load receiving sequence; The time and capacity of each load transfer are determined according to the load transfer mapping relationship, each load rate, the capacity upper limit value and the time interval value, and the load transfer operation is repeated until the load distribution balancing degree is greater than or equal to the preset balancing degree threshold value, and the load transfer is stopped.

3. The fault restoration status assessment based distribution network load restoration system, as recited in claim 1, wherein, The power recovery data of each recovery node in a recovery time interval under current recovery control parameters is acquired, wherein the power recovery data includes recovery rate data, insulation strength data and line damping data, and the power recovery data specifically comprises: The power distribution network topology relationship is acquired, the line corresponding to the recovery node is determined based on the power distribution network topology relationship, the current recovery time of the recovery node is determined in real time after the current of the line recovers from zero to a target value, the current recovery time is calculated to obtain a current recovery speed sequence, the output power difference value of the recovery node is determined based on the current recovery time to obtain a power transfer amount sequence; The current recovery speed sequence and the power transfer amount sequence are multiplied, and the load recovery rate time sequence is obtained by dividing the rated power; the load recovery rate time sequence is subjected to time domain difference operation, and the root mean square value of the load recovery rate time sequence in a preset sliding time window is calculated to obtain the recovery rate data; The insulation resistance instantaneous value sequence in the recovery time interval is acquired, each insulation resistance instantaneous value is compared with the rated insulation resistance value to obtain the insulation strength data; The output power oscillation waveform data in the recovery time interval is acquired, the output power oscillation waveform is subjected to exponential decay function fitting to obtain a damping ratio instantaneous value sequence, and the absolute value of the difference between each damping ratio instantaneous value and a damping ratio reference value is obtained as the line damping data.

4. The fault restoration status assessment based electric distribution network load restoration system, as recited in claim 3, wherein, The power recovery data is evaluated for synchronization by a correlation coefficient method, specifically: A sliding time window method is used to calculate the Pearson correlation coefficient of the insulation strength data and the line damping data, the absolute value of the Pearson correlation coefficient is taken as the synchronization index, and the synchronization index is subjected to percentage linear mapping to obtain the evaluation value.

5. The fault restoration status assessment based distribution network load restoration system, as recited in claim 1, wherein, The power distribution network load recovery system based on fault recovery state evaluation further comprises a monitoring module connected with the data acquisition module and the control module respectively, for continuously monitoring the current load recovery rate and the current voltage stability during execution of the load recovery control instruction according to the recovery control parameter, wherein the current voltage stability is determined by the percentage of the difference between the actual voltage and the rated voltage to the rated voltage; If the current load recovery rate is less than the preset rate threshold and the duration is greater than the preset time threshold, and the voltage stability is less than the preset voltage threshold, the capacity upper limit value is increased and the time interval value is extended; If the difference between the current load recovery rate and the preset rate threshold is greater than a first preset difference threshold, and the difference between the current voltage stability and the preset voltage threshold is greater than a second preset difference threshold, the capacity upper limit value is reduced and the time interval value is shortened.

6. The fault restoration status assessment based electric distribution network load restoration system, as recited in claim 3, wherein, Before the insulation resistance instantaneous value sequence in the recovery time interval is acquired, the following steps are further included: Raw monitoring data of the insulation resistance are acquired, the abnormal values in the raw monitoring data are identified and removed by a 3σ criterion, and the removed raw monitoring data are smoothed by a sliding average filtering method to obtain the insulation resistance instantaneous value sequence.

7. The fault restoration status assessment based electric distribution network load restoration system, as recited in claim 3, wherein, The line recovery risk level is determined according to the power recovery data and the recovery time interval, specifically: The line recovery risk is divided into three levels of low risk, medium risk and high risk; calculating a ratio of a mean value of the restoration rate data and the restoration time interval to obtain a risk assessment coefficient; if the risk assessment coefficient is less than a first risk threshold, determining as low risk, if the risk assessment coefficient is greater than or equal to the first risk threshold and less than a second risk threshold, determining as medium risk, if the risk assessment coefficient is greater than or equal to the second risk threshold, determining as high risk, wherein the second risk threshold is greater than the first risk threshold.

8. The fault restoration status assessment based electric distribution network load restoration system, as recited in claim 3, wherein, The power distribution network load restoration system based on fault restoration state assessment further comprises a data storage and analysis module for storing restoration rate data, insulation strength data, line damping data, restoration control parameters in each fault restoration process, and actual restoration data after restoration of the power distribution network through the restoration control parameters; performing clustering analysis on the stored data based on a preset period to identify typical restoration characteristics corresponding to each fault type; optimizing the preset parameter mapping table and the preset stability threshold according to each of the typical restoration characteristics, and controlling load restoration of the power distribution network according to the optimized preset parameter mapping table and preset stability threshold.

9. A power distribution network load restoration method based on failure restoration state assessment, characterized by, comprising: In the load restoration process after fault clearance of the power distribution network, real-time power restoration data of each restoration node in a restoration time interval under a current restoration control parameter is obtained, wherein the restoration time interval refers to a time interval from the fault clearance time to the current time; synchronicity of the power restoration data is evaluated by a correlation coefficient method to obtain an evaluation value for representing line restoration stability of the power distribution network, if the evaluation value is greater than or equal to a preset stability threshold, a line restoration risk level corresponding to the current restoration control parameter is determined according to the power restoration data and the restoration time interval; when the line restoration risk level is greater than a preset risk threshold, a restoration control parameter is determined according to the line restoration risk level and a preset parameter mapping table, the current restoration control parameter is adjusted based on the restoration control parameter, and load restoration of the power distribution network is controlled by a power distribution network control device according to the restoration control parameter, wherein the restoration control parameter is used to generate a load restoration sequence and a load restoration rate, including a capacity upper limit value for limiting single load input capacity to suppress power oscillation, and a time interval value for controlling load input interval to maintain node voltage stability.

10. The method for power distribution network load restoration based on failure restoration state assessment as claimed in claim 9, wherein, The power distribution network load restoration method based on fault restoration state assessment further comprises: real-time transmission power of each line in the power distribution network is obtained, load rates corresponding to each of the lines are obtained by calculating each of the real-time transmission power and a corresponding rated transmission capacity, and load distribution balance degree is calculated by calculating a standard deviation of each of the load rates; when the load distribution balance degree is less than a preset balance degree threshold, high load lines with load rates greater than an average load rate and low load lines with load rates less than or equal to the average load rate are identified; According to the load recovery sequence, a load transfer sequence is determined in the high-load line, and a load receiving sequence is determined in the low-load line, and a load transfer mapping relationship between the high-load line and the low-load line is determined according to the load transfer sequence and the load receiving sequence; According to the load transfer mapping relationship, the load rate, the capacity upper limit value and the time interval value, a time and a capacity of each load transfer are determined, and a load transfer operation is repeated until the load distribution balance degree is greater than or equal to the preset balance degree threshold, and the load transfer is stopped.

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