Pre-fault alarm method and system for improving self-healing power restoration effect of distribution network

Abstract

The invention discloses a pre-fault alarm method and system for improving the self-healing power restoration effect of a distribution network, and the method comprises the steps: collecting data of a target power grid, carrying out the preprocessing, and constructing a pre-outage range analysis model; abstracting the pre-outage range analysis model into a graph model composed of nodes and edges, automatically constructing a feeder segment model in the graph model by using an A * algorithm, and simulating a fault and a protection action; an ant colony algorithm is utilized to simulate an extreme fault point, the self-healing power restoration effect is calculated, and early warning and differential management and control are achieved. According to the method, fault behaviors in a complex system are simulated and predicted based on a heuristic algorithm, and propagation and evolution processes of faults are simulated according to characteristics, equipment defects and protection conditions of the system, so that the influence of the factors on power failure when a line fault occurs is predicted, distribution transformers of self-healing power recovery effects of different fault points are calculated, an alarm is given in advance, and the fault recovery efficiency is improved. Operation and maintenance are managed and controlled differentially, and rectification suggestions are provided. And pre-fault warning for improving the self-healing power restoration effect of the distribution network is realized.

Description

Technical Field

[0001] The present invention relates to the technical field of power network maintenance, and in particular to a pre-fault alarm method and system for improving the effectiveness of self-healing and power restoration of a distribution network. Background Art

[0002] As an important part of the power system, the stability and reliability of the distribution network directly affect the power supply to end users. In traditional distribution network management, when faced with sudden faults, fault handling is usually done by manual or semi-automatic systems, which are often slow to respond and inefficient. With the advancement of technology and the complexity of the power grid, a more intelligent and automated solution is needed to achieve rapid fault detection and restore power supply, especially in the event of a large-scale power outage.

[0003] Self-healing technology provides a forward-looking solution for fault management in distribution networks. By leveraging advanced data acquisition, modeling technology, and graphical simulation operations, distribution networks can predict and identify potential problem points before a fault occurs, and perform real-time analysis and processing through intelligent algorithms. This technology not only improves the speed and accuracy of fault handling, but also significantly reduces the economic losses and social impact caused by faults. In this way, the self-healing ability of the distribution network has been significantly enhanced, and it can quickly switch and restore power supply when a fault occurs, greatly improving the overall resilience and reliability of the power grid.

[0004] With the integration and development of information technology and operation technology, distribution network management is shifting towards full automation and intelligence. The use of a pre-fault warning system and a self-healing power restoration effectiveness calculation model can not only provide early warning before a fault occurs, but also predict the consequences of a fault based on real-time data analysis and automatically select the optimal recovery path. This integrated technical solution provides efficient, flexible and reliable technical support for the operation and maintenance of the distribution network, and is an important development direction for modern power grid management. Summary of the invention

[0005] In view of the above-mentioned problems, the present invention is proposed.

[0006] Therefore, the technical problem solved by the present invention is: to optimize the layout of lines with low power restoration efficiency, to upgrade the control of key defects that affect the scope of power outages, and to maximize the self-healing success rate and power restoration efficiency.

[0007] To solve the above technical problems, the present invention provides the following technical solutions: a pre-fault warning method for improving the effectiveness of self-healing and power restoration of distribution networks, comprising: collecting data of the target power grid and performing pre-processing to construct a pre-power outage range analysis model; abstracting the pre-power outage range analysis model into a graph model composed of nodes and edges, and automatically constructing a feeder segmentation model in the graph model using the A* algorithm to simulate faults and protection actions; using an ant colony algorithm to simulate extreme fault points, calculate the effectiveness of self-healing and power restoration, and achieve early warning and differentiated management and control.

[0008] As a preferred scheme of the pre-fault alarm method for improving the effectiveness of self-healing and power restoration of distribution networks described in the present invention, the pre-power outage range analysis model includes exporting SVG graphic files and model files from the GIS system to realize the integration of GIS graphics and models, forming a pre-power outage range basic model based on the power grid topology, exporting self-healing line and self-healing switch ledger information from the distribution network OMS system, and integrating it with the GIS graphics and models to realize the definition and association of self-healing switches and lines, establish a self-healing segmentation model, integrate the pre-power outage range basic model and the self-healing segmentation model, and construct a pre-power outage range analysis model.

[0009] As a preferred solution of the pre-fault alarm method for improving the effectiveness of self-healing and power restoration of distribution networks described in the present invention, the graph model includes, when constructing a pre-power outage analysis model, abstracting the complex power system into a graph model composed of nodes and edges, each node in the graph model represents an electric device, and the edge represents the physical connection between these devices.

[0010] As a preferred solution of the pre-fault alarm method for improving the effectiveness of self-healing power restoration of distribution network described in the present invention, the A* algorithm includes using the evaluation function f(n) to find the optimal path from the starting point to the target point, which is expressed as:

[0011] f(n)=g(n)+h(n)

[0012] The actual path cost is calculated by combining physical loss, maintenance cost and economic cost, and is expressed as:

[0013]

[0014] The current and resistance of the power grid are considered when calculating the estimated cost. The resistance and current load are combined to calculate the energy loss, and then the estimated cost from node n to the target node is evaluated, which is expressed as:

[0015]

[0016] Combining the improved heuristic function h(n) with the actual path cost g(n), we get a new evaluation function, expressed as:

[0017]

[0018] Where f(n) represents the total evaluation cost of node n; g(n) represents the actual cost from the starting point to node n; h(n) represents the estimated minimum cost from node n to the target; P represents the path from the starting point to node n; k represents the weight factor; I in Represents the current between each connection i and n on the path; R in Represents the resistance between each connection i and n on the path; M in represents the aging cost; E in represents the installation and maintenance cost; N(n) represents the set of neighbor nodes of node n; L nj represents the resistance of the wire from node i to j; C nj represents the wire current load from node i to j; V th Represents the threshold voltage.

[0019] As a preferred solution of the pre-fault alarm method for improving the effectiveness of self-healing and power restoration of distribution networks described in the present invention, the ant colony algorithm includes: when solving, the fault point that causes the largest power outage range of the distribution network is regarded as an extreme fault point, the power outage range of the distribution network is used as the objective function of the ant colony algorithm, and the numbers of all possible fault points in the distribution network are used as the input of the algorithm. The ant colony algorithm is iteratively solved to find the position of the extreme fault point that causes the largest power outage range of the distribution network. The process of the ant colony algorithm is expressed as follows: numbering each node in the power grid topology structure and numbering each bus outlet in the power grid topology structure; establishing an extreme fault point acquisition model based on the ant colony algorithm; randomly generating a fault and bringing it into the simulation environment of the model to solve the flow data of each node; judging the power outage range according to the flow data of each node; repeating the process of randomly generating fault solution flow data and judging the power outage range M times; collecting simulation results in real time, and bringing the simulation results into the ant colony algorithm for solution; obtaining the bus outlet number and power outage range corresponding to the extreme fault point, and then determining the position of the extreme fault point.

[0020] As a preferred solution of the pre-fault alarm method for improving the effectiveness of self-healing and power restoration of distribution networks described in the present invention, wherein: the extreme fault point obtaining model includes: the pheromone update formula of the ant colony algorithm is expressed as:

[0021] τ ij (t+1)=(1-ρ)·τ ij (t)+Δτ ij

[0022] Considering the three-phase short circuit, it is assumed that the power loss of node k is transferred to other nodes during the fault, and the energy loss is simulated by multiplying the square of the current by the resistance, which is expressed as:

[0023]

[0024] Integrate the power flow data and pheromone update, adjust the pheromone update formula to reflect the impact of the fault, and express it as:

[0025]

[0026] Among them, τ ij represents the pheromone concentration on the path from node i to j at time t; ρ represents the pheromone volatility; Δτ ij represents the increase in pheromone left by ants; ΔP k represents the power change of node k caused by the fault; I k represents the current at node k; R k represents the resistance of node k; K represents the set of all possible fault points; ξ represents the pheromone intensity adjustment coefficient; S ij represents the maximum capacity of the line from node i to j; according to the final pheromone concentration, the bus outlet corresponding to the path with the highest pheromone is selected as the extreme fault point.

[0027] As a preferred solution of the pre-fault alarm method for improving the self-healing and power restoration effect of the distribution network according to the present invention, wherein: calculating the self-healing and power restoration effect includes calculating the self-healing and power restoration effect according to the obtained extreme fault point, expressed as:

[0028]

[0029] Among them, P represents the effectiveness of self-healing and power restoration of the distribution network; T N Indicates the number of transformers that resume operation after self-healing under extreme fault conditions; T M Indicates the number of all transformers within the power outage range.

[0030] In the second aspect, the present invention also provides a pre-fault warning system for improving the effectiveness of self-healing and power restoration of distribution networks, including a data acquisition module, which collects and pre-processes data of the target power grid, integrates data from the GIS system and the distribution network OMS system, and constructs a pre-power outage range analysis model; a fault simulation module, which abstracts the pre-power outage range analysis model into a graph model composed of nodes and edges, and uses the A* algorithm to automatically construct a feeder segmentation model in the graph model, simulates faults and protection actions, and optimizes the fault processing path; a power restoration effectiveness calculation module, which introduces the power grid's flow data and the characteristics of the busbar outlet to optimize the pheromone update of the ant colony, uses the ant colony algorithm to simulate extreme fault points, and calculates the self-healing and power restoration effectiveness based on the obtained extreme fault points, thereby achieving early warning and differentiated management and control.

[0031] In a third aspect, the present invention further provides a computing device, comprising: a memory and a processor;

[0032] The memory is used to store computer executable instructions, and the processor is used to execute the computer executable instructions. When the computer executable instructions are executed by the processor, the steps of the pre-fault alarm method for improving the effectiveness of self-healing and power restoration of the distribution network are implemented.

[0033] In a fourth aspect, the present invention further provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the steps of the pre-fault alarm method for improving the effectiveness of self-healing and power restoration of distribution networks.

[0034] Beneficial effects of the invention: The method of the invention simulates and predicts fault behaviors in complex systems based on heuristic algorithms, simulates the propagation and evolution of faults according to system characteristics, equipment defects and protection conditions, so as to predict the impact of power outages caused by the above factors when line faults occur, calculate the self-healing and power restoration effects of different fault points, issue warnings in advance, differentiate management and control of operation and maintenance, and put forward rectification suggestions. Pre-fault warnings that improve the effectiveness of self-healing and power restoration of distribution networks. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work. Among them:

[0036] Figure 1 A flowchart of obtaining an extreme fault point based on an ant colony algorithm to improve the effectiveness of self-healing and power restoration of a distribution network provided by an embodiment of the present invention;

[0037] Figure 2 A visualization system diagram of a pre-fault warning method for improving the effectiveness of self-healing and power restoration of distribution networks provided in accordance with a second embodiment of the present invention. DETAILED DESCRIPTION

[0038] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0039] Example 1

[0040] Reference Figure 1 , which is an embodiment of the present invention, provides a pre-fault alarm method for improving the effectiveness of self-healing and power restoration of distribution networks, comprising:

[0041] S1: Collect and pre-process the data of the target power grid and build a pre-power outage range analysis model.

[0042] Furthermore, the data is exported from the OCS system and pre-processed such as cleaning and collection.

[0043] Export SVG graphic files and model files from the GIS system to integrate GIS graphics and models, and form a basic model of the expected power outage range based on the power grid topology.

[0044] The self-healing line and self-healing switch ledger information is exported from the distribution network OMS system and integrated with the GIS map model to realize the definition and association of self-healing switches and lines and establish a self-healing segmented model.

[0045] The two models are integrated to construct a pre-power outage range analysis model, which provides a basis for subsequent pre-power outage range analysis.

[0046] By importing and analyzing the morning exercise results and combining them with imported telesignal signals, such as "device offline", "power module abnormality", and other abnormal signals, switch defects are collected and superimposed on the pre-power outage analysis model to provide data support for simulating equipment failures.

[0047] Furthermore, we use front-end development technology combined with a graphics library to develop a graphical interface and fault simulation function, and use D3.js to perform real-time graphics visualization and user interaction, so as to intuitively understand the operation of the power system and perform fast and intuitive graphics simulation operations and fault simulation.

[0048] The feeder is displayed based on the GIS single-line diagram, and the fault or defect information is imported into the single-line diagram to calculate the effectiveness of self-healing and power restoration. The power outage range is colored based on the single-line diagram to intuitively display the power outage range. At the same time, it supports the display of summary information such as the expected maximum power outage rate and the expected maximum number of power outage users of each healing feeder group in the form of a dashboard.

[0049] S2: The pre-power outage range analysis model is abstracted into a graph model consisting of nodes and edges. The A* algorithm is used to automatically construct a feeder segmentation model in the graph model to simulate faults and protection actions.

[0050] Furthermore, when constructing the pre-power outage analysis model, the complex power system will be abstracted into a graph model consisting of nodes and edges. In this model, each node represents an electric device, such as a feeder, transformer, switch, etc., and the edge represents the physical connection between these devices, such as a line segment. The heuristic search algorithm A* search automatically constructs the feeder segmentation model. Taking into account the action logic of the protection device, the self-healing power restoration effectiveness calculation model is used to calculate indicators such as the pre-maximum power outage rate, the pre-maximum number of power outage users, and the self-healing power restoration effectiveness, and supports visual simulation demonstrations.

[0051] Furthermore, the A* algorithm involves finding the optimal path from the starting point to the target point using an evaluation function f(n), expressed as:

[0052] f(n)=g(n)+h(n)

[0053] The actual path cost is calculated by combining physical loss, maintenance cost and economic cost, and is expressed as:

[0054]

[0055] The current and resistance of the power grid are considered when calculating the estimated cost. The current and resistance will affect the power loss and system reliability, and affect the estimated cost of reaching the target node. The resistance and current load are combined to calculate the energy loss, and then the estimated cost from node n to the target node is evaluated, which is expressed as:

[0056]

[0057] Combining the improved heuristic function h(n) with the actual path cost g(n), we get a new evaluation function, expressed as:

[0058]

[0059] Where f(n) represents the total evaluation cost of node n; g(n) represents the actual cost from the starting point to node n; h(n) represents the estimated minimum cost from node n to the target; P represents the path from the starting point to node n; k represents the weight factor; I in Represents the current between each connection i and n on the path; R in Represents the resistance between each connection i and n on the path; M in represents the aging cost; E in represents the installation and maintenance cost; N(n) represents the set of neighbor nodes of node n; L nj represents the resistance of the wire from node i to j; C nj represents the wire current load from node i to j; V th Represents the threshold voltage.

[0060] It should be noted that the A* algorithm provides an effective way to balance the cost and effect in the search process. It can lead to the goal faster through the heuristic function, which is crucial for quickly responding to power grid failures and formulating recovery plans.

[0061] The heuristic function of the A* algorithm can be flexibly adjusted according to the specific configuration and real-time status of the power grid, such as current load, line capacity and other factors, which helps to more accurately simulate and handle complex power grid fault conditions. Therefore, the present invention selects the A* algorithm as the heuristic search algorithm, uses an exponential function to adjust the impact of energy consumption when designing the heuristic function, and simulates the impact of energy loss in the power grid by the product of resistance and current, thereby providing decision support for the automated configuration of the power grid.

[0062] S3: Use the ant colony algorithm to simulate extreme fault points, calculate the effectiveness of self-healing and power restoration, and achieve early warning and differentiated management and control.

[0063] Furthermore, when the network topology and the sectional switch configuration results have been determined, the ant colony algorithm is used to simulate the extreme fault point. When solving the problem, the fault point that causes the largest power outage range in the distribution network is regarded as the extreme fault point, the power outage range of the distribution network is used as the objective function of the ant colony algorithm, and the numbers of all possible fault points in the distribution network are used as the input of the algorithm. The ant colony algorithm is used to iteratively solve the problem and find the location of the extreme fault point that causes the largest power outage range in the distribution network, which can be expressed as:

[0064] A. Pre-fault alarm to improve the effectiveness of self-healing and power restoration of distribution network Each node in the power grid topology is numbered as n. i (i=1, 2…, I).

[0065] B. Pre-fault alarm to improve the effectiveness of self-healing and power restoration of distribution network Each bus outlet in the power grid topology is numbered as g j (j=1, 2…, J).

[0066] C. To improve the effectiveness of self-healing and power restoration of distribution networks, a pre-fault warning model for obtaining extreme fault points based on ant colony algorithm is established.

[0067] D. Pre-fault alarm to improve the effectiveness of self-healing and power restoration of distribution networks randomly generates a fault and brings it into the simulation environment of the model to solve the power flow data of each node.

[0068] E. The pre-fault alarm that improves the effectiveness of self-healing and power restoration of the distribution network determines the scope of the power outage based on the power flow data of each node.

[0069] F. To improve the effectiveness of self-healing power restoration in the distribution network, the pre-fault alarm repeats steps D and E M times.

[0070] G. The pre-fault alarm that improves the effectiveness of self-healing and power restoration of the distribution network collects simulation results in real time (busbar outlet number and its corresponding power outage range), and brings the simulation results into the ant colony algorithm for solution.

[0071] H. The pre-fault alarm that improves the effectiveness of self-healing and power restoration of the distribution network can obtain the bus outlet number and power outage range corresponding to the extreme fault point, and thus determine the location of the extreme fault point.

[0072] Furthermore, in order to find the fault point of the power grid, it is necessary to consider the special properties of the power grid, such as the current flow direction, the propagation and impact range of the fault, introduce the power grid flow data and the characteristics of the bus outlet, and the pheromone update formula of the ant colony algorithm is expressed as:

[0073] τ ij (t+1)=(1-ρ)·τ ij (t)+Δτ ij

[0074] Considering the three-phase short circuit, it is assumed that the power loss of node k is transferred to other nodes during the fault, and the energy loss is simulated by multiplying the square of the current by the resistance, which is expressed as:

[0075]

[0076] Integrate the power flow data and pheromone update, adjust the pheromone update formula to reflect the impact of the fault, and express it as:

[0077]

[0078] Among them, τ ij represents the pheromone concentration on the path from node i to j at time t; ρ represents the pheromone volatility; Δτ ij represents the increase in pheromone left by ants; ΔP k represents the power change of node k caused by the fault; I k represents the current at node k; R k represents the resistance of node k; K represents the set of all possible fault points; ξ represents the pheromone intensity adjustment coefficient; S ij represents the maximum capacity of the line from node i to j; according to the final pheromone concentration, the bus outlet corresponding to the path with the highest pheromone is selected as the extreme fault point.

[0079] Furthermore, the self-healing power restoration effect is calculated based on the extreme fault point, which is expressed as:

[0080]

[0081] Among them, P represents the effectiveness of self-healing and power restoration of the distribution network; T N Indicates the number of transformers that resume operation after self-healing under extreme fault conditions; T M Indicates the number of all transformers within the power outage range.

[0082] By calculating the effectiveness of self-healing and power restoration, we can obtain the maximum power outage rate and the maximum number of users who will experience power outages. We can classify and manage users according to their power consumption characteristics, importance, and service requirements, and formulate different power outage plans and safeguard measures. For important users, such as hospitals and government agencies, their power supply should be guaranteed first, and measures should be taken to ensure no or minimal power outages. For general users, we can reasonably arrange power outage plans and times based on their power demand and the impact of the fault. This will achieve differentiated management and control of power outages.

[0083] The present embodiment also provides a pre-fault warning system for the effectiveness of self-healing and power restoration of the distribution network, including a data acquisition module, which collects and pre-processes the data of the target power grid, integrates the data of the GIS system and the distribution network OMS system, and constructs a pre-power outage range analysis model; a fault simulation module, which abstracts the pre-power outage range analysis model into a graph model composed of nodes and edges, and uses the A* algorithm to automatically construct a feeder segmentation model in the graph model, simulates faults and protection actions, and optimizes the fault processing path; a power restoration effectiveness calculation module, which introduces the power grid's flow data and the characteristics of the busbar outlet to optimize the pheromone update of the ant colony, uses the ant colony algorithm to simulate extreme fault points, and calculates the self-healing and power restoration effectiveness based on the obtained extreme fault points, thereby realizing early warning and differentiated management and control.

[0084] This embodiment also provides a computing device, including a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute computer-executable instructions to implement the pre-fault alarm method proposed in the above embodiment to improve the effectiveness of self-healing and power restoration of the distribution network.

[0085] This embodiment also provides a storage medium having a computer program stored thereon. When the program is executed by a processor, the pre-fault alarm method for improving the effectiveness of self-healing and power restoration of the distribution network as proposed in the above embodiment is implemented.

[0086] The storage medium proposed in this embodiment and the pre-fault alarm method for improving the effectiveness of self-healing and power restoration of the distribution network proposed in the above embodiment belong to the same inventive concept. The technical details not described in detail in this embodiment can be referred to the above embodiment, and this embodiment has the same beneficial effects as the above embodiment.

[0087] Through the above description of the implementation method, the technicians in the relevant field can clearly understand that the present invention can be implemented by means of software and necessary general hardware, and of course it can also be implemented by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as a computer floppy disk, read-only memory (ReadOnly, Memory, ROM), random access memory (RandomAccessMemory, RAM), flash memory (FLASH), hard disk or optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform the methods of various embodiments of the present invention.

[0088] Example 2

[0089] Reference Figure 2 , which is an embodiment of the present invention, provides a pre-fault alarm method for improving the effectiveness of self-healing and power restoration of distribution networks. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through simulation experiments.

[0090] In order to realize intelligent pre-power outage range analysis and implement differentiated self-healing pre-power outage management and control strategies, the following strategies are adopted:

[0091] 1. Digital feeder section switch configuration: First, the configuration strategy of the feeder section switch is digitized and input as a parameter into the distribution network self-healing restoration effectiveness calculation model.

[0092] 2. Simulate faults and protection actions: The model can simulate the location of faults in the distribution network by combining it with the fault simulator. On this basis, the model will consider the possible normal and abnormal actions (such as refusal to operate, malfunction) of the switch and simulate the change process of the entire protection action.

[0093] 3. Intelligent algorithms and simulation: Based on the network topology of the power grid, the model uses intelligent algorithms for simulation and calculation, which helps to retrieve information such as the scope of power outages caused by faults.

[0094] 4. Calculation of self-healing and power restoration effectiveness: The self-healing and power restoration effectiveness calculation model is used to calculate the maximum power outage range, the self-healing and power restoration effectiveness of the feeder, and the maximum number of power outage users.

[0095] 5. Visual simulation: The model also supports animation simulation, making the entire self-healing process more intuitive and easier for us to understand and manage.

[0096] The beneficial effects of the present invention are verified through simulation experiments. A simulated distribution network based on a real-world data model is constructed. This network includes 100 nodes, each of which represents an electrical device, such as a transformer, a distribution board, etc. The nodes are connected by wires to form a complex network topology.

[0097] Random faults, such as three-phase short circuits, are generated in the model to simulate the response of the distribution network under different circumstances. The A* algorithm is used to calculate the optimal path from the fault point to each self-healing node. The evaluation function combines physical loss, maintenance cost, and economic cost. The ant colony algorithm is further used for multiple rounds of iterations. In each round of iteration, a new fault is simulated and the scope of the power outage caused by it is calculated. By comparing the results of each round, the most likely extreme fault point is found. Some experimental data are shown in the following table.

[0098] Table 1 Experimental data table

[0099] Parameters / Nodes Node 1 Node 2 Node 3 Node 4 Node 5 Node 6 Current load(A) 85 90 95 80 88 92 Line resistance (Ω) 0.2 0.15 0.18 0.22 0.19 0.16 Pheromone concentration 0.72 0.65 0.75 0.68 0.71 0.67 Power loss (kW) 1.45 1.21 1.63 1.41 1.49 1.33 Power outage range (%) 12 9 14 10 11 13 Estimated costs 420 390 450 410 430 420

[0100] Compared with traditional methods, the method of the present invention shows significant advantages in reducing the scope and cost of power outages. By combining the A* algorithm and the ant colony algorithm to optimize the fault processing path and the prediction of the fault point, the average power outage scope is reduced to 10%-14%. In addition, by optimizing the fault response process, the average estimated cost is reduced to about US$415.

[0101] The pre-fault alarm method proposed in the present invention can respond to power grid faults more quickly, provide early warnings, and reduce the power outage time and impact range of the power system through real-time data analysis and the application of advanced algorithms. It shows obvious innovation and application advantages, and demonstrates how to improve the operating efficiency and reliability of the power grid through technological innovation, which is in line with the trend of modern power systems towards intelligence and automation.

[0102] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A pre-fault alarm method for improving the effectiveness of self-healing and power restoration in distribution networks, characterized in that: include: Collect and pre-process the data of the target power grid and build a pre-power outage range analysis model; The predicted power outage range analysis model is abstracted into a graph model consisting of nodes and edges. The A* algorithm is used to automatically construct a feeder segmentation model in the graph model to simulate faults and protection actions. Ant colony algorithm is used to simulate extreme fault points, calculate the effectiveness of self-healing and power restoration, and achieve early warning and differentiated management and control.

2. The pre-fault alarm method for improving the effectiveness of self-healing and power restoration of distribution networks as claimed in claim 1, characterized in that: The pre-power outage range analysis model includes exporting SVG graphic files and model files from the GIS system to realize the integration of GIS graphics and models, forming a pre-power outage range basic model based on the power grid topology, exporting self-healing line and self-healing switch ledger information from the distribution network OMS system and integrating it with the GIS graphics and models to realize the definition and association of self-healing switches and lines, establish a self-healing segmentation model, integrate the pre-power outage range basic model and the self-healing segmentation model, and construct a pre-power outage range analysis model.

3. The pre-fault alarm method for improving the effectiveness of self-healing and power restoration of distribution networks as claimed in claim 2, characterized in that: The graph model includes, when constructing a pre-power outage analysis model, abstracting a complex power system into a graph model consisting of nodes and edges, wherein each node in the graph model represents an electric device, and the edge represents the physical connection between these devices.

4. The pre-fault alarm method for improving the effectiveness of self-healing and power restoration of distribution networks as claimed in claim 3, characterized in that: The A* algorithm includes using an evaluation function f(n) to find the optimal path from the starting point to the target point, expressed as: f(n)=g(n)+h(n) The actual path cost is calculated by combining physical loss, maintenance cost and economic cost, and is expressed as: The current and resistance of the power grid are considered when calculating the estimated cost. The resistance and current load are combined to calculate the energy loss, and then the estimated cost from node n to the target node is evaluated, which is expressed as: Combining the improved heuristic function h(n) with the actual path cost g(n), we get a new evaluation function, expressed as: Where f(n) represents the total evaluation cost of node n; g(n) represents the actual cost from the starting point to node n; h(n) represents the estimated minimum cost from node n to the target; P represents the path from the starting point to node n; k represents the weight factor; I in Represents the current between each connection i and n on the path; R in Represents the resistance between each connection i and n on the path; M in represents the aging cost; E in represents the installation and maintenance cost; N(n) represents the set of neighbor nodes of node n; L nj represents the resistance of the wire from node i to j; C nj represents the wire current load from node i to j; V th Represents the threshold voltage.

5. The pre-fault alarm method for improving the effectiveness of self-healing and power restoration of distribution networks as claimed in claim 4, characterized in that: The ant colony algorithm includes, when solving, regarding the fault point that causes the largest power outage range of the distribution network as the extreme fault point, taking the power outage range of the distribution network as the objective function of the ant colony algorithm, and taking the numbers of all possible fault points in the distribution network as the input of the algorithm, and solving iteratively through the ant colony algorithm to find the position of the extreme fault point that causes the largest power outage range of the distribution network. The process of the ant colony algorithm is expressed as follows: Numbering each node in the power grid topology structure, and numbering each busbar outlet in the power grid topology structure; Establish an extreme fault point finding model based on ant colony algorithm; A fault is randomly generated and brought into the simulation environment of the model to solve the power flow data of each node; Determine the scope of power outage based on the power flow data of each node; Repeat the process of randomly generating fault solving power flow data and determining the power outage range M times; Collect simulation results in real time and bring them into the ant colony algorithm for solution; By obtaining the busbar outlet number and power outage range corresponding to the extreme fault point, the location of the extreme fault point can be determined.

6. The pre-fault alarm method for improving the effectiveness of self-healing and power restoration of distribution networks as claimed in claim 5, characterized in that: The extreme fault point obtaining model includes: the pheromone update formula of the ant colony algorithm is expressed as: t ij (t+1)=(1-ρ)·τ ij (t)+Δτ ij Considering the three-phase short circuit, it is assumed that the power loss of node k is transferred to other nodes during the fault, and the energy loss is simulated by multiplying the square of the current by the resistance, which is expressed as: Integrate the power flow data and pheromone update, adjust the pheromone update formula to reflect the impact of the fault, and express it as: Among them, τ ij represents the pheromone concentration on the path from node i to j at time t; ρ represents the pheromone volatility; Δτ ij represents the increment of pheromone left by ants; ΔP k represents the power change of node k caused by the fault; I k represents the current at node k; R k represents the resistance of node k; K represents the set of all possible fault points; ξ represents the pheromone intensity adjustment coefficient; S ij represents the maximum capacity of the line from node i to j; according to the final pheromone concentration, the bus outlet corresponding to the path with the highest pheromone is selected as the extreme fault point.

7. The pre-fault alarm method for improving the effectiveness of self-healing and power restoration of distribution networks as claimed in claim 6, characterized in that: Calculating the self-healing power restoration effect includes calculating the self-healing power restoration effect according to the obtained extreme fault point, which is expressed as: Among them, P represents the effectiveness of self-healing and power restoration of the distribution network; T N Indicates the number of transformers that resume operation after self-healing under extreme fault conditions; T M Indicates the number of all transformers within the power outage range.

8. A pre-fault warning system for improving the effectiveness of self-healing and power restoration of distribution networks using the method described in any one of claims 1 to 7, characterized in that: include, The data acquisition module collects and pre-processes the data of the target power grid, integrates the data of the GIS system and the distribution network OMS system, and builds a pre-power outage range analysis model; The fault simulation module abstracts the pre-power outage range analysis model into a graph model consisting of nodes and edges, and uses the A* algorithm to automatically build a feeder segmentation model in the graph model to simulate faults and protection actions and optimize the fault handling path. The power restoration effectiveness calculation module introduces the power grid's flow data and the characteristics of the busbar outlet to optimize the ant colony's pheromone update, uses the ant colony algorithm to simulate extreme fault points, and calculates the self-healing power restoration effectiveness based on the obtained extreme fault points, thereby achieving early warning and differentiated management and control.

9. A computing device comprising: Memory and processor; The memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions. When the computer-executable instructions are executed by the processor, the steps of the method described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium storing computer-executable instructions, wherein the computer-executable instructions, when executed by a processor, implement the steps of the method according to any one of claims 1 to 7.

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