A temperature control optimization method for distribution cabinet based on improved dead leaf butterfly optimization algorithm

Abstract

The present invention discloses a distribution cabinet temperature control optimization method based on an improved leaf butterfly optimization algorithm, comprising: S1, constructing a distribution cabinet temperature control mathematical model to be optimized based on the distribution cabinet temperature control requirements; S2, constructing a distribution cabinet temperature PID control system; S3, introducing an improved leaf butterfly optimizer; S4, using the improved leaf butterfly optimizer to adjust the cabinet temperature PID control parameters in the distribution cabinet temperature PID control system, and obtaining the optimal control parameters through optimization; S5, setting the optimal control parameters obtained through optimization as the parameters of the temperature PID controller in the distribution cabinet temperature PID control system to optimize the temperature control effect. The present invention introduces and improves the leaf butterfly optimization algorithm, which can improve the stability of the distribution cabinet temperature control system, allowing the temperature to stabilize at a preset temperature value more quickly, thereby preventing large temperature fluctuations from damaging related equipment or electronic components in the distribution cabinet.

Description

Technical Field

[0001] The present invention belongs to the technical field of control optimization, and in particular relates to a temperature control optimization method for a power distribution cabinet based on an improved dead leaf butterfly optimization algorithm. Background Art

[0002] As a crucial piece of equipment in power systems, distribution cabinets are widely used in a variety of fields, including industrial control, building power, data centers, and new energy. Their primary function is to distribute, protect, control, and monitor electrical energy, ensuring the safe and stable operation of the entire electrical system. However, as the load density of distribution systems continues to increase, the number of electrical components integrated within distribution cabinets has increased significantly, leading to a corresponding increase in heat generation. Excessive temperatures within distribution cabinets can accelerate component aging, reduce equipment reliability, and even cause safety incidents such as fires. Therefore, temperature control of distribution cabinets requires paramount attention.

[0003] Currently, temperature control in power distribution cabinets typically relies on devices such as temperature sensors and fans, coupled with PID controllers for automatic adjustment. PID controllers are widely used in industrial temperature control due to their simple structure, strong real-time performance, and ease of implementation. However, power distribution cabinet temperature control systems often face complex operating conditions such as nonlinearity, time-varying performance, and external interference, making it difficult for traditional PID control to achieve ideal control results. In particular, improper initial parameter tuning can lead to problems such as slow system response, large overshoot, and large steady-state errors.

[0004] Traditional PID parameter tuning methods primarily include empirical methods, trial-and-error methods, and model-based analysis. Empirical methods rely on the engineer's experience and system response curves, are highly subjective, and are difficult to adapt to diverse operating conditions. Trial-and-error methods are time-consuming, inefficient, and susceptible to human error. Model-based analysis methods require high accuracy of the system model and struggle to cope with complex systems characterized by time lag, nonlinearity, and disturbances. Therefore, efficiently and accurately tuning PID controller parameters to improve the robustness and control performance of distribution cabinet temperature control systems has become a pressing technical challenge in current research and engineering practice. Summary of the Invention

[0005] In order to overcome the technical problems described in the above background technology, the present invention provides a distribution cabinet temperature control optimization method based on an improved dead leaf butterfly optimization algorithm. The dead leaf butterfly optimization algorithm is introduced and improved, which can improve the stability of the distribution cabinet temperature control system, so that the temperature can be stabilized at the preset temperature value more quickly, avoiding large temperature fluctuations that may damage related equipment or electronic components in the distribution cabinet.

[0006] The technical solution of the present invention is: a distribution cabinet temperature control optimization method based on an improved dead leaf butterfly optimization algorithm, comprising the following steps:

[0007] S1. Based on the temperature control requirements of the power distribution cabinet, a mathematical model for the temperature control of the power distribution cabinet to be optimized is constructed;

[0008] S2. Build a power distribution cabinet temperature PID control system, including a cabinet temperature threshold setting module, a cabinet temperature PID controller module, an improved dead leaf butterfly optimization algorithm module, a cabinet temperature adjustment module, and a cabinet temperature monitoring module;

[0009] S3, introduce the improved dead leaf butterfly optimization algorithm, the specific improvement strategy is:

[0010] S31, introduce three update strategies, namely adaptive collective color change strategy, differential imitation color change strategy and adaptive recall color change strategy, to improve the exploration phase of the dead leaf butterfly optimization algorithm;

[0011] S32. Introduce the elite-guided differential migration strategy to improve the migration mechanism of the dead leaf butterfly optimization algorithm, and select the top population with the best fitness value from the current population. % of individuals, forming the elite population , by performing differential mutation on the elite population to generate migration targets , for ordinary individuals selected for migration, For the individuals in the selected elite population, a perturbation strategy of Gaussian mutation is used;

[0012] S4. Use the improved dead leaf butterfly optimization algorithm to adjust the cabinet temperature PID control parameters in the distribution cabinet temperature PID control system, and obtain the best control parameters by searching for the best 、 、 ;

[0013] S5. The three optimal control parameters obtained by optimizing the improved dead leaf butterfly optimization algorithm are set as the parameters of the temperature PID controller in the temperature PID control system of the distribution cabinet to optimize the temperature control effect.

[0014] Furthermore, in step S1, the temperature control mathematical model of the power distribution cabinet is:

[0015] ,

[0016] in, Indicates the final change in the temperature inside the power distribution cabinet under the action of the unit control quantity, which is used to reflect the sensitivity of the system to the cooling adjustment input. and It is the process time constant that reflects the inertia of the distribution cabinet during the heating and cooling process. It is the pure delay time that represents the time lag between the action of the regulating device and the detection of a significant temperature change by the temperature sensor. is the independent variable of the Laplace transform.

[0017] Furthermore, in the distribution cabinet temperature PID control system constructed in step S2, the cabinet temperature threshold is obtained through the cabinet temperature threshold setting module, and the current temperature difference is obtained based on the actual cabinet temperature monitored by the temperature monitoring module and the cabinet temperature threshold. The current temperature difference is input into the PID controller module, and the parameters of the PID controller module are optimized through the improved dead leaf butterfly optimization algorithm module. The optimized PID controller outputs the control quantity, and the control quantity is input into the cabinet temperature adjustment module for adjustment.

[0018] Furthermore, in step S31, the exploration phase of the improved leaf butterfly optimization algorithm includes the following steps: When the update is performed, randomly select one from the adaptive collective color-changing strategy, differential imitation color-changing strategy, and adaptive recall color-changing strategy, specifically:

[0019] C1, the adaptive collective color change strategy includes the current individual based on its own and the global optimal individual The corresponding formula is:

[0020] ,

[0021] in, is the updated position, is the individual position of the current iteration, is the adaptive weight, and is the random disturbance term;

[0022] C2, the differential imitation color change strategy involves generating a new position by the differential vectors of three different individuals randomly selected from the population. The corresponding formula is:

[0023] ,

[0024] in, is the updated position, 、 、 are the positions of three different individuals selected randomly, and F is the dynamic scaling factor;

[0025] C3, the adaptive recall color change strategy includes the current individual based on its own and the individual's historical optimal position The corresponding formula is:

[0026] ,

[0027] in, is the updated position, is the individual position of the current iteration, is the adaptive weight, and is a random disturbance term.

[0028] Furthermore, the adaptive weight and the probability of choosing the exploration phase With the number of iterations The square of the ratio of the maximum number of iterations T is nonlinearly attenuated, and the corresponding formulas are:

[0029] ,

[0030] ,

[0031] Where T is the maximum number of iterations, and are the maximum and minimum values ​​of the adaptive weight respectively.

[0032] Furthermore, the migration mechanism of the improved leaf butterfly optimization algorithm in step S32 includes the following steps:

[0033] S321, when continuous When the global optimal solution is not improved after the first iteration, the migration mechanism is triggered, and the top p% of individuals are selected according to the fitness value to form an elite population, where is the preset number of stagnation tolerances;

[0034] S322, by three random individuals in the elite population 、 and Perform differential mutation to generate an elite-guided migration target , ;

[0035] S323: For non-elite migrants, guide them to migrate towards elite-guided destinations Perform random step moves, , where D is the dimension of the solution space;

[0036] S324. Apply a Gaussian mutation perturbation that decays with the number of iterations to the individuals in the elite population to perform local fine search.

[0037] Furthermore, in step S324, the Gaussian mutation applied to the elite population individuals has a standard deviation of With the number of iterations Linearly decreasing, the mathematical expression is:

[0038] ,

[0039] Among them, the standard deviation of the Gaussian distribution With the number of iterations linearly decreasing, and , is the current number of iterations, and T is the maximum number of iterations.

[0040] Furthermore, in step S4, the improved dead leaf butterfly optimization algorithm is used to adjust the cabinet temperature PID control parameters in the distribution cabinet temperature PID control system, including the following steps:

[0041] S41, set the ratio ,integral ,differential The range of parameter values ​​constitutes the search space for optimization;

[0042] S42, initializing a population of the improved dead leaf butterfly optimization algorithm, wherein each individual represents a set of PID parameters;

[0043] S43, using the time product absolute error integral criterion of the distribution cabinet temperature PID control system as the fitness function, iteratively executing the population update and migration steps of the improved dead leaf butterfly optimization algorithm;

[0044] S44. When the preset maximum number of iterations is reached or the fitness value converges, the algorithm terminates and outputs the current optimal set of PID parameters as the final result.

[0045] By adopting the above technical solution, the beneficial effects of the present invention are as follows: the dead leaf butterfly optimization algorithm is improved, the exploration phase of the dead leaf butterfly optimization algorithm is improved by introducing differential mutation and adaptive weight strategies, the migration mechanism of the dead leaf butterfly optimization algorithm is improved by introducing an elite-guided differential migration strategy, and the overall optimization performance of the algorithm is improved. In the imitation color change of the exploration phase, the current individual is allowed to learn from a random individual, but the learning direction and step size are determined by the difference vectors of the other two random individuals. This method can generate more diverse new positions and greatly enhance the global exploration capability. In the collective color change and recall color change of the exploration phase, an adaptive inertia weight that changes with the number of iterations t is introduced. In the early stage, it explores more towards the global optimum, and in the later stage, it conducts more detailed searches near the individual historical optimum. If the stagnation condition is triggered and migration behavior is required, the migration is no longer a blind random jump, but is guided by the collective wisdom of the elite population. This increases the probability of finding a better solution area. At the same time, different migration strategies are adopted for ordinary individuals and elite individuals, which not only ensures the global exploration ability of the algorithm and the bold jumps of ordinary individuals, but also protects the high-quality solutions that have been found and the prudent fine-tuning of elite individuals, achieving a dynamic balance between exploration and utilization. In this way, the improved dead leaf butterfly optimization algorithm is used for the optimization of the PID parameters of the distribution cabinet temperature, which can improve the stability of the distribution cabinet temperature control system, so that the temperature can be stabilized at the temperature preset value faster, avoiding large temperature fluctuations that damage related equipment or electronic components in the distribution cabinet. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 It is a schematic flow diagram of the present invention.

[0047] Figure 2 This is a model diagram of the power distribution cabinet temperature PID control system of the present invention.

[0048] Figure 3 This is a comparison chart of the optimal ITAE of the standard dead leaf butterfly optimization algorithm and the improved dead leaf butterfly optimization algorithm.

[0049] Figure 4 is the optimization parameter value of the standard dead leaf butterfly optimization algorithm and the improved dead leaf butterfly optimization algorithm Comparison picture.

[0050] Figure 5 is the optimization parameter value of the standard dead leaf butterfly optimization algorithm and the improved dead leaf butterfly optimization algorithm Comparison picture.

[0051] Figure 6 is the optimization parameter value of the standard dead leaf butterfly optimization algorithm and the improved dead leaf butterfly optimization algorithm Comparison picture.

[0052] Figure 7 This is a comparison chart of the PID control effects of the standard dead leaf butterfly optimization algorithm and the improved dead leaf butterfly optimization algorithm. DETAILED DESCRIPTION

[0053] Example 1: Figure 1-Figure 7 As shown, the present invention provides a distribution cabinet temperature control optimization method based on an improved dead leaf butterfly optimization algorithm, comprising steps S1 to S5.

[0054] S1. Construction of a mathematical model for the temperature control of the power distribution cabinet. The temperature change of the power distribution cabinet mainly involves three aspects: internal heat generation, heat retention, and heat dissipation (including passive heat dissipation and active cooling). It can be equivalent to a second-order plus delay model with inertia and pure delay characteristics. Therefore, the mathematical model for the temperature control of the power distribution cabinet is:

[0055] ,

[0056] in, Indicates the final change in the temperature inside the power distribution cabinet under the action of the unit control quantity, which is used to reflect the sensitivity of the system to the cooling adjustment input. and It is the process time constant that reflects the inertia of the distribution cabinet during the heating and cooling process. It is the pure delay time that represents the time lag between the action of the regulating device and the detection of a significant temperature change by the temperature sensor. is the independent variable of the Laplace transform.

[0057] Therefore, for industrial power distribution cabinets with inverters inside and forced air cooling by fans, = , represents the thermal inertia of the core components =180 seconds, indicating the thermal inertia of the air circulation inside the cabinet = 50 seconds, representing the combined delay of air flow and sensor = 20 seconds, the transfer function formula is:

[0058] .

[0059] S2. Construct a PID control system and optimization target for the power distribution cabinet temperature, including a cabinet temperature threshold setting module, a cabinet temperature PID controller module, an improved dead leaf butterfly optimization algorithm module, a cabinet temperature adjustment module, and a cabinet temperature monitoring module. The output u(t) of the PID controller consists of three parts: proportional, integral, and differential:

[0060] ,

[0061] The optimization variable is the proportional gain , integration time , differential time ,and 、 、 There is a one-to-one correspondence, so this optimization process is to find the optimal PID control parameters.

[0062] At the same time, the optimization goal is to minimize the time product absolute error integral ITAE when the system is subjected to a step disturbance (such as a sudden increase in external ambient temperature or a sudden increase in internal device load). It can impose a heavier penalty on errors that exist for a long time and effectively obtain a control effect with fast response and small overshoot. The corresponding fitness function formula is:

[0063] ,

[0064] in is the set temperature threshold, The current temperature inside the power distribution cabinet.

[0065] S3. Introduce an improved dead leaf butterfly optimization algorithm, and its specific improvement strategy is the same as executing steps S31 and S32.

[0066] S31, the hybrid update strategy of the exploration and utilization phase, in each iteration, for each individual in the population , first calculate an exploration probability that decays nonlinearly with the number of iterations t ,

[0067] ,

[0068] Where T is the maximum number of iterations.

[0069] Define an adaptive inertia weight , The value decreases nonlinearly from 0.9 to 0.2, that is:

[0070] .

[0071] A dynamic perturbation amplitude scaling factor is also defined , The value decreases linearly from 1 to 0.2, that is:

[0072] .

[0073] The base perturbation vector used in all update strategies Defined as:

[0074] ,

[0075] in represents a three-dimensional random vector whose components follow the standard normal distribution, and I is the identity matrix.

[0076] Then generate a random number R in [0,1], if , then it enters the exploration phase and randomly selects one of the following three strategies C1, C2, and C3 to execute.

[0077] C1, the adaptive collective color change strategy includes the current individual based on its own and the global optimal individual The corresponding formula is:

[0078] ,

[0079] in, is the updated position, is the individual position of the current iteration, is the adaptive weight, is a random number that follows a standard normal distribution. is the basic perturbation vector.

[0080] C2, the differential imitation color change strategy involves generating a new position by the differential vectors of three different individuals randomly selected from the population. The corresponding formula is:

[0081] ,

[0082] in, is the updated position, 、 、 are three different individuals randomly selected, F is the dynamic scaling factor, , so that the step size of differential mutation is also adaptively adjusted with the iterative process.

[0083] C3, the adaptive recall color change strategy includes the current individual based on its own and the individual's historical optimal position The corresponding formula is:

[0084] ,

[0085] in, is the updated position, is the individual position of the current iteration, is the adaptive weight, is a random number that follows a standard normal distribution. is the basic perturbation vector.

[0086] if , then enter the utilization phase and execute a method that combines the global optimal , individual historical optimality And the weighted average update strategy of neighboring individual information:

[0087] ,

[0088] in Is the population except Outside of oneself The individual with the closest Euclidean distance, 、 、 are three independent random weights uniformly distributed between [0,1].

[0089] Generate New Location Then perform a bounds check to ensure that it is within the search space [lb,ub], and then calculate its fitness value If it is better than the current individual, then update the individual position and the individual's historical optimal position, and if it is better than the global optimal, then update the global optimal solution and the optimal fitness value .

[0090] S32. Introducing an elite-guided differential migration mechanism, including steps S321 to S324.

[0091] S321, set a stagnation counter, if the global optimal solution is continuous = 3 times without improvement, the migration mechanism is triggered. At this time, all individuals in the current population are sorted according to their fitness values, and the best 10% (no less than 3 individuals) are selected to form the elite population. .

[0092] S322, from the elite population Randomly select three different individuals 、 and , generating an elite-guided migration target through differential mutation .

[0093] .

[0094] S323. For the migration of non-elite individuals, the proportion of individuals who have not been selected as elites is =20% random selection Individuals migrate, and these selected individuals Migration target move:

[0095] ,

[0096] Where rand(1,D) is a D-dimensional random vector with components uniformly distributed on [0,1]. D is the dimension of the optimization problem, and the current value is 3. Indicates that after implementing the migration target The new position vector calculated after the migration operation is the updated state of the individual and will be used for subsequent fitness evaluation and population update. Represents an individual The original position vector before performing the migration operation is the starting state of this individual in the current iteration step.

[0097] S324. In order to enhance the local exploration ability of the elite population, a multiplicative Gaussian mutation perturbation that decays with the number of iterations is applied to each individual in the elite population:

[0098] ,

[0099] Among them, the standard deviation of the Gaussian distribution With the number of iterations linearly decreasing, and , T is the maximum number of iterations.

[0100] All new individuals generated by migration and disturbance also need to undergo boundary checking and fitness evaluation, and the population, individual optimal and global optimal information are updated accordingly. After the migration mechanism is triggered, the stagnation counter is reset to 0.

[0101] S4. Use the improved dead leaf butterfly optimization algorithm to adjust the cabinet temperature PID control parameters in the distribution cabinet temperature PID control system, and obtain the best control parameters by searching for the best 、 、 , including steps S401 to S404.

[0102] S401. Parameter initialization, that is, setting algorithm parameters, including population size N=50, optimization dimension D=3, maximum number of iterations T=50, setting the search space of PID parameters, for example, lower limit lb=[−80,−80,−40], upper limit ub=[0 0 0], setting the stagnation tolerance to 3, the proportion of migrating individuals to 0.2, and the proportion of elite population to 0.1.

[0103] S402, population initialization, that is, randomly generating N individuals in the search space set in step S401, each individual represents a set of [Kp, Ki, Kd] parameters.

[0104] S403, iterative optimization: using ITAE integral as the fitness function, in the loop from t=1 to T, execute all the update steps of the improved leaf butterfly optimization algorithm described in S3, including the hybrid update strategy and the migration mechanism triggered when the conditions are met.

[0105] S404, terminate and output: After reaching the maximum number of iterations T, the algorithm terminates and outputs the global optimal solution recorded at this time , which is the best PID parameter found [ , , ].

[0106] S5. The three optimal control parameters obtained by optimizing the improved dead leaf butterfly optimization algorithm are set as the parameters of the temperature PID controller in the temperature PID control system of the distribution cabinet to optimize the temperature control effect.

[0107] In the distribution cabinet temperature PID control system constructed in step S2, the cabinet temperature threshold is obtained through the cabinet temperature threshold setting module, and the current temperature difference is obtained based on the actual cabinet temperature monitored by the temperature monitoring module and the cabinet temperature threshold. The current temperature difference is input into the PID controller module, and the parameters of the PID controller module are optimized through the improved dead leaf butterfly optimization algorithm module. The optimized PID controller outputs the control quantity, and the control quantity is input into the cabinet temperature adjustment module for adjustment.

[0108] By converting the above technical solution into a project code running in Matlab, the corresponding distribution cabinet temperature PID control system model is established in Simulink, and the parameters of the improved dead leaf butterfly optimization algorithm are initialized, including the algorithm population size N=50, problem dimension d=3, maximum number of iterations 50, upper limit ub=[0 0 0] of the search space, and lower limit lb=[-80 -80 -40]. After running the project code, the optimal ITAE comparison curve of the dead leaf butterfly optimization algorithm and the improved dead leaf butterfly optimization algorithm is obtained, as shown in the figure. Figure 3 As shown in the figure, the improved leaf butterfly optimization algorithm reaches the optimal value at about the 24th iteration. Compared with the standard dead leaf butterfly optimization algorithm, it has better optimization accuracy and faster convergence speed, which can better improve the control performance of the distribution cabinet temperature PID control system, thereby achieving better temperature control effect inside the distribution cabinet.

[0109] And as Figure 4-Figure 6 As shown in the figure, the PID controller in the temperature PID control system of the distribution cabinet is tuned by the dead leaf butterfly optimization algorithm and the improved dead leaf butterfly optimization algorithm respectively. The optimal PID control parameters obtained by the dead leaf butterfly optimization algorithm are =-11.068, =-0.026, =-21.539, the optimal PID control parameters obtained by the improved dead leaf butterfly optimization algorithm are =-3.476, =-0.016, =-7.064, and then input the optimal control parameters into the PID controller of the distribution cabinet temperature PID control system to obtain the corresponding optimal control effect, such as Figure 7As shown in the figure, the comparison diagram of the PID optimization effects of the dead leaf butterfly optimization algorithm and the improved dead leaf butterfly optimization algorithm is shown. The target input value is a step signal of -1, which is used to simulate the temperature drop process in the temperature control of the distribution cabinet. The response speed of the response curve of the PID control effect optimized by the improved dead leaf butterfly optimization algorithm is relatively fast and the overshoot is relatively low, so that the temperature inside the distribution cabinet can reach a steady state faster after adjustment, and the control effect of the temperature inside the cabinet is better.

Claims

1. A temperature control optimization method for a power distribution cabinet based on an improved dead leaf butterfly optimization algorithm, characterized by: The steps include: S1. Based on the temperature control requirements of the power distribution cabinet, a mathematical model for the temperature control of the power distribution cabinet to be optimized is constructed; S2. Build a power distribution cabinet temperature PID control system, including a cabinet temperature threshold setting module, a cabinet temperature PID controller module, an improved dead leaf butterfly optimization algorithm module, a cabinet temperature adjustment module, and a cabinet temperature monitoring module; S3, introduce the improved dead leaf butterfly optimization algorithm, the specific improvement strategy is: S31. In the exploration phase, when the generated random number is less than When the update is performed, randomly select one from the adaptive collective color-changing strategy, differential imitation color-changing strategy, and adaptive recall color-changing strategy, specifically: C1, the adaptive collective color change strategy includes the current individual based on its own and the global optimal individual The corresponding formula is: , in, is the updated position, is the individual position of the current iteration, is the adaptive weight, and is the random disturbance term; C2, the differential imitation color change strategy involves generating a new position by the differential vectors of three different individuals randomly selected from the population. The corresponding formula is: ， in, is the updated position, 、 、 are the positions of three different individuals selected randomly, and F is the dynamic scaling factor; C3, the adaptive recall color change strategy includes the current individual based on its own and the individual's historical optimal position The corresponding formula is: ， in, is the updated position, is the individual position of the current iteration, is the adaptive weight, and is the random disturbance term; S32. Introduce the elite-guided differential migration strategy to improve the migration mechanism of the dead leaf butterfly optimization algorithm, and select the top population with the best fitness value from the current population. % of individuals, forming the elite population , by performing differential mutation on the elite population to generate migration targets , for ordinary individuals selected for migration, For the individuals in the selected elite population, a perturbation strategy of Gaussian mutation is used; S4. Use the improved dead leaf butterfly optimization algorithm to adjust the PID control parameters of the cabinet temperature in the distribution cabinet temperature PID control system, and obtain the best control parameters by searching for the best 、 、 ; S5. The three optimal control parameters obtained by optimizing the improved dead leaf butterfly optimization algorithm are set as the parameters of the temperature PID controller in the temperature PID control system of the distribution cabinet to optimize the temperature control effect.

2. The method for optimizing temperature control of a power distribution cabinet based on an improved dead leaf butterfly optimization algorithm according to claim 1, characterized in that: In step S1, the temperature control mathematical model of the power distribution cabinet is: ， in, Indicates the final change in the temperature inside the power distribution cabinet under the action of the unit control quantity, which is used to reflect the sensitivity of the system to the cooling adjustment input. and It is the process time constant that reflects the inertia of the distribution cabinet during the heating and cooling process. It is the pure delay time that represents the time lag between the action of the regulating device and the detection of a significant temperature change by the temperature sensor. is the independent variable of the Laplace transform.

3. The method for optimizing temperature control of a power distribution cabinet based on an improved dead leaf butterfly optimization algorithm according to claim 2, characterized in that: In the distribution cabinet temperature PID control system constructed in step S2, the cabinet temperature threshold is obtained through the cabinet temperature threshold setting module, and the current temperature difference is obtained based on the actual cabinet temperature monitored by the temperature monitoring module and the cabinet temperature threshold. The current temperature difference is input into the PID controller module, and the parameters of the PID controller module are optimized through the improved dead leaf butterfly optimization algorithm module. The optimized PID controller outputs the control quantity, and the control quantity is input into the cabinet temperature adjustment module for adjustment.

4. The method for optimizing temperature control of a power distribution cabinet based on an improved dead leaf butterfly optimization algorithm according to claim 3 is characterized in that: Adaptive weights and the probability of choosing the exploration phase With the number of iterations The square of the ratio of the maximum number of iterations T is nonlinearly attenuated, and the corresponding formulas are: ， ， Where T is the maximum number of iterations, and are the maximum and minimum values ​​of the adaptive weight respectively.

5. The method for optimizing temperature control of a power distribution cabinet based on an improved dead leaf butterfly optimization algorithm according to claim 4 is characterized in that: The migration mechanism of the improved leaf butterfly optimization algorithm in step S32 includes the following steps: S321, when continuous When the global optimal solution is not improved after the first iteration, the migration mechanism is triggered, and the top p% of individuals are selected according to the fitness value to form an elite population, where is the preset number of stagnation tolerances; S322, by three random individuals in the elite population 、 and Perform differential mutation to generate an elite-guided migration target , ; S323: For non-elite migrants, guide them to migrate towards elite-guided destinations Perform random step moves, , where D is the dimension of the solution space; S324. Apply a Gaussian mutation perturbation that decays with the number of iterations to the individuals in the elite population to perform local fine search.

6. The method for optimizing temperature control of a power distribution cabinet based on an improved dead leaf butterfly optimization algorithm according to claim 5, characterized in that: In step S324, the Gaussian mutation applied to the elite population individuals has a standard deviation of With the number of iterations Linearly decreasing, the mathematical expression is: ， Among them, the standard deviation of the Gaussian distribution With the number of iterations linearly decreasing, and , is the current number of iterations, and T is the maximum number of iterations.

7. The method for optimizing temperature control of a power distribution cabinet based on an improved dead leaf butterfly optimization algorithm according to claim 6, characterized in that: In step S4, the improved dead leaf butterfly optimization algorithm is used to adjust the cabinet temperature PID control parameters in the distribution cabinet temperature PID control system, including the following steps: S41, set the ratio ,integral ,differential The range of parameter values ​​constitutes the search space for optimization; S42, initializing a population of the improved dead leaf butterfly optimization algorithm, wherein each individual represents a set of PID parameters; S43, using the time product absolute error integral criterion of the distribution cabinet temperature PID control system as the fitness function, iteratively executing the population update and migration steps of the improved dead leaf butterfly optimization algorithm; S44. When the preset maximum number of iterations is reached or the fitness value converges, the algorithm terminates and outputs the current optimal set of PID parameters as the final result.

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