Fault diagnosis and positioning method for power supply multi-module parallel system

Abstract

The invention discloses a fault diagnosis and positioning method for a power supply multi-module parallel system in combination with a genetic algorithm and an ant colony algorithm. The method comprises the following steps: firstly, acquiring electrical characteristic quantity of each power supply module in the system, calculating an average value, and calculating the cost of each module state according to the characteristic quantity; and then, potential fault modules are rapidly screened by using an ant colony algorithm, a high-fitness state combination is used as an initial population of a genetic algorithm, global optimization is performed through selection, crossover and mutation operations of the genetic algorithm, and an optimal solution is gradually approached. And finally, an optimal module state combination is output by judging a fitness threshold value or a termination condition of the maximum number of iterations, a fault module in the system is represented in an array form, 0 represents normal, and 1 represents a fault. The method has the advantages of high efficiency and accuracy, is suitable for a complex multi-module parallel system, is simple to implement and easy to expand, and remarkably reduces the system maintenance cost and the false detection and omission ratio.

Description

Technical Field

[0001] The present invention relates to the fault diagnosis and location technology of a multi-module parallel power supply system, and specifically to a fault diagnosis and location method combining genetic algorithm and ant colony algorithm. Background Art

[0002] With the development of modern power electronics technology, multi-module parallel systems have been widely used in the fields of power, communication, and industrial control. The characteristic of a multi-module parallel system is that multiple power modules work together to meet the needs of different loads, with the advantages of high efficiency, high reliability, and flexible expansion. However, due to the large number of modules and complex structure in the system, the failure of any one module may have a significant impact on the overall performance of the system and even pose a safety hazard. Therefore, how to achieve fast and accurate fault diagnosis and location is the key issue to ensure the stable operation of the system.

[0003] Existing fault diagnosis methods mostly rely on circuit-level diagnosis technology, which has problems such as high hardware cost and complex maintenance. Therefore, a system-level fault diagnosis scheme combining genetic algorithm and ant colony algorithm is proposed to achieve efficient and low-cost fault detection and location. Summary of the Invention

[0004] A fault diagnosis and location method for a multi-module parallel power supply system is as follows:

[0005] (1) Collect the electrical characteristic values of each power module, define the operating state of the module, where 0 represents normal and 1 represents abnormal, and calculate the state correlation between modules;

[0006] (2) Initialize the parameters of the ant colony algorithm, including the number of ants, the initial value of pheromone concentration, the evaporation coefficient, the heuristic factor, etc., and construct a pheromone distribution matrix;

[0007] (3) Simulate path selection through the ant colony algorithm, and based on pheromone concentration and heuristic information, use the state probability function to determine the state of each module, and quickly screen out several potential module state combinations;

[0008] (4) Update the pheromone distribution, strengthen the potential paths with high fitness, and gradually optimize the search results through the pheromone evaporation and accumulation mechanism;

[0009] (5) Use the potential state combinations screened by the ant colony algorithm as the initial population of the genetic algorithm;

[0010] (6) Optimize the population through the selection, crossover, and mutation operations of the genetic algorithm, evaluate each solution in the population based on the fitness function, and gradually iterate and optimize to approach the optimal solution;

[0011] (7) Output the optimization result of the genetic algorithm, finally determine the faulty modules in the system, and represent them in the form of an array. In the array, 0 indicates normal and 1 indicates faulty.

[0012] Optionally, the fitness function is evaluated based on the differences between the module characteristic values, calculates the fault possibility of each state combination, and the higher the fitness value, the higher the reliability of the fault diagnosis result.

[0013] Optionally, the state determination of the ant colony algorithm is based on pheromone concentration and heuristic information, where the heuristic information is determined by the deviation of the characteristic value of the module from its normal value, and the state probability function is used to calculate the state selection probability of each module.

[0014] Optionally, the selection process of the genetic algorithm adopts the roulette wheel selection mechanism, selects individuals according to the proportion of fitness values; the crossover operation adopts the single-point crossover method; the mutation operation increases the population diversity by randomly changing the individual states.

[0015] Optionally, the output module state array can directly reflect the positions of the faulty modules and can be used for subsequent maintenance or the formulation of module replacement strategies. Description of the Drawings

[0016] Figure 1 Schematic diagram of the fault diagnosis and location method combining the genetic algorithm and the ant colony algorithm for the multi-module parallel system of the charging station power supply in the embodiment;

[0017] Figure 2 Schematic diagram of the structure of the multi-module parallel system of the charging station power supply in the embodiment. Detailed Embodiment

[0018] Now refer to the drawings to introduce the exemplary embodiments of the present invention. However, the present invention can be implemented in many different forms and is not limited to the embodiments described herein. These embodiments are provided to disclose the present invention in detail and completely and to fully convey the scope of the present invention to those skilled in the art. The terms in the exemplary embodiments shown in the drawings are not limitations on the present invention. In the drawings, the same units / components use the same reference numerals.

[0019] Unless otherwise specified, the terms (including scientific and technical terms) used herein have the ordinary meaning understood by those skilled in the art. Additionally, it can be understood that the terms defined in the commonly used dictionary should be understood to have a meaning consistent with the context of their related fields and should not be understood as idealized or overly formal meanings.

[0020] (1) Collect the electrical characteristic values of each power module, define the operating state of the module, where 0 indicates normal and 1 indicates abnormal, and calculate the state correlation between the modules;

[0021] (2) Initialize the parameters of the ant colony algorithm, including the number of ants, the initial value of pheromone concentration, the evaporation coefficient, the heuristic factor, etc., and construct a pheromone distribution matrix;

[0022] (3) Simulate path selection through the ant colony algorithm. Based on the pheromone concentration and heuristic information, use the state probability function to determine the state of each module, and quickly screen out several potential module state combinations;

[0023] (4) Update the pheromone distribution, strengthen the potential paths with high fitness, and gradually optimize the search results through the pheromone evaporation and accumulation mechanism;

[0024] (5) Use the potential state combinations screened by the ant colony algorithm as the initial population of the genetic algorithm;

[0025] (6) Optimize the population through the selection, crossover, and mutation operations of the genetic algorithm. Evaluate each solution in the population based on the fitness function, and gradually iterate and optimize to approach the optimal solution;

[0026] (7) Output the optimization result of the genetic algorithm, finally determine the faulty modules in the system, and represent them in the form of an array. In the array, 0 represents normal and 1 represents faulty.

[0027] Optionally, the fitness function is evaluated based on the differences between the module eigenvalue, calculate the fault possibility of each state combination, and the higher the fitness value, the higher the reliability of the fault diagnosis result.

[0028] Optionally, the state determination of the ant colony algorithm is based on the pheromone concentration and heuristic information, where the heuristic information is determined by the deviation of the module eigenvalue from its normal value, and the state probability function is used to calculate the state selection probability of each module.

[0029] Optionally, the selection process of the genetic algorithm adopts the roulette wheel selection mechanism, selects individuals according to the proportion of fitness values; the crossover operation adopts the single-point crossover method; the mutation operation randomly changes the individual state to increase the population diversity.

[0030] Optionally, the output module state array can directly reflect the positions of the faulty modules and can be used for subsequent maintenance or the formulation of module replacement strategies.

[0031] The present invention will be further described below in conjunction with embodiments:

[0032] The method of the present invention, as Figure 1 shown, includes:

[0033] S1. Collect the electrical characteristic quantities F of each power module in the system and calculate the average value Calculate the cost d of each module state according to the characteristic quantityij , namely:

[0034]

[0035]

[0036] S2. Initialize the parameters related to the ant colony algorithm, including the pheromone concentration τ ij , and the heuristic information η ij .

[0037]

[0038] S3. Select the module state combination through the state transition formula of the ant colony, namely:

[0039]

[0040] where α and β are the weights of the pheromone and the heuristic information respectively.

[0041] S4. Update the pheromone concentration of each state combination:.

[0042] τ ij =(1 - ρ)·τ ij +Δτ ij (5)

[0043] where ρ represents the evaporation coefficient, and Δτ ij is the increment.

[0044] S5. Use the high-fitness state combination output by the ant colony algorithm as the initial population of the genetic algorithm.

[0045] S6. Optimize the population through the selection, crossover, and mutation operations of the genetic algorithm, evaluate each solution in the population based on the fitness function, and gradually iterate and optimize to approach the optimal solution.

[0046] (1) Define the fitness function:

[0047]

[0048] (2) Select the population individuals through the roulette wheel selection method, perform single-point crossover operations to generate offspring, and randomly mutate the offspring states with a set mutation probability.

[0049] S7. Judge the iteration termination condition until the maximum iteration number is reached or the difference in population fitness is less than the set threshold. If the condition is met, terminate the algorithm and output the optimal solution; otherwise, return to step S6 to continue optimization.

[0050] S8. Output the optimized optimal module state combination, finally determine the faulty modules in the system, and represent them in the form of an array, where 0 represents normal and 1 represents faulty.

[0051] S = [s 1 , s 2 ,......, s N (7)

[0052] Combining the advantages of the ant colony algorithm and the genetic algorithm, the present invention has the characteristics of high efficiency, accuracy, and strong real-time performance. By quickly screening potential faults through the ant colony algorithm and performing global optimization through the genetic algorithm, the diagnostic efficiency and accuracy are ensured; it has strong adaptability, can cope with the dynamic changes of the system, and is applicable to complex multi-module systems; at the same time, it is simple to implement and easy to expand, reducing the system maintenance cost and the rates of false detection and missed detection, and is an efficient and reliable fault diagnosis solution.

[0053] Those skilled in the art should understand that the embodiments of the present application can be provided as methods, systems, or computer program products. Therefore, the present application can adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code. The solutions in the embodiments of the present application can be implemented in various computer languages, for example, object-oriented programming languages such as Java and interpreted scripting languages such as JavaScript.

[0054] The present application is described with reference to the flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or block in the flowchart and / or block diagram, and the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing devices generate means for implementing the functions specified in Figure 1 one process or multiple processes and / or blocks Figure 1 one block or multiple blocks

[0055] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory generate a manufactured article including instruction means, and the instruction means implements the functions specified in Figure 1 one process or multiple processes and / or blocks Figure 1The functions specified in one or more boxes. These computer program instructions can also be loaded onto a computer or other programmable data processing device, so that a series of operation steps are executed on the computer or other programmable device to generate a computer-implemented process. Thus, the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in one or more processes and / or boxes. Figure 1 One or more processes and / or boxes Figure 1 The steps of the functions specified in one or more boxes.

[0056] Although the preferred embodiments of the present application have been described, those skilled in the art can make additional changes and modifications to these embodiments once they learn the basic creative concepts. Therefore, the appended claims are intended to be construed to include the preferred embodiments as well as all changes and modifications falling within the scope of the present application. Obviously, those skilled in the art can make various changes and variations to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the claims of the present application and their equivalent technologies, the present application also intends to include these modifications and variations.

Claims

1. A fault diagnosis and location method for a multi-module parallel power system, the method comprising: (1) Collect the electrical characteristic values ​​of each power module, define the operating status of the module, where 0 represents normal and 1 represents abnormal, and calculate the status association between modules; (2) Initialize the parameters of the ant colony algorithm, including the number of ants, the initial value of the pheromone concentration, the evaporation coefficient, the heuristic factor, etc., and construct the pheromone distribution matrix; (3) The ant colony algorithm is used to simulate path selection. Based on pheromone concentration and heuristic information, the state probability function is used to determine the state of each module, and several potential module state combinations are quickly screened out; (4) Update pheromone distribution, strengthen potential paths with high fitness, and gradually optimize search results through pheromone evaporation and accumulation mechanisms; (5) The potential state combination screened by the ant colony algorithm is used as the initial population of the genetic algorithm; (6) The population is optimized through the selection, crossover and mutation operations of the genetic algorithm, each solution in the population is evaluated based on the fitness function, and the optimization is gradually iterated to approach the optimal solution; (7) Output the optimization results of the genetic algorithm, and finally determine the faulty module in the system. The faulty module is represented in the form of an array, where 0 represents normal and 1 represents faulty.

2. The method according to claim 1, characterized in that: The fitness function is evaluated based on the difference between the module characteristic values ​​and calculates the fault possibility of each state combination. The higher the fitness value, the higher the reliability of the fault diagnosis result.

3. The method according to claim 1, characterized in that: The state determination of the ant colony algorithm is based on pheromone concentration and heuristic information, wherein the heuristic information is determined by the deviation of the characteristic value of the module from its normal value, and the state probability function is used to calculate the state selection probability of each module.

4. The method according to claim 1, characterized in that The selection process of the genetic algorithm adopts a roulette selection mechanism to select individuals according to the fitness value ratio; the crossover operation adopts a single-point crossover method; and the mutation operation increases population diversity by randomly changing the individual state.

5. The method according to claim 1, characterized in that The output module status array can directly reflect the location of the faulty module and can be used for subsequent maintenance or formulation of module replacement strategies.

Images