An integrated matching design method for power router

Abstract

The present invention discloses an integrated matching design method for an electric energy router, relating to the technical field of electric energy router design. Based on the position update mechanisms of the manta ray foraging optimization algorithm and the aurora optimization algorithm, the individual position update formulas in the exploration and development phases of the snake optimization algorithm are improved to obtain an improved snake optimization algorithm. The structural design parameters of the electric energy router are used as the individual snake positions of the improved snake optimization algorithm. With the goal of maximizing the performance indicators 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 at the maximum number of iterations are then determined as the integrated matching design solution for the electric energy router. This method can improve the design performance of electric energy routers.

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 for a power router. Background Art

[0002] Energy routers are power electronic devices that distribute, convert, and control electrical energy. They play a key role in smart grids and distributed energy systems. For example, as key components in smart grids, energy routers enable efficient access and management of distributed energy resources, optimize grid energy distribution, and improve grid flexibility and reliability. They contribute to intelligent grid operation, reduce transmission losses, and enhance overall grid performance. Therefore, the integrated and matched design of energy routers is essential.

[0003] In the prior art, intelligent optimization algorithms are usually used to optimize power parameters in optimization design in the power field. However, the intelligent optimization algorithms in the prior art are prone to falling 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 problems, which can improve the design effect of the power router.

[0005] The present invention adopts the following technical solutions:

[0006] The present invention provides an integrated matching design method for an electric energy router, comprising:

[0007] Based on the position update mechanism of the manta ray foraging optimization algorithm and the aurora optimization algorithm, the individual position update formula in the exploration and 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 of selecting different position update modes, the optimal position of the snake individual in the current iteration, the movement 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 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 movement step size of the snake individual, and the adaptive weight coefficient that changes with the number of iterations.

[0008] The structural design parameters of the electric energy router are used as the individual snake positions of 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 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.

[0009] Alternatively, the individual position update formula in the exploration phase of the improved snake optimization algorithm is:

[0010] ;

[0011] ;

[0012] ;

[0013] ;

[0014] in, For the t +1 iteration i The location of individual male snakes; The randomly selected t The position of the male snake 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 snake in the iteration; the ability to find food for individual female snakes; For the t The first iteration i The location of individual male snakes; For the t The first iteration i The location of individual female snakes; For the t The optimal position of individuals in the snake population in the iteration is 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.

[0015] Optionally, the individual position update formula in the combat mode of the development phase of the improved snake optimization algorithm is:

[0016] ;

[0017] ;

[0018] 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 At the iteration, randomly generated communication learning snake individuals are generated within the snake population; To learn the weights, Indicates the amount of food, , is a constant.

[0019] 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:

[0020] According to t The individual positions at the iteration are used to construct candidate power routers;

[0021] 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.

[0022] Optionally, the candidate power router is tested to obtain values of performance indicators of the candidate power router, including:

[0023] Testing the candidate power routers to determine the transmission power, transmission efficiency and availability of the candidate power routers;

[0024] Normalizing the transmission power, transmission efficiency, and availability of the candidate power routers to obtain normalized values of transmission power, transmission efficiency, and availability;

[0025] 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.

[0026] Optionally, the calculation formula for the performance index of the candidate power router is:

[0027] ;

[0028] 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.

[0029] Optionally, the candidate power router is tested to obtain values of performance indicators of the candidate power router, including:

[0030] Testing the candidate power router to obtain the transmission power, transmission efficiency or availability of the candidate power router;

[0031] The transmission power, transmission efficiency or availability of the candidate power router is determined as the value of the performance indicator of the candidate power router.

[0032] The present invention provides an integrated matching design device for an electric energy router, comprising:

[0033] An improved module is used to improve the individual position update formulas in the exploration and development phases of the snake optimization algorithm based on the position update mechanisms of the manta ray foraging optimization algorithm and the aurora optimization algorithm, thereby obtaining an improved snake optimization algorithm. The improved snake optimization algorithm's individual position update formula in the exploration phase is constructed based on the random switching probability of selecting different position update modes, the optimal position of the individual snake in the current iteration, the individual snake's movement step, and an adaptive weight coefficient that varies with the number of iterations. The improved snake optimization algorithm's individual position update formula in the development phase of the combat mode is constructed based on the optimal position of the individual snake in the current iteration, randomly generated communication learning snakes within the population, the individual snake's movement step, and an adaptive weight coefficient that varies with the number of iterations.

[0034] The optimization module is used to use the structural design parameters of the electric energy router as the individual snake positions of the improved snake optimization algorithm, and to optimize the structural design parameters according to the improved snake optimization algorithm with the goal of maximizing the value of the performance index of the electric energy router until the improved snake optimization algorithm reaches a preset maximum number of iterations. 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 based on the performance of the electric energy router designed according to the structural design parameters at each iteration.

[0035] 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.

[0036] 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. When the processor executes the program, the integrated matching design method for the power router is implemented.

[0037] At least one of the above technical solutions adopted by the present invention can achieve the following beneficial effects:

[0038] 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 positions of randomly selected males and females 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 a 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 a 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 individual snakes in the current iteration, the individual snake movement step size, and an adaptive weight coefficient that varies with the number of iterations. This increases the diversity of the algorithm's search directions, avoids local optima within each iteration, and thus improves algorithm performance. Furthermore, the individual position update formula in the combat mode during the development phase of the improved snake optimization algorithm comprehensively considers the optimal position of individual snakes in the current iteration, the randomly generated communication learning snakes within the population, the individual snake movement step size, and the adaptive weight coefficient that varies with the number of iterations. This avoids overemphasizing individual competition and causing the algorithm to converge to a local optimum prematurely, thereby improving algorithm performance. The improved snake optimization algorithm is then used to optimize the structural design parameters of the power router, resulting in the optimal structural design parameters. The power router designed based on these optimal structural design parameters has a higher design efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] 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:

[0040] Figure 1 A schematic flow chart of an integrated matching design method for a power router provided by the present invention;

[0041] Figure 2 A schematic flow chart of a snake optimization algorithm provided by the present invention;

[0042] Figure 3 A schematic flow chart of an integrated matching design method for a power router provided by the present invention;

[0043] Figure 4 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;

[0044] 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

[0045] To make the objectives, technical solutions, and advantages of the present invention more clear, the technical solutions of the present invention will be clearly and completely described below in conjunction with specific embodiments of the present invention and corresponding drawings. Obviously, the embodiments described are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0046] The Snake Optimization Algorithm (SOA) is a new metaheuristic algorithm proposed in 2022. It is a novel intelligent optimization algorithm that mimics the unique mating behavior of snakes. Each snake (male or female) strives to find the best mate, given sufficient food and low temperatures. This algorithm can be applied to the design of integrated matching for power routers. However, the SOA still has some flaws, making it prone to local optimality and low convergence accuracy. This often fails to achieve ideal design results when designing integrated matching for power routers.

[0047] Based on this, the present invention provides an integrated matching design method for an electric energy router. By using the position update mechanism of the manta ray foraging optimization algorithm and the aurora optimization algorithm, the snake optimization algorithm is improved. Then, the structural design parameters of the electric energy router are optimized using the improved snake optimization algorithm, thereby obtaining the optimal structural design parameters, so that the designed electric energy router achieves the best design effect.

[0048] The execution subject of the integrated matching design method of the power router provided in the present invention can be a server set up on the business platform, or a device such as a desktop computer, a laptop computer, etc. that can execute the solution of the present invention.

[0049] The technical solutions provided by various embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0050] Figure 1 This is a flow chart of an integrated matching design method for a power router in the present invention, which specifically includes the following steps:

[0051] S101, based on 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 development stage 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 size of the snake individual, and the adaptive weight coefficient that changes with the number of iterations.

[0052] The snake optimization algorithm's fundamental principle is that mating behavior between males and females is influenced by several factors. Snakes typically mate during the cooler temperatures of late spring and early summer, but the mating process depends not only on temperature but also on food availability. If temperatures are low and food is plentiful, competing males will fight for the attention of females. Females have the final say on whether to mate. If mating occurs, they begin laying eggs in a nest or burrow and leave once the eggs are laid.

[0053] The Snake Optimization Algorithm is inspired by the mating behavior of snakes. If the temperature is low and food is available, snakes will mate; otherwise, they will simply search for food or consume existing food. Based on this, the search process for the Snake Optimization Algorithm is divided into two phases: exploration and exploitation. The exploration phase describes environmental factors, namely, cold locations and food. In this phase, the snake does not simply search for food in its surroundings.

[0054] Development includes numerous transition phases to improve the algorithm's search efficiency. If food is available but the temperature is high, the snakes will focus solely on eating the available food. Finally, if food is available and the area is cold, mating occurs. There are several scenarios within the mating process: fighting mode or mating mode. In fighting mode, each male fights for the best female, while each female strives to select the best male. In mating mode, the occurrence of mating behavior within each pair depends on the amount of food available. If mating occurs within the search space, the female has the potential to lay eggs, which hatch into new snakes.

[0055] The mathematical model of the snake optimization algorithm is described as follows:

[0056] The snake population is initialized as shown in formula (1).

[0057] (1);

[0058] 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.

[0059] Divide the snake population into two groups: males and females. Assume that the number of males is 50% and the number of females is 50%. The snake population is divided into two groups: males and females. Use the following two formulas to divide the population:

[0060] (2);

[0061] (3);

[0062] in, N The size of the snake population; is the number of male snakes; is the number of female snakes.

[0063] 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 females and food location .

[0064] Temperature can be defined by the following formula:

[0065] (4);

[0066] in, t is the current iteration number;T is the maximum number of iterations.

[0067] Food quantity It can be defined by the following formula:

[0068] (5);

[0069] in, is a constant, which can be set to 0.5.

[0070] Exploration phase (no food): If (Threshold ), the snakes search for food by choosing any random location and updating their positions. To simulate the exploration phase, the male position update formula is as follows:

[0071] (6);

[0072] in, For the t +1 iteration i Individual positions of male snakes; The randomly selected t The position of the male snake in the iteration; rand is a random number in the range [0,1]; is a constant, take 0.05; is the ability of male snakes to find food, and the calculation formula is shown in formula (7).

[0073] (7);

[0074] in, is the position of a randomly selected male snake The fitness value of The position of the male snake The fitness value of .

[0075] The female position update formula is as follows:

[0076] (8);

[0077] in, For the t +1 iteration i The location of individual female snakes; The randomly selected t The position of the female snake 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).

[0078] (9);

[0079] 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 .

[0080] Development stage (with food): Under the conditions, if the temperature , then the temperature is hot. The snake will only look for food, and the position update formula is as follows:

[0081] (10);

[0082] 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 is the food position; rand is a random number in the range [0,1]; is a constant, take 2.

[0083] exist Under the conditions, if the temperature , then the temperature is cold and the snake will be in fighting mode or mating mode.

[0084] (1) Battle Mode

[0085] (11);

[0086] 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.

[0087] (12);

[0088] 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.

[0089] and It can be calculated by the following formula:

[0090] (13);

[0091] (14);

[0092] in, is the optimal position of an individual 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 snake (male or female).

[0093] (2) Mating pattern

[0094] (15);

[0095] (16);

[0096] in, rand is a random number in the range [0,1]; and The mating abilities of males and females can be calculated using the following formula:

[0097] (17);

[0098] (18);

[0099] in, The position of the male snake The fitness value of The position of the female snake The fitness value of .

[0100] 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 updated according to formula (19) and formula (20).

[0101] (19);

[0102] (20);

[0103] in, The male snake with the worst fitness value in the male snake group is ranked 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].

[0104] like Figure 2 As shown, Figure 2 Flowchart of the snake optimization algorithm, which includes the following steps:

[0105] S201, set parameters: population size N, maximum number of iterations T, upper and lower boundaries of individual snake positions.

[0106] Among them, the upper and lower boundaries of the individual snake positions represent the upper and lower boundaries of the problem optimization.

[0107] S202, initialize the position of the snake population according to formula (1).

[0108] S203, divide the snake population into two sub-populations (males and females) of equal size according to formulas (2) and (3).

[0109] S204, calculating the fitness value of each individual snake in the male population and the female population, and finding the optimal position of the individual.

[0110] Among them, the individual best position includes the individual best position in the snake population , the optimal position of individuals in the female snake population and optimal position of individuals in male snake populations .

[0111] S205, define the temperature according to formula (4) Temp , calculate the amount of food according to formula (5) Q。

[0112] S206, judgment Q Is it less than 0.25?

[0113] Among them, if Q If it is less than 0.25, execute step S207; otherwise, execute step S208.

[0114] S207, exploration phase: Calculate the positions of male and female individuals according to formulas (6) and (8), respectively.

[0115] S208, judgment Temp Is it greater than 0.6?

[0116] Among them, if Temp If it is greater than 0.6, execute step S209; otherwise, execute step S210.

[0117] S209, calculate the individual's position according to formula (10).

[0118] S210, random number rand Is it greater than 0.6?

[0119] Among them, if the random number rand If it is greater than 0.6, execute step S211; otherwise, execute step S212.

[0120] S211, development phase: Entering combat mode, the positions of male and female individuals are calculated according to formulas (11) and (12) respectively.

[0121] S212, development stage: enter the mating mode and calculate the positions of male and female individuals according to formulas (15) and (16) respectively.

[0122] S213, replace the positions of the worst male and worst female individuals according to formulas (19) and (20), respectively.

[0123] S214, comparing the fitness values of the snake individuals before and after the individual position update, and retaining the position of the individual with the superior fitness value.

[0124] S215, judging whether the stopping condition is satisfied.

[0125] The stopping condition may be whether the number of iterations reaches a preset maximum number of iterations.

[0126] S216, output the position of the optimal snake individual and its fitness value.

[0127] However, the snake optimization algorithm has the following two key deficiencies:

[0128] (1) Exploration stage: The position update method of male (female) snake individuals 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.

[0129] (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.

[0130] The above two key deficiencies result in the failure to achieve the best design effect when using the snake optimization algorithm for the integrated matching design of the power router.

[0131] Therefore, according to the position updating mechanism of the manta ray foraging optimization algorithm and the aurora optimization algorithm, the individual position updating formula in the exploration phase and the exploitation phase combat mode of the snake optimization algorithm is improved, and the improved snake optimization algorithm is obtained.

[0132] Specifically, improvements were made to the exploration phase: During the SOA exploration phase, the snake optimization algorithm's method for updating the positions of individual male (female) snakes relied solely on the positions of the selected male (female). This lacked diversity in search directions, preventing it from effectively covering the entire problem search space and potentially causing the algorithm to become trapped in a local optimum. To more effectively improve the algorithm's performance, the position update mechanisms of the manta ray foraging optimization algorithm and the aurora optimization algorithm were introduced to improve the snake's position update method. This method comprehensively considers factors such as the selection of different position update modes based on switching probabilities, the optimal position of the individual snake in this iteration, the individual snake's movement step size, and an adaptive weight coefficient that changes with the number of iterations. This improves the diversity of the algorithm's search directions, avoids local optimums within each iteration, and thus improves the algorithm's performance.

[0133] In the exploration phase of SOA, the manta ray foraging optimization algorithm and the aurora optimization algorithm are introduced. The individual position update formula of the improved snake optimization algorithm in the exploration phase is:

[0134] (twenty one);

[0135] (twenty two);

[0136] (twenty three);

[0137] (twenty four);

[0138] in, For the t +1 iteration i The location of individual male snakes; The randomly selected t The position of the male snake 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 snake in the iteration; the ability to find food for individual female snakes; For the t The first iteration i The location of individual male snakes; For the t The first iteration i The location of individual female snakes; For the t The optimal position of individuals in the snake population in the iteration is 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.

[0139] Improvements to the combat model during the development phase: During the SOA development phase, the combat model of the snake optimization algorithm emphasized only individual competition and lacked a communication and cooperation mechanism, making it prone to falling into local optimality. To more effectively improve the algorithm's performance, we introduced the position update mechanism of the manta ray foraging optimization algorithm and the aurora optimization algorithm, improving the way individual snake positions are updated. This approach comprehensively considers factors such as the optimal position of the individual snake in the current iteration, the randomly generated communicating learning snakes within the population, the individual snake's movement step size, and the adaptive weight coefficient that changes with the number of iterations. This avoids overemphasizing individual competition, which can lead to premature convergence to a local optimal solution, thereby improving the algorithm's performance.

[0140] In the combat mode of the development phase of SOA, the manta ray foraging optimization algorithm and the aurora optimization algorithm are introduced. The individual position update formula in the combat mode of the development phase of the improved snake optimization algorithm is:

[0141] (25);

[0142] (26);

[0143] 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 At the iteration, randomly generated communication learning snake individuals are generated within the snake population; To learn weights, satisfy , Indicates the amount of food, , is a constant.

[0144] S102, using the structural design parameters of the power router as the individual snake positions of the improved snake optimization algorithm, with the goal of maximizing the value of the performance index of the power router, 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 power router; the value of the performance index of the power router is determined based on the performance of the power router designed according to the structural design parameters at each iteration.

[0145] 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.

[0146] 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 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.

[0147] The transmission power of a power router refers to the amount of electrical energy it can transmit per unit time. Its transmission efficiency, typically expressed as a percentage, is the ratio of its output power to its input power. It's a key indicator of a router's ability to effectively convert input power into output power, reflecting the device's energy efficiency during transmission. Availability refers to the router's ability to perform its specified functions under specified conditions and within a specified timeframe.

[0148] The candidate power router is tested to determine the transmission power, transmission efficiency and availability of the candidate power router, including: using circuit simulation software to establish a circuit model based on the topology and working principle of the candidate power router, running the circuit model through the circuit simulation software to simulate the transmission power characteristics of the candidate power router, determining the transmission power of the candidate power router, and calculating the input power of the input end and the output power of the output end of the candidate power router, 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.

[0149] A large number of random simulation experiments are conducted on candidate power routers using the Monte Carlo method. In each simulation, a fault event is randomly generated based on 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] Optionally, the calculation formula for the performance index of the candidate power router is:

[0155] (27);

[0156] 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 .

[0157] The performance indicator of the candidate power router may also be transmission power, 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, transmission efficiency or availability of the candidate power router; and determining the transmission power, transmission efficiency or availability of the candidate power router as the value of the performance indicator of the candidate power router.

[0158] 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 transmission power and transmission efficiency; the normalized values of transmission power and transmission efficiency are weighted to obtain the values of the performance indicators of the candidate power routers.

[0159] 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.

[0160] like Figure 3 As shown, Figure 3 The present invention provides a flow chart of an integrated matching design method for a power router, which specifically includes the following steps:

[0161] 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.

[0162] Among them, performance parameters include port type, port quantity, etc.

[0163] S302, establishing a fitness function of the power router integrated matching design scheme based on the improved snake optimization algorithm.

[0164] Among them, the fitness function can be shown as formula (27).

[0165] S303, perform parameter setting.

[0166] The parameter settings mainly include: the size of the population (that is, the number of snake individuals) N; The maximum number of iterations (i.e. the condition for stopping the iteration) T ; Problem optimization lower bound ; Problem optimization upper bound .

[0167] S304, initialize the snake population position according to formula (1).

[0168] S305, divide the snake population into two sub-populations (males and females) of equal size according to formulas (2) and (3).

[0169] 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 optimal position of the individual.

[0170] Among them, the individual best position includes the individual best position in the snake population , the optimal position of individuals in the female snake population and optimal position of individuals in male snake populations .

[0171] S307, define the temperature according to formula (4) Temp , calculate the food quantity Q according to formula (5).

[0172] S308, judgment Q Is it less than 0.25?

[0173] Among them, if Q If the value is less than 0.25, go to step S309; otherwise, go to step S310.

[0174] S309, exploration phase: Calculate the positions of male and female individuals according to formulas (21) and (22), respectively.

[0175] S310, judgment Temp Is it greater than 0.6?

[0176] If Temp>0.6, execute step S311; otherwise, execute step S312.

[0177] S311, calculate the position of the individual according to formula (10).

[0178] S312, determine the random number rand Is it greater than 0.6?

[0179] Among them, if the random number rand If it is greater than 0.6, execute step S313; otherwise, execute step S314.

[0180] S313, development phase: Entering combat mode, the positions of male and female individuals are calculated according to formulas (25) and (26) respectively.

[0181] S314, development phase: Entering the mating mode, the positions of male and female individuals are calculated according to formulas (15) and (16), respectively.

[0182] S315, replace the positions of the worst male and worst female individuals according to formulas (19) and (20), respectively.

[0183] S316, comparing the fitness values of the snake individuals before and after the individual position update, and retaining the position of the individual with the superior fitness value.

[0184] S317, judging whether the stop condition is met.

[0185] S318, outputting the optimal position of the individual snake, that is, the integrated matching design scheme of the power router.

[0186] The integrated matching design scheme is the optimal power router integrated matching design scheme.

[0187] In one embodiment, the improved snake optimization algorithm ISOA proposed in the present invention was compared with the snake optimization algorithm SOA using MATLAB as the simulation platform. To ensure the fairness of the experiment, the population size of all algorithms was set to 30 and the maximum number of iterations was set to 300.

[0188] Figure 4 is the iterative process curve, from Figure 4 It can be intuitively seen that the ISOA method converges faster than the SOA algorithm and has better convergence accuracy. Simulation results show that the ISOA algorithm has stronger search capabilities and obtains a better integrated matching design for the power router, verifying the effectiveness of the algorithm.

[0189] 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.

[0190] The above is an integrated matching design method for 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 for a power router, which includes:

[0191] An improved module is used to improve the individual position update formulas in the exploration and development phases of the snake optimization algorithm based on the position update mechanisms of the manta ray foraging optimization algorithm and the aurora optimization algorithm, thereby obtaining an improved snake optimization algorithm. The improved snake optimization algorithm's individual position update formula in the exploration phase is constructed based on the random switching probability of selecting different position update modes, the optimal position of the individual snake in the current iteration, the individual snake's movement step, and an adaptive weight coefficient that varies with the number of iterations. The improved snake optimization algorithm's individual position update formula in the development phase of the combat mode is constructed based on the optimal position of the individual snake in the current iteration, randomly generated communication learning snakes within the population, the individual snake's movement step, and an adaptive weight coefficient that varies with the number of iterations.

[0192] The optimization module is used to use the structural design parameters of the electric energy router as the individual snake positions of the improved snake optimization algorithm, and to optimize the structural design parameters according to the improved snake optimization algorithm with the goal of maximizing the value of the performance index of the electric energy router 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 value of the performance index of the electric energy router is determined based on the performance of the electric energy router designed according to the structural design parameters at each iteration.

[0193] The specific definitions of the integrated matching design device for an energy router can be found in the definitions of the integrated matching design method for an energy router described above and will not be repeated here. Each module in the integrated matching design device for an energy router described above can be implemented in whole or in part through software, hardware, or a combination thereof. Each of these modules can be embedded in or independent of a processor in a computer device in hardware form, or stored in a computer device memory in software form, so that the processor can call and execute the corresponding operations of each of these modules.

[0194] 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.

[0195] The present invention also provides Figure 5 The structural diagram of the computer equipment 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. Of course, it 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.

[0196] Those skilled in the art will appreciate that all or part of the processes in the above-described method embodiments can be implemented by instructing the relevant hardware using a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes in the above-described method embodiments. Any reference to memory, storage, database, or other media used in the various embodiments provided herein may include at least one of non-volatile and volatile memory. Non-volatile memory may include read-only memory (ROM), magnetic tape, floppy disk, flash memory, or optical storage. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM can take various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0197] The technical features of the above embodiments can be combined arbitrarily. In order 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. A method for designing an integrated matching system for a power router, characterized in that: include: Based on 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 of selecting different position update modes, the optimal position of the snake individual in the current iteration, the movement step size of the snake individual, and an 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 movement step size of the snake individual, and an adaptive weight coefficient that changes with the number of iterations; The structural design parameters of the power router are used as the individual snake positions of the improved snake optimization algorithm. The goal is to maximize the performance index of the power 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 power router. The value of the performance index of the power router is determined by the performance of the power router designed according to the structural design parameters in each iteration; The individual position update formula of the improved snake optimization algorithm in the exploration phase is: Among them, X i,m (t+1) is the position of the i-th male snake individual in the t+1-th iteration; X rand,m (t) is the position of the randomly selected male snake in the tth iteration; rand is a random number in the range [0,1]; A m Ability to find food for male snakes; X i,f (t+1) is the position of the i-th female snake in the t+1-th iteration; X rand,f (t) is the position of the randomly selected female snake individual in the tth iteration; A f The ability to find food for female snakes; X i,m (t) is the position of the i-th male snake individual in the t-th iteration; X i,f (t) is the position of the i-th female snake individual in the t-th iteration; X food (t) is the optimal position of the individual in the snake population at the tth iteration, i.e., the food position; λ1 and λ2 are random numbers in the range [0,1]; r is a random number in the range [0,1]; α is the moving step length of the male or female snake; c2 is a constant; X max and X min are the upper and lower boundaries of the snake's individual position; ω is the adaptive weight coefficient that changes with the number of iterations t; σ is a random number in [0,1], and T is the maximum number of iterations; The individual position update formula in the combat mode during the development phase of the improved snake optimization algorithm is: X i,m (t+1)=X i,m (t)×cosα+X food (t)×sinω+X L (t)×ξ+c3×FM×rand×(Q×X best,f -X i,m (t)); X i,f (t+1)=X i,f (t)×cosα+X food (t)×sinω+X L (t)×ξ+c3×FF×rand×(Q×X best,m -X i,f (t)); Among them, X best,f is the optimal position of individuals in the female snake population; FM is the male fighting ability; c3 is a constant; X best,m is the optimal position of an individual in the male snake population; FF is the female fighting ability; X L (t) is the randomly generated communication learning snake individual within the snake population at the tth iteration; ξ is the learning weight, Q represents the amount of food, c1 is a constant.

2. 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 process of obtaining the fitness value of the improved snake optimization algorithm at the tth iteration includes: According to the individual positions at the tth iteration, a candidate power router is constructed; The candidate power router is tested to obtain a value of a performance indicator of the candidate power router; the value of the performance indicator of the candidate power router is the fitness value of the improved snake optimization algorithm at the tth iteration.

3. The method according to claim 2, 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.

4. The method according to claim 3, characterized in that The calculation formula for the performance index of the candidate power router is: fit=ω1P+ω2Q+ω3U; Where fit is the performance index value of the candidate power router, ω1, ω2, and ω3 are the weights of transmission power, transmission efficiency, and availability, respectively; P, Q, and U are the normalized values of transmission power, transmission efficiency, and availability, respectively.

5. The method according to claim 2, 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, transmission efficiency or availability of the candidate power router; The transmission power, transmission efficiency or availability of the candidate power router is determined as a value of the performance indicator of the candidate power router.

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