Power failure repair path optimization method and system under extreme weather influence

Abstract

The invention is suitable for the technical field of fault first-aid repair, and provides a power fault first-aid repair path optimization method and system under the influence of extreme weather, and the method comprises the steps: building a power fault first-aid repair path initial scheduling model based on the shortest total first-aid repair time and the minimum loss under the influence of extreme weather as parameters; setting a constraint condition to allocate at least one first-aid repair team for each fault point, wherein the first-aid repair capability of the first-aid repair team under the influence of extreme weather is an integer greater than zero and the loss caused by the influence of extreme weather is less than the loss of the regional fault point; determining a target parameter variable according to the constraint condition, and optimizing the target parameter variable by using an improved genetic algorithm to obtain an optimized power fault repair path optimization scheduling model; and inputting the obtained power fault first-aid repair response data into the power fault first-aid repair path optimization scheduling model to generate a target first-aid repair path. According to the method, the loss amount generated by extreme weather influence factors is considered, and the accuracy of path prediction is effectively improved.

Description

Technical Field

[0001] The present invention relates to the technical field of emergency repair of faults, and in particular to a method and system for optimizing an emergency repair path of a power fault under the influence of extreme weather. Background Art

[0002] With the improvement of the service quality of the power system, in order to ensure the service quality, it is particularly important to quickly locate the fault and solve the fault problem. The purpose of selecting the best repair path for the power communication network is to quickly and accurately find the fault location and carry out timely repairs. At present, a hierarchical analysis method is proposed for the selection of the best repair path. The subjective weighting method is used to obtain the user's preference information. The hierarchical analysis method is introduced to combine qualitative analysis with quantitative analysis to obtain subjective weights, and then the objective weights are obtained by the weighting method. The two weights are combined to determine the best path. However, the calculation process of this method takes a long time, which makes the efficiency relatively low, and the optimization efficiency under the influence of extreme weather is relatively low.

[0003] In view of this, a method and system for optimizing the emergency repair path of power failures under the influence of extreme weather is needed. Summary of the invention

[0004] The embodiments of the present application provide a method and system for optimizing power failure repair paths under the influence of extreme weather, which are used to solve the problem of low optimization efficiency under the influence of extreme weather.

[0005] A first aspect of an embodiment of the present application provides a method for optimizing a power failure repair path under the influence of extreme weather, comprising:

[0006] Constructing an initial dispatching model for power failure repair paths based on target variables as parameters, wherein the target variables are minimizing the total repair time and minimizing the loss under the influence of extreme weather;

[0007] Setting constraints, wherein each fault point is assigned at least one repair team, the repair capacity of the repair team under the influence of extreme weather is an integer greater than zero, and the loss amount caused by the extreme weather is less than the loss amount of the regional fault point;

[0008] Determine the target parameter variable according to the constraint condition, and optimize the target parameter variable by using an improved genetic algorithm to obtain an optimized power fault repair path optimization scheduling model;

[0009] The power failure repair response data is acquired in real time, and the response data is input into the power failure repair path optimization scheduling model to generate a target repair path based on the response data.

[0010] Furthermore, the expression of the initial scheduling model of the power failure repair path is as follows:

[0011] minF(X)={f1(X),f2(X)};

[0012] Where: X=(x1,x2,…,x m ,…,x n ), x1,x2,…,x m The m in the figure is the dimension of the repair sequence of the fault point, and x m ,…,x n is the charge number affected by extreme weather, f1(X) is the variable with the shortest total repair time, and f2(X) is the variable with the minimum loss under the influence of extreme weather.

[0013] Furthermore, the expression of the variable f1(X) with the shortest total repair time is as follows:

[0014]

[0015] Where: z is the number of fault repair teams, K is the number of fault points, T is the fault repair time, t i is the repair time of the i-th fault point, t wk The waiting time for power failure, x vk are the constraints for the fault point and fault team.

[0016] Furthermore, the expression of the minimum loss variable f2(X) under the influence of extreme weather is as follows:

[0017]

[0018] Where: i is the loss caused by the extreme weather factors at the i-th fault point, l i is the power outage loss at the i-th fault point.

[0019] Furthermore, the expression of the constraint condition that at least one repair team is assigned to each fault point is as follows:

[0020]

[0021] The repair capability of the repair team under the influence of the extreme weather conditions Q i The expression with the constraint of an integer greater than zero is as follows:

[0022] Q i ≥0;

[0023] The amount of loss caused by the extreme weather impact g i Less than the regional fault point loss G i The expressions for the constraints are as follows:

[0024] βgi <G i ;

[0025] Where: β is the impact ratio of extreme weather.

[0026] Furthermore, the optimization of the target parameter variables by using an improved genetic algorithm includes:

[0027] The chromosomes in the parameter variables are encoded in two layers, the first layer uses integer encoding and the second layer uses binary encoding;

[0028] Setting initial parameters, including initial fault repair path, iteration and search times;

[0029] Determine the fitness function and calculate the individual fitness value;

[0030] Set the selection strategy and stopping criteria, perform crossover and mutation operations, and determine the target parameter variable when the set number of iterations is reached.

[0031] Furthermore, the expressions of the selection strategy and the stopping criterion are as follows:

[0032]

[0033]

[0034] Where: p(x i ) is the probability of each individual being inherited to the next generation, q(x i ) is the probability of chromosome being selected.

[0035] A second aspect of the embodiment of the present application provides a system for optimizing the emergency repair path of power failures under the influence of extreme weather, including:

[0036] An initial dispatch model building unit is used to build an initial dispatch model for power failure repair path based on target variables as parameters, wherein the target variables are the shortest total repair time and the smallest loss under the influence of extreme weather;

[0037] A constraint setting unit, used to set constraint conditions, wherein the constraint conditions include that at least one repair team is assigned to each fault point, the repair capacity of the repair team under the influence of extreme weather is an integer greater than zero, and the loss amount caused by the extreme weather is less than the loss amount of the regional fault point;

[0038] An optimization scheduling model determination unit is used to determine the target parameter variables according to the constraint conditions, and optimize the target parameter variables using an improved genetic algorithm to obtain an optimized power fault repair path optimization scheduling model;

[0039] The target repair path generation unit is used to obtain power failure repair response data in real time, and input the response data into the power failure repair path optimization scheduling model to generate a target repair path based on the response data.

[0040] A third aspect of an embodiment of the present application provides a computer device, including a memory, a processor, and computer-readable instructions stored in the memory and executable on the processor, wherein when the processor executes the computer-readable instructions, the steps of the method for optimizing the path of emergency repair of power failures under the influence of extreme weather as described in any one of the above are implemented.

[0041] A fourth aspect of an embodiment of the present application provides a readable storage medium, which stores computer-readable instructions. When the computer-readable instructions are executed by a processor, the steps of the method for optimizing the path of power failure repair under extreme weather conditions as described in any one of the above are implemented.

[0042] It can be seen from the above technical solutions that the embodiments of the present application have the following advantages:

[0043] This application first constructs an initial scheduling model for power failure repair paths based on determining the target variables as the shortest total repair time and the minimum loss under the influence of extreme weather, and then sets constraints that at least one repair team is assigned to each fault point, the repair capacity of the repair team under the influence of extreme weather is an integer greater than zero, and the amount of loss caused by the extreme weather is less than the amount of loss at the regional fault point; then the target parameter variables are determined according to the constraints, and the target parameter variables are optimized using an improved genetic algorithm to obtain an optimized scheduling model, and finally, after obtaining the repair response data, it is input into the optimized scheduling model to generate the optimal repair path. This application considers the amount of loss caused by extreme weather influencing factors under the setting of extreme weather influence conditions, so that the fault repair path generated for this factor of extreme weather is more accurate, and the accuracy of path prediction is effectively improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 This is a schematic diagram of an embodiment of a method for optimizing a power failure repair path under the influence of extreme weather in the present invention;

[0045] Figure 2 This is a structural block diagram of an embodiment of a system for optimizing a power failure repair path under the influence of extreme weather in the present invention;

[0046] Figure 3 The figure is a structural block diagram of a computer device in the present invention. DETAILED DESCRIPTION

[0047] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0048] In this embodiment, the method for optimizing the power fault repair path under the influence of extreme weather is used to improve the prediction accuracy of the fault repair path generated for the factor of extreme weather. In this embodiment, the implementation method can be implemented in the system, can be implemented in the server, and can also be implemented in the terminal, without specific limitation.

[0049] Embodiment 1

[0050] See also Figure 1 , a method for optimizing the repair path of power failures under the influence of extreme weather in the present invention comprises the following steps:

[0051] S11. Construct an initial dispatching model for power failure repair path based on the target variable as a parameter, where the target variable is to minimize the total repair time and minimize the loss under the influence of extreme weather;

[0052] In this embodiment, the emergency repair of power failures under the influence of extreme weather is a process based on multiple objective optimizations, which involve the location of the emergency repair site, the emergency repair time required for each fault point, the number of emergency repair teams, the road conditions of the emergency repair path, and the power outage losses involved in the emergency repair process, etc. Here, the results of the influence of extreme weather factors are considered, and the objective function is constructed with the shortest total emergency repair time and the minimum power outage losses under the influence of extreme weather as the target variables, and the initial path scheduling model is constructed based on the determination of the objective function.

[0053] The specific expression of the objective function is as follows:

[0054] minF(X)={f1(X),f2(X)};

[0055] Where: X=(x1,x2,…,x m ,…,x n ), x1,x2,…,x m The m in the figure is the dimension of the repair sequence of the fault point, and x m ,…,x n is the charge number affected by extreme weather, f1(X) is the variable with the shortest total repair time, and f2(X) is the variable with the minimum loss under the influence of extreme weather.

[0056] The expression of the variable f1(X) with the shortest total repair time is as follows:

[0057]

[0058] Where: z is the number of fault repair teams, K is the number of fault points, T is the fault repair time, t i is the repair time of the i-th fault point, t wk The waiting time for power failure, x vk are the constraints for the fault point and fault team.

[0059] The expression of the variable f2(X) with the minimum loss under the influence of extreme weather is as follows:

[0060]

[0061] Where: i is the loss caused by the extreme weather factors at the i-th fault point, l i is the power outage loss at the i-th fault point.

[0062] S12. Set constraints, including that at least one repair team is assigned to each fault point, the repair capacity of the repair team under the influence of extreme weather is an integer greater than zero, and the loss amount caused by extreme weather is less than the loss amount of the regional fault point;

[0063] In this embodiment, the constraints set include that at least one repair team x is assigned to each fault point. vk , The repair capability of the repair team under the influence of extreme weather conditions i is an integer greater than zero and the amount of loss caused by extreme weather impact g i Less than the regional fault point loss G i , the expressions are as follows:

[0064]

[0065] Q i ≥0;

[0066] βg i <G i ;

[0067] Where: β is the impact ratio of extreme weather.

[0068] S13. Determine the target parameter variables according to the constraint conditions, and optimize the target parameter variables using the improved genetic algorithm to obtain an optimized power failure repair path optimization scheduling model;

[0069] In this embodiment, optimizing the target parameter variable further includes the following steps:

[0070] 1. Perform double-layer encoding on the chromosomes in the parameter variables, with integer encoding in the first layer and binary encoding in the second layer;

[0071] The chromosomes are encoded in two layers. The first layer is integer encoding, which indicates the order of emergency repair points. The second layer is binary encoding, which indicates the selection of emergency repair paths.

[0072] 2. Set initial parameters, including initial fault repair path, iteration and search times;

[0073] The initial path of fault repair is randomly generated, and then the initial solution of the repair team for repair is constructed in the sub-path. Then, all the initial solutions are converted into individuals according to the above encoding method for iteration and search.

[0074] 3. Determine the fitness function and calculate the individual fitness value;

[0075] Here, since the objective function is the shortest path distance, the smaller the objective function value, the better the result, so the objective function is set as the fitness function.

[0076] 4. Set the selection strategy and stopping criteria, then perform crossover and mutation operations. When the set number of iterations is reached, determine the target parameter variable.

[0077] Specifically, the roulette wheel selection strategy is adopted here, that is, the fitness value of the individual is proportional to the probability of the chromosome being selected. The specific selection strategy refers to the probability of each individual being inherited to the next generation; the stopping criterion means that the probability of the chromosome being selected is the cumulative probability of each individual.

[0078] The expressions for the selection strategy and stopping criterion are as follows:

[0079]

[0080]

[0081] Where: p(x i ) is the probability of each individual being inherited to the next generation, q(x i ) is the probability of chromosome being selected.

[0082] S14. Acquire power failure emergency repair response data in real time, and input the response data into the power failure emergency repair path optimization scheduling model to generate a target emergency repair path based on the response data.

[0083] In this embodiment, the power failure repair response data refers to model parameter data, such as the location of the fault, the amount of charge, the power outage time, and weather-related data of extreme weather. After the response data is obtained, basic preprocessing is performed on the data so that the data structure conforms to the structure of the model input parameters. The preprocessed data is input into the optimization scheduling model, and the optimal repair path corresponding to the response data is generated according to the optimized model. That is, based on the knowledge of the weather data of extreme weather, the extreme weather influencing factors are used as variables to analyze the path.

[0084] The above embodiment sets factors under the influence of extreme weather when constructing the scheduling model, so that the accuracy of the output structure of the scheduling model considering the influence of extreme weather is higher.

[0085] Embodiment 2

[0086] See also Figure 2 , an embodiment of a power failure repair path optimization system under the influence of extreme weather in the present invention comprises the following steps:

[0087] An initial dispatch model building unit 101 is used to build an initial dispatch model for power failure repair paths based on target variables as parameters, wherein the target variables are the shortest total repair time and the smallest loss under the influence of extreme weather;

[0088] The constraint condition setting unit 102 is used to set the constraint conditions, wherein the constraint conditions include that at least one repair team is assigned to each fault point, the repair capacity of the repair team under the influence of extreme weather is an integer greater than zero, and the loss amount caused by the extreme weather is less than the loss amount of the regional fault point;

[0089] The optimization scheduling model determination unit 103 is used to determine the target parameter variables according to the constraint conditions, and optimize the target parameter variables using the improved genetic algorithm to obtain the optimized power fault repair path optimization scheduling model;

[0090] The target repair path generation unit 104 is used to obtain power failure repair response data in real time, and input the response data into the power failure repair path optimization scheduling model to generate a target repair path based on the response data.

[0091] For the specific definition of the optimization system, please refer to the definition of the optimization method above, which will not be repeated here. Each module in the above optimization system can be implemented in whole or in part by software, hardware and a combination thereof. The above modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory in the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.

[0092] Embodiment 3

[0093] See also Figure 3 The present invention provides a computer device, including a memory, a processor, and computer-readable instructions stored in the memory and executable on the processor. When the processor executes the computer-readable instructions, the steps of the above method are implemented.

[0094] Those of ordinary skill in the art will appreciate that the units of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition of each example has been generally described in terms of function in the above description. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0095] In the embodiments provided by the present invention, it should be understood that the division of units is only a logical function division, and there may be other division methods in actual implementation, such as multiple units can be combined into one unit, one unit can be split into multiple units, or some features can be ignored. In addition, each functional unit in each embodiment of the present invention can be integrated into a processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units.

[0096] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, read-only memory (ROM, Read-0nlyMemory), random access memory (RAM, RandomAccessMemory), mobile hard disk, magnetic disk or optical disk, etc., which can store program code.

[0097] It can be understood 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 above embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the above embodiments, or replace some or all of the technical features therein by equivalents. These modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present invention, and they should all be included in the scope of the claims and specification of the present invention.

Claims

1. A method for optimizing the repair path of power failures under extreme weather conditions, characterized in that: include: Constructing an initial dispatching model for power failure repair paths based on target variables as parameters, wherein the target variables are minimizing the total repair time and minimizing the loss under the influence of extreme weather; Setting constraints, wherein each fault point is assigned at least one repair team, the repair capacity of the repair team under the influence of extreme weather is an integer greater than zero, and the loss amount caused by the extreme weather is less than the loss amount of the regional fault point; Determine the target parameter variable according to the constraint condition, and optimize the target parameter variable by using an improved genetic algorithm to obtain an optimized power fault repair path optimization scheduling model; The power failure repair response data is acquired in real time, and the response data is input into the power failure repair path optimization scheduling model to generate a target repair path based on the response data.

2. The method for optimizing the power failure repair path under the influence of extreme weather according to claim 1 is characterized in that: The expression of the initial dispatching model of the power failure repair path is as follows: minF(X)={f1(X),f2(X)}; Where: X=(x1,x2,…,x m ,…,x n ), x1, x2, …, x m The m in the figure is the dimension of the repair sequence of the fault point, and x m ,…,x n is the charge number affected by extreme weather, f1(X) is the variable with the shortest total repair time, and f2(X) is the variable with the minimum loss under the influence of extreme weather.

3. The method for optimizing the power failure repair path under the influence of extreme weather according to claim 2 is characterized in that: The expression of the variable f1(X) with the shortest total repair time is as follows: Where: z is the number of fault repair teams, K is the number of fault points, T is the fault repair time, t i is the repair time of the i-th fault point, t wk The waiting time for power failure, x vk are the constraints for the fault point and fault team.

4. The method for optimizing the repair path of power failures under the influence of extreme weather according to claim 3 is characterized in that: The expression of the minimum loss variable f2(X) under the influence of extreme weather is as follows: Where: i is the loss caused by the extreme weather factors at the i-th fault point, l i is the power outage loss at the i-th fault point.

5. The method for optimizing the repair path of power failures under the influence of extreme weather according to claim 4 is characterized in that: The expression for the constraint condition that at least one repair team is assigned to each fault point is as follows: The repair capability of the repair team under the influence of the extreme weather conditions Q i The expression with the constraint of an integer greater than zero is as follows: Q i ≥0； The amount of loss caused by the extreme weather impact g i Less than the regional fault point loss G i The expressions for the constraints are as follows: bg i <G i ; Where: β is the impact ratio of extreme weather.

6. The method for optimizing the repair path of power failures under the influence of extreme weather according to claim 1 is characterized in that: The method of optimizing the target parameter variables by using an improved genetic algorithm includes: The chromosomes in the parameter variables are encoded in two layers, the first layer uses integer encoding and the second layer uses binary encoding; Setting initial parameters, including initial fault repair path, iteration and search times; Determine the fitness function and calculate the individual fitness value; Set the selection strategy and stopping criteria, perform crossover and mutation operations, and determine the target parameter variable when the set number of iterations is reached.

7. The method for optimizing the repair path of power failures under the influence of extreme weather according to claim 6 is characterized in that: The expressions of the selection strategy and stopping criteria are as follows: Where: p(x i ) is the probability of each individual being inherited to the next generation, q(x i ) is the probability of chromosome being selected.

8. A power failure repair path optimization system under extreme weather conditions, characterized in that: include: An initial dispatch model building unit is used to build an initial dispatch model for power failure repair path based on target variables as parameters, wherein the target variables are the shortest total repair time and the smallest loss under the influence of extreme weather; A constraint setting unit, used to set constraint conditions, wherein the constraint conditions include that at least one repair team is assigned to each fault point, the repair capacity of the repair team under the influence of extreme weather is an integer greater than zero, and the loss amount caused by the extreme weather is less than the loss amount of the regional fault point; An optimization scheduling model determination unit is used to determine the target parameter variables according to the constraint conditions, and optimize the target parameter variables using an improved genetic algorithm to obtain an optimized power fault repair path optimization scheduling model; The target repair path generation unit is used to obtain power failure repair response data in real time, and input the response data into the power failure repair path optimization scheduling model to generate a target repair path based on the response data.

9. A computer device comprising a memory, a processor, and computer-readable instructions stored in the memory and executable on the processor, characterized in that: When the processor executes the computer-readable instructions, the steps of the method for optimizing the path of emergency repair of power failures under the influence of extreme weather as described in any one of claims 1 to 7 are implemented.

10. A readable storage medium storing computer-readable instructions, characterized in that: When the computer-readable instructions are executed by a processor, the steps of the method for optimizing the path of emergency repair of power failures under the influence of extreme weather as described in any one of claims 1 to 7 are implemented.

Images