Line anti-band sequential optimization control method and system under power distribution network fault

Abstract

The application belongs to the technical field of power distribution network optimal dispatching, and particularly discloses a line reverse-band sequential optimal control method and system under power distribution network fault, which comprises the following steps: obtaining a tie switch state matrix of a current period; updating the tie switch state matrix of the current period by using a genetic algorithm to obtain a tie switch state matrix of a next period; individuals in a population of the genetic algorithm correspond to tie switch state sequences of each node of the power grid in the next period; in each iteration process of the genetic algorithm, a preset sequential optimization criterion is used to sort the advantages and disadvantages of the individuals in the population to obtain a sorting result; after a preset iteration stop condition is reached, a historical optimal individual is determined according to the sorting results obtained in each iteration, and a scheme represented by the historical optimal individual is taken as an optimal tie switch control scheme of the power grid in the next period. The application initiatively evaluates the operation state of the power grid, and improves the intelligent level of decision-making.

Description

Technical Field

[0001] This invention belongs to the field of distribution network optimization and scheduling technology, specifically relating to a method and system for reverse-band sequential optimization control of lines under distribution network faults. Background Technology

[0002] The power distribution network is a crucial link in the power system connecting the transmission network and users, bearing the mission of providing a safe and stable power supply. Due to its wide distribution, complex terrain, and long-term exposure to the natural environment, it is susceptible to failures caused by factors such as lightning strikes and equipment aging. Power outages not only affect production and daily life but may also trigger secondary disasters. Therefore, quickly and safely restoring power to non-faulty areas has become a core issue.

[0003] Currently, three mainstream fault recovery technologies have emerged in the industry, but all have significant limitations and are difficult to adapt to the needs of complex distribution networks. The first is the traditional manual dispatching and upstream power supply reverse-current scheme, suitable for simple distribution networks. However, manual load calculation by dispatchers is difficult, easily leading to overload of upstream power supplies and lines, causing safety hazards; manually formulated plans are influenced by subjective experience, making it difficult to guarantee optimality, and fault location is time-consuming, unable to meet the emergency needs of large-scale distribution networks. The second is a semi-automatic recovery scheme based on feeder automation (FA), which quickly locates and isolates faults through automated equipment, reducing the dispatching burden to some extent. However, the formulation of this reverse-current scheme still requires manual intervention, failing to address the limitations of manual decision-making; the reverse-current control uses a fixed strategy, unable to adapt to dynamic changes in operating status; and in complex scenarios, fault misjudgment is prone to occur, resulting in insufficient reliability. The third is a preliminary automated reconfiguration scheme based on intelligent algorithms, using genetic algorithms and other methods to assist in formulating reverse-current schemes, currently in the pilot stage. However, these algorithms are mostly aimed at a single optimization objective, with poor practicality, insufficient dynamic adaptability, and insufficient consideration of distributed power supply constraints, limiting their applicability.

[0004] With the increasing complexity of distribution network topologies, rising load densities, and large-scale integration of distributed power sources, users' demands for power supply reliability are constantly rising. The limitations of existing technical solutions are becoming increasingly apparent, making it difficult to meet the actual needs of emergency recovery from current distribution network faults. Currently, the field of distribution network fault recovery urgently needs to leverage existing technological advancements to overcome the core challenges of current solutions and further improve the safety, efficiency, and rationality of fault recovery. Therefore, in-depth research on line reverse-current decision-making under distribution network faults has urgent engineering necessity and practical significance. Summary of the Invention

[0005] The purpose of this invention is to provide a method and system for sequential optimization control of line reverse band under distribution network faults, so as to realize sequential optimization control, improve the automation level and optimization effect of distribution network fault recovery, enhance the intelligence level of decision-making, improve the safety, efficiency and rationality of fault recovery, and ensure the fit between the decision-making scheme and actual needs.

[0006] To achieve the above objectives, the present invention employs the following technical solution: According to one aspect of the present invention, a method for reverse-band sequential optimization control of lines under distribution network faults is provided, comprising the following steps: Obtain the contact switch status matrix for the current time period; A genetic algorithm is used to update the tie switch state matrix for the current time period to obtain the tie switch state matrix for the next time period. The individuals in the genetic algorithm population correspond to the tie switch state sequence of each node in the power grid for the next time period. In each iteration of the genetic algorithm, a preset sequential optimization criterion is used to sort the individuals in the population according to their quality to obtain the sorting result. After the preset iteration stop condition is met, the historical best individual is determined based on the ranking results obtained from each iteration. The scheme represented by the historical best individual is used as the optimal interconnection switch control scheme for the power grid in the next period.

[0007] The tie switch status matrix describes the current or specific time-period open / closed status of all tie switches in the distribution network. Each element in the matrix corresponds to a tie switch, and its value typically indicates whether the switch is closed or open. The tie switch status matrix can be obtained in several ways. For example, the system operator can manually input the current open / closed status information of all tie switches to form the matrix. Alternatively, the matrix can be automatically read from the distribution network's Supervisory Control and Data Acquisition (SCADA) system or Energy Management System (EMS). Furthermore, data can be collected through periodic inspections or sensor networks and compiled into the tie switch status matrix.

[0008] Genetic Algorithm (GA) is a heuristic search algorithm that simulates the process of biological evolution. It optimizes solutions to problems by mimicking biological mechanisms such as natural selection, heredity, crossover, and mutation. In the power distribution network reconfiguration problem, GA is used to explore a large number of tie switch combinations to find the optimal configuration that satisfies a specific optimization objective. In this embodiment, each individual in the GA population corresponds to the tie switch state sequence of each node in the power grid for the next time period. Specifically, one implementation is to encode the open / closed state of each tie switch into binary bits (e.g., 0 represents open, 1 represents closed), and then concatenate the binary bits of all tie switches to form a binary string, which represents an individual in the GA.

[0009] Compared to traditional fault recovery methods that rely on manual scheduling and experience-based judgment, the above-mentioned technical solution, by introducing a genetic algorithm for sequential optimization of tie switch states, automates and intelligentizes fault recovery decision-making. When a line fault occurs, based on the acquired tie switch state matrix for the current time period, the genetic algorithm searches for a large number of tie switch configuration schemes in a short time, and evaluates and sorts them according to preset sequential optimization criteria. This avoids the complexity and subjectivity of manual calculation, significantly improving the efficiency and accuracy of decision-making.

[0010] The above-mentioned technical solution, through the iterative process of sequential optimization criteria and genetic algorithm, can dynamically adapt to changes in the power grid operating state and comprehensively consider multiple optimization objectives. It not only considers the power grid state in the current period, but also generates the optimal interconnection switch control scheme for the next period through the optimization process. This makes the reverse-band control strategy no longer fixed, but can be dynamically adjusted according to the real-time operating data and forecast of the power grid, thereby generating a more comprehensive and reasonable fault recovery scheme. It effectively solves the limitations of the existing schemes that use fixed strategies for reverse-band control and cannot adapt to dynamic changes in operating state, and improves the adaptability and reliability of the fault recovery scheme.

[0011] According to one embodiment of the present invention, the step of updating the contact switch state matrix for the current time period using a genetic algorithm to obtain the contact switch state matrix for the next time period includes: Obtain the original state matrix of the power grid lines; The original state matrix of the power grid lines is corrected using the current time period's tie switch state matrix to obtain the current time period's power grid line state matrix; Based on the power grid line state matrix for the current time period, the individuals in the genetic algorithm population are traversed to update the tie switch state matrix for the current time period, so that the state information in the tie switch state matrix is ​​consistent with the tie switch state corresponding to each individual in the genetic algorithm population, and the tie switch state matrix for the next time period is obtained.

[0012] Obtaining the original state matrix of the power grid lines provides information on the initial topology and connections of the distribution network before any faults or operations occur. This typically includes line connections, the initial states of switches (open or closed), and transformer connections. The original state matrix of the power grid lines can be obtained by directly reading preset or historical power grid topology data from the distribution network's SCADA system or GIS (Geographic Information System), or it can be manually entered or parsed from design drawings and stored in a database.

[0013] The original state matrix is ​​corrected using the current tie switch state matrix to accurately reflect the actual operating topology of the distribution network at the current moment. Each individual in the genetic algorithm population represents a candidate control scheme for the tie switches in the next time period. This scheme is used to update the tie switch state matrix for the current time period, ensuring it is consistent with the tie switch state corresponding to that individual, thereby generating a tie switch state matrix representing the grid topology for the next time period. In this way, each candidate solution of the genetic algorithm can be transformed into a specific grid topology configuration, enabling subsequent performance evaluations such as load shedding rate and line load rate. This ensures that the genetic algorithm can accurately simulate and evaluate the impact of different control schemes on the grid operating state during the optimization process, making the optimization results closer to actual needs. Therefore, using the candidate solutions generated by the genetic algorithm to guide the updating of the tie switch state matrix, thereby generating a tie switch state matrix representing the grid topology for the next time period, concretizes the abstract optimization results into an operable grid control scheme.

[0014] According to one embodiment of the present invention, the preset sequential optimization criterion is as follows: For the g-th generation population, the quality of all individuals in the g-th generation population is determined by prioritizing the minimum load shedding rate of the next time period, then the maximum line load rate of the next time period, and finally the number of tie switch switching times of the next time period. The optimal individual of the g-th generation population is then selected.

[0015] The minimum load shedding rate for the next time period reflects the reliability of power supply and the degree to which user electricity demand is met; the maximum line load rate for the next time period measures the safety and stability of grid operation, preventing equipment damage or secondary faults caused by line overload; the number of tie switch switching operations for the next time period reflects the frequency and complexity of grid operation, and is closely related to equipment wear, operation and maintenance costs, and operational risks. Pre-defined sequential optimization criteria ensure that, in multi-objective optimization, the more important objectives are prioritized for satisfaction.

[0016] The genetic algorithm comprehensively evaluates each individual in the current generation (generation g) of the population and selects the best-performing individual. Through this progressive and prioritized evaluation process, the criterion can accurately determine the quality of all individuals in the generation g and ultimately select the optimal individual that best balances power supply reliability, network stability, and operational economy. This detailed and prioritized evaluation mechanism greatly enhances the genetic algorithm's ability to find the optimal tie-line control scheme in complex distribution network fault scenarios, enabling it to converge to high-quality solutions more effectively and overcoming the suboptimal results that may result from relying solely on fuzzy optimization criteria.

[0017] According to one embodiment of the present invention, the minimum load shedding rate, the maximum line load rate, and the number of tie switch switching times for the next time period are obtained by the following method: Collect power grid operation data and power grid line status matrix for the current time period; Based on the current power grid operation data, predict the power grid operation data for the next period; The power grid line state matrix for the current period is corrected based on the tie switch state matrix for the next period to obtain the power grid line state matrix for the next period. Based on the predicted power grid operation data for the next time period and the power grid line state matrix for the next time period, the minimum load shedding rate and the maximum line load rate for each individual in the population for the next time period are obtained using the constructed mathematical model of the current operation of the distribution network. Based on the power grid line state matrix for the current time period and the power grid line state matrix for the next time period, the number of tie switch switching times for the next time period is calculated. The constructed mathematical model of the current operation of the distribution network uses the minimum load shedding rate for the next time period as the objective and uses linear programming to solve for the optimal power flow of the distribution network for the next time period. The minimum load shedding rate and the maximum line load rate for the next time period are obtained through the optimal power flow calculation.

[0018] To predict power grid operation data for the next period, a trained prediction model can be used. This model can be built based on statistical methods, such as time series analysis models (e.g., ARIMA, exponential smoothing), or based on machine learning methods, such as neural networks (e.g., Long Short-Term Memory Networks (LSTM), Gated Recurrent Units (GRU), Support Vector Machines (SVM), or Random Forests). These models are trained and optimized using historical data to improve prediction accuracy.

[0019] The minimum load shedding rate for the next period is calculated using the following formula:

[0020] In the formula, J t+1 The objective function representing the minimum load shedding rate P L,N,i,t+1 Indicates the first t Total power demand of the power grid during the +1 period; P L,i,t+1 Indicates the actual total power supplied by the power grid; v Indicates the number of power grid nodes; i For temporary variables; The maximum load factor of the line in the next time period is calculated according to the following formula:

[0021] In the formula, d t+1,j,i Indicates the first t +1 period i Line load rate; d t+1 Indicates the first t Maximum load rate of power grid lines during +1 time period; I t+1,j,i Indicates the first t +1 time period node j Flow to Node i The line current; I N,j,i node j With nodes i The rated current that the lines between them can carry; The number of times the communication switch will be switched in the next time period is calculated according to the following formula: ; In the formula, N ,t+1 Indicates the first t The number of times the communication switch was switched during the +1 time period; b t,i,j Indicates the first tNodes in the power grid line state matrix for a given time period i To the node j The connection status of the lines between them; b t+1,i,j Indicates the first t Nodes in the power grid line state matrix during time +1 i To the node j The status of the connection between the lines.

[0022] According to one embodiment of the present invention, the constructed mathematical model of the current operation of the distribution network uses the minimum load shedding rate of the next time period as the objective function. Linear programming is used to solve for the optimal power flow of the distribution network in the next time period. The minimum load shedding rate and the maximum line load rate of the next time period are obtained through optimal power flow calculation. The constraints of the objective function include distribution network node balance equation constraints, line loss equation constraints, and renewable energy generation constraints.

[0023] The minimum load shedding rate for the next period is calculated using the following formula:

[0024] In the formula, J t+1 The objective function representing the minimum load shedding rate P L,N,i,t+1 Indicates the first t Total power demand of the power grid during the +1 period; P L,i,t+1 Indicates the actual total power supplied by the power grid; v Indicates the number of power grid nodes; i For temporary variables; The maximum load factor of the line in the next time period is calculated according to the following formula:

[0025] In the formula, d t+1,j,i Indicates the first t +1 period i Line load rate; d t+1 Indicates the first t Maximum load rate of power grid lines during +1 time period; I t+1,j,i Indicates the first t +1 time period node j Flow to Node i The line current; I N,j,i node j With nodes i The rated current that the lines between them can carry; The number of times the communication switch will be switched in the next time period is calculated according to the following formula: ; In the formula, N ,t+1 Indicates the first t The number of times the communication switch was switched during the +1 time period; b t,i,j Indicates the first t Nodes in the power grid line state matrix for a given time period i To the node j The connection status of the lines between them; b t+1,i,j Indicates the first t Nodes in the power grid line state matrix during time +1 i To the node j The status of the connection between the lines.

[0026] The constructed mathematical model of the current operation of the distribution network is based on the minimum load shedding rate of the next period as the objective function. The constraints of the objective function include the distribution network node balance equation constraints, line loss equation constraints, and new energy power generation constraints.

[0027] A well-constructed mathematical model of the current operation of a distribution network aims to simulate its behavior under specific operating conditions in order to predict or evaluate its performance. This model can be based on physical laws (such as Kirchhoff's laws) and equipment characteristics (such as transformer and line parameters), or it can be based on a data-driven machine learning model to provide a quantitative framework for analyzing key indicators such as power flow distribution, voltage levels, and power losses. Setting the minimum load shedding rate for the next time period as the objective function ensures that the primary goal in distribution network optimization control is to minimize load losses caused by faults or reconfigurations. This aligns with the core requirement of ensuring power supply reliability in power system operation.

[0028] The constraints of the distribution network node balance equations are as follows: ; In the formula, P t,i,WD Indicates the first t The first time period i Wind power generation capacity at each distribution network node; P t,i,PV Indicates the first t The first time period i Photovoltaic power generation at each distribution network node; P t,i,L Indicates the first t The first time period i Load power at each distribution network node; P t,i,j Indicates the first tEach time period is determined by nodes. i Flow to Node j Line power; P t,j,i Indicates the first t Each time period is determined by nodes. j Flow to Node i Line power; P t,j,i Indicates the first t The time period is determined by the first j The first distribution network node receives the first i Line power of each distribution network node P t,j,i The resulting line losses; The line loss equation is constrained as follows: ; In the formula, I t,j,i Indicates the first t Each time period starts from the starting node j To the terminal node i The line current on the line; r j,i Indicates the first t Each time period starts from the starting node j To the terminal node i The line impedance on the line; Constraints on new energy power generation include constraints on wind power generation, constraints on photovoltaic power generation, and constraints on load power consumption. The constraints for wind power generation are as follows: ; In the formula, P t,i,WD Indicates the first t Time period i The power generation capacity of wind power generation at each node; P i,WD,min Indicates the first t Time period i Minimum power generation of wind power at each node; P i,WD,max Indicates the first t Time period i The maximum power output of wind power generation at each node; The constraints on photovoltaic power generation are as follows: ; In the formula, P t, i,PV Indicates the first t Time period i The power generation capacity of photovoltaic power generation at each node; Pi,PV,min Indicates the first t Time period i Minimum power generation of photovoltaic power generation at each node; P i,PV,max Indicates the first t Time period i The maximum power output of photovoltaic power generation at each node; The load power consumption constraints are as follows: ; In the formula, P t,i,L Indicates the first t Time period i The power consumption of each node load; P i,L,min Indicates the first t Time period i Minimum power consumption of each node load; P i,L,max Indicates the first t Time period i The maximum power consumption of each node load.

[0029] According to one embodiment of the present invention, after obtaining the minimum load shedding rate and the maximum line load rate for the next time period, the method further includes the following steps: The minimum load shedding rate and the maximum line load rate for the next time period are compared with the preset grid reconfiguration signal for the next time period, and the grid reconfiguration is determined based on the comparison results. If grid reconfiguration is required, a genetic algorithm is used to update the tie switch state matrix for the current time period to obtain the tie switch state matrix for the next time period. Based on the tie switch state matrix for the next time period, the grid line state matrix for the current time period is corrected to obtain the grid line state matrix for the next time period.

[0030] Grid reconfiguration refers to the process of adjusting the grid topology by changing the opening and closing states of tie switches and sectionalizing switches in the distribution network, thereby optimizing operational objectives. The preset grid reconfiguration signal for the next time period can be one or a set of pre-defined thresholds or conditions. For example, the signal is triggered when the minimum load shedding rate exceeds a certain percentage, or the maximum load rate exceeds a certain safety limit. By evaluating the currently predicted grid operating state, it is determined whether optimization by changing the grid topology is necessary. Based on the comparison results, the system determines whether grid reconfiguration is required. If the comparison results indicate that grid reconfiguration is necessary—for example, when the minimum load shedding rate and the maximum line load rate for the next time period exceed the acceptable range of the preset grid reconfiguration signal for the next time period—the system will use a genetic algorithm to update the tie switch state matrix for the current time period to obtain the tie switch state matrix for the next time period.

[0031] According to one embodiment of the present invention, after obtaining the optimal interconnection switch control scheme for the next power grid time period, the method further includes the following steps: Update the state matrix of the tie switch for the next time period based on the optimal tie switch control scheme for the next time period, and obtain the optimal state matrix of the tie switch for the next time period. The power grid line state matrix is ​​corrected based on the optimal state matrix of the tie switch in the next time period to obtain the power grid line reconfiguration strategy matrix. Based on the predicted power grid operation data for the next period and the power grid line reconfiguration strategy matrix, the minimum load shedding rate and the maximum line load rate for the next reconfigured period are obtained by using the constructed mathematical model of the current operation of the distribution network. The minimum load shedding rate and the maximum line load rate of the next reconstructed time period are compared with the minimum load shedding rate and the maximum line load rate of the next time period, and the final tie switch control scheme is determined based on the preset judgment criteria.

[0032] By adopting the above technical solution and setting up a closed-loop verification and final decision-making process for the reconfiguration scheme, the effectiveness of the fault reverse transmission scheme can be verified, avoiding invalid or poor-quality reconfiguration operations.

[0033] According to one aspect of the present invention, a line reverse-band sequential optimization control device under distribution network faults is provided, comprising: The information acquisition module is used to obtain the contact switch status matrix for the current time period; The model calculation module is used to update the tie switch state matrix of the current time period using a genetic algorithm to obtain the tie switch state matrix of the next time period; the individuals in the population of the genetic algorithm correspond to the tie switch state sequence of each node of the power grid in the next time period; in each iteration of the genetic algorithm, the individuals in the population are sorted according to the quality of the individuals using a preset sequential optimization criterion to obtain the sorting result; The control scheme determination module is used to determine the historical best individual based on the ranking results obtained in each iteration after the preset iteration stop condition is met. The scheme represented by the historical best individual is used as the optimal interconnection switch control scheme for the next period of the power grid.

[0034] According to one aspect of the present invention, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the line reverse-band sequential optimization control method under distribution network faults according to any of the above embodiments.

[0035] According to one aspect of the present invention, a computer-readable storage medium is provided, which stores a computer program that, when executed by a processor, implements the line reverse-band sequential optimization control method under distribution network faults according to any of the above embodiments.

[0036] Compared with the prior art, the present invention has at least the following beneficial effects: 1. This invention provides a sequential optimization control method for line reverse transmission when a fault occurs in a distribution network. This method overcomes the limitations of single-objective optimization by using a preset sequential optimization criterion, and realizes intelligent decision-making with hierarchical and orderly selection of multiple indicators. It fits the priority requirements of actual operation and maintenance of the distribution network, solves the problem of multiple indicators conflicting and unable to select the best in an orderly manner in the existing technology, and avoids the defects of existing manual dispatching schemes such as high load calculation difficulty, easy overload, and non-optimal decision-making, as well as the defects of traditional intelligent algorithms with single-objective optimization and disconnect from engineering practice.

[0037] 2. This invention introduces a genetic algorithm to sequentially optimize the state of the interconnection switch, realizing a direct transformation from algorithm optimization to switch control strategy. This replaces manual calculation and decision-making, significantly reducing the workload of dispatchers, achieving a precise correspondence between the switch state and individual algorithm components, while avoiding decision-making biases caused by subjective human experience. This improves the scientificity and efficiency of fault recovery decision-making, and realizes the automation and intelligence of fault recovery decision-making.

[0038] 3. This invention achieves global optimality of the interconnection switch control scheme by sorting and iteratively optimizing the individuals in the genetic algorithm population, avoiding the local optimality problem caused by manual decision-making or simple algorithms in the prior art, ensuring that the fault reverse transmission scheme is the engineering optimal solution under multiple index constraints, and effectively reducing safety hazards such as line overload and ineffective switch operation.

[0039] The present invention provides a line reverse-band sequential optimization control device, electronic device, and computer-readable storage medium under distribution network faults, which also solves the problems mentioned in the background section. Attached Figure Description

[0040] The accompanying drawings, which form part of this specification, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an undue limitation of the invention. In the drawings: Figure 1 This is a flowchart of the line reverse-band sequential optimization control method under distribution network faults in Embodiment 1 of the present invention; Figure 2 This is a structural block diagram of the line reverse-band sequential optimization control device under distribution network faults according to Embodiment 2 of the present invention; Figure 3 This is a schematic diagram of the electronic device according to Embodiment 3 of the present invention.

[0041] Reference numerals: Electronic device 100; Memory 101; Processor 102; Computer program 103; Communication bus 104. Detailed Implementation

[0042] The present invention will now be described in detail with reference to the accompanying drawings and embodiments. It should be noted that, unless otherwise specified, the embodiments and features described herein can be combined with each other.

[0043] Example 1 A method for reverse-band sequential optimization control of lines under distribution network faults, such as... Figure 1 As shown, it includes the following steps: Obtain the contact switch status matrix for the current time period; A genetic algorithm is used to update the tie switch state matrix for the current time period to obtain the tie switch state matrix for the next time period. The individuals in the genetic algorithm population correspond to the tie switch state sequence of each node in the power grid for the next time period. In each iteration of the genetic algorithm, a preset sequential optimization criterion is used to sort the individuals in the population according to their quality to obtain the sorting result. After the preset iteration stop condition is met, the historical best individual is determined based on the ranking results obtained from each iteration. The scheme represented by the historical best individual is used as the optimal interconnection switch control scheme for the power grid in the next period.

[0044] It should be noted that, in this embodiment, the current time period is the [number]th [time period]. t The next time period is the [period]. t +1 time period.

[0045] Furthermore, the pre-defined sequential optimization criteria are as follows: For the g-th generation population, the quality of all individuals in the g-th generation population is determined by prioritizing the minimum load shedding rate of the next time period, then the maximum line load rate of the next time period, and finally the number of tie switch switching times of the next time period. The optimal individual of the g-th generation population is then selected.

[0046] The reverse-band sequential optimization control method for distribution network faults provided in this embodiment demonstrates significant technical contributions in addressing the problems of low fault recovery efficiency, insufficient reliability, and difficulty in adapting to complex power grid changes in existing technologies.

[0047] Compared to traditional manual dispatching methods, this approach, by introducing predictive models and genetic algorithms, achieves intelligent prediction of power grid operation data and automatic optimization of tie switch status. Traditional methods, where dispatchers manually calculate loads, are prone to overload and poor contingency plan optimization. This approach, however, can automatically generate optimal control schemes based on multi-objective optimization (minimum load shedding rate, maximum line load rate, and number of tie switch switching operations), avoiding the subjectivity and limitations of manual decision-making and significantly improving the rationality and safety of the scheme.

[0048] The above method can not only quickly locate and isolate faults, but also dynamically adapt to changes in the power grid's operating status. It can dynamically adjust the configuration of tie switches in each time period based on real-time collected and predicted power grid data, ensuring that the optimal recovery strategy can be obtained under different operating conditions, thereby improving the flexibility and reliability of power grid operation.

[0049] The above method comprehensively considers three key indicators: minimum load shedding rate, maximum line load rate, and number of tie switch switching times. It also sets clear judgment criteria for multi-objective optimization, overcoming the limitations of existing preliminary schemes based on intelligent algorithms, which often target a single optimization objective and lack dynamic adaptability. This results in a more comprehensive and practical control scheme that can better balance power supply reliability, equipment safety, and operational economy, thereby improving the overall performance of distribution network fault recovery.

[0050] It should be noted that the trained prediction model is a neural network algorithm model obtained through training based on historical data of wind power generation, photovoltaic power generation, and load electricity consumption from the distribution network. The specific training process is as follows: S(1.1) collects historical data of the distribution network, including historical data of wind power generation, historical data of photovoltaic power generation, and historical data of load electricity consumption. Here, we take nodes as an example. i This will be illustrated using historical data collection as an example.

[0051] For nodes i Historical data on wind power generation and photovoltaic power generation were collected at 15-minute intervals. The resulting historical data on wind power generation and photovoltaic power generation are as follows:

[0052] In the formula, P i,WD,m,n as well as P i,PV,m,n They represent the first n Heavenly m Historical power generation data matrix of wind and solar power plants during different time periods k Indicates the number of days in historical data. m andn For temporary variables, m (1,24×4), n (1, k Each row of the matrix represents the power generation data for one day.

[0053] For nodes i The data collection time was 15 minutes, and the data acquisition length was [missing information]. k The historical electricity load data for the day is as follows:

[0054] In the formula, P i,L,m,n Indicates the first n Heavenly m Time-of-use load electricity consumption historical data matrix k Indicates the number of days in historical data. m and n For temporary variables, m (1,24×4), n (1, k Each row of the matrix represents the historical electricity consumption data for one day.

[0055] S(1.2) uses a neural network algorithm model to train prediction models for wind power generation, photovoltaic power generation, and load electricity consumption (new energy power generation prediction is the lower limit value, and load electricity consumption prediction is the upper limit value), with a prediction time interval of 15 minutes. The specific neural network type can be selected according to the application scenario, such as: backpropagation neural network (BP neural network model), long short-term memory network model (LSTM model), convolutional neural network model (CNN model), or Transformer model, etc., which are neural networks with prediction functions.

[0056] (1) A wind power generation prediction model is trained based on a neural network model, and the collected data is used to predict wind power generation. k Historical wind power generation data from the past 10 days was used as the raw data for training the neural network model. The trained model needs to possess the following functionality: [data missing - likely related to a specific function or feature]. t Each period, based on the past n Wind power generation data for one time period, predicting future... n Wind power generation data for two time periods, specifically n 1 and n The time length represented by 2 is determined based on the required prediction accuracy. n 1≥2· n 2 is used to ensure the accuracy of the neural network model's predictions of future data; here, it is used as... n 1=32 andn 2=16, one time period represents 15 minutes, that is, the input data P WD,in,t-n1+1,t The output data includes data from 32 time periods within the past 32 × 15 min = 480 minutes. P WD,s This represents the data for 16 time periods within the predicted future 16 × 15 min = 240 min. (Input data matrix for wind power generation forecasting) and output data vector As shown below:

[0057] In the formula, as well as These represent the input data matrix and the wind power generation prediction data matrix, respectively. P WD,in,t as well as P WD,s,t+1 These represent the inputs of the first and second generations. t The time period wind power generation vector and the predicted first t Wind power generation forecast vector for time period +1 P WD,in,t,v as well as P WD,s,t+1,v These represent the wind power generation in the [number]th [year]. t Time period v Power generation data of individual nodes and wind power generation in t +1 time period in v Predicted data for each node, n 1 and n 2 indicates the input wind power generation capacity. P WD,in,t The number of time periods included and the obtained wind power forecast. P WD,s,t+1 The number of time periods included, each time period representing 15 minutes.

[0058] (2) A photovoltaic power generation prediction model is trained based on a neural network model, and the collected data is used to predict the photovoltaic power generation. k Historical photovoltaic power generation data from the past few days was used as the raw data for training the neural network model. The trained model needs to possess the following functionality: based on past data... n Photovoltaic power generation data for one time period, predicting future... n Photovoltaic power generation data for two time periods, specifically n 1 and n The time length represented by 2 is determined based on the required prediction accuracy. n 1≥2· n2 is used to ensure the accuracy of the neural network model's predictions of future data; here, it is used as... n 1=32 and n 2=16, one time period represents 15 minutes, that is, the input data P PV,in The output data includes data from 32 time periods within the past 32 × 15 min = 480 minutes. P PV,s This represents the data for 16 time periods within the predicted future 16 × 15 min = 240 min. It is the input data matrix for the neural network model in photovoltaic power generation prediction. and output data matrix As shown below:

[0059] In the formula, as well as These represent the input data matrix and the photovoltaic power generation prediction data matrix, respectively. P PV,in,t as well as P PV,s,t+1 These represent the inputs of the first and second generations. t The power generation vector of the photovoltaic power plant during the time period and the predicted first t The photovoltaic power generation prediction vector for the +1 time period P PV,in,t,v as well as P PV,s,t+1,v These represent the photovoltaic power generation in the [number]th [year]. t Time period v Power generation data of each node and photovoltaic power generation in t +1 time period in v Predicted data for each node, n 1 and n 2 indicates the input photovoltaic power generation capacity. P PV,in,t The number of time periods included and the obtained photovoltaic power generation forecast P PV,s,t+1 The number of time periods included, each time period representing 15 minutes.

[0060] (3) Train a load power prediction model based on a neural network model, and use the collected data... k Historical electricity load data for the past few days is used as the raw data for training the neural network model. The trained model needs to have the following functionality: based on past data... n Electricity load data for one time period, predicting future electricity consumption. n Electricity consumption data for two time periods, specifically n 1 and nThe time length represented by 2 is determined based on the required prediction accuracy. n 1≥2· n 2 is used to ensure the accuracy of the neural network model's predictions of future data; here, it is used as... n 1=32 and n 2=16, one time period represents 15 minutes, that is, the input data P L,in,t The output data includes data from 32 time periods within the past 32 × 15 min = 480 minutes. P L,s,t+1 This represents the data for 16 time periods within the predicted future 16 × 15 min = 240 min. It is the input data matrix of the neural network model in load power prediction. and output data matrix As shown below:

[0061] In the formula, as well as This represents the load power input matrix and the load power prediction matrix. P L,in,t as well as P L,s,t+1 These represent the inputs of the first and second generations. t The time period load power vector and the predicted first t Forecast power vector for load electricity consumption during the +1 period P L,in,t,v as well as P L,s,t+1,v These represent the load electricity consumption in the [number]th [period]. t Time period v The power consumption data and load of each node are in t +1 time period in v Electricity consumption forecast data for each node, n 1 and n 2 indicates the input load power. P L,in,t The number of time periods included and the obtained power load forecast. P L,s,t+1 The number of time periods included, each time period representing 15 minutes.

[0062] The specific operation flow of the line reverse-band sequential optimization control method under distribution network faults in this embodiment is as follows: S1. Collect the power grid operation data for the current time period, the tie switch status matrix for the current time period, and the power grid line status matrix for the current time period.

[0063] (1) Grid operation data includes wind power generation, photovoltaic power generation, and load power consumption. Specifically, for the current time period... t Power grid operation data, including the current time period t Wind power vector P t,i,WD Current period t Photovoltaic power vector P t,i,PV and the current time period t Load power vector P t,i,L .

[0064] When the power grid coexists v When the node is reached, obtain the first node. t The first time period i Wind power vector of each node P t,i,WD Photovoltaic power vector P t,i,PV and load power vector P t,i,L Wind power generation data, photovoltaic power generation data, and load power consumption data are node data. That is, a node configured with a photovoltaic power station will have photovoltaic power generation data, a node configured with a wind power station will have wind power generation data, and a node with electricity load will have load power consumption data, as shown below:

[0065] In the formula, P t,WD , P t,PV as well as P t,L They represent the first t Wind power generation vector, photovoltaic power generation vector, and load power consumption vector for each time period P t,i,WD , P t,i,PV as well as P t,i,L They represent the first t The first time period i The wind power generation capacity, photovoltaic power generation capacity, and load power consumption of each node. v The vector dimension for wind power, solar power generation, and load power consumption data represents the number of grid nodes. v .

[0066] (2) Current time period's contact switch status matrix U t The construction is based on the status information of the handover switch.

[0067] Interconnection switch position matrix M This indicates whether a tie switch exists on the line, as shown below:

[0068] In the formula, M Represents the position matrix of the handover switch. m v,v-1 Indicates the first v The node to the first v Does a tie switch exist on the line between -1 nodes? If the -1 node... v The node to the first v If there is a tie switch on the line between -1 nodes, then m v,v-1 =1, if the first v The node to the first v If there is no tie switch on the line between -1 nodes, then m v,v-1 =0.

[0069] When the power grid coexists v When there are nodes, obtain the current time period for each contact switch. t The status information is as follows:

[0070] In the formula, U t Indicates the contact switch at the 1st t The state matrix for each time period u t,v,v-1 Indicates the first v The node to the first v The interconnection switch between -1 nodes is in the... t Status information for each time period, u t,v,v-1 For a 0-1 variable, when u t,v,v-1 A value of 1 indicates that the tie switch is closed and the line is in a normal connection state. u t,v,v-1 A value of 0 indicates that the tie switch is open, and the corresponding line is disconnected. All elements on the diagonal. u t,i,i The value must be 0, meaning that this line and its corresponding connecting switch do not exist. i Indicates a temporary variable. i =1,2,…, v .

[0071] (3) The current power grid line state matrix is ​​obtained by correcting the original power grid line state matrix based on the current tie switch state matrix.

[0072] China's power grid v Establish the original state matrix of the power grid line using nodes. b As shown below:

[0073] In the formula, b Represents the original state matrix of the power grid line. b v,v-1 Indicates from the first v The node to the first v -1 node's line connection status information, b v,v-1 For a 0-1 variable, when b v,v-1 A value of 0 indicates that there is no connection between the two nodes. b v,v-1 A value of 1 indicates a path exists between the two nodes. All elements on the diagonal. b i,i The value must be 0, meaning this route does not exist. i =1,2,…, v , i This represents a temporary variable. The original state matrix of the power grid line. b This indicates the original connection status of the power grid lines.

[0074] Based on the current time period's contact switch state matrix U t For the original state matrix of the power grid line b Correction to obtain the first t Power grid line state matrix for each time period b t Here, the first... t Each time period is based on the contact switch status matrix U t For the original state matrix of the power grid line b Taking the correction as an example, the correction method is explained. Based on the handover switch at the... t The contact switch status matrix for each time period U t For the original state matrix of the power grid line b Make the following modifications:

[0075] In the formula, b t Indicates the first t The power grid line state matrix for each time period, b t,v,v-1 Indicates from the first v The node to the firstv -1 node's line in the first t Status information for each time period, b t,v,v-1 For a 0-1 variable, when b t,v,v-1 A value of 0 indicates that the connection between the two nodes is broken. b t,v,v-1 A value of 1 indicates a normal line connection. The significance of this step lies in the fact that, according to the first... t The state matrix of the tie switch in each time period corrects the connection or disconnection status of power grid lines, and is used as the first time period tie switch state matrix in subsequent optimal power flow calculations. t Input the line status calculated for each time period. b v,v-1 Represented in the original state matrix of the power grid line b From the first v The node to the first v -1 node connection status information, u t,v,v-1 Indicates the first t The contact switch status matrix for each time period U t In the middle, the first v The node to the first v The interconnection switch between -1 nodes is in the... t Status information for each time period.

[0076] S2. Power grid line maintenance.

[0077] Based on whether the power grid detects a fault on a certain line, insufficient power supply to some loads, or a line under maintenance, it determines whether a line reverse-band optimization control decision is needed. Specifically, all lines in the power grid are monitored for line fault signals, line maintenance signals, line load shedding signals, or other situations affecting the normal power transmission of lines, and reverse-band optimization decision signals are established accordingly. K Inverse optimization decision signal K The value is 0 when the power grid is operating normally. It becomes 0 when a line fault signal, line maintenance signal, line load shedding signal, or other situation affecting the normal transmission of power by the line is detected. K If it becomes 1; K= If 1 is the case, then reverse-band optimization control of the line is required.

[0078] Based on the reverse band optimization decision signal K Determine the current time period t Whether or not line reverse-band optimization control is needed, if K =1, then the current time period t Line reverse-band optimization control decision needs to be made, proceed to step S3; if K =0, then the current time periodt The power grid is operating normally and does not require line reverse-band optimization control decisions in the next time period. t +1 returns to step S1 to start the next time period. t +1 line reverse band optimization control decision detection.

[0079] S3. Based on the power grid operation data for the current period, use the trained prediction model to predict the power grid operation data for the next period; use a genetic algorithm to update the tie switch state matrix for the current period to obtain the tie switch state matrix for the next period; based on the tie switch state matrix for the next period, correct the power grid line state matrix for the current period to obtain the power grid line state matrix for the next period.

[0080] Individuals in the genetic algorithm population correspond to the state sequences of interconnecting switches at various nodes of the power grid. The genetic algorithm is used to update the interconnecting switch state matrix for the current time period to obtain the interconnecting switch state matrix for the next time period. Before the step of correcting the power grid line state matrix for the current time period based on the interconnecting switch state matrix for the next time period to obtain the power grid line state matrix for the next time period, the following steps are also included: S3-1. Based on the predicted power grid operation data for the next period, the minimum load shedding rate and the maximum line load rate for the next period are obtained using the constructed mathematical model of the current operation of the distribution network. The constructed mathematical model of the current operation of the distribution network is used to solve the optimal power flow of the distribution network for the next period with the minimum load shedding rate of the next period as the target. The minimum load shedding rate and the maximum line load rate for the next period are obtained through the optimal power flow calculation.

[0081] (1) For the first t For each time period, a trained prediction model is used to predict the power grid operation data for the next time period, i.e., to obtain... P WD,s,t+1 , P PV,s,t+1 as well as P L,s,t+1 , Used here n 2=16, meaning the prediction time period is 16 time intervals, with each prediction time interval being 15 minutes apart, as shown below:

[0082] In the formula, P WD,s,t+1 This represents a vector of wind power generation prediction data. P WD,s,t+1 Indicates the first obtained from the prediction t Predicted wind power generation capacity for the +1 time period P PV,s,t+1 This represents a vector of photovoltaic power generation prediction data.P PV,s,t+1 The predicted first t Predicted photovoltaic power generation for the +1 time period P L,s,t+1 This represents the load power prediction vector. P L,s,t+1 Indicates the first obtained from the prediction t Predicted power consumption during the +1 period n 2 represents the obtained predicted data vector P WD,s,t+1 , P PV,s,t+1 as well as P L,s,t+1 The number of time periods included, each time period representing 15 minutes.

[0083] (2) The constructed mathematical model of the current operation of the distribution network is based on the minimum load shedding rate of the next period as the objective function. The constraints of the objective function include the distribution network node balance equation constraint, the line loss equation constraint, the equation constraint between the node inflow power and the node voltage, and the new energy generation constraint.

[0084] Distribution network node balance equations represent the equations for nodes. i The balance state of inflow and outflow power, i.e., the node i Inflow power equals node i The outflow power, and the constraints of the distribution network node balance equations are as follows: ; In the formula, P t,i,WD Indicates the first t The first time period i Wind power generation capacity at each distribution network node; P t,i,PV Indicates the first t The first time period i Photovoltaic power generation at each distribution network node; P t,i,L Indicates the first t The first time period i Load power at each distribution network node; P t,i,j Indicates the first t Each time period is determined by nodes. i Flow to Node j Line power; P t,j,i Indicates the first t Each time period is determined by nodes. j Flow to Node i Line power; Pt,j,i Indicates the first t The time period is determined by the first j The first distribution network node receives the first i Line power of each distribution network node P t,j,i The resulting line losses.

[0085] The line loss equation is constrained as follows: ; In the formula, I t,j,i Indicates the first t Each time period starts from the starting node j To the terminal node i The line current on the line; r j,i Indicates the first t Each time period starts from the starting node j To the terminal node i The line impedance on the line.

[0086] For the t The first time period i For a distribution network node, based on the node voltage equation, the active and reactive power equations in polar coordinate form are derived, and the equality constraints between the node power inflow and the node voltage are as follows: ; In the formula, P t,j,i Indicates the first i Inject active power into each node. V i Represents a node i voltage, G ij as well as B ij Representing nodes respectively i and nodes j The real and imaginary parts of the mutual admittance, θ i This represents the node phase angle.

[0087] Constraints on new energy power generation include constraints on wind power generation, constraints on photovoltaic power generation, and constraints on load power consumption.

[0088] The constraints for wind power generation are as follows: ; In the formula, P t,i,WD Indicates the first t Time period i The power generation capacity of wind power generation at each node; Pi,WD,min Indicates the first t Time period i Minimum power generation of wind power at each node; P i,WD,max Indicates the first t Time period i The maximum power output of wind power generation at each node.

[0089] The constraints on photovoltaic power generation are as follows: ; In the formula, P t, i,PV Indicates the first t Time period i The power generation capacity of photovoltaic power generation at each node; P i,PV,min Indicates the first t Time period i Minimum power generation of photovoltaic power generation at each node; P i,PV,max Indicates the first t Time period i The maximum power output of photovoltaic power generation at each node.

[0090] The load power consumption constraints are as follows: ; In the formula, P t,i,L Indicates the first t Time period i The power consumption of each node load; P i,L,min Indicates the first t Time period i Minimum power consumption of each node load; P i,L,max Indicates the first t Time period i The maximum power consumption of each node load.

[0091] (3) Solve for the optimal power flow of the distribution network using the minimum load shedding rate as the objective function. The minimum load shedding rate for the next time period. J t+1 As shown below:

[0092] In the formula, J t+1 The objective function representing the minimum load shedding rate P L,N,i,t+1 Indicates the first t Total power demand of the power grid during the +1 period; P L,i,t+1Indicates the actual total power supplied by the power grid; v Indicates the number of power grid nodes; i It is a temporary variable.

[0093] Calculate the first using the optimal power flow algorithm. t +1 time period distribution network power flow situation, obtain the first t +1 time period objective function value J t+1 That is, the total load shedding rate of the power grid. J t+1 The value.

[0094] Calculate the maximum line load rate for the next time period. d t+1 Line load factor represents the ratio of line current to line rated current. A higher line load factor indicates a greater load on the line. In power grid operation, excessively high line load factors are generally avoided. The calculation method is as follows:

[0095] In the formula, d t+1,j,i Indicates the first t +1 period i Line load rate; d t+1 Indicates the first t Maximum load rate of power grid lines during +1 time period; I t+1,j,i Indicates the first t +1 time period node j Flow to Node i The line current; I N,j,i node j With nodes i The rated current that the line can carry.

[0096] S3-2. Compare the minimum load shedding rate and the maximum line load rate of the next time period with the preset grid reconfiguration signal for the next time period, and determine whether grid reconfiguration is required based on the comparison results.

[0097] If the minimum load shedding rate for the next period J t+1 Not equal to 0 or the maximum line load rate in the next time period d t+1 Greater than the set threshold QThen, the distribution network structure needs to be restructured, proceeding to the steps of "updating the current time period's tie switch state matrix using a genetic algorithm to obtain the next time period's tie switch state matrix; and correcting the current time period's power grid line state matrix based on the next time period's tie switch state matrix to obtain the next time period's power grid line state matrix." If the minimum load shedding rate for the next time period... J t+1 The line's maximum load rate is equal to 0 and will be used in the next time period. d t+1 If the value is less than the set threshold, maintain the current running state and return to step S1. The specific judgment logic is as follows:

[0098] In the formula, Q This is a preset line load rate threshold; the specific value is set according to the line type in the actual application. F t+1 Indicates the first t +1 time period grid reconfiguration signal. If F t+1 =1 then the first t +1 time period requires grid reconfiguration, proceeding to the steps of "updating the current time period's tie switch state matrix using a genetic algorithm to obtain the next time period's tie switch state matrix; correcting the current time period's grid line state matrix based on the next time period's tie switch state matrix to obtain the next time period's grid line state matrix"; if F t+1 =0 then the first t The +1 time period does not require a reconstruction operation; at this point, return to step S1 and begin the next time period. t +1 line reverse band optimization control decision detection.

[0099] If grid reconfiguration is required, a genetic algorithm is used to update the tie switch state matrix for the current time period to obtain the tie switch state matrix for the next time period. Based on the tie switch state matrix for the next time period, the grid line state matrix for the current time period is corrected to obtain the grid line state matrix for the next time period.

[0100] S3-3. Use a genetic algorithm to update the tie switch state matrix for the current time period to obtain the tie switch state matrix for the next time period; based on the tie switch state matrix for the next time period, correct the power grid line state matrix for the current time period to obtain the power grid line state matrix for the next time period.

[0101] It should be noted that the genetic algorithm can be a BP neural network, LSTM model, or Transformer model, etc. Each individual in the genetic algorithm population corresponds to the state sequence of the interconnection switch of the power grid node.

[0102] The genetic algorithm is used to update the communication switch state matrix for the current time period to obtain the communication switch state matrix for the next time period. The specific steps include: s3-3-1. Obtain the original state matrix of the power grid lines b .

[0103] s3-3-2. Use the current time period's tie switch state matrix to compare with the original state matrix of the power grid lines. b The correction is performed to obtain the power grid line state matrix for the current time period. b t .

[0104] s3-3-3. Power grid line state matrix based on the current time period b t The process iterates through the individuals in the genetic algorithm population, updates the connection switch state matrix for the current time period, and ensures that the state information in the connection switch state matrix is ​​consistent with the connection switch state corresponding to each individual in the genetic algorithm population, thereby obtaining the connection switch state matrix for the next time period.

[0105] For the t +1 time period, initialize the genetic algorithm parameters, including the crossover probability in the genetic algorithm. P c Variation rate P m Define the population size as h max Define the number of iterations. g =1,2,…, g max , define the first g The first generation of the population h individual X g,h This represents a sequence of handshake switch states, where one individual represents the first... t One possible combination of switching modes for all tie switches in the power grid during the +1 time period can be represented as 1× a A 0-1 vector of dimension a Indicates the number of tie switches in the power grid, then the first... g The first generation of the population h individual X g,h As shown below: ; In the formula, X g,h Indicates the first g The first generation of the population h Individual, x g,h,a It is a 0-1 variable, representing the first... gThe first generation of the population h Among the individual entities, the state of the a-th contact switch is... x g,h,a =0 indicates that the communication switch is on. x g,h,a =1 indicates that the contact switch is closed. a This represents the total number of tie switches in the power grid. At this point, for the [number]th [unit]... g The entire population constitutes the population individual matrix. X g As shown below:

[0106] In the formula, X g Indicates the first g The population matrix of generations. h max Indicates population size, a This indicates the number of interconnecting switches in the power grid.

[0107] Based on the first step S1 t Power grid line state matrix for each time period b t , using the g The first generation of the population h individual X g,h State matrix of handover switches U t Update to obtain the first t +1 Time Period Communication Switch Status Matrix U t+1 , of which g The first generation of the population h individual X g,h Each handshake switch in the matrix corresponds to a handshake switch state matrix. U t One of the elements in the contact switch state matrix U t The status information in the first g The first generation of the population h individual X g,h The status of the communication switch remains consistent.

[0108] For the t +1 time period, according to the first g The first generation of the population h individual X g,h Obtain the state matrix of the power grid interconnection switch U t+1Using the grid interconnection switch state matrix U t+1 Update the original power grid line matrix b Obtain the power grid line in the t State matrix for time period +1 b t+1 As shown below:

[0109] In the formula, b t+1 Indicates the power grid line at the 1st t The state matrix for time interval +1 b t+1,v,v-1 Indicates from the first v The node to the first v -1 node's line in the first t Status information for the +1 time period b t+1,v,v-1 For a 0-1 variable, when b t+1,v,v-1 A value of 0 indicates that the connection between the two nodes is broken. b t+1,v,v-1 A value of 1 indicates a normal line connection. This step is based on the... t +1 Time Period Communication Switch Status Matrix U t+1 Correcting the connection or disconnection status of power grid lines will be used as the first step in subsequent optimal power flow calculations. t Input the line status calculated for the +1 time period.

[0110] S4. Based on the predicted power grid operation data for the next time period and the power grid line state matrix for the next time period, the minimum load shedding rate and the maximum line load rate for each individual in the population for the next time period are obtained using the constructed mathematical model of the current operation of the distribution network; the number of tie switch switching times for the next time period is calculated based on the power grid line state matrix for the current time period and the power grid line state matrix for the next time period.

[0111] Construct a mathematical model of the distribution network, using the first step obtained in step S3-1. t +1 time period grid wind power generation forecast data vector P WD,s,t+1 Photovoltaic power generation prediction data vector P PV,s,t+1 and load power consumption forecast data vector P L,s,t+1 The power flow is optimized using the minimum load shedding rate as the objective function, and the grid load shedding rate is calculated based on the predicted data. J t+1 Maximum line load rate d t+1and the number of times the contact switch is switched N ,t+1 As shown below:

[0112] In the formula, J t+1 , P L,N,i,t+1 as well as P L,i,t+1 Let each represent the objective function for the minimum load shedding rate, and the second and third represent the objective functions for the minimum load shedding rate. t Total power demand of the power grid and actual total power supply of the power grid during the +1 period v Indicates the number of power grid nodes. i It is a temporary variable.

[0113] Maximum line load rate for the next time period d t+1 As shown below:

[0114] In the formula, d t+1,j,i , d t+1 , I t+1,j,i as well as I N,j,i They represent the first t +1 period i Line load rate, number t The maximum load rate of the power grid lines during the +1 time period, the t +1 time period node j Flow to Node i The line current and the nodes j With nodes i The rated current that the line can carry. v This indicates the number of nodes in the power grid.

[0115] Power grid interconnection switch switching frequency index N ,t+1 The calculation method is as follows:

[0116] In the formula, N ,t+1 , b t,i,j as well as b t+1,i,j They represent the first t +1 time period interconnection switch switching frequency index, power grid line status matrixb t The Middle t Time period nodes i To the node j The connection status of the lines (values ​​are 0 or 1, where 0 indicates disconnection and 1 indicates connection) and the power grid line status matrix. b t+1 The Middle t +1 time period node i To the node j The connection status of the line between (the value is 0 or 1, where 0 indicates disconnection and 1 indicates connection).

[0117] For the first g Population individual matrix of the generation X g Use the minimum load shedding rate for the next time period given in step S4. J t+1 Maximum line load rate for the next time period d t+1 and the number of times the contact switch is switched N ,t+1 The calculation method for the population individual matrix X g Each individual in X g,h Calculate the power grid load shedding rate respectively. J t+1,h Maximum line load rate d t+1,h and the number of times the contact switch is switched N ,t+1,h , h =1,2,…, h max Forming an individual indicator matrix D g As shown below:

[0118]

[0119] In the formula, D g Indicates the first g Individual indicator matrix of the generation population, D g,1 Indicates the first g The individual index vector of the first individual in the population. h max Indicates the population size, i.e., the number of species. g The number of individuals contained in a generational population. Jg , d g as well as N g Both are column vectors, representing the first, second, and third columns respectively. g The power grid load shedding rate index vector, the maximum line load rate index vector, and the tie switch switching count index vector with population.

[0120] S5. Based on the minimum load shedding rate, the maximum line load rate, and the number of tie switch switching times in the next time period, the individual in the population is judged as superior or inferior according to the preset first judgment criteria, and the individuals in the population are sorted from superior to inferior according to the results of the superior and inferior judgment to obtain the population sorting matrix.

[0121] The first preset judgment criterion is: first consider the minimum load shedding rate of the next time period, then consider the maximum line load rate of the next time period, and finally consider the order of the number of tie switch switching times of the next time period.

[0122] For the first g Total number of generations h max For each individual, a sequential selection approach is used to prioritize the grid load shedding rate. J t+1,h Then consider the maximum line load rate. d t+1,h Finally, consider the number of times the contact switch is turned over. N ,t+1,h Priority order, determine all h max The merits and demerits of each individual are considered, and the optimal individual is ultimately selected. X g,best And record the best individual in history X b = X g,best The specific rules for judging the merits of individuals are as follows: S5-1. Selection J g Power grid load shedding rate index for all individuals in the population J t+1 minimum value J t+1,min When only one has the minimum load shedding rate J t+1,min individual X g,h1 At that time, this individual is the optimal individual. X g,best To make the best individual in history X b = Xg,h1 When there are two or more instances with the same minimum load shedding rate... J t+1,min When an individual is not found, it is considered that the optimal individual has not been found. X g,best Proceed to step S5-2.

[0123] S5-2. When there are two or more entities with the same minimum load shedding rate J t+1,min When considering individual cases, the maximum load rate of the line should be taken as the reference. d t+1 Minimum as the metric, select d g Maximum line load rate for all individuals in the population d t+1 minimum value d t+1,min minimum value d t+1,min Corresponding individuals X g,h2 As the optimal individual X g,best To make the best individual in history X b = X g,best When there are two or more individual lines, the maximum load factor is: d g minimum value d t+1,min When this occurs, it is considered that the optimal individual has not been found. X g,best Proceed to step S5-3.

[0124] S5-3. When there are two or more entities with the same minimum load shedding rate. J t+1,min When considering an individual, the number of times the contact switch is switched is used as an indicator. N ,t+1,h The minimum value is used as the selection criterion to choose the indicator vector of the number of handover switches. N g The minimum number of handover switch switching operations is required. N ,t+1,min individual X g,h3 As the optimal individual X g,best ,make X b = X g,best .

[0125] S6. Based on the obtained sorting matrix, perform population iterative optimization until the preset maximum number of iterations is reached, and then output the historical best individual as the optimal interconnection switch control scheme for the next period.

[0126] S6-1. Based on the judgment logic for the superiority or inferiority of individuals in the population established in step S5, obtain the first... g Population individual matrix of the generation X g The evaluation of the merits and demerits of all individuals in the group, and the evaluation of the merits and demerits of the group based on this evaluation. g Population individual matrix of the generation X g Reorder the individuals. X g,best Placed in the sorting matrix And The first line, the g The other individuals in the population are sorted row by row in descending order of quality. A larger row number indicates a lower quality rating for that individual. The sorting matrix... O g as follows:

[0127] In the formula, And Indicates the first g The ranking matrix obtained by sorting the population from best to worst based on the evaluation of the quality of the individuals in each generation. h max Indicates population size, x g,o,best,1 This represents the state of the first communication switch in the communication switch state vector corresponding to the globally optimal individual obtained after ranking by quality. x g,o,best,1 It is a 0-1 variable.

[0128] S6-2. Based on the obtained sorting matrix And ,set up G The sorting matrix represents the number of individuals retained during the genetic algorithm iterations. And The Middle G +1 line to the h max Individuals in the population of a given row are eliminated, i.e., removed from the sorting matrix. And The Middle G +1 line to the h max The sequence of connection switch states represented by individuals in rows G is used to generate a shared sequence of states based on the retained connection switch state vectors of individuals from the first to the Gth rows, through selection, crossover, and mutation operations using a genetic algorithm. h max - G A new population of individuals restores the population size to [a certain value].h max The first one, will be the first g The population retains G individuals and the newly generated individuals. h max - G A new population of individuals constitutes the first g Population individual matrix in +1st genetic algorithm optimization X g+1 As shown below:

[0129] In the formula, X g+1 Indicates the first g Population individual matrix of generation +1, h max Indicates population size, a Indicates the number of interconnecting switches in the power grid. x g+1,1,1 Indicates the first g The first individual generated in the +1 iteration X g+1,1 The state of the first handshake switch in the handshake switch state vector. x g+1,1,1 It is a 0-1 variable.

[0130] S6-3. Settings n op,max This represents the maximum number of iterations in the genetic algorithm. g < n op,max ,but g = g +1, return to step 4.2, and continue the genetic algorithm optimization iteration until... g ≥ n op,max ;like g ≥ n op,max Then the iteration ends, and the historical best individual obtained at this time is obtained. X b That is, the result obtained after optimization by the genetic algorithm. t Optimal handover switch control scheme for +1 time period X b The obtained optimal handover switch control scheme X t+1,b as follows:

[0131] In the formula, X t+1,b This represents the result obtained after optimization by the genetic algorithm. tThe optimal grid interconnection switch control scheme for the +1 time period X t+1,b,1 express t The state of the first tie switch in the optimal grid tie switch control scheme for the +1 time period. X t+1,b,1 For a 0-1 variable, a This indicates the total number of interconnecting switches in the power grid.

[0132] Furthermore, after obtaining the optimal interconnection switch control scheme for the next power grid time period, the following steps are also included: S7-1. Optimal Power Grid Interconnection Switch Control Scheme Based on the Next Time Period X t+1,b Update the communication switch state matrix for the next time period to obtain the optimal communication switch state matrix for the next time period. U t+1,b As shown below:

[0133] In the formula, U t+1,b Indicates the contact switch at the 1st t The optimal state matrix for time interval +1 u t+1,b,v,v-1 Indicates the first v The node to the first v The interconnection switch between -1 nodes is in the... t Optimal state information for time period +1 u t+1,b,v,v-1 For a 0-1 variable, when u t+1,b,v,v-1 A value of 0 indicates that the tie switch is open or that there is no tie switch on this line. u t+1,b,v,v-1 A value of 1 indicates that the handshake switch is closed. All elements on the diagonal. u t+1,i,i The value must be 0, meaning that this line and its corresponding connecting switch do not exist. i Indicates a temporary variable. i =1,2,…, v .

[0134] S7-2. Optimal state matrix of the contact switch based on the next time period U t+1,b The power grid line state matrix is ​​corrected to obtain the power grid line reconfiguration strategy matrix. b t+1,b As shown below:

[0135] In the formula, b t+1,bIndicates the power grid line at the 1st t The power grid reconfiguration strategy matrix for the +1 time period b t+1,b,v,v-1 Indicates from the first v The node to the first v -1 node's line in the first t Status information for the +1 time period b t+1,b,v,v-1 For a 0-1 variable, when b t+1,b,v,v-1 A value of 0 indicates that the connection switch between the two nodes is open, causing the line to be disconnected. b t+1,b,v,v-1 A value of 1 indicates that the tie switch is closed, allowing the line to be connected normally.

[0136] S7-3. Based on the predicted power grid operation data for the next time period and the power grid line reconfiguration strategy matrix b t+1,b The minimum load shedding rate for the next time period after reconfiguration is obtained by using the established mathematical model of the current operation of the distribution network. and the maximum load rate of the line in the next time period after reconstruction .

[0137] S7-4. Minimum load shedding rate for the next reconfigured period. The maximum load rate of the line in the next time period after reconstruction. Minimum load shedding rate for the next period J t+1 And the maximum load rate of the line in the next time period. d t+1 By comparing the results, the final control scheme for the interconnection switch is determined based on the preset judgment criteria.

[0138] The preset judgment criteria are as follows: If < J t+1 Then the optimal individual control communication switch is used; if > J t+1 Then maintain the status quo; if = J t+1 and < d t+1 Then, the optimal individual control communication switch will be used; if = J t+1 and > d t+1 If so, then the status quo will remain. Details are as follows:

[0139] In the formula, C t+1 This indicates the line reverse-band optimization control decision signal, if C t+1 =1 then in t +1 time period for line reverse band optimization control, if C t+1 =0 t No line reverse-band optimization control is performed during the +1 time period.

[0140] S7-5. Complete the line reverse-band optimization control decision for time period t+1. t = t +1, return to step S1.

[0141] The sequential optimization control method for line reversal under distribution network faults provided in this embodiment differs from the traditional passive response after a fault occurs through real-time monitoring. This technology utilizes predicted future renewable energy output and load data to assess the grid's operating status (load shedding rate, load rate) in advance, and proactively determines whether network reconfiguration needs to be initiated based on this assessment. This proactive "prediction-assessment-decision" model enhances the foresight and intelligence of the control.

[0142] Traditional genetic algorithms can only solve problems optimally, but they are difficult to solve practical problems involving multiple indicators and multi-level judgment logic. The control method in this embodiment combines the idea of ​​sequential selection to realize three-level sequential selection for power distribution network reconstruction. It clarifies the priority of multiple optimization objectives, ensuring that the reliability of power supply is guaranteed first, and then the operation economy and equipment safety are optimized, so that the decision is more in line with the actual operation and maintenance needs.

[0143] The control method in this embodiment generates an optimal reconfiguration scheme based on predicted data. Before execution, it recalculates the actual effect of the scheme (such as the improvement in load shedding rate and load rate) and compares it with the consequences of maintaining the status quo. Operation is only executed when the new scheme is confirmed to be superior. This "optimization-verification-execution" feedback mechanism effectively avoids invalid or poor-quality reconfiguration operations, improving the reliability and economy of control.

[0144] Example 2 A sequential optimization control device for line reverse-band under distribution network faults, such as Figure 2 As shown, it includes: The information acquisition module is used to obtain the contact switch status matrix for the current time period; The model calculation module is used to update the tie switch state matrix of the current time period using a genetic algorithm to obtain the tie switch state matrix of the next time period; the individuals in the population of the genetic algorithm correspond to the tie switch state sequence of each node of the power grid in the next time period; in each iteration of the genetic algorithm, the individuals in the population are sorted according to the quality of the individuals using a preset sequential optimization criterion to obtain the sorting result; The control scheme determination module is used to determine the historical best individual based on the ranking results obtained in each iteration after the preset iteration stop condition is met. The scheme represented by the historical best individual is used as the optimal interconnection switch control scheme for the next period of the power grid.

[0145] Example 3 like Figure 3 As shown, the present invention also provides an electronic device 100 for a line reverse-band sequential optimization control method under distribution network faults; the electronic device 100 includes a memory 101, at least one processor 102, a computer program 103 stored in the memory 101 and capable of running on at least one processor 102, and at least one communication bus 104.

[0146] The memory 101 can be used to store the computer program 103. The processor 102 implements the steps of the method for analyzing and finding abnormal power line losses in Embodiment 1 by running or executing the computer program stored in the memory 101 and calling the data stored in the memory 101.

[0147] The memory 101 may primarily include a program storage area and a data storage area. The program storage area may store the operating system, application programs required for at least one function (such as sound playback function, image playback function, etc.), etc.; the data storage area may store data created based on the use of the electronic device 100 (such as audio data), etc. In addition, the memory 101 may include non-volatile memory, such as hard disk, RAM, plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, at least one disk storage device, flash memory device, or other non-volatile solid-state storage device.

[0148] At least one processor 102 may be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. Processor 102 may be a microprocessor or any conventional processor. Processor 102 is the control center of electronic device 100, connecting various parts of electronic device 100 via various interfaces and lines.

[0149] The memory 101 in the electronic device 100 stores multiple instructions to implement a line anti-band sequential optimization control method under distribution network faults, and the processor 102 can execute multiple instructions to achieve the following: Obtain the contact switch status matrix for the current time period; A genetic algorithm is used to update the tie switch state matrix for the current time period to obtain the tie switch state matrix for the next time period. The individuals in the genetic algorithm population correspond to the tie switch state sequence of each node in the power grid for the next time period. In each iteration of the genetic algorithm, a preset sequential optimization criterion is used to sort the individuals in the population according to their quality to obtain the sorting result. After the preset iteration stop condition is met, the historical best individual is determined based on the ranking results obtained from each iteration. The scheme represented by the historical best individual is used as the optimal interconnection switch control scheme for the power grid in the next period.

[0150] Example 4 If the modules / units integrated in the electronic device 100 are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of 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 computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, and read-only memory (ROM).

Claims

1. A method for reverse-band sequential optimization control of lines under distribution network faults, characterized in that, Includes the following steps: Obtain the contact switch status matrix for the current time period; A genetic algorithm is used to update the tie switch state matrix for the current time period to obtain the tie switch state matrix for the next time period. The individuals in the genetic algorithm population correspond to the tie switch state sequence of each node in the power grid for the next time period. In each iteration of the genetic algorithm, a preset sequential optimization criterion is used to sort the individuals in the population according to their quality to obtain the sorting result. After the preset iteration stop condition is met, the historical best individual is determined based on the ranking results obtained from each iteration. The scheme represented by the historical best individual is used as the optimal interconnection switch control scheme for the power grid in the next period.

2. The method for reverse-band sequential optimization control of lines under distribution network faults according to claim 1, characterized in that, The step of updating the communication switch state matrix for the current time period using a genetic algorithm to obtain the communication switch state matrix for the next time period includes: Obtain the original state matrix of the power grid lines; The original state matrix of the power grid lines is corrected using the current time period's tie switch state matrix to obtain the current time period's power grid line state matrix; Based on the power grid line state matrix for the current time period, the individuals in the genetic algorithm population are traversed to update the tie switch state matrix for the current time period, so that the state information in the tie switch state matrix is ​​consistent with the tie switch state corresponding to each individual in the genetic algorithm population, and the tie switch state matrix for the next time period is obtained.

3. The method for reverse-band sequential optimization control of lines under distribution network faults according to claim 1, characterized in that, The preset sequential optimization criteria are as follows: For the g-th generation population, the quality of all individuals in the g-th generation population is determined by prioritizing the minimum load shedding rate of the next time period, then the maximum line load rate of the next time period, and finally the number of tie switch switching times of the next time period. The optimal individual of the g-th generation population is then selected.

4. The method for reverse-band sequential optimization control of lines under distribution network faults according to claim 3, characterized in that, The minimum load shedding rate, the maximum line load rate, and the number of tie switch switching times for the next time period are obtained using the following method: Collect power grid operation data and power grid line status matrix for the current time period; Based on the power grid operation data for the current period, predict the power grid operation data for the next period; The power grid line state matrix for the current period is corrected based on the tie switch state matrix for the next period to obtain the power grid line state matrix for the next period. Based on the predicted power grid operation data for the next time period and the power grid line state matrix for the next time period, the minimum load shedding rate and the maximum line load rate for each individual in the population for the next time period are obtained using the constructed mathematical model of the current operation of the distribution network; the number of tie switch switching times for the next time period is calculated based on the power grid line state matrix for the current time period and the power grid line state matrix for the next time period. The established mathematical model of the current operation of the distribution network is used to determine the minimum load shedding rate for the next time period. The optimal power flow of the distribution network for the next time period is solved using linear programming. The minimum load shedding rate and the maximum line load rate for the next time period are obtained through the optimal power flow calculation.

5. The method for reverse-band sequential optimization control of lines under distribution network faults according to claim 4, characterized in that, The minimum load shedding rate for the next time period is calculated according to the following formula: In the formula, J t+1 The objective function representing the minimum load shedding rate P L,N,i,t+1 Indicates the first t Total power demand of the power grid during the +1 period; P L,i,t+1 This indicates the actual total power supplied by the power grid; v Indicates the number of power grid nodes; i For temporary variables; The maximum load factor of the line in the next time period is calculated according to the following formula: In the formula, d t+1,j,i Indicates the first t +1 period i Line load rate; d t+1 Indicates the first t Maximum load rate of power grid lines during +1 time period; I t+1,j,i Indicates the first t +1 time period node j Flow to Node i The line current; I N,j,i node j With nodes i The rated current that the lines between them can carry; The number of times the communication switch will be switched in the next time period is calculated according to the following formula: ； In the formula, N ,t+1 Indicates the first t The indicator of the number of times the communication switch is switched during the +1 time period; b t,i,j Indicates the first t Nodes in the power grid line state matrix for a given time period i To the node j The connection status of the lines between them; b t+1,i,j Indicates the first t Nodes in the power grid line state matrix during time +1 i To the node j The status of the connection between the lines.

6. The method for reverse-band sequential optimization control of lines under distribution network faults according to claim 4, characterized in that, The constructed mathematical model for the current operation of the distribution network uses the minimum load shedding rate for the next time period as the objective function. The constraints of the objective function include the distribution network node balance equation constraints, the line loss equation constraints, and the new energy generation constraints.

7. The method for reverse-band sequential optimization control of lines under distribution network faults according to claim 1, characterized in that, After obtaining the optimal interconnection switch control scheme for the next power grid time period, the following steps are also included: Update the state matrix of the tie switch for the next time period based on the optimal tie switch control scheme for the next time period, and obtain the optimal state matrix of the tie switch for the next time period. The power grid line state matrix is ​​corrected based on the optimal state matrix of the tie switch in the next time period to obtain the power grid line reconfiguration strategy matrix. Based on the predicted power grid operation data for the next period and the power grid line reconfiguration strategy matrix, the minimum load shedding rate and the maximum line load rate for the next reconfigured period are obtained by using the constructed mathematical model of the current operation of the distribution network. The minimum load shedding rate and the maximum line load rate of the next reconstructed time period are compared with the minimum load shedding rate and the maximum line load rate of the next time period, and the final tie switch control scheme is determined based on the preset judgment criteria.

8. A line reverse-band sequential optimization control device under distribution network fault conditions, characterized in that, include: The information acquisition module is used to obtain the contact switch status matrix for the current time period; The model calculation module is used to update the tie switch state matrix of the current time period using a genetic algorithm to obtain the tie switch state matrix of the next time period; the individuals in the population of the genetic algorithm correspond to the tie switch state sequence of each node of the power grid in the next time period; in each iteration of the genetic algorithm, the individuals in the population are sorted according to the quality of the individuals using a preset sequential optimization criterion to obtain the sorting result; The control scheme determination module is used to determine the historical best individual based on the ranking results obtained from each iteration after the preset iteration stop condition is met. The scheme represented by the historical best individual is used as the optimal interconnection switch control scheme for the next period of the power grid.

9. An electronic device, characterized in that, It includes a processor and a memory, wherein the processor is used to execute a computer program stored in the memory to implement the line reverse-band sequential optimization control method under distribution network faults as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores at least one instruction, which, when executed by a processor, implements the line reverse-band sequential optimization control method under distribution network faults as described in any one of claims 1 to 7.

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