Integrated matching design method of electric energy router

Abstract

The invention discloses an integrated matching design method of an electric energy router, and relates to the technical field of electric energy router design. According to position updating mechanisms of the manta ray foraging optimization algorithm and the aurora optimization algorithm, improving individual position updating formulas in combat modes in an exploration stage and a development stage of the snake optimization algorithm to obtain an improved snake optimization algorithm; the structural design parameters of the electric energy router are used as snake individual positions of the improved snake optimization algorithm, the maximum value of the performance index of the electric energy router is used as a target, and the structural design parameters are optimized according to the improved snake optimization algorithm until the improved snake optimization algorithm reaches a preset maximum number of iterations; and determining the optimal structural design parameter as the integrated matching design scheme of the electric energy router when the maximum number of iterations is reached. The method can improve the design effect of the electric energy router.

Description

Technical Field

[0001] The present invention relates to the technical field of power router design, and in particular to an integrated matching design method of a power router. Background Art

[0002] The power router is a power electronic device with power distribution, conversion and control functions, and plays a key role in smart grids and distributed energy systems. For example, as a key device in smart grids, the power router can achieve efficient access and management of distributed energy, optimize the power distribution of the power grid, and improve the flexibility and reliability of the power grid. It helps to realize the intelligent operation of the power grid, reduce transmission losses, and improve the overall performance of the power grid. Therefore, the integrated matching design of the power router is extremely necessary.

[0003] In the prior art, in the optimization design in the power field, intelligent optimization algorithms are usually used to optimize power parameters. However, the intelligent optimization algorithms in the prior art are prone to fall into local optimality and low convergence accuracy when optimizing design parameters, resulting in poor design effects of power routers designed using existing intelligent optimization algorithms. Summary of the invention

[0004] Based on this, it is necessary to provide an integrated matching design method for a power router to address the above technical issues, which can improve the design effect of the power router.

[0005] The present invention adopts the following technical solutions: The present invention provides an integrated matching design method for an electric energy router, comprising: According to the position update mechanism of the manta ray foraging optimization algorithm and the aurora optimization algorithm, the individual position update formula in the exploration phase and the development phase combat mode of the snake optimization algorithm is improved to obtain an improved snake optimization algorithm; the individual position update formula in the exploration phase of the improved snake optimization algorithm is constructed based on the random switching probability to select different position update modes, the optimal position of the snake individual in the current iteration, the moving step length of the snake individual, and the adaptive weight coefficient that changes with the number of iterations; the individual position update formula in the development phase combat mode of the improved snake optimization algorithm is constructed based on the optimal position of the snake individual in the current iteration, the randomly generated communication learning snake individuals within the population, the moving step length of the snake individual, and the adaptive weight coefficient that changes with the number of iterations; The structural design parameters of the electric energy router are used as the individual positions of the snakes in the improved snake optimization algorithm. The goal is to maximize the value of the performance index of the electric energy router. The structural design parameters are optimized according to the improved snake optimization algorithm until the improved snake optimization algorithm reaches a preset maximum number of iterations. The optimal structural design parameters when the maximum number of iterations is reached are determined as the integrated matching design scheme of the electric energy router; the values ​​of the performance indexes of the electric energy router are determined according to the performance of the electric energy router designed according to the structural design parameters at each iteration.

[0006] Optionally, the individual position update formula in the exploration phase of the improved snake optimization algorithm is: ; ; ; ; in, For the t +1 iteration i The location of the male snakes; The randomly selected t The position of the male snake individuals in the iteration; rand is a random number in the range [0,1]; the ability to find food for individual male snakes; For the t +1 iteration i The location of individual female snakes; The randomly selected t The position of the female snakes in the iteration; the ability to find food for individual female snakes; For the t The iteration i The location of the male snakes; For the t The iteration i The location of individual female snakes; For the t The optimal position of individuals in the snake population in the iteration, i.e., the food position; , is a random number in the range [0,1]; r is a random number in [0,1]; is the moving step length of male or female snake individuals; is a constant; and are the upper and lower boundaries of the individual snake positions, respectively; As the number of iterations t Changing adaptive weight coefficients; is a random number in [0,1], is the maximum number of iterations.

[0007] Optionally, the individual position update formula in the combat mode of the development phase of the improved snake optimization algorithm is: ; ; in, The optimal position for individuals in a population of female snakes; rand is a random number in the range [0,1]; for male fighting ability; is a constant; The optimal position for individuals in a population of male snakes; for female fighting ability; For the t In the iteration, the randomly generated communication learning snake individuals are generated within the snake population; To learn the weights, Indicates the amount of food, , is a constant.

[0008] Optionally, the structural design parameters of the power router include port type and port number; the performance index of the power router is the fitness value of the improved snake optimization algorithm; the improved snake optimization algorithm is in the first t The process of obtaining the fitness value at the iteration includes: According to t The individual positions at the iteration are used to construct candidate power routers; The candidate power routers are tested to obtain the values ​​of the performance indicators of the candidate power routers; the values ​​of the performance indicators of the candidate power routers are the values ​​of the improved snake optimization algorithm in the first t The fitness value at the iteration.

[0009] Optionally, the candidate power router is tested to obtain the value of the performance indicator of the candidate power router, including: Testing the candidate power routers to determine the transmission power, transmission efficiency and availability of the candidate power routers; Normalizing the transmission power, transmission efficiency and availability of the candidate power routers respectively to obtain normalized values ​​of the transmission power, transmission efficiency and availability; The normalized values ​​of transmission power, transmission efficiency and availability are weighted to obtain the values ​​of the performance indicators of the candidate power routers.

[0010] Optionally, the calculation formula for the value of the performance indicator of the candidate power router is: ; in, is the value of the performance index of the candidate power router, are the weights of transmission power, transmission efficiency and availability, respectively. are the normalized values ​​of transmission power, transmission efficiency and availability, respectively.

[0011] Optionally, the candidate power router is tested to obtain the value of the performance indicator of the candidate power router, including: Testing the candidate power router to obtain the transmission power or transmission efficiency or availability of the candidate power router; The transmission power or transmission efficiency or availability of the candidate power router is determined as the value of the performance indicator of the candidate power router.

[0012] The present invention provides an integrated matching design device for an electric energy router, comprising: The improved module is used to improve the individual position update formula in the exploration phase and the development phase combat mode of the snake optimization algorithm according to the position update mechanism of the manta ray foraging optimization algorithm and the aurora optimization algorithm, so as to obtain the improved snake optimization algorithm; the individual position update formula in the exploration phase of the improved snake optimization algorithm is constructed according to the random switching probability to select different position update modes, the optimal position of the snake individual in the current iteration, the moving step length of the snake individual, and the adaptive weight coefficient that changes with the number of iterations; the individual position update formula in the combat mode of the development phase of the improved snake optimization algorithm is constructed according to the optimal position of the snake individual in the current iteration, the randomly generated communication learning snake individuals within the population, the moving step length of the snake individual, and the adaptive weight coefficient that changes with the number of iterations; The optimization module is used to use the structural design parameters of the electric energy router as the individual positions of the snakes of the improved snake optimization algorithm, and to maximize the value of the performance index of the electric energy router. The structural design parameters are optimized according to the improved snake optimization algorithm until the improved snake optimization algorithm reaches a preset maximum number of iterations, and the structural design parameters corresponding to the maximum number of iterations are determined as the integrated matching design scheme of the electric energy router; the value of the performance index of the electric energy router is determined according to the performance of the electric energy router designed according to the structural design parameters at each iteration.

[0013] The present invention provides a computer-readable storage medium, wherein the storage medium stores a computer program, and when the computer program is executed by a processor, the integrated matching design method of the power router is implemented.

[0014] The present invention provides a computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the above-mentioned integrated matching design method of the power router when executing the program.

[0015] At least one of the above technical solutions adopted by the present invention can achieve the following beneficial effects: In the present invention, in the exploration phase of the snake optimization algorithm, the position update method of male and female snake individuals only relies on the randomly selected male and female positions to update, and the search direction lacks diversity, and cannot effectively cover the entire problem search space, which may cause the algorithm to fall into the local optimum. In the development phase, the snake optimization algorithm combat mode only emphasizes individual competition, lacks a communication and cooperation mechanism, and is prone to fall into the local optimum. The present invention introduces the position update mechanism of the manta ray foraging optimization algorithm and the aurora optimization algorithm in the exploration phase and the development phase combat mode of the snake optimization algorithm, so that the individual position update formula of the improved snake optimization algorithm in the exploration phase comprehensively considers the random switching probability. The algorithm selects different position update modes, the optimal position of the individual snake in the current iteration, the moving step of the individual snake, and the adaptive weight coefficient that changes with the number of iterations, so as to improve the diversity of the algorithm search direction and avoid the local optimum in each iteration, thereby improving the performance of the algorithm; in addition, the individual position update formula in the combat mode of the improved snake optimization algorithm development phase comprehensively considers the optimal position of the individual snake in the current iteration, the randomly generated communication learning snake individuals within the population, the moving step of the individual snake, and the adaptive weight coefficient that changes with the number of iterations, thereby avoiding over-emphasis on individual competition, causing the algorithm to converge to the local optimal solution too early, thereby improving the performance of the algorithm. Then, the structural design parameters of the power router are optimized according to the improved snake optimization algorithm, so as to obtain the optimal structural design parameters. The design efficiency of the power router designed according to the optimal structural design parameters is relatively high. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings: Figure 1 A schematic diagram of a flow chart of an integrated matching design method for an electric energy router provided by the present invention; Figure 2 A schematic diagram of a process flow of a snake optimization algorithm provided by the present invention; Figure 3 A schematic diagram of a flow chart of an integrated matching design method for an electric energy router provided by the present invention; Figure 4 It is a schematic diagram of the iterative process curve of the improved snake optimization algorithm ISOA and the snake optimization algorithm SOA proposed in the present invention; Figure 5 A schematic diagram of a computer device for implementing an integrated matching design method for an electric energy router provided by the present invention. DETAILED DESCRIPTION

[0017] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with the specific embodiments of the present invention and the corresponding drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0018] Snake optimization algorithm (SOA) is a new meta-heuristic algorithm proposed in 2022. This algorithm is a new intelligent optimization algorithm that imitates the special mating behavior of snakes. For each snake (male / female), if the amount of food is sufficient and the temperature is very low, it will try to get the best partner. This algorithm can be applied to the integrated matching design problem of power routers. However, the snake optimization algorithm still has some defects, which makes the algorithm easy to fall into local optimality and low convergence accuracy. When performing integrated matching design of power routers, it often fails to achieve the ideal design effect.

[0019] Based on this, the present invention provides an integrated matching design method for an electric energy router, which improves the snake optimization algorithm through the position update mechanism of the manta ray foraging optimization algorithm and the aurora optimization algorithm, and then optimizes the structural design parameters of the electric energy router through the improved snake optimization algorithm, thereby obtaining the optimal structural design parameters, so that the designed electric energy router achieves the best design effect.

[0020] The execution subject of the integrated matching design method of the power router provided in the present invention may be a server arranged on a business platform, or a device such as a desktop computer, a notebook computer, etc. that can execute the solution of the present invention.

[0021] The technical solutions provided by various embodiments of the present invention are described in detail below in conjunction with the accompanying drawings.

[0022] Figure 1 The figure is a flow chart of an integrated matching design method of a power router in the present invention, which specifically includes the following steps: S101, according to the position update mechanism of the manta ray foraging optimization algorithm and the aurora optimization algorithm, the individual position update formula in the exploration stage and the development stage combat mode of the snake optimization algorithm is improved to obtain an improved snake optimization algorithm; the individual position update formula in the exploration stage of the improved snake optimization algorithm is constructed based on the random switching probability to select different position update modes, the optimal position of the snake individual in the current iteration, the moving step size of the snake individual, and the adaptive weight coefficient that changes with the number of iterations; the individual position update formula in the combat mode of the development stage of the improved snake optimization algorithm is constructed based on the optimal position of the snake individual in the current iteration, the randomly generated communication learning snake individuals within the population, the moving step size of the snake individual, and the adaptive weight coefficient that changes with the number of iterations.

[0023] The basic principle of the snake optimization algorithm is that the mating behavior between males and females is affected by a number of factors. Snakes usually mate in late spring and early summer when the temperature is lower, but the snake mating process depends not only on temperature but also on the availability of food. If the temperature is low and food is plentiful, competing males will fight each other to attract the attention of females. Females have the right to decide whether to mate. If mating occurs, females begin to lay eggs in a nest or cave and leave as soon as the eggs come out.

[0024] The snake optimization algorithm is inspired by the mating behavior of snakes. If the temperature is low and food is available, the snake's mating behavior occurs; otherwise, the snake will only look for food or eat the existing food. Based on this, the search process of the snake optimization algorithm will be considered in two stages: exploration and exploitation. Exploration describes environmental factors, namely cold places and food. This stage does not exist in which the snake only looks for food in the environment around it.

[0025] The development includes many transition stages to improve the algorithm's search efficiency. If food is available but the temperature is high, the snakes will focus only on eating the available food. Finally, if food is available and the area is cold, the mating process occurs. There are several situations in the mating process, namely fighting mode or mating mode. In fighting mode, each male will fight to get the best female and each female will try to choose the best male. In mating mode, the occurrence of mating behavior for each pair of snakes depends on the amount of food availability. In the search space, if mating behavior occurs, the female has the possibility to lay eggs, which hatch into new snakes.

[0026] The mathematical model of the snake optimization algorithm is described as follows: The snake population is initialized as shown in formula (1).

[0027] (1); in, For the i The location of individual snakes; is a random number in the range [0,1]; and are the upper and lower boundaries of the individual snake positions respectively.

[0028] Divide the snake population into two groups: male and female: Assume that the number of males is 50% and the number of females is 50%. The snake population is divided into two groups: male and female. Use the following 2 formulas to divide the population: (2); (3); in, N The size of the snake population; is the number of male snakes; is the number of female snakes.

[0029] Evaluate each group and define the temperature and food quantity : Find the best individual in each group and get the best male And the best female and food location .

[0030] Temperature can be defined by the following formula: (4); in, t is the current iteration number; T is the maximum number of iterations.

[0031] Food quantity It can be defined by the following formula: (5); in, is a constant, which can be taken as 0.5.

[0032] Exploration phase (no food): If (Threshold ), the snakes search for food by choosing any random location and update their positions. To simulate the exploration phase, the male position update formula is as follows: (6); in, For the t +1 iteration i Individual positions of male snakes; The randomly selected t The position of the male snake individuals in the iteration; rand is a random number in the range [0,1]; is a constant, taking 0.05; is the ability of male snakes to find food, and the calculation formula is shown in formula (7): (7); in, is the position of a randomly selected male snake The fitness value of The position of the male snake The fitness value of .

[0033] The female position update formula is as follows: (8); in, For the t +1 iteration i The location of individual female snakes; The randomly selected t The position of the female snakes in the iteration; rand is a random number in the range [0,1]; is the ability of female snakes to find food, and the calculation formula is shown in formula (9).

[0034] (9); in, The ability to find food for randomly selected female snake individuals The fitness value of The position of the female snake The fitness value of .

[0035] Development stage (with food): Under the condition of , then the temperature is hot. The snake will only look for food, and the position update formula is as follows: (10); in, For the t +1 iteration i The location of individual snakes (male or female), For the t The first iteration i The location of individual snakes (male or female); For the t The optimal position of individuals in the snake population in the iteration, i.e., the food position; rand is a random number in the range [0,1]; is a constant, take 2.

[0036] exist Under the condition of , then the temperature is cold and the snake will be in fighting mode or mating mode.

[0037] (1) Battle Mode (11); in, The optimal position for individuals in a population of female snakes; rand is a random number in the range [0,1]; For male fighting ability.

[0038] (12); in, The optimal position for individuals in a population of male snakes; rand is a random number in the range [0,1]; For female fighting ability.

[0039] and It can be calculated by the following formula: (13); (14); in, is the optimal position for individuals in the female snake population The fitness value of is the optimal position of an individual in the male snake population The fitness value of For snake individuals The fitness value of a male or female snake.

[0040] (2) Mating pattern (15); (16); in, rand is a random number in the range [0,1]; and are the mating capacities of males and females, respectively, and can be calculated by the following formula: (17); (18); in, The position of the male snake The fitness value of The position of the female snake The fitness value of .

[0041] After mating is completed, according to the random number Mating is successful if , then the mating is successful, and the position of the male snake individual with the worst fitness value and the position of the female snake individual are selected and updated according to formula (19) and formula (20).

[0042] (19); (20); in, The male snake with the worst fitness value in the male snake group is t +1 position at iteration; The female snake with the worst fitness value in the female snake group is ranked t +1 position at iteration; rand is a random number in the range [0,1].

[0043] like Figure 2 As shown, Figure 2 Flowchart of the snake optimization algorithm, which includes the following steps: S201, setting parameters: population size N, maximum number of iterations T, upper and lower boundaries of individual snake positions.

[0044] Among them, the upper and lower boundaries of the individual snake positions represent the upper and lower boundaries of the problem optimization.

[0045] S202, initializing the position of the snake population according to formula (1).

[0046] S203, dividing the snake population into two sub-populations (males and females) of equal size according to formulas (2) and (3).

[0047] S204, calculating the fitness value of each individual snake in the male population and the female population, and finding the best position of the individual.

[0048] Among them, the individual best position includes the individual best position in the snake population , the optimal position of individuals in a population of female snakes and optimal position of individuals in a population of male snakes .

[0049] S205, define the temperature according to formula (4) Temp , calculate the amount of food according to formula (5) Q。

[0050] S206, judgment Q Is it less than 0.25?

[0051] Among them, if Q If it is less than 0.25, execute step S207; otherwise, execute step S208.

[0052] S207, exploration phase: Calculate the positions of male and female individuals according to formulas (6) and (8), respectively.

[0053] S208, judgment Temp Is it greater than 0.6?

[0054] Among them, if Temp If it is greater than 0.6, execute step S209; otherwise, execute step S210.

[0055] S209, calculating the position of the individual according to formula (10).

[0056] S210, random number rand Is it greater than 0.6?

[0057] Among them, if the random number rand If it is greater than 0.6, execute step S211; otherwise, execute step S212.

[0058] S211, development phase: Entering combat mode, the positions of male and female individuals are calculated according to formulas (11) and (12) respectively.

[0059] S212, development phase: Entering the mating mode, the positions of male and female individuals are calculated according to formulas (15) and (16), respectively.

[0060] S213, replace the positions of the worst male and worst female individuals according to formulas (19) and (20), respectively.

[0061] S214, comparing the fitness values ​​of the snake individuals before and after the individual positions are updated, and retaining the position of the individual with the superior fitness value.

[0062] S215, determining whether the stop condition is met.

[0063] The stopping condition may be whether the number of iterations reaches a preset maximum number of iterations.

[0064] S216, output the position of the optimal snake individual and its fitness value.

[0065] However, the snake optimization algorithm has the following two key deficiencies: (1) Exploration stage: The position update method of male (female) snakes in the snake optimization algorithm only relies on the position of randomly selected males (females). The search direction lacks diversity and cannot effectively cover the entire problem search space, which may cause the algorithm to fall into a local optimum.

[0066] (2) Development stage: In the combat mode of the snake optimization algorithm, only individual competition is emphasized, and there is a lack of communication and cooperation mechanism, which makes it easy to fall into local optimality.

[0067] The above two key deficiencies result in failure to achieve the best design effect when using the snake optimization algorithm for integrated matching design of power routers.

[0068] Therefore, according to the position updating mechanism of manta ray foraging optimization algorithm and aurora optimization algorithm, the individual position updating formula in the combat mode of exploration phase and development phase of snake optimization algorithm was improved to obtain the improved snake optimization algorithm.

[0069] Specifically, improvements in the exploration phase: In the SOA exploration phase, the position update method of male (female) snakes in the snake optimization algorithm only relies on the position of the selected male (female) to update, and the search direction lacks diversity, which cannot effectively cover the entire problem search space, and may cause the algorithm to fall into a local optimum. In order to more effectively improve the performance of the algorithm, the position update mechanism of the manta ray foraging optimization algorithm and the aurora optimization algorithm is introduced to improve the position update method of individual snakes. The individual snake position is updated by comprehensively considering factors such as selecting different position update modes according to the switching probability, the optimal position of the individual snake in this iteration, the individual snake's moving step length, and the adaptive weight coefficient that changes with the number of iterations, so as to improve the diversity of the algorithm's search direction, avoid the local optimum in each iteration, and thus improve the performance of the algorithm.

[0070] In the exploration phase of SOA, the manta ray foraging optimization algorithm and the aurora optimization algorithm are introduced, and the individual position update formula of the improved snake optimization algorithm in the exploration phase is: (twenty one); (twenty two); (twenty three); (twenty four); in, For the t +1 iteration i The location of the male snakes; The randomly selected t The position of the male snake individuals in the iteration; rand is a random number in the range [0,1]; the ability to find food for individual male snakes; For the t +1 iteration i The location of individual female snakes; The randomly selected t The position of the female snakes in the iteration; the ability to find food for individual female snakes; For the t The iteration i The location of the male snakes; For the t The iteration i The location of individual female snakes; For the t The optimal position of individuals in the snake population in the iteration, i.e., the food position; , is a random number in the range [0,1]; r is a random number in [0,1]; is the moving step length of male or female snake individuals; is a constant; and are the upper and lower boundaries of the individual snake positions, respectively; As the number of iterations t Changing adaptive weight coefficients; is a random number in [0,1], is the maximum number of iterations.

[0071] Improvement of combat mode in the development stage: In the SOA development stage, the combat mode of the snake optimization algorithm only emphasizes individual competition, lacks a communication and cooperation mechanism, and is prone to fall into local optimality. In order to more effectively improve the performance of the algorithm, the position update mechanism of the manta ray foraging optimization algorithm and the aurora optimization algorithm is introduced to improve the individual snake position update method. The individual snake position is updated by comprehensively considering the optimal position of the individual snake in this iteration, the randomly generated communication learning snakes within the population, the individual snake's moving step length, and the adaptive weight coefficient that changes with the number of iterations. It avoids over-emphasizing individual competition, which causes the algorithm to converge to the local optimal solution too early, thereby improving the performance of the algorithm.

[0072] In the development phase combat mode of SOA, the manta ray foraging optimization algorithm and the aurora optimization algorithm are introduced. The individual position update formula in the development phase combat mode of the improved snake optimization algorithm is: (25); (26); in, The optimal position for individuals in a population of female snakes; rand is a random number in the range [0,1]; for male fighting ability; is a constant; The optimal position for individuals in a population of male snakes; for female fighting ability; For the t In the iteration, the randomly generated communication learning snake individuals are generated within the snake population; To learn the weights, , Indicates the amount of food, , is a constant.

[0073] S102, taking the structural design parameters of the electric energy router as the individual positions of the snakes of the improved snake optimization algorithm, taking the maximum value of the performance index of the electric energy router as the goal, optimizing the structural design parameters according to the improved snake optimization algorithm until the improved snake optimization algorithm reaches a preset maximum number of iterations, and determining the optimal structural design parameters when the maximum number of iterations is reached as the integrated matching design scheme of the electric energy router; the value of the performance index of the electric energy router is determined according to the performance of the electric energy router designed according to the structural design parameters at each iteration.

[0074] Optionally, the structural design parameters of the power router are the individual positions of the improved snake optimization algorithm; the structural design parameters of the power router include the port type and the number of ports; the performance index of the power router is the fitness value of the improved snake optimization algorithm; the improved snake optimization algorithm is in the first t The process of obtaining the fitness value at the iteration includes: t The individual positions at the first iteration are used to construct candidate power routers; the candidate power routers are tested to obtain the values ​​of the performance indicators of the candidate power routers; the values ​​of the performance indicators of the candidate power routers are the values ​​of the improved snake optimization algorithm at the first iteration. t The fitness value at the iteration.

[0075] Optionally, the candidate power router is tested to obtain the values ​​of the performance indicators of the candidate power router, including: testing the candidate power router to determine the transmission power, transmission efficiency and availability of the candidate power router; normalizing the transmission power, transmission efficiency and availability of the candidate power router respectively to obtain the normalized values ​​of the transmission power, transmission efficiency and availability; weighting the normalized values ​​of the transmission power, transmission efficiency and availability to obtain the values ​​of the performance indicators of the candidate power router.

[0076] Among them, the transmission power of the power router refers to the amount of power that the power router can transmit per unit time; the transmission efficiency of the power router refers to the ratio of its output power to its input power, usually expressed as a percentage. It is an important indicator to measure the ability of the power router to effectively convert input power into output power, reflecting the energy utilization efficiency of the device during the power transmission process; availability refers to the ability of the power router to complete the specified function under specified conditions and within a specified time.

[0077] The candidate power router is tested to determine the transmission power, transmission efficiency and availability of the candidate power router, including: a circuit model can be established using circuit simulation software according to the topological structure and working principle of the candidate power router, and the circuit model is run through the circuit simulation software to simulate the transmission power characteristics of the candidate power router, thereby determining the transmission power of the candidate power router, and at the same time calculating the input power of the input end of the candidate power router and the output power of the output end, and determining the ratio of the output power of the output end to the input power of the input end as the transmission efficiency of the candidate power router.

[0078] A large number of random simulation experiments are conducted on candidate power routers using the Monte Carlo method. In each simulation experiment, fault events are randomly generated according to the set fault probability and fault duration, and the proportion of time that the candidate power router is in normal working state in the simulation is calculated. Through multiple simulation experiments, the availability of the candidate power router is calculated, that is, the ratio of normal working time to total time.

[0079] The transmission power, transmission efficiency and availability of the candidate power routers are normalized and mapped to the interval [0, 1] to eliminate the influence of different dimensions.

[0080] Normalized value of transmission power ,in, and are the minimum and maximum transmission power in the design requirements, is the transmission power of the candidate power router.

[0081] Normalized value of transmission efficiency ,in, and are the minimum and maximum transmission efficiency in the design requirements, is the transmission efficiency of the candidate power router.

[0082] Normalized value of availability ,in, and are the minimum and maximum values ​​of availability in the design requirements, is the availability of the candidate power router.

[0083] Optionally, the calculation formula for the value of the performance indicator of the candidate power router is: (27); in, is the value of the performance index of the candidate power router, are the weights of transmission power, transmission efficiency and availability, respectively. are the normalized values ​​of transmission power, transmission efficiency and availability, respectively. The value of can be determined according to the specific application scenario and design focus. For example, if the transmission efficiency is high, it can be appropriately increased. The value of .

[0084] The performance indicator of the candidate power router may also be transmission power or transmission efficiency or availability. Therefore, testing the candidate power router to obtain the value of the performance indicator of the candidate power router includes: testing the candidate power router to obtain the transmission power or transmission efficiency or availability of the candidate power router; determining the transmission power or transmission efficiency or availability of the candidate power router as the value of the performance indicator of the candidate power router.

[0085] It should be noted that the values ​​of the performance indicators of the candidate power routers can also be determined after considering any two indicators of transmission power, transmission efficiency, and availability. For example, taking transmission power and transmission efficiency as an example, the candidate power routers are tested to determine the transmission power and transmission efficiency of the candidate power routers; the transmission power and transmission efficiency of the candidate power routers are normalized respectively to obtain the normalized values ​​of the transmission power and transmission efficiency; the normalized values ​​of the transmission power and transmission efficiency are weighted to obtain the values ​​of the performance indicators of the candidate power routers.

[0086] The values ​​of the performance indicators of the candidate power routers may be adjusted by adding other performance indicators or constraints according to actual conditions, and the embodiments of the present application are not limited thereto.

[0087] like Figure 3 As shown, Figure 3 A flowchart of an integrated matching design method for an electric energy router provided by the present invention specifically includes the following steps: S301, initializing the structural design parameters of the power router according to the basic elements and performance parameters of the power router to be designed.

[0088] Among them, performance parameters include port type, port quantity, etc.

[0089] S302, establishing a fitness function of an integrated matching design scheme of an electric energy router based on an improved snake optimization algorithm.

[0090] Among them, the fitness function can be shown as formula (27).

[0091] S303, perform parameter setting.

[0092] The parameter settings mainly include: the size of the population (i.e. the number of individual snakes) N ; The maximum number of iterations (i.e. the condition for stopping the iteration) T ; Problem optimization lower bound ; Problem optimization upper bound .

[0093] S304, initializing the position of the snake population according to formula (1).

[0094] S305, dividing the snake population into two sub-populations (males and females) of equal size according to formulas (2) and (3).

[0095] S306, calculating the fitness value of each individual snake in the male population and the female population according to the fitness function, and finding the best position of the individual.

[0096] Among them, the individual best position includes the individual best position in the snake population , the optimal position of individuals in a population of female snakes and optimal position of individuals in a population of male snakes .

[0097] S307, define the temperature according to formula (4) Temp , calculate the food quantity Q according to formula (5).

[0098] S308, judgment Q Is it less than 0.25?

[0099] Among them, if Q If it is less than 0.25, execute step S309; ​​otherwise, execute step S310.

[0100] S309, exploration phase: Calculate the positions of male and female individuals according to formulas (21) and (22), respectively.

[0101] S310, judgment Temp Is it greater than 0.6?

[0102] If Temp>0.6, execute step S311, otherwise, execute step S312.

[0103] S311, calculate the position of the individual according to formula (10).

[0104] S312, determine the random number rand Is it greater than 0.6?

[0105] Among them, if the random number rand If it is greater than 0.6, execute step S313; otherwise, execute step S314.

[0106] S313, development phase: Entering combat mode, the positions of male and female individuals are calculated according to formulas (25) and (26), respectively.

[0107] S314, development phase: Entering the mating mode, the positions of male and female individuals are calculated according to formulas (15) and (16), respectively.

[0108] S315, replace the positions of the worst male and worst female individuals according to formulas (19) and (20), respectively.

[0109] S316, comparing the fitness values ​​of the snake individuals before and after the individual positions are updated, and retaining the position of the individual with the superior fitness value.

[0110] S317, judging whether the stop condition is met.

[0111] S318, outputting the optimal individual snake position, that is, the integrated matching design scheme of the power router.

[0112] The integrated matching design scheme is the optimal power router integrated matching design scheme.

[0113] In one embodiment, MATLAB is used as a simulation platform to select the improved snake optimization algorithm ISOA proposed in the present invention and the snake optimization algorithm SOA for comparison. To ensure the fairness of the experiment, the population size of all algorithms is set to 30 and the maximum number of iterations is set to 300.

[0114] Figure 4 is the iterative process curve, from Figure 4 It can be intuitively found that the convergence speed of ISOA method is faster than that of SOA algorithm, and the convergence accuracy of ISOA algorithm is better than that of SOA algorithm. The simulation results show that ISOA algorithm has stronger search ability and obtains a better integrated matching design scheme of power router, which verifies the effectiveness of the algorithm.

[0115] When applying the integrated matching design method of the power router provided by the present invention, it is not necessary to Figure 1 The steps are executed in the order shown. The specific execution order of the steps can be determined according to needs, and the present invention does not limit this.

[0116] The above is an integrated matching design method of a power router provided by one or more embodiments of the present invention. Based on the same idea, the present invention also provides a corresponding integrated matching design device of a power router, which includes: The improved module is used to improve the individual position update formula in the exploration phase and the development phase combat mode of the snake optimization algorithm according to the position update mechanism of the manta ray foraging optimization algorithm and the aurora optimization algorithm, so as to obtain the improved snake optimization algorithm; the individual position update formula in the exploration phase of the improved snake optimization algorithm is constructed according to the random switching probability to select different position update modes, the optimal position of the snake individual in the current iteration, the moving step length of the snake individual, and the adaptive weight coefficient that changes with the number of iterations; the individual position update formula in the combat mode of the development phase of the improved snake optimization algorithm is constructed according to the optimal position of the snake individual in the current iteration, the randomly generated communication learning snake individuals within the population, the moving step length of the snake individual, and the adaptive weight coefficient that changes with the number of iterations; The optimization module is used to use the structural design parameters of the electric energy router as the individual positions of the snakes of the improved snake optimization algorithm, and to maximize the value of the performance index of the electric energy router. The structural design parameters are optimized according to the improved snake optimization algorithm until the improved snake optimization algorithm reaches a preset maximum number of iterations, and the optimal structural design parameters when the maximum number of iterations is reached are determined as the integrated matching design scheme of the electric energy router; the value of the performance index of the electric energy router is determined according to the performance of the electric energy router designed according to the structural design parameters at each iteration.

[0117] For the specific definition of the integrated matching design device of the power router, please refer to the definition of the integrated matching design method of the power router above, which will not be repeated here. Each module in the above-mentioned integrated matching design device of the power router can be implemented in whole or in part by software, hardware and a combination thereof. The above-mentioned 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 of the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.

[0118] The present invention also provides a computer-readable storage medium, which stores a computer program, which can be used to execute the above Figure 1 An integrated matching design method for an electric energy router is provided.

[0119] The present invention also provides Figure 5 The structural diagram of the computer device shown in FIG. Figure 5 As shown in the figure, at the hardware level, the computer device includes a processor, an internal bus, a network interface, a memory, and a non-volatile memory, and may also include other hardware required for the business. The processor reads the corresponding computer program from the non-volatile memory into the memory and then runs it to achieve the above Figure 1 An integrated matching design method for an electric energy router is provided.

[0120] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided by the present invention can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory or optical memory, etc. Volatile memory can include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0121] The technical features of the above embodiments may be arbitrarily combined. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of the present invention.

Claims

1. An integrated matching design method for an electric energy router, characterized in that: include: According to the position update mechanism of the manta ray foraging optimization algorithm and the aurora optimization algorithm, the individual position update formula in the exploration phase and the development phase combat mode of the snake optimization algorithm is improved to obtain an improved snake optimization algorithm; the individual position update formula in the exploration phase of the improved snake optimization algorithm is constructed according to the random switching probability to select different position update modes, the optimal position of the snake individual in the current iteration, the moving step length of the snake individual, and the adaptive weight coefficient that changes with the number of iterations; the individual position update formula in the development phase combat mode of the improved snake optimization algorithm is constructed according to the optimal position of the snake individual in the current iteration, the randomly generated communication learning snake individuals within the population, the moving step length of the snake individual, and the adaptive weight coefficient that changes with the number of iterations; The structural design parameters of the power router are used as the individual positions of the snakes in the improved snake optimization algorithm. The maximum value of the performance index of the power router is taken as the goal. The structural design parameters are optimized according to the improved snake optimization algorithm until the improved snake optimization algorithm reaches the preset maximum number of iterations. The optimal structural design parameters when the maximum number of iterations is reached are determined as the integrated matching design scheme of the power router. The value of the performance index of the power router is determined according to the performance of the power router designed according to the structural design parameters in each iteration.

2. The method according to claim 1, characterized in that: The individual position update formula of the improved snake optimization algorithm in the exploration phase is: ； ； ； ； in, For the t +1 iteration i The location of the male snakes; The randomly selected t The position of the male snake individuals in the iteration; rand is a random number in the range [0,1]; the ability to find food for individual male snakes; For the t +1 iteration i The location of individual female snakes; The randomly selected t The position of the female snakes in the iteration; the ability to find food for individual female snakes; For the t The iteration i The location of the male snakes; For the t The iteration i The location of individual female snakes; For the t The optimal position of individuals in the snake population in the iteration, i.e., the food position; , is a random number in the range [0,1]; r is a random number in [0,1]; is the moving step length of male or female snake individuals; is a constant; and are the upper and lower boundaries of the individual snake positions, respectively; As the number of iterations t Changing adaptive weight coefficients; is a random number in [0,1], is the maximum number of iterations.

3. The method according to claim 2, characterized in that The individual position update formula in the combat mode of the improved snake optimization algorithm development phase is: ； ； in, The optimal position for individuals in a population of female snakes; rand is a random number in the range [0,1]; for male fighting ability; is a constant; The optimal position for individuals in a population of male snakes; for female fighting ability; For the t In the iteration, the randomly generated communication learning snake individuals within the snake population; To learn the weights, Indicates the amount of food, , is a constant.

4. The method according to claim 1, characterized in that The structural design parameters of the power router include port type and port number; the performance index of the power router is the fitness value of the improved snake optimization algorithm; The improved snake optimization algorithm is t The process of obtaining the fitness value at the iteration includes: According to t The individual positions at the iteration are used to construct candidate power routers; The candidate power router is tested to obtain the value of the performance index of the candidate power router; the value of the performance index of the candidate power router is the value of the improved snake optimization algorithm in the first t The fitness value at the iteration.

5. The method according to claim 4, characterized in that The testing of the candidate power router to obtain the value of the performance indicator of the candidate power router includes: Testing the candidate power router to determine the transmission power, transmission efficiency and availability of the candidate power router; Normalizing the transmission power, transmission efficiency and availability of the candidate power router, respectively, to obtain normalized values ​​of the transmission power, transmission efficiency and availability; The normalized values ​​of transmission power, transmission efficiency and availability are weighted to obtain the values ​​of the performance indicators of the candidate power routers.

6. The method according to claim 5, characterized in that The calculation formula for the performance index of the candidate power router is: ； in, is the value of the performance index of the candidate power router, are the weights of transmission power, transmission efficiency and availability, respectively. are the normalized values ​​of transmission power, transmission efficiency and availability, respectively.

7. The method according to claim 4, characterized in that The testing of the candidate power router to obtain the value of the performance indicator of the candidate power router includes: Testing the candidate power router to obtain the transmission power or transmission efficiency or availability of the candidate power router; The transmission power or transmission efficiency or availability of the candidate power router is determined as the value of the performance indicator of the candidate power router.

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