A coal mine power outage emergency handling method and system

Abstract

The present invention relates to the technical field of emergency handling of power outages in coal mines, and in particular to a method and system for emergency handling of power outages in coal mines. According to the actual situation of the coal mine, an objective function is constructed; a first algorithm is set for the existing objective function; and the first algorithm is used to iteratively solve the problem. The improved whale optimization algorithm based on the orthogonal design principle applies the orthogonal design method to the parameter design of the whale algorithm, and also designs a nonlinear convergence factor. For the scenario of emergency handling of power outages in coal mines, the handling efficiency is higher, it is easier to obtain the global optimal solution, and the convergence speed is faster.

Description

Technical Field

[0001] The present invention relates to the technical field of emergency handling of power outage faults in coal mines, and in particular to a method and system for emergency handling of power outage faults in coal mines. Background Art

[0002] Fault handling for coal mine users involves the deployment of various human and material resources, as well as the overall planning of the handling work. It is a complex optimization problem with nonlinearity, multiple variables, and multiple constraints. At present, the main methods for solving such problems include traditional optimization methods such as Newton's method and interior point method, as well as heuristic swarm intelligence algorithms based on probability search.

[0003] However, traditional mathematical models or single-objective optimization methods: These methods cannot adapt to the needs of the current development of power grid technology. A multi-objective method is needed to dynamically plan the repair process of the distribution network. There are two conflicting goals in emergency disposal, that is, while restoring power as quickly as possible, it is also necessary to consider how to minimize the cost of repairing power supply equipment. This makes the emergency disposal plan a complex multi-constraint optimization problem. For existing technologies, such as particle swarm optimization (PSO), although the PSO algorithm is popular because of its simple structure and few parameter adjustment requirements, its performance is greatly affected by parameter selection; the standard whale algorithm, although it has a strong local search ability, has a slow convergence speed, which limits its optimization ability in large-scale complex problems; the non-dominated sorting genetic algorithm II (NSGA-II) maintains the diversity of the population through non-dominated sorting and crowding distance. However, NSGA-II needs to calculate the crowding distance, which will lead to significant computational overhead when the population size is large.

[0004] At present, in general, the multi-objective optimization methods used in fault emergency disposal have the following characteristics: high computational complexity and high computational cost, especially when searching for the global optimal solution; random parameter adjustment, many multi-objective optimization algorithms contain multiple parameters, which have a great impact on algorithm performance, but often need to be adjusted based on experience; universality for specific scenarios, more effective for specific types of problems, but not so effective for other problems. Summary of the invention

[0005] In view of the above problems existing in the prior art, the present invention is proposed.

[0006] The technical problem of the present invention is to solve the problem of coal mine power outage accident handling efficiency, involving a multi-objective optimization algorithm in the field of emergency repair. Specifically, the technical problem to be solved is how to achieve multi-objective optimization of the coal mine power outage accident handling process, combine the multi-objective optimization method, and study the improvement of the whale optimization algorithm based on the orthogonal design principle to improve the efficiency of accident handling.

[0007] Traditional multi-objective optimization algorithms may have problems such as random parameter adjustment and weak search capabilities. Using the whale algorithm combined with the orthogonal parameter design principle and nonlinear convergence factor can improve the rationality of parameter selection, enhance global search capabilities, and improve the efficiency of emergency response.

[0008] In order to solve the above technical problems, the present invention provides the following technical solutions: a method for emergency handling of power outages in coal mines, comprising: constructing an objective function according to the actual situation of the coal mine;

[0009] For the existing objective function, set the first algorithm;

[0010] The first algorithm is used to iterate the solution.

[0011] As a preferred solution of the coal mine power outage emergency handling method described in the present invention, wherein: the constructing of the objective function includes constructing the objective function with the goal of minimizing the total economic loss.

[0012] As a preferred solution of the coal mine power outage emergency handling method described in the present invention, the existing objective function includes a multi-objective function constructed for the coal mine power outage accident handling process.

[0013] As a preferred solution of the coal mine power outage emergency handling method described in the present invention, the first algorithm setting includes determining the value of the adaptive parameter through the orthogonal design principle and designing a new nonlinear convergence factor.

[0014] As a preferred solution of the coal mine power outage emergency handling method described in the present invention, the iterative solution using the first algorithm includes an optimization result of an output repair sequence based on the first algorithm.

[0015] As a preferred solution of the coal mine power outage emergency handling method described in the present invention, the first algorithm includes inputting relevant information, calculating the shortest path from each repair station to the fault point based on the input relevant information, using the shortest path to calculate the shortest travel time between each point, and initializing, calculating individual fitness, updating individual position, finding the optimal individual, and confirming whether the maximum number of iterations has been reached.

[0016] As an optimal solution of the coal mine power outage emergency handling method described in the present invention, the confirmation of whether the maximum number of iterations has been reached includes, if the maximum number has been reached, the iteration ends, if the maximum number of iterations has not been reached, the individual fitness is recalculated.

[0017] As a preferred solution of the coal mine power failure emergency handling system described in the present invention, it includes: a function construction module, an algorithm setting module, and a calculation module;

[0018] Function construction module, constructs the target function according to the actual situation of the coal mine;

[0019] An algorithm setting module performs a first algorithm setting for an existing objective function;

[0020] The calculation module uses the first algorithm to perform iterative solution.

[0021] A computer device comprises a memory and a processor, wherein the memory stores a computer program, and is characterized in that when the processor executes the computer program, the steps of any one of the methods for emergency handling of coal mine power outages are implemented.

[0022] A computer-readable storage medium having a computer program stored thereon, characterized in that when the computer program is executed by a processor, the steps of any one of the methods for emergency handling of coal mine power outages are implemented.

[0023] Beneficial effects of the invention: The orthogonal design principle is used to improve the whale optimization algorithm. The orthogonal design method is applied to the parameter design of the whale algorithm, and a nonlinear convergence factor is also designed. For the emergency handling of coal mine power outages, the handling efficiency is higher, the global optimal solution is easier to obtain, and the convergence speed is faster. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0025] Figure 1 A schematic diagram of data acquisition equipment connection and technical flow of a coal mine power outage emergency response method provided by an embodiment of the present invention.

[0026] Figure 2 A schematic diagram of a convergence curve of a coal mine power outage emergency handling method provided by an embodiment of the present invention.

[0027] Figure 3 A schematic diagram of a whale optimization algorithm improved by using an orthogonal design principle for a method for emergency handling of power outages in coal mines provided by an embodiment of the present invention.

[0028] Figure 4 A schematic diagram of the effects of an improved whale algorithm and a standard whale algorithm for a method for emergency handling of coal mine power outages provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0029] 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.

[0030] Example 1

[0031] Reference Figure 1-Figure 4 , which is the first embodiment of the present invention, and provides a method for emergency handling of power outages in coal mines, such as Figure 1 As shown, including:

[0032] S1: Construct the objective function according to the actual situation of the coal mine.

[0033] S2: Perform the first algorithm setting for the existing objective function.

[0034] S3: Use the first algorithm to perform iterative solution.

[0035] In this embodiment, the first algorithm is an orthogonal design principle improved whale optimization algorithm;

[0036] Step 1: Input relevant information such as fault location, repair point location, maximum number of iterations, number of individuals in the population, etc.

[0037] Step 2: Calculate the shortest path from each repair station to the fault point based on the input fault location information;

[0038] Step 3: Use the shortest path to calculate the shortest travel time between points;

[0039] Step 4: Initialize the whale population;

[0040] Step 5: Calculate individual fitness, update individual positions, and find the optimal individual;

[0041] Step 6: Determine whether the maximum number of iterations has been reached. If so, end the iteration. If not, return to step 5 to continue solving.

[0042] Step 7: Output the optimization results of the repair sequence and end.

[0043] In an embodiment of the present application, constructing the objective function includes constructing the objective function with the goal of minimizing total economic losses.

[0044] In the embodiment of the present application, after a power outage occurs in a coal mine, the power supply of the distribution network should be restored in the shortest possible time with the lowest maintenance cost. Therefore, when designing an emergency repair model for coal mine power outages, the overall objective function should be aimed at minimizing the total economic loss. When establishing a distribution network repair model, the repair cost of the distribution network repair target and the distance between the point to be repaired and the maintenance station should be the main factors, and the target distance, environmental complexity weight, comprehensive cost, node location and other influencing factors should be comprehensively considered. The above-mentioned influencing factors are shown in formulas (1)-(4).

[0045]

[0046] ω∈(ω 1 ,,(x r -x i ) 2 ω 2 ,…,ω N )#(2)

[0047]

[0048] (x,y)=[(x 1 ,y 1 ),(x 2 ,y 2 ),…,(x i ,y i )]#(4)

[0049] Where d is the target distance, (x r ,y r ) is the coordinate of the maintenance station, (x i ,y i ) is the location of the i-th fault node; ω is the environmental complexity weight; C is the comprehensive cost, m is the number of maintenance stations, and n is the number of distribution network nodes; where (x, y) represents the location of the distribution network node. According to the above influencing factors, the established model is combined into a multi-objective equation, as shown in formula (5).

[0050]

[0051] A multi-objective equation constructed for the coal mine power outage accident handling process;

[0052] In the embodiment of the present application, the first algorithm is an innovation for the parameter setting of the standard whale algorithm, and an orthogonal design principle is used to improve the whale optimization algorithm. The present invention makes two improvements to the standard whale algorithm.

[0053] In the embodiment of the present application, the first improvement in the standard whale algorithm is that parameter A in the standard whale algorithm is used to balance the global and local search capabilities, and the value of parameter A is affected by the convergence factor a. When a is small, the algorithm has a stronger local search capability and can speed up the search; when a is large, it has a stronger global search capability and is not easily trapped in the local optimum. The size of A is also related to the number of iterations. As the number of iterations decreases linearly from 2 to 0, the global search capability of the algorithm also decreases linearly, resulting in insufficient search space in the early stage of the search, affecting the search speed in the later stage. Based on this, a formula for the nonlinear convergence factor a is proposed, so that in the early stage of the iteration, a is at a higher value and gradually decreases, thereby improving the global search capability. In the later stage of the iteration, a is at a low value, which improves the local search capability and speeds up the search speed. The formula is shown in formula (6), and the convergence curve is shown in the attached figure. Figure 2 shown.

[0054]

[0055] In an embodiment of the present application, the second improvement in the standard whale algorithm is that in the standard whale algorithm, p is a random number between 0 and 1, which is used to control the algorithm's conversion between global search and local search; it is used to determine which predation strategy the whale adopts. When p is less than a certain threshold (usually 0.5), the whale will adopt a bubble net predation strategy, that is, approaching the prey through spiral motion. On the contrary, if p is greater than or equal to this threshold, the whale will adopt an encirclement and contraction strategy, that is, directly approaching the current optimal solution. Since p is random, the algorithm exhibits unstable behavior during the search process, and the convergence speed is affected. Based on this, the present invention proposes an adaptive parameter based on the orthogonal design principle, so that the value of p can be adaptively adjusted according to the number of iterations or the progress of the algorithm, thereby improving the efficiency of the algorithm. The formula is shown in formula (7), where the segmented values ​​of p and t are obtained by the orthogonal design principle.

[0056]

[0057] In general, the present invention determines the value of the adaptive parameter through the orthogonal design principle, and designs a new nonlinear convergence factor, so that the improved whale optimization algorithm can be more efficient and accurate. The overall flow chart is as shown in the attached Figure 3 shown.

[0058] For the emergency response scenario of coal mine power outage, the response efficiency is higher, it is easier to obtain the global optimal solution, and the convergence speed is faster. Figure 4 shown.

[0059] It should be noted that the existing objective function includes a multi-objective function constructed for the coal mine power outage accident handling process.

[0060] Furthermore, the first algorithm setting includes determining the value of the adaptive parameter through the orthogonal design principle and designing a new nonlinear convergence factor.

[0061] It should be noted that the iterative solution using the first algorithm includes repairing the optimization result of the sequence based on the output of the first algorithm.

[0062] In an optional embodiment, the first algorithm may be an improved genetic algorithm for optimizing the emergency repair process of coal mine power outage accidents. The following are specific implementation steps:

[0063] Step 1: Input relevant information such as fault location, repair point location, maximum number of iterations, number of individuals in the population, etc. At the same time, initialize the parameters of the genetic algorithm, such as crossover rate, mutation rate, etc.

[0064] Step 2: Based on the input fault location information, calculate the shortest path from each repair station to the fault point. Use the shortest path as the initial population for subsequent optimization.

[0065] Step 3: Use the shortest path to calculate the shortest travel time between points as the basis for individual fitness evaluation.

[0066] Step 4: Initialize the genetic algorithm population and randomly generate a certain number of individuals, each of which represents a repair plan.

[0067] Step 5: Calculate the fitness of each individual in the population. The higher the fitness, the better the repair plan. Select excellent individuals according to the fitness for crossover and mutation operations.

[0068] Step 6: Perform crossover operations on the selected individuals to generate new repair plans. At the same time, perform mutation operations on some individuals to increase the diversity of the population.

[0069] Step 7: Determine whether the maximum number of iterations has been reached. If not, return to step 5 to continue optimization; otherwise, proceed to step 8.

[0070] Genetic algorithms have high efficiency and accuracy in optimizing the emergency repair process of coal mine power outage accidents, which helps to reduce the total economic losses.

[0071] In an optional embodiment, the first algorithm may be an improved particle swarm optimization (PSO) algorithm for optimizing the emergency repair process of coal mine power outage accidents. The following are specific implementation steps:

[0072] Step 1: Input relevant information such as fault location, repair point location, maximum number of iterations, number of individuals in the population, etc. At the same time, initialize the parameters of the PSO algorithm, such as inertia weight, learning factor, etc.

[0073] Step 2: Based on the input fault location information, calculate the shortest path from each repair station to the fault point. Use the shortest path as the initial population for subsequent optimization.

[0074] Step 3: Use the shortest path to calculate the shortest travel time between points as the basis for individual fitness evaluation.

[0075] Step 4: Initialize the PSO algorithm population and randomly generate a certain number of particles, each particle representing a repair plan.

[0076] Step 5: Calculate the fitness of each particle in the population. The higher the fitness, the better the repair plan. Update the speed and position of the particle according to the fitness.

[0077] Step 6: Update the global optimal solution and the individual optimal solution so that the particles move closer to the optimal solution during the search process.

[0078] Step 7: Determine whether the maximum number of iterations has been reached. If not, return to step 5 to continue optimization; otherwise, proceed to step 8.

[0079] Step 8: Output the optimization result of the repair sequence, that is, the best repair plan. End the PSO algorithm.

[0080] The improved particle swarm optimization algorithm has a high convergence speed and global search capability in optimizing the emergency repair process of coal mine power outage accidents, which helps to minimize the total economic losses.

[0081] Example 2

[0082] The second embodiment of the present invention is different from the previous embodiment in that:

[0083] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including 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 methods described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc., which can store program codes.

[0084] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0085] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering or, if necessary, processing in another suitable manner, and then stored in a computer memory.

[0086] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, a plurality of steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0087] Example 3

[0088] A third embodiment of the present invention provides a coal mine power failure emergency handling system, characterized in that it includes a function building module, an algorithm setting module, and a calculation module;

[0089] Function construction module, constructs the target function according to the actual situation of the coal mine;

[0090] An algorithm setting module performs a first algorithm setting for an existing objective function;

[0091] The calculation module uses the first algorithm to perform iterative solution.

[0092] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the present invention. 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.

[0093] Example 4

[0094] The fourth embodiment of the present invention provides a simulation experiment process of a method for emergency handling of power outages in coal mines. In order to test the performance of the algorithm proposed by the present invention, the above algorithm is compared with NSGAⅡ and IDBEA on the basis of the basic test ZDT. In order to more comprehensively present the experimental results of the comparative experiment, two comprehensive indicators, Hypervolume (HV) and Inverse Generational Distance (IGD), are used as evaluation indicators. IGD evaluates the calculation results by the average distance from each point in the true Pareto optimal solution set (TPF) to the nearest point in the approximate Pareto optimal solution set (APF); HV evaluates the quality of the solution set by calculating the volume of the hypercube between a reference point and the approximate Pareto front (Pareto Front). When the HV index of the non-dominated solution set is large, it indicates that the solution set is both diverse and convergent, that is, it contains a wide range of solutions, and these solutions are also close to the true Pareto front.

[0095] The following table shows that the three algorithms were independently run 30 times on the MATLAB R2021b platform and statistically significant conclusions were drawn.

[0096] Table 1. Comparison of the results of the traditional algorithm and the algorithm of the present invention on the ZDT benchmark and IGD index

[0097]

[0098] Table 2. Comparison of the results of the traditional algorithm and the proposed algorithm on the ZDT benchmark and HV index

[0099]

[0100]

[0101] It can be seen from Table 1 that the IGD value of the algorithm of the present invention in ZED1 is significantly better than that of NSGAII and IDBEA. In the three comparisons, the IGD values ​​of two groups are lower than those of the NSGAII group, and the IGD values ​​of three groups are lower than those of the IDBEA group. In the comparison between NSGAII and the algorithm of the present invention, the IGD value of the algorithm of the present invention in ZED1 and 3 is better than that of NSGAII. Comparing IDBEA and the algorithm of the present invention, it is found that Ti-MOPSO has the best IGD value on ZED1 and 2. In summary, the algorithm of the present invention exhibits competitive optimization capabilities on the ZDT test problem, and the results of Table 2 can further verify that the algorithm of the present invention has excellent performance. In all three test instances, the present invention obtained the best HV value in the ZED1 and 2 tests. The statistical results show that the algorithm of the present invention achieved good performance in the test experiment.

Claims

1. A method for emergency handling of power outage in coal mines, characterized by: include, According to the actual situation of coal mine, construct the objective function; For the existing objective function, set the first algorithm; The first algorithm is used to iterate the solution.

2. A method for emergency handling of power outage in coal mines according to claim 1, characterized in that: The constructing of the objective function includes constructing the objective function with the goal of minimizing the total economic loss.

3. A method for emergency handling of coal mine power outages as claimed in claim 2, characterized in that: The existing objective function includes a multi-objective function constructed for the coal mine power outage accident handling process.

4. A method for emergency handling of power outage in coal mines as claimed in claim 3, characterized in that: The first algorithm setting includes determining the value of the adaptive parameter through the orthogonal design principle and designing a new nonlinear convergence factor.

5. A method for emergency handling of power outage in coal mines as claimed in claim 4, characterized in that: The iterative solution using the first algorithm includes repairing the optimization result of the sequence based on the output of the first algorithm.

6. A method for emergency handling of power outage in coal mines as claimed in claim 5, characterized in that: The first algorithm includes inputting relevant information, calculating the shortest path from each repair station to the fault point based on the input relevant information, using the shortest path to calculate the shortest travel time between each point, and initializing, calculating individual fitness, updating individual positions, finding the optimal individual, and confirming whether the maximum number of iterations has been reached.

7. A method for emergency handling of power outage in coal mines according to claim 6, characterized in that: The confirming whether the maximum number of iterations is reached includes: if the maximum number is reached, the iteration ends; if the maximum number of iterations is not reached, the individual fitness is recalculated.

8. A system based on the method for emergency handling of coal mine power outages according to any one of claims 1 to 7, characterized in that: Including function building module, algorithm setting module, and calculation module; Function construction module, constructs the target function according to the actual situation of the coal mine; An algorithm setting module performs a first algorithm setting for an existing objective function; The calculation module uses the first algorithm to perform iterative solution.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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