Switched reluctance generator off angle optimization method based on improved whale optimization algorithm

Abstract

The switching reluctance motor shut-off angle optimization method based on the improved whale algorithm includes the following steps: establishing a torque ripple coefficient expression to evaluate the degree of switching reluctance motor shut-off angle optimization as the optimization objective; transforming the torque ripple coefficient expression into an objective function according to the optimization objective, thus transforming the shut-off angle optimization problem into a nonlinear programming problem; proposing an improved whale optimization algorithm to optimize and solve the switching reluctance motor shut-off angle optimization problem; the improved whale optimization algorithm uses a Piecewise chaotic mapping to initialize the population, employs a nonlinear convergence factor and introduces adaptive inertia weights to update the position, thereby obtaining the optimal shut-off angle of the switching reluctance motor; to improve the search capability of the whale optimization algorithm, this invention introduces a Piecewise chaotic mapping to increase the diversity of the population, and employs a nonlinear convergence factor and adaptive inertia weights, thereby improving the algorithm's ability to perform global search in the early stage and accurate local search in the later stage.

Description

Technical Field

[0001] This invention belongs to the field of control parameter optimization technology for switched reluctance motors, specifically a method for optimizing the deactivation angle of switched reluctance motors based on an improved whale optimization algorithm. Background Technology

[0002] Switched reluctance motors (SRMs) offer advantages such as simple structure, reliable performance, low cost, high conversion efficiency, and adjustable speed, making them one of the preferred high-speed drive motors for electric vehicles. When operating at medium and low speeds, the phase current amplitude can be limited within a certain range through current chopping control. By adjusting the turn-on and turn-off angles, the waveform of the phase current is altered, ultimately changing the output torque. The turn-off angle has a far greater impact on the SRM than the turn-on angle. Furthermore, the frequent start-stop conditions of electric vehicles, i.e., frequent changes in speed and load, can exacerbate torque ripple in the SRM if the turn-off angle parameter is not selected appropriately. Therefore, setting suitable turn-off angle parameters is necessary under different operating conditions to reduce torque ripple.

[0003] To address the aforementioned issues, some literature utilizes nonlinear model simulations of switched reluctance motors, manually adjusting the turn-off angle parameters to obtain the turn-off angle with the lowest torque ripple under different operating conditions as the optimal turn-off angle. However, the optimal turn-off angle of switched reluctance motors varies slightly under different speeds and loads. This method requires multiple simulation experiments, and relying solely on manual parameter trial and error is time-consuming and cannot guarantee that the optimal turn-off angle is found.

[0004] In addition, some literature utilizes a genetic algorithm (GA) to optimize the shut-off angle parameter. Leveraging the global optimization capability of the GA, the reciprocal of the torque ripple coefficient is used as the fitness function to find the optimal shut-off angle under different operating conditions. This method overcomes the limitations of manual trial and error by globally optimizing the shut-off angle. However, the GA has slow convergence speed, many control variables, and complex program code, involving gene encoding and decoding. Furthermore, the GA's ability to search local areas is poor, and it cannot achieve optimal results when used for shut-off angle optimization. Therefore, existing methods for optimizing the shut-off angle of switched reluctance motors still have shortcomings. Summary of the Invention

[0005] To address the aforementioned technical problems, this invention provides a method for optimizing the break-off angle of switched reluctance motors based on the Improved Whale Optimization Algorithm (IWOA). This method overcomes the shortcomings of existing optimization methods for the break-off angle parameters of switched reluctance motors, reduces optimization time, improves algorithm convergence speed, and avoids the algorithm getting stuck in local optima during later local development, while ensuring the optimization effect.

[0006] The technical solution adopted in this invention is as follows:

[0007] The method for optimizing the deactivation angle of a switched reluctance motor based on an improved whale optimization algorithm includes the following steps:

[0008] Step 1: Build a model of the switched reluctance motor control system and set the initial control parameters;

[0009] Step 2: Determine the optimization target for the shut-off angle of the switched reluctance motor;

[0010] Step 3: Transform the optimization objective of Step 2 into an objective function, and transform the shut-off angle optimization problem into solving the problem of finding the maximum and minimum values ​​of the objective function;

[0011] Step 4: Optimize and solve the problem of switching reluctance motor deactivation angle optimization based on the improved whale optimization algorithm.

[0012] In step 1, the switched reluctance motor control system model includes: a PID module, a current hysteresis control module, a power converter module, a switched reluctance motor model, a commutation control module, an angle input module, and a speed calculation module.

[0013] The PID module is used to generate a reference current to control the speed loop;

[0014] The current hysteresis control module is used to perform hysteresis control on the phase current so that it tracks the reference current.

[0015] Power converter module for driving switched reluctance motors;

[0016] The commutation control module is used to detect the rotor position and calculate and generate switching signals;

[0017] Angle input module, used to set the values ​​of the opening angle and the closing angle;

[0018] The speed calculation module is used to calculate the current motor speed based on the detected rotor position;

[0019] The switched reluctance motor (SRM) model is a 12 / 8-pole nonlinear model, as shown in Figure 8(a). The torque and inductance characteristics of the SRM are obtained based on finite element analysis, and the torque and inductance modules are modeled using Lookup Table (2-D). The model of phase A winding is shown in Figure 8(b). Phases B and C are the same as phase A winding models, with a 30° interval between adjacent phases. The model is then constructed based on the input rotor position angle θ and phase current i. A Output the current inductance value and the torque value provided by phase A winding, respectively.

[0020] In step 2, minimizing the torque ripple in the commutation interval is taken as the optimization target for the shut-off angle of the switched reluctance motor. Vibration in a switched reluctance motor is usually due to large torque ripple in the commutation interval, which is affected by the shut-off angle. Therefore, optimizing the shut-off angle helps suppress torque ripple.

[0021] In step 3, the optimization objective is to suppress torque ripple, which is actually a problem of minimizing torque ripple. This involves setting the root mean square value T of the instantaneous torque as the target value. ripple Define the objective function as:

[0022]

[0023] Where: T, T L Here, τ represents the instantaneous actual torque and the load torque, respectively, and τ is the simulation time. This formula can be used to measure the degree to which the actual torque deviates from the reference torque. When the value is larger, the actual torque deviates further from the reference torque, the torque ripple is large, and the control effect is poor; when the value is smaller, the actual torque is closer to the reference torque, the torque ripple is small, and the control effect is good.

[0024] In step 4, the improved whale optimization algorithm uses Piecewise chaotic mapping to initialize the population, adopts a nonlinear convergence factor and introduces adaptive inertia weight to update the position, thereby improving the algorithm's accuracy and ability to escape local optima, and thus obtaining the optimal turn-off angle of the switched reluctance motor.

[0025] The improved whale optimization algorithm employs the following strategies:

[0026] Improved initial population:

[0027] The population is initialized using a Piecewise chaotic map, and the expression for the Piecewise chaotic map is:

[0028]

[0029] Where: control parameter p∈[0,0.5], y k ∈(0,1), after multiple experiments, taking p = 0.4 can make y k It is uniformly mapped over the interval (0,1).

[0030] Assuming the whale population size is N and the search space is j-dimensional, the position of the i-th whale in the j-dimensional space can be represented using the Piecewise chaotic mapping as follows:

[0031] x i,j =lb j +y i,j (ub j -lb j ), i = 1, 2, 3, ..., N

[0032] Where: x i,j Let lb represent the position of the i-th whale in the j-th dimension. j y represents the lower limit of the whale's location range. i,j ub represents the mapping value of the Piecewise chaotic mapping in (0,1). j N represents the upper limit of the whale's location range, and N represents the size of the whale population.

[0033] According to the principle of the whale optimization algorithm, the location of the target prey is the global optimal solution of the objective function.

[0034] Improved random search phase:

[0035] A nonlinear convergence factor and adaptive inertia weights are used to improve the whale's search and foraging behavior. The mathematical model is as follows:

[0036]

[0037] Where t is the current iteration number; X(t) is the current position of the individual whale; X rand (t) represents a whale individual randomly selected from the current group; A and C are coefficient vectors, λ is the adaptive inertia weight; X(t+1) represents the position of the next whale individual.

[0038] The expressions for A and C are:

[0039]

[0040] Where r1 and r2 are random numbers in [0,1]; a is a nonlinear convergence factor.

[0041] Improved encirclement and predation phase:

[0042] When |A|<1, the whale enters the contraction and encirclement phase. A nonlinear convergence factor and adaptive inertia weights are used to improve the whale's predation behavior. The mathematical model is as follows:

[0043] D = |C·X * (t)-X(t)|

[0044] X(t+1)=λX * (t)-A·D;

[0045] Where t is the current iteration number, and X(t) is the current position of the individual whale; X * (t) is the prey position vector; X(t+1) represents the position of the next individual whale.

[0046] Improved spiral bubble net movement stage:

[0047] Whales may employ a spiral bubble-web movement pattern during hunting, meaning their position is updated in a spiral motion. By introducing adaptive inertia weights, the mathematical model of this spiral hunting behavior is modified as follows:

[0048]

[0049] Where D′ represents the distance between the current individual and the current best individual; b is a constant representing the logarithmic spiral shape; l is a random number in [-1,1]; and e is the natural constant.

[0050] The whale's behavior is determined by the magnitude of the parameter |A| and the probability factor P. When P ≥ 0.5, it is in the spiral bubble-web movement phase; when P < 0.5, it is in the random search phase and the encirclement and predation phase. The mathematical model is as follows:

[0051]

[0052] The expression for the nonlinear convergence factor α is as follows:

[0053]

[0054] Where: t is the current iteration number, t max This represents the maximum number of iterations.

[0055] The expression for the adaptive inertia weight λ is as follows:

[0056]

[0057] Where: t is the current iteration number, t max The maximum number of iterations is n, where n is the control coefficient and n = 4.

[0058] Step 4, the optimization solution, includes the following steps:

[0059] Step 4.1: Set the rotational speed and load, initialize the population, and set the shut-off angle θ. off The corresponding modules in the SRM control system simulation model are assigned values ​​and run by assigning values ​​to variables.

[0060] Step 4.2: Convert the output of the SRM control system simulation model into a scalar and call it into the IWOA program to calculate the objective function value;

[0061] Step 4.3: Calculate the nonlinear convergence factor a, the adaptive inertia weight λ, and update the individual whale position;

[0062] Step 4.4: The new individual position is assigned to the corresponding module in the SRM control system simulation model and calculated. The algorithm stops when it reaches the maximum number of iterations, and the optimal shut-off angle is obtained.

[0063] This invention discloses a method for optimizing the deactivation angle of a switched reluctance motor based on an improved whale optimization algorithm. The technical effects are as follows:

[0064] 1): This invention applies the Whale Optimization Algorithm (WOA) to the optimization of the deactivation angle parameter of switched reluctance motors. At the same time, it improves WOA to avoid the problems of slow convergence speed, too many parameters, and insufficient local development capability of genetic algorithms. In addition, the principle is simple and easy to implement, and the optimization effect is good.

[0065] 2) To maintain population diversity and improve the algorithm's global exploration in the early stages and its local fine-tuning in the later stages, this invention uses chaotic mapping to initialize the population, proposes a nonlinear convergence factor and adaptive inertia weight, which accelerates the convergence speed in the early stages and effectively avoids the algorithm falling into local optima in the later stages. These improvements enhance the algorithm's optimization capabilities, enabling it to better solve the problem of finding the optimal angle in switched reluctance motors. Attached Figure Description

[0066] The present invention will be further described below with reference to the accompanying drawings and embodiments:

[0067] Figure 1 This is a flowchart of the switching reluctance motor deactivation angle optimization method based on the improved whale optimization algorithm described in this invention.

[0068] Figure 2 This is a structural diagram of the switched reluctance motor control system model of the present invention.

[0069] Figure 3 This is a flowchart of the IWOA described in this invention.

[0070] Figure 4(a) shows the convergence curves of the IWOA, WOA and GA algorithms for the Sphere test function in the embodiments of the present invention.

[0071] Figure 4(b) shows the convergence curves of the IWOA, WOA and GA algorithms for the Ackleys test function in the embodiments of the present invention.

[0072] Figure 5 This is a convergence curve of IWOA for optimizing the deactivation angle of a switched reluctance motor in the embodiment of the present invention.

[0073] Figure 6 The above is a waveform diagram of the SRM output torque before and after IWOA optimization of the turn-off angle in the embodiment of the present invention.

[0074] Figure 7 The image shows the SRM phase current waveforms before and after IWOA optimization of the turn-off angle in the embodiment of the present invention.

[0075] Figure 8(a) shows the static torque characteristic of the switched reluctance motor.

[0076] Figure 8(b) is a model diagram of the A-phase winding of a switched reluctance motor. Detailed Implementation

[0077] The switching reluctance motor shut-off angle optimization method based on the improved whale optimization algorithm includes the following steps: establishing a torque ripple coefficient expression to evaluate the optimization degree of the switching reluctance motor shut-off angle as the optimization objective; transforming the torque ripple coefficient expression into an objective function according to the optimization objective, and transforming the shut-off angle optimization problem into a nonlinear programming problem; proposing an improved whale optimization algorithm to optimize and solve the switching reluctance motor shut-off angle optimization problem; the improved whale optimization algorithm uses Piecewise chaotic mapping to initialize the population, uses a nonlinear convergence factor and introduces adaptive inertia weights to update the position, thereby obtaining the optimal shut-off angle of the switching reluctance motor; to improve the search capability of the whale optimization algorithm, this invention introduces Piecewise chaotic mapping to increase the diversity of the population, and uses a nonlinear convergence factor and adaptive inertia weights, which improves the algorithm's ability to perform global search in the early stage and accurate local search in the later stage.

[0078] Specifically, the following steps are included:

[0079] Step 1: Build a model of the switched reluctance motor control system and set the initial control parameters:

[0080] This invention utilizes Matlab / Simulink software to build a model of a switched reluctance motor control system. The built model of the switched reluctance motor control system is as follows: Figure 2 As shown, the system includes a PID module, a current hysteresis control module, a power converter module, a switched reluctance motor model, a commutation control module, an angle input module, and a speed calculation module. All modules are built into the Matlab / Simulink software. The improved whale optimization algorithm is implemented using Matlab programming through Matlab-Function. The parameters of the switched reluctance motor are shown in Table 1.

[0081] Table 1 Parameters of Switched Reluctance Motor

[0082] Stator / rotor pole number 12 / 8 <![CDATA[Rated speed n / (r·min -1 )]]> 1500 Rated power P / kW 0.75 <![CDATA[moment of inertia J / (kg·m 2 )]]> 0.0017 Rated voltage U / V 240 <![CDATA[Friction coefficient D / (N·s·m -1 )]]> 0.00813

[0083] To ensure the reliability of the comparative experiment, all control parameters remained the same except for the turn-off angle parameter, including the PID control parameter K. P =3, K I =1.8, K D =0, opening angle θ on =0°.

[0084] In this embodiment, taking the working condition of load = 2 N·m and speed = 500 rpm as an example, the output torque signal of the switched reluctance motor is collected to construct the objective function of the turn-off angle; of course, users can select the required output signal to construct the objective function according to the actual engineering application, and are not limited to the output torque signal provided in this embodiment.

[0085] Step 2: Minimize the torque ripple in the commutation interval as the target for optimizing the shut-off angle of the switched reluctance motor. In this embodiment, reducing torque ripple is the optimization target. Users can choose other optimization targets according to their actual applications.

[0086] Step 3: Transform the optimization objective into an objective function, and transform the shut-off angle optimization problem into a problem of finding the maximum and minimum values ​​of the objective function;

[0087] According to the optimization objective of this embodiment, the root mean square value T of the instantaneous torque is... ripple Define the objective function as:

[0088]

[0089] Where: T, T L These represent the instantaneous actual torque and the load torque, respectively, with τ being the simulation time.

[0090] Step 4: An improved whale optimization algorithm is proposed to optimize the shut-off angle problem of the switched reluctance motor. The improved whale optimization algorithm uses a Piecewise chaotic mapping to initialize the population, adopts a nonlinear convergence factor and introduces an adaptive inertia weight to update the position, thereby improving the algorithm's accuracy and ability to escape local optima, and thus obtaining the optimal shut-off angle of the switched reluctance motor.

[0091] The mathematical model of the improved whale optimization algorithm is described as follows:

[0092] (1) Population initialization: The population is initialized using a Piecewise chaotic map. The expression for the Piecewise chaotic map is as follows:

[0093]

[0094] Assuming the whale population size is N and the search space is j-dimensional, the position of the i-th whale in the j-dimensional space can be represented using the Piecewise chaotic mapping.

[0095] x i,j =lb j +y i,j (ub j -lb j ), i = 1, 2, 3, ..., N;

[0096] Traditional whale optimization algorithms use random population initialization, which can lead to uneven population distribution and get stuck in local optima, affecting the algorithm's final optimization performance. This invention introduces a Piecewise chaotic mapping to initialize the population, resulting in a more even population distribution, enhanced population diversity, and avoidance of getting stuck in local optima.

[0097] The target prey's location corresponds to the global optimal solution of the objective function;

[0098] (2) Random search phase: A nonlinear convergence factor and adaptive inertia weight are used to improve the random foraging behavior of whales. The expression for the nonlinear convergence factor is as follows:

[0099]

[0100] Where: t is the current iteration number, T max This represents the maximum number of iterations.

[0101] The adaptive inertia weight expression is as follows:

[0102]

[0103] Where: t is the current iteration number, t max The maximum number of iterations is n, and n is the control coefficient, where n = 4.

[0104] The mathematical model for improving whale foraging behavior is as follows:

[0105] D = |C·X rand (t)-X(t)|

[0106] X(t+1)=λX rand (t)-A·D;

[0107] Where t is the current iteration number, X(t) is the current position of the individual whale, and X rand (t) represents a randomly selected whale individual from the current swarm, A and C are coefficient vectors, and λ is the adaptive inertia weight. The expressions for A and C are:

[0108] A = 2a·r1-a

[0109] C = 2·r²;

[0110] Where r1 and r2 are random numbers in [0,1]; a is a nonlinear convergence factor.

[0111] (3) Encirclement and Predation Phase: When |A|<1, the whale enters the contraction and encirclement phase. The nonlinear convergence factor and adaptive inertia weight are used to improve the whale's predation behavior. The mathematical model is as follows:

[0112] D = |C·X * (t)-X(t)|

[0113] X(t+1)=λX * (t)-A·D;

[0114] Where: t is the current iteration number, X(t) is the current position of the individual whale, X * (t) is the prey position vector, and λ is the adaptive inertia weight.

[0115] (4) Spiral Bubble-Net Movement Phase: Whales may employ a spiral bubble-net movement pattern during foraging, meaning their position is updated in a spiral motion. Introducing adaptive inertia weights modifies the mathematical model of this spiral foraging behavior as follows:

[0116]

[0117] Where D′ represents the distance between the current individual and the current best individual, b is a constant representing the logarithmic spiral shape, and l is a random number in [-1,1].

[0118] The whale's behavior is determined by the magnitude of the parameter |A| and the probability factor P. When P ≥ 0.5, it is in the spiral bubble-web movement phase; when P < 0.5, it is in the random search phase and the encirclement and predation phase. The mathematical model is as follows:

[0119]

[0120] To improve the algorithm's search capability in the early stages of iteration and maintain population diversity, a lower weight should be used to reduce the guidance of the current optimal solution on the search direction; in the later stages of iteration, a higher weight should be applied to the current optimal solution to enhance its guidance on the search direction. Therefore, the adaptive inertia weight λ proposed in this invention exhibits a non-linear increasing trend with the number of iterations. Traditional whale optimization algorithms use a linear convergence factor α. This invention uses a non-linear convergence factor, with the decay rate accelerating with the number of iterations, which is beneficial for improving the convergence speed in the early stages and enhancing the global search capability. The flowchart of the improved whale optimization algorithm is as follows: Figure 3 As shown.

[0121] To verify the computational performance of the improved whale optimization algorithm, Sphere and Ackleys were selected as test functions. The function expressions are shown in Table 2. The algorithm was then compared with the basic whale optimization algorithm and the basic genetic algorithm. Specific parameter settings were as follows: dimension 5, population size 35, and maximum number of iterations 1500.

[0122] Table 2 Test function parameter settings

[0123]

[0124] Figure 4 shows the convergence curves of the Improved Whale Optimization Algorithm (IWOA), the Whale Optimization Algorithm (WOA), and the Genetic Algorithm (GA) for different test functions. The figure shows that the IWOA proposed in this invention outperforms the traditional GA and WOA in terms of convergence algebra, convergence speed, and global optimum for different test functions. Test results demonstrate that IWOA has excellent optimization capabilities, thus enabling it to find the optimal turn-off angle parameter of the switched reluctance motor more quickly and accurately.

[0125] The improved whale optimization algorithm is used to solve the optimization problem of the deactivation angle of a switched reluctance motor. The steps are as follows:

[0126] (1) Set parameters: population size N, dimension j, maximum number of iterations t max Piecewise chaotic mapping initializes individual whale populations and records their current positions;

[0127] (2) Establish the objective function based on the optimization objective;

[0128] (3) The optimal shut-off angle corresponding to the minimum objective function is obtained by using the improved whale optimization algorithm proposed in this invention;

[0129] (4) Reach the maximum number of iterations and output the optimal shut-off angle result.

[0130] To verify the effectiveness of the improved whale optimization algorithm proposed in this invention in the problem of optimizing the deflection angle of switched reluctance motors, simulation experiments were conducted based on a practical example. The relevant parameters were set as follows: population size N = 20, dimension j = 1, and maximum number of iterations t. max =50, parameter optimization range is [7.5, 22.5]. Simulation results are as follows. Figure 5 , Figure 6 , Figure 7 As shown.

[0131] Figure 5 The graph shows the convergence curve of IWOA for optimizing the deactivation angle of switched reluctance motors. It can be seen that the algorithm converges after 30 iterations, which is very fast.

[0132] Figure 6 The waveforms show the output torque of the SRM before and after IWOA optimization of the turn-off angle. It is clear that the output torque ripple is smaller after the turn-off angle optimization, indicating that the turn-off angle found by IWOA is more suitable for the operation of the switched reluctance motor under this condition and can effectively suppress the torque ripple in the commutation interval.

[0133] Figure 7The images show the three-phase current waveforms of the SRM before and after IWOA optimization of the turn-off angle. It is clear that the phase current is more stable and the peak current is smaller after the turn-off angle optimization, indicating that this turn-off angle effectively reduces the fluctuation of the phase current during commutation, resulting in smoother phase current and output torque. This demonstrates that IWOA has a very good optimization effect on the turn-off angle of the switched reluctance motor.

[0134] In summary, the method provided by this invention has a faster convergence speed and higher optimization accuracy than traditional GA and WOA methods, and is more suitable for optimizing the turn-off angle of switched reluctance motors.

Claims

1. A method for optimizing the deactivation angle of a switched reluctance motor based on an improved whale optimization algorithm, characterized in that... Includes the following steps: Step 1: Build a model of the switched reluctance motor control system and set the initial control parameters; Step 2: Determine the optimization target for the shut-off angle of the switched reluctance motor; Step 3: Transform the optimization objective of Step 2 into an objective function, and transform the shut-off angle optimization problem into solving the problem of finding the maximum and minimum values ​​of the objective function; Step 4: Optimize and solve the problem of switching reluctance motor deactivation angle based on the improved whale optimization algorithm; In step 3, the optimization objective is to suppress torque ripple, which is actually a problem of minimizing torque ripple. This involves minimizing the root mean square value of the instantaneous torque. T ripple Define the objective function as: ； in: T , T L These are the instantaneous actual torque and the load torque values, respectively. τ For simulation time; In step 4, the improved whale optimization algorithm uses Piecewise chaotic mapping to initialize the population, adopts a nonlinear convergence factor and introduces adaptive inertia weight to update the position, thereby improving the algorithm accuracy and the ability to escape local optima, and thus obtaining the optimal turn-off angle of the switched reluctance motor. The improved whale optimization algorithm employs the following strategies: Improved initial population: The population is initialized using a Piecewise chaotic map, and the expression for the Piecewise chaotic map is: ； Wherein: control parameters p ∈[0, 0.5], y k ∈(0,1); Assuming the whale population size is N The search space is j The dimension can be represented using the Piecewise chaotic mapping. i Only one whale in the first j The position in 3D space is: ； in: x i,j Indicates the first i Only one whale in the first j Position in 3D space lb j This indicates the lower limit of the whale's location range. y i,j This represents the mapping value of the Piecewise chaotic mapping in (0,1). ub j This indicates the upper limit of the whale's location range. N Indicates the size of a whale population; The target prey's location corresponds to the global optimal solution of the objective function; Improved random search phase: A nonlinear convergence factor and adaptive inertia weights are used to improve the whale's search and foraging behavior. The mathematical model is as follows: ； in, t This represents the current iteration number; X ( t () represents the current location of the individual whale; X rand ( t () is a whale individual randomly selected from the current group; A and C These are the coefficient vectors, λ For adaptive inertia weights; X ( t +1 indicates the location of the next individual whale; A , C The expression is: ； in, r 1 and r 2 is a random number in the range [0,1]. a It is a nonlinear convergence factor; Improved encirclement and predation phase: When |A|<1, the whale enters the contraction and encirclement phase. A nonlinear convergence factor and adaptive inertia weights are used to improve the whale's predation behavior. The mathematical model is as follows: ； in, t This represents the current iteration number. X ( t () represents the current location of the individual whale; X * ( t ) represents the prey's position vector; X ( t +1 indicates the location of the next individual whale; Improved spiral bubble net movement stage: After introducing adaptive inertia weights, the mathematical model of spiral predation behavior is changed to: ； in, D′ This represents the distance between the current individual and the current best individual; b It is a constant representing the shape of a logarithmic spiral; l It is a random number within the range [-1, 1]; e It is a natural constant; Based on parameter |A| and probability factor P The size of a whale determines its behavior. P ≥0.5, in the spiral bubble web movement stage; when P <0.5 indicates the region is in the random search and encirclement predation phases. The mathematical model is as follows: ； Nonlinear convergence factor a The expression is as follows: ； in: t This represents the current iteration number. t max This represents the maximum number of iterations. Adaptive inertia weights λ The expression is as follows: ； in: t This represents the current iteration number. t max The maximum number of iterations, n This is the control factor.

2. The method for optimizing the deactivation angle of a switched reluctance motor based on the improved whale optimization algorithm according to claim 1, characterized in that: In step 1, the switched reluctance motor control system model includes: a PID module, a current hysteresis control module, a power converter module, a switched reluctance motor model, a commutation control module, an angle input module, and a speed calculation module. The PID module is used to generate a reference current to control the speed loop; The current hysteresis control module is used to perform hysteresis control on the phase current so that it tracks the reference current. Power converter module for driving switched reluctance motors; The commutation control module is used to detect the rotor position and calculate and generate switching signals; Angle input module, used to set the values ​​of the opening angle and the closing angle; The speed calculation module is used to calculate the current motor speed based on the detected rotor position.

3. The method for optimizing the deactivation angle of a switched reluctance motor based on the improved whale optimization algorithm according to claim 1, characterized in that: In step 2, minimizing the torque ripple in the commutation interval is used as the optimization target for the shut-off angle of the switched reluctance motor.

4. The method for optimizing the deactivation angle of a switched reluctance motor based on the improved whale optimization algorithm according to claim 1, characterized in that: Step 4, the optimization solution includes the following steps: Step 4.1: Set the rotation speed and load, initialize the population, and set the shut-off angle. θ off The corresponding modules in the SRM control system simulation model are assigned values ​​and run by assigning values ​​to variables. Step 4.2: Convert the output of the SRM control system simulation model into a scalar and call it into the IWOA program to calculate the objective function value; Step 4.3: Calculate the nonlinear convergence factor a Adaptive inertia weights λ And update the location of individual whales; Step 4.4: The new individual position is assigned to the corresponding module in the SRM control system simulation model and calculated. The algorithm stops when it reaches the maximum number of iterations, and the optimal shut-off angle is obtained.

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