Cluster system simulation solving method and device, storage medium and computer equipment

Abstract

According to the cluster system simulation solving method and device, the storage medium and the computer equipment provided by the invention, during simulation solving, the cluster system is divided into the multiple independent subsystems according to the topological structure of the cluster system, and the digital model of each subsystem is constructed, so that the model matrix dimension is reduced, and the single-step calculation amount is reduced; and then differential solution is carried out on the digital model of each subsystem to obtain a differential solution result, and then algebraic solution is carried out, so that mutual containment of the two subsystems is avoided, and the speed is increased. Before algebraic solution, dependence parameters of the cluster system are determined according to the controlled relationship among the subsystems, and whether the cluster system has an algebraic ring is determined and judged based on the dependence parameters; if yes, carrying out iterative solution on each digital model by adopting a Newton-Raphson iterative algorithm to avoid error accumulation caused by closed-loop dependence; and if not, sequentially solving the algebraic equations in the digital models by adopting a display function, and improving the solving efficiency to a millisecond level.

Description

Technical Field

[0001] The present application relates to the field of system simulation technology, and in particular to a cluster system simulation solution method, device, storage medium and computer equipment. Background Art

[0002] Power electronics and motor systems are essential components of modern power engineering and automation technology. With the continuous advancement of electrical engineering and its applications, power electronics and motor systems are becoming increasingly complex, their scale is increasing, and they are exhibiting cluster characteristics. Power electronics and motors, as well as power electronics within each other, are tightly coupled and interact with each other, resulting in complex dynamic behavior. Therefore, efficient simulation of power electronics and motor clusters is becoming increasingly important. However, the complex structure of power electronics and motor clusters often requires a significant amount of time for simulation.

[0003] Currently, power electronics and motor cluster system models typically take the mathematical form of differential algebraic equations (DAEs). Existing simulation solutions for the complex coupling of power electronics and motor systems typically rely on multiple iterations to solve the algebraic constraints in the DAEs, or transform the DAEs into ordinary differential equations (ODEs) using implicit integration algorithms. These simulation solutions guarantee solution accuracy but increase computational complexity and time overhead, resulting in a trade-off between efficiency and accuracy. Summary of the Invention

[0004] The purpose of this application is to solve at least one of the above-mentioned technical deficiencies, especially the technical defect that the cluster system simulation solution method in the prior art has a balance between efficiency and accuracy.

[0005] This application provides a cluster system simulation solution method, the method comprising:

[0006] Dividing the cluster system into multiple independent subsystems according to the topological structure of the cluster system, and constructing a digital model of each subsystem; wherein each subsystem is connected by signals through a controlled source;

[0007] Based on the state variables of each subsystem at the current moment, the differential equations in each digital model are differentially solved to obtain the state variables of each subsystem at the next moment;

[0008] Determining dependent parameters of the cluster system according to the controlled relationships between the subsystems, and determining whether an algebraic loop exists in the cluster system based on the dependent parameters;

[0009] If so, the Newton-Raphson iterative algorithm is used to iteratively solve the algebraic equations in each digital model to obtain the controlled input variables and output variables of each subsystem at the next moment;

[0010] If not, the explicit function is used to sequentially solve the algebraic equations in each digital model to obtain the controlled input variables and output variables of each subsystem at the next moment.

[0011] Optionally, the mathematical expression of the digital model includes:

[0012]

[0013]

[0014]

[0015] Where, Represents a differential equation; represents the state variable predicted by the i-th subsystem; Represents the actual state variables of the i-th subsystem; represents the independent input variable of the i-th subsystem; represents the controlled input variable of the i-th subsystem; t represents time; and All represent algebraic equations; and They represent the output variables of the i-th subsystem and the j-th subsystem respectively; N represents the total number of subsystems in the cluster system.

[0016] Optionally, the differential equations in each digital model are differentially solved based on the state variables of each subsystem at the current moment to obtain the state variables of each subsystem at the next moment, including:

[0017] For each subsystem, obtain the state variables of the subsystem at the current moment;

[0018] According to the differential equation in the digital model corresponding to the subsystem, the Runge-Kutta method is used to solve the derivative of the state variable at the current moment, and the derivative is integrated to the next moment to obtain the state variable at the next moment.

[0019] Optionally, the controlled relationship includes a first association relationship and a second association relationship; the dependent parameter includes a first dependent parameter and a second dependent parameter;

[0020] Determining the dependent parameters of the cluster system according to the controlled relationship between the subsystems includes:

[0021] Expanding the output variables and controlled input variables of each subsystem to obtain output scalar elements corresponding to the output variables and controlled input scalar elements corresponding to the controlled input variables;

[0022] For each subsystem, determining a first association relationship between an output scalar element and a controlled input scalar element in the subsystem, and generating a first dependency parameter according to the first association relationship;

[0023] A second association relationship between the controlled input scalar element in the subsystem and the output scalar elements of other subsystems is determined, and a second dependent parameter is generated according to the second association relationship.

[0024] Optionally, the determining, based on the dependent parameter, whether an algebraic ring exists in the cluster system includes:

[0025] Constructing a dependency matrix of the cluster system according to the dependency parameters, and determining whether an element of the dependency matrix is 0;

[0026] If so, determining that no algebraic ring exists in the cluster system;

[0027] If not, it is determined that an algebraic ring exists in the cluster system.

[0028] Optionally, the judgment formula of the dependency matrix includes:

[0029]

[0030] Where, and are all elements in the dependency matrix, representing the first dependency parameter and the second dependency parameter in the dependency parameters respectively; n represents the total number of subsystems in the cluster system; represents the i-th subsystem; represents the i-th controlled input scalar element in the controlled input variables; Represents the i-th output scalar element in the output variable.

[0031] Optionally, the method further comprises:

[0032] After obtaining the state variables, controlled input variables, and output variables of each subsystem at the next moment, determining whether the next moment is the last step in the system simulation duration;

[0033] If yes, then the simulation solution of the cluster system is terminated and the solution result is output;

[0034] If not, continue to perform simulation and solve the cluster system at the next moment.

[0035] This application also provides a cluster system simulation solution device, including:

[0036] A system partitioning module is used to divide the cluster system into multiple independent subsystems according to the topological structure of the cluster system and to construct a digital model of each subsystem; wherein each subsystem is connected to the signal by a controlled source;

[0037] A differential solution module is used to perform differential solution on the differential equations in each digital model based on the state variables of each subsystem at the current moment, and obtain the state variables of each subsystem at the next moment;

[0038] an algebraic loop determination module, configured to determine dependent parameters of the cluster system according to the controlled relationships between the subsystems, and determine whether an algebraic loop exists in the cluster system based on the dependent parameters;

[0039] A first algebraic solution module is configured to iteratively solve the algebraic equations in each digital model using a Newton-Raphson iterative algorithm when an algebraic loop exists in the cluster system, so as to obtain the controlled input variables and output variables of each subsystem at the next moment;

[0040] The second algebraic solving module is used to sequentially solve the algebraic equations in each digital model using an explicit function when no algebraic loop exists in the cluster system, so as to obtain the controlled input variables and output variables of each subsystem at the next moment.

[0041] The present application also provides a storage medium storing computer-readable instructions. When the computer-readable instructions are executed by one or more processors, the one or more processors execute the steps of the cluster system simulation solution method as described in any one of the above embodiments.

[0042] The present application also provides a computer device, comprising: one or more processors, and a memory;

[0043] The memory stores computer-readable instructions, and when the computer-readable instructions are executed by the one or more processors, the steps of the cluster system simulation solution method as described in any one of the above embodiments are performed.

[0044] It can be seen from the above technical solutions that the embodiments of the present application have the following advantages:

[0045] The cluster system simulation and solution method, device, storage medium and computer equipment provided in this application can, when simulating and solving the cluster system, first divide the cluster system into multiple subsystems according to the topological structure of the cluster system. The subsystems are independent of each other but connected by controlled source signals. Therefore, a digital model of each subsystem is constructed separately, which can reduce the matrix dimension of the entire cluster system model and thus reduce the amount of single-step calculations. Then, based on the state variables of each subsystem at the current moment, the differential equations in each digital model can be differentially solved to obtain the state variables of each subsystem at the next moment. Here, the differential equations of the digital model are solved first to avoid the mutual restraint between the differential equations and the algebraic equations, thereby improving the speed. Then, before solving the algebraic equations, the dependent parameters of the cluster system can be determined according to the controlled relationship between the subsystems, and based on the dependent parameters, it can be determined whether the cluster system has an algebraic loop. Then, different solution methods can be adopted according to the judgment results to avoid blind iterative solution and reduce the overall simulation efficiency. For example, when an algebraic loop exists, the Newton-Raphson iterative algorithm can be used to iteratively solve the algebraic equations in each digital model, so that the exact solution can be approximated through multiple iterations, avoiding the accumulation of errors caused by closed-loop dependence; when an algebraic loop does not exist, the explicit function can be used to sequentially solve the algebraic equations in each digital model, thereby improving the solution efficiency to the millisecond level; through these two methods, the present application can efficiently algebraically solve the controlled input variables and output variables of each subsystem at the next moment. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0047] Figure 1 A schematic diagram of a flow chart of a cluster system simulation solution method provided in an embodiment of the present application;

[0048] Figure 2 A schematic diagram of the structure of a cluster system simulation solution device provided in an embodiment of the present application;

[0049] Figure 3 A schematic diagram of the internal structure of a computer device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0050] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0051] Currently, power electronics and motor cluster system models typically take the mathematical form of differential algebraic equations (DAEs). Existing simulation solutions for the complex coupling of power electronics and motor systems typically rely on multiple iterations to solve the algebraic constraints in the DAEs, or transform the DAEs into ordinary differential equations (ODEs) using implicit integration algorithms. These simulation solutions guarantee solution accuracy but increase computational complexity and time overhead, resulting in a trade-off between efficiency and accuracy.

[0052] Based on this, this application proposes the following technical solutions, please refer to the following for details:

[0053] In one embodiment, Figure 1 As shown, Figure 1 A schematic diagram of a cluster system simulation solution method provided in an embodiment of the present application; the present application also provides a schematic diagram of a cluster system simulation solution method, which specifically includes the following:

[0054] S110: Divide the cluster system into multiple independent subsystems according to the topological structure of the cluster system, and construct a digital model of each subsystem; wherein each subsystem is connected to a signal through a controlled source.

[0055] In this step, when simulating and solving the cluster system, the computer equipment can first divide the cluster system into multiple subsystems based on the topological structure of the cluster system. The subsystems are independent of each other but connected by controlled source signals. Therefore, constructing a digital model of each subsystem separately can reduce the matrix dimension of the entire cluster system model, thereby reducing the amount of single-step calculations.

[0056] A controlled source is an input signal whose value isn't directly given, but rather is generated by a function calculated from the outputs of other subsystems. For example, in a cluster system, the voltage output of one subsystem is used to control the current input of another. Therefore, information exchange and physical coupling exist between the subsystems in the cluster system of this application.

[0057] Specifically, to improve the computational efficiency and manageability of cluster system simulations, computing equipment can first divide the entire system into several topologically disconnected subsystems based on the topological characteristics of the cluster system's components. Each subsystem is relatively independent in structure, but controlled sources form cross-subsystem signal transmission paths between them. This modularization of the cluster system allows for the construction of digital models for each subsystem, represented by differential-algebraic equations (DAEs).

[0058] It can be understood that through topological division, the present application can effectively split the mathematical model of the entire cluster system into multiple low-dimensional subsystem models, so that the dimension of the overall state variable matrix of the cluster system is greatly reduced, thereby significantly reducing the calculation scale and amount within each simulation step, and improving the speed and efficiency of simulation processing.

[0059] S120: Differentially solving the differential equations in each digital model based on the state variables of each subsystem at the current moment to obtain the state variables of each subsystem at the next moment.

[0060] In this step, after the digital model of each subsystem is constructed through step S110, the computer device can perform differential solutions on the differential equations in each digital model based on the state variables of each subsystem at the current moment to obtain the state variables of each subsystem at the next moment. Solving the differential equations of the digital model first can avoid the mutual restraint between the differential equations and the algebraic equations, thereby improving the speed.

[0061] It is understandable that digital models are composed of differential equations and algebraic equations, and algebraic equations often involve signal dependencies between multiple subsystems. If processed together with differential equations, the complexity of the overall system equations will be significantly increased. Therefore, when solving digital models, computer equipment can adopt a strategy of prioritizing the solution of differential equations. That is, within each time step, the state evolution equations of each subsystem are first solved, and the process of the system state changing over time is deduced. This will clarify the state change trend of the system within the current step and provide a stable input basis for the algebraic equations, thereby reducing the difficulty of algebraic solution and improving the overall solution efficiency and stability of the simulation.

[0062] S130: Determine dependency parameters of the cluster system according to the controlled relationships between the subsystems, and determine whether an algebraic loop exists in the cluster system based on the dependency parameters.

[0063] In this step, after the differential solution of the digital model is obtained through step S120, the computer device can determine the dependent parameters of the cluster system based on the controlled relationship between each subsystem, and determine whether an algebraic loop exists in the cluster system based on the dependent parameters. Different algebraic solution methods can then be adopted based on the judgment results to avoid blind iterative solutions and reduce the overall simulation efficiency.

[0064] A controlled relationship refers to the fact that certain input quantities in a subsystem are not given independently, but are obtained through functional operations on the output quantities of other subsystems. Therefore, this application can use dependency parameters to quantify and express the mapping relationship between such controlled inputs and external outputs. An algebraic loop refers to a closed-loop dependency relationship between subsystems, where each subsystem has its own input and output. For example, the input of subsystem A is controlled by the output of subsystem B, and the input of subsystem B is controlled by the output of subsystem A.

[0065] It's understandable that after the computer solves the differential equations for each subsystem, the system's state variables at the current time step are updated to the next moment. At this point, the computer needs to process the coupling relationships between the subsystems—that is, the input of one subsystem is controlled by the output of other subsystems. Therefore, the computer can determine the dependent parameters of the cluster system based on the controlled relationships between the subsystems. By analyzing these dependent parameters, it constructs a structural diagram that reflects the signal flow and control influences within the system. Based on this structural diagram, it can determine whether an algebraic loop exists within the entire cluster system at the current moment, and then select an appropriate algebraic solution strategy to improve overall simulation efficiency.

[0066] S140: If an algebraic loop exists, the Newton-Raphson iterative algorithm is used to iteratively solve the algebraic equations in each digital model to obtain the controlled input variables and output variables of each subsystem at the next moment.

[0067] In this step, after the cluster system is identified as an algebraic loop in step S130, if an algebraic loop is formed, the computer device can use the Newton-Raphson iterative algorithm to iteratively solve the algebraic equations in each digital model, so that the exact solution can be approximated through multiple iterations to obtain the controlled input variables and output variables of each subsystem at the next moment, avoiding error accumulation due to closed-loop dependence.

[0068] It is understandable that the existence of algebraic loops in the cluster system indicates that a mutually dependent closed path is formed between the controlled input variables of a subsystem and the output variables of other subsystems, resulting in the inability to analyze the algebraic variables in sequence. At this time, the computer equipment can use the Newton-Raphson iterative algorithm based on this structural feature to iteratively solve the algebraic equations in the digital model of each subsystem to obtain the controlled input variables and output variables of each subsystem at the next moment.

[0069] Among them, the Newton-Raphson iterative algorithm refers to a classic numerical approximation method that can gradually converge to the exact solution of the algebraic equation by continuously correcting the variable values based on the initial values. It can be used to deal with closed-loop structures with complex coupling between such variables and where explicit solutions are difficult to obtain.

[0070] Therefore, through multiple rounds of iterative solutions using the Newton-Raphson iterative algorithm, computer equipment can effectively approximate the true values of the controlled input variables and output variables corresponding to each subsystem in the system at the current time step, thereby ensuring the accuracy and stability requirements of system simulation. At the same time, it avoids direct calculation errors or solution uncertainties caused by closed-loop dependencies, prevents errors from propagating and accumulating among multiple subsystems, and ensures that the overall simulation process has high convergence and accuracy.

[0071] S150: If no algebraic loop exists, the algebraic equations in each digital model are sequentially solved using an explicit function to obtain the controlled input variables and output variables of each subsystem at the next moment.

[0072] In this step, after the cluster system is identified as an algebraic loop in step S130, if no algebraic loop is formed, the computer device can use the display function to sequentially solve the algebraic equations in each digital model to obtain the controlled input variables and output variables of each subsystem at the next moment, thereby improving the solution efficiency to the millisecond level.

[0073] It is understandable that there are no algebraic loops in the cluster system, which means that the controlled input variables between each subsystem and the output variables of other subsystems do not form a mutually dependent closed path, which means that the relationship between the algebraic variables has a clear calculation order and a non-circular dependency structure. At this time, the computer equipment can directly use explicit function forms to sequentially solve the algebraic equations in the digital models of each subsystem.

[0074] Specifically, within the current time step, the state variables of each subsystem have been obtained by solving the differential equations, so the variable input conditions required in the algebraic equations are complete. The computer equipment can substitute the controlled input variables of the subsystem one by one according to the dependency order and simultaneously derive the corresponding output variables. Since there is no iterative dependency between variables in this process, the entire algebraic solution process can be completed using a non-iterative analytical calculation method, which greatly simplifies the operation process and computing resource overhead. Compared with the traditional general iterative method, this sequential solution mechanism can significantly improve the model solution speed without reducing the accuracy, thereby improving the execution efficiency of the algebraic solution process to the millisecond level.

[0075] In the above embodiment, when simulating and solving the cluster system, the cluster system can be first divided into multiple subsystems according to the topological structure of the cluster system. The subsystems are independent of each other but connected by controlled source signals. Therefore, the digital model of each subsystem is constructed separately, which can reduce the matrix dimension of the entire cluster system model and thus reduce the amount of single-step calculations. Then, based on the state variables of each subsystem at the current moment, the differential equations in each digital model can be differentially solved to obtain the state variables of each subsystem at the next moment. Here, solving the differential equations of the digital model first can avoid the mutual restraint between the differential equations and the algebraic equations, thereby improving the speed. Then, before solving the algebraic equations, the dependent parameters of the cluster system can be determined according to the controlled relationship between the subsystems, and based on the dependent parameters, it can be determined whether the cluster system has an algebraic loop. Then, different solution methods can be adopted according to the judgment results to avoid blind iterative solution and reduce the overall simulation efficiency. For example, when an algebraic loop exists, the Newton-Raphson iterative algorithm can be used to iteratively solve the algebraic equations in each digital model, so that the exact solution can be approximated through multiple iterations, avoiding the accumulation of errors caused by closed-loop dependence; when an algebraic loop does not exist, the explicit function can be used to sequentially solve the algebraic equations in each digital model, thereby improving the solution efficiency to the millisecond level; through these two methods, the present application can efficiently algebraically solve the controlled input variables and output variables of each subsystem at the next moment.

[0076] In one embodiment, the mathematical expression of the digital model in step S110 may include:

[0077]

[0078]

[0079]

[0080] Where, Represents a differential equation; represents the state variable predicted by the i-th subsystem; Represents the actual state variables of the i-th subsystem; represents the independent input variable of the i-th subsystem; represents the controlled input variable of the i-th subsystem; t represents time; and All represent algebraic equations; and They represent the output variables of the i-th subsystem and the j-th subsystem respectively; N represents the total number of subsystems in the cluster system.

[0081] It's understandable that the mathematical model of a subsystem is presented as a differential algebraic equation, consisting primarily of three formulas. The first formula represents a differential equation, determining how state variables change over time. The second and third formulas represent algebraic equations. The second formula determines the subsystem's external output variables, while the third describes how the subsystem's control inputs depend on the outputs of other subsystems, thereby establishing the coupling relationship of the entire system.

[0082] In one embodiment, the process of performing differential solutions on the differential equations in each digital model based on the current state variables of each subsystem in step S120 to obtain the state variables of each subsystem at the next moment may include:

[0083] S121: For each subsystem, obtain the state variables of the subsystem at the current moment.

[0084] S122: According to the differential equation in the digital model corresponding to the subsystem, the Runge-Kutta method is used to solve the derivative of the state variable at the current moment, and the derivative is integrated to the next moment to obtain the state variable at the next moment.

[0085] In this embodiment, for each subsystem, the computer device can obtain the state variable of the subsystem at the current moment, and then use the Runge-Kutta method to solve the derivative of the state variable at the current moment based on the differential equation in the digital model corresponding to the subsystem, and integrate the derivative to the next moment to obtain the state variable at the next moment.

[0086] Among them, the Runge-Kutta method refers to a commonly used explicit multi-step integration method, which can accurately approximate the derivative value of the state variable at the current moment by multiple weighted evaluations of the derivative function without introducing iterations.

[0087] Specifically, the computer device can obtain the state variables of the subsystem at the current simulation moment as the initial conditions for solving the differential equation. Then, based on the differential equation structure in the digital model, the Runge-Kutta method with higher numerical accuracy is used to solve the current state variables, and the derivative of the state variable at the current moment is obtained. The derivative is used as the integral input, and the derivative is numerically integrated within the set step size range to derive the state variables of the subsystem at the next simulation moment, thereby realizing the accurate evolution of the state variables from the current moment to the future moment, and ensuring the numerical stability and accuracy of the differential equation solution.

[0088] In one embodiment, the controlled relationship in step S130 may include a first association relationship and a second association relationship; the dependent parameter may include a first dependent parameter and a second dependent parameter; wherein the process of determining the dependent parameter of the cluster system based on the controlled relationship between each subsystem may include:

[0089] S131: Expand the output variables and controlled input variables of each subsystem to obtain output scalar elements corresponding to the output variables and controlled input scalar elements corresponding to the controlled input variables.

[0090] S132: For each subsystem, determine a first association relationship between an output scalar element and a controlled input scalar element in the subsystem, and generate a first dependency parameter according to the first association relationship.

[0091] S133: Determine a second association relationship between the controlled input scalar element in the subsystem and the output scalar elements of other subsystems, and generate a second dependent parameter according to the second association relationship.

[0092] In this embodiment, when determining the dependency parameters of the cluster system, the computer device may first perform scalar expansion on the output variables and controlled input variables of each subsystem to obtain output scalar elements corresponding to the output variables and controlled input scalar elements corresponding to the controlled input variables. For each subsystem, the computer device may determine a first association between the output scalar element and the controlled input scalar element in that subsystem and generate a first dependency parameter based on the first association. Furthermore, the computer device may determine a second association between the controlled input scalar element in that subsystem and the output scalar elements of other subsystems and generate a second dependency parameter based on the second association.

[0093] Among them, the first dependency parameter can be used to characterize whether the internal output variable of the subsystem is directly controlled by its own controlled source input, that is, whether the output depends on its own input. In this application, it can be expressed as , represents the internal j-th output scalar element of subsystem i Whether it depends on the kth controlled input scalar element in this subsystem The second dependency parameter can be used to characterize the controlled coupling relationship between subsystems, that is, whether the input of a subsystem depends on the output of other subsystems. In this application, it can be expressed as , represents the wth controlled input scalar element in the i-th subsystem between subsystems Whether it depends on the kth output scalar element in the jth subsystem .

[0094] Specifically, the computer device can convert the output variable of subsystem i into Expand into a combination of scalar elements to subsequently represent the relationship between each output variable and each controlled input variable. The expansion is specifically expressed as follows:

[0095]

[0096] Where, arrive Indicates output variables Each output scalar element in the output variable Depend on scalar elements, is the number of output scalar elements in subsystem i, which is also the output variable The length of the vector.

[0097] Based on the above formula, the algebraic equation in the digital model By decomposing it, we can get the following formula:

[0098]

[0099] Where, Represents the controlled input variable the kth scalar element of ; Indicates the first dependency parameter introduced, and its value can be expressed as follows:

[0100]

[0101] Then, the computer device can calculate the algebraic equation Rewrite it as follows:

[0102]

[0103] Where, 、 and Represent the controlled input variables The first, the (last) first and w-th scalar element, is the controlled input variable length.

[0104] Therefore, each controlled input variable determined in the algebraic equations of the numerical model The controlled input scalar elements of can be expressed as follows:

[0105]

[0106] Where, Indicates the second dependent parameter introduced, and its value definition can be expressed as follows:

[0107]

[0108] In one embodiment, the process of determining whether an algebraic loop exists in the cluster system based on the dependency parameters in step S130 may include:

[0109] S134: constructing a dependency matrix of the cluster system according to the dependency parameters, and determining whether any element in the dependency matrix is 0.

[0110] S135: If yes, determine that no algebraic ring exists in the cluster system.

[0111] S136: If not, determine that an algebraic ring exists in the cluster system.

[0112] In this embodiment, when determining an algebraic ring, the computer device may construct a dependency matrix of the cluster system based on the dependency parameters and determine whether any element in the dependency matrix is 0. If any element is 0, it can be determined that no algebraic ring exists in the cluster system; if no element is 0, it can be determined that an algebraic ring exists in the cluster system.

[0113] It can be understood that the dependency matrix can fully describe the dependencies between various subsystems and between the controlled inputs and outputs within them. This dependency matrix, with dependency parameters as units, reflects whether there are direct or indirect functional dependencies between all controlled input and output variables in the system. Its elements are typically 0 or 1, indicating that there is or is no dependency between the corresponding variables.

[0114] Specifically, if at least one element in the dependency matrix is 0, it means that there is a blocked link in the dependency path, that is, there is at least one controlled input variable that does not need to rely on the corresponding output variable for calculation within the current step size. Therefore, the computer device can infer that there is no algebraic loop in the cluster system; conversely, if all elements in the dependency matrix are not 0, it means that a closed dependency path is formed between all controlled input scalar elements and output scalar elements in the cluster system, and the calculation of any variable inevitably depends on the result of another variable, thus forming a typical algebraic loop structure.

[0115] In one embodiment, the judgment formula of the dependency matrix in step S134 may include:

[0116]

[0117] Where, and are all elements in the dependency matrix, representing the first dependency parameter and the second dependency parameter in the dependency parameters respectively; n represents the total number of subsystems in the cluster system; represents the i-th subsystem; represents the i-th controlled input scalar element in the controlled input variables; Represents the i-th output scalar element in the output variable.

[0118] In this embodiment, using the above formula, the computer device can trace the dependency paths of the cluster system. When the dependency parameter corresponding to a pair of variables is 0, it indicates that there is a broken dependency relationship on this path. In this case, the computer device can determine that there is no algebraic loop in the cluster system. Conversely, if the dependency parameters corresponding to all variables are not 0, it indicates that the variables between all subsystems form a closed dependency chain, thus forming an algebraic loop.

[0119] In one embodiment, the method may further include:

[0120] S160: After obtaining the state variables, controlled input variables, and output variables of each subsystem at the next moment, it is determined whether the next moment is the last step in the system simulation duration.

[0121] S170: If yes, then end the simulation solution of the cluster system and output the solution result.

[0122] S180: If not, continue to simulate and solve the cluster system at the next moment.

[0123] In this embodiment, after obtaining the state variables, controlled input variables, and output variables of each subsystem at the next moment, the computer device can determine whether the next moment is the last step in the system simulation duration. If so, the computer device can terminate the cluster system simulation and output the solution results. If not, the computer device can continue to simulate the cluster system at the next moment.

[0124] Specifically, when the next moment is the last step in the system simulation duration, the entire simulation process of the cluster system has been completed. At this point, the computer device can terminate the subsequent calculation process and organize and output the solution results of all recorded state variables, control inputs, and output variables evolving over time to analyze system behavior or support subsequent control strategy formulation. Conversely, it means that the cluster system has not yet completed all simulation steps. At this time, the computer device can continue to enter the simulation solution process at the next moment based on the current calculation results, and repeat operations such as state evolution, algebraic solution, and algebraic loop judgment until all steps within the simulation time range are completed, thereby ensuring that the dynamic response process of the entire power electronics and motor equipment cluster system can be completely and accurately simulated and recorded.

[0125] The cluster system simulation solution device provided in an embodiment of the present application is described below. The cluster system simulation solution device described below and the cluster system simulation solution method described above can be referenced to each other.

[0126] In one embodiment, Figure 2 As shown, Figure 2This is a schematic diagram of the structure of a cluster system simulation and solution device provided in an embodiment of the present application. The present application also provides a cluster system simulation and solution device, including a system partitioning module 210, a differential solution module 220, an algebraic loop judgment module 230, a first algebraic solution module 240, and a second algebraic solution module 250, specifically including the following:

[0127] The system partitioning module 210 is used to divide the cluster system into multiple independent subsystems according to the topological structure of the cluster system and to construct a digital model of each subsystem; wherein each subsystem is connected to a signal through a controlled source.

[0128] The differential solution module 220 is used to perform differential solution on the differential equations in each digital model based on the state variables of each subsystem at the current moment, so as to obtain the state variables of each subsystem at the next moment.

[0129] The algebraic loop determination module 230 is configured to determine dependency parameters of the cluster system according to the controlled relationships between the subsystems, and determine whether an algebraic loop exists in the cluster system based on the dependency parameters.

[0130] The first algebraic solution module 240 is used to iteratively solve the algebraic equations in each digital model using the Newton-Raphson iterative algorithm when an algebraic loop exists in the cluster system, so as to obtain the controlled input variables and output variables of each subsystem at the next moment.

[0131] The second algebraic solving module 250 is used to sequentially solve the algebraic equations in each digital model using an explicit function when there is no algebraic loop in the cluster system, so as to obtain the controlled input variables and output variables of each subsystem at the next moment.

[0132] In the above embodiment, when simulating and solving the cluster system, the cluster system can be first divided into multiple subsystems according to the topological structure of the cluster system. The subsystems are independent of each other but connected by controlled source signals. Therefore, the digital model of each subsystem is constructed separately, which can reduce the matrix dimension of the entire cluster system model and thus reduce the amount of single-step calculations. Then, based on the state variables of each subsystem at the current moment, the differential equations in each digital model can be differentially solved to obtain the state variables of each subsystem at the next moment. Here, solving the differential equations of the digital model first can avoid the mutual restraint between the differential equations and the algebraic equations, thereby improving the speed. Then, before solving the algebraic equations, the dependent parameters of the cluster system can be determined according to the controlled relationship between the subsystems, and based on the dependent parameters, it can be determined whether the cluster system has an algebraic loop. Then, different solution methods can be adopted according to the judgment results to avoid blind iterative solution and reduce the overall simulation efficiency. For example, when an algebraic loop exists, the Newton-Raphson iterative algorithm can be used to iteratively solve the algebraic equations in each digital model, so that the exact solution can be approximated through multiple iterations, avoiding the accumulation of errors caused by closed-loop dependence; when an algebraic loop does not exist, the explicit function can be used to sequentially solve the algebraic equations in each digital model, thereby improving the solution efficiency to the millisecond level; through these two methods, the present application can efficiently algebraically solve the controlled input variables and output variables of each subsystem at the next moment.

[0133] In one embodiment, the system partitioning module 210 may include:

[0134]

[0135]

[0136]

[0137] Where, Represents a differential equation; represents the state variable predicted by the i-th subsystem; Represents the actual state variables of the i-th subsystem; represents the independent input variable of the i-th subsystem; represents the controlled input variable of the i-th subsystem; t represents time; and All represent algebraic equations; and They represent the output variables of the i-th subsystem and the j-th subsystem respectively; N represents the total number of subsystems in the cluster system.

[0138] In one embodiment, the differential solution module 220 may include:

[0139] The variable acquisition submodule is used to obtain the state variables of each subsystem at the current moment.

[0140] The differential solution submodule is used to solve the derivative of the state variable at the current moment using the Runge-Kutta method according to the differential equation in the digital model corresponding to the subsystem, and integrate the derivative to the next moment to obtain the state variable at the next moment.

[0141] In one embodiment, the controlled relationship in the algebraic loop judgment module 230 may include a first association relationship and a second association relationship; the dependent parameter may include a first dependent parameter and a second dependent parameter; the algebraic loop judgment module 230 may further include:

[0142] The variable expansion submodule is used to expand the output variables and controlled input variables of each subsystem to obtain the output scalar elements corresponding to the output variables and the controlled input scalar elements of the controlled input variables.

[0143] The first parameter generating submodule is used to determine, for each subsystem, a first association relationship between an output scalar element and a controlled input scalar element in the subsystem, and generate a first dependent parameter according to the first association relationship.

[0144] The second parameter generating submodule is used to determine a second association relationship between the controlled input scalar element in the subsystem and the output scalar elements of other subsystems, and to generate a second dependent parameter according to the second association relationship.

[0145] In one embodiment, the algebraic ring determination module 230 may further include:

[0146] The matrix judgment submodule is used to construct the dependency matrix of the cluster system according to the dependency parameters and to judge whether there is an element of 0 in the dependency matrix.

[0147] The first determining submodule is configured to determine that no algebraic ring exists in the cluster system when an element of the dependency matrix is 0.

[0148] The second determining submodule is configured to determine whether an algebraic ring exists in the cluster system when no element of the dependency matrix is zero.

[0149] In one embodiment, the judgment formula of the dependency matrix in the matrix judgment submodule may include:

[0150]

[0151] Where, and are all elements in the dependency matrix, representing the first dependency parameter and the second dependency parameter in the dependency parameters respectively; n represents the total number of subsystems in the cluster system; represents the i-th subsystem; represents the i-th controlled input scalar element in the controlled input variables; Represents the i-th output scalar element in the output variable.

[0152] In one embodiment, the apparatus may further include:

[0153] The step judgment module is used to judge whether the next moment is the last step in the system simulation time after solving the state variables, controlled input variables and output variables of each subsystem at the next moment.

[0154] The result output module is used to end the simulation solution of the cluster system and output the solution result when the next moment is the last step in the system simulation time.

[0155] The iterative solution module is used to continue to simulate and solve the cluster system at the next moment when the next moment is not the last step in the system simulation time.

[0156] In one embodiment, the present application also provides a storage medium storing computer-readable instructions. When the computer-readable instructions are executed by one or more processors, the one or more processors execute the steps of the cluster system simulation solution method as described in any of the above embodiments.

[0157] In one embodiment, the present application also provides a computer device having computer-readable instructions stored therein. When the computer-readable instructions are executed by one or more processors, the one or more processors execute the steps of the cluster system simulation solution method as described in any of the above embodiments.

[0158] Schematically, as Figure 3 As shown, Figure 3 This is a schematic diagram of the internal structure of a computer device provided in an embodiment of the present application. The computer device 300 can be provided as a server. Figure 3 Computer device 300 includes a processing component 302, which further includes one or more processors, and memory resources represented by memory 301 for storing instructions executable by processing component 302, such as application programs. The application programs stored in memory 301 may include one or more modules, each corresponding to a set of instructions. Furthermore, processing component 302 is configured to execute the instructions to perform the cluster system simulation solution method according to any of the above-described embodiments.

[0159] The computer device 300 may further include a power supply component 303 configured to perform power management of the computer device 300, a wired or wireless network interface 304 configured to connect the computer device 300 to a network, and an input / output (I / O) interface 305. The computer device 300 may operate based on an operating system stored in the memory 301, such as Windows Server™, Mac OS X™, Unix™, Linux™, Free BSD™, or the like.

[0160] Those skilled in the art will understand that Figure 3 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0161] Finally, it should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or device comprising a series of elements includes not only those elements, but also other elements not explicitly listed, or elements inherent to such process, method, article, or device. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of additional identical elements in the process, method, article, or device comprising the element.

[0162] The various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The various embodiments can be combined as needed, and the same or similar parts can be referenced to each other.

[0163] The above description of the disclosed embodiments is intended to enable one skilled in the art to implement or use the present application. Various modifications to these embodiments will be readily apparent to one skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application is not limited to the embodiments shown herein, but is intended to conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A cluster system simulation solution method, characterized in that: The method comprises: Dividing the cluster system into multiple independent subsystems according to the topological structure of the cluster system, and constructing a digital model of each subsystem; wherein each subsystem is connected by signals through a controlled source; Based on the state variables of each subsystem at the current moment, the differential equations in each digital model are differentially solved to obtain the state variables of each subsystem at the next moment; Determining dependent parameters of the cluster system according to the controlled relationships between the subsystems, and determining whether an algebraic loop exists in the cluster system based on the dependent parameters; If so, the Newton-Raphson iterative algorithm is used to iteratively solve the algebraic equations in each digital model to obtain the controlled input variables and output variables of each subsystem at the next moment; If not, the explicit function is used to sequentially solve the algebraic equations in each digital model to obtain the controlled input variables and output variables of each subsystem at the next moment.

2. The cluster system simulation solution method according to claim 1, characterized in that: The mathematical expression of the digital model includes: Where, represents a differential equation; represents the state variable predicted by the i-th subsystem; Represents the actual state variables of the i-th subsystem; represents the independent input variable of the i-th subsystem; represents the controlled input variable of the i-th subsystem; t represents time; and All represent algebraic equations; and They represent the output variables of the i-th subsystem and the j-th subsystem respectively; N represents the total number of subsystems in the cluster system.

3. The cluster system simulation solution method according to claim 1, characterized in that: The differential equations in each digital model are differentially solved based on the state variables of each subsystem at the current moment to obtain the state variables of each subsystem at the next moment, including: For each subsystem, obtain the state variables of the subsystem at the current moment; According to the differential equation in the digital model corresponding to the subsystem, the Runge-Kutta method is used to solve the derivative of the state variable at the current moment, and the derivative is integrated to the next moment to obtain the state variable at the next moment.

4. The cluster system simulation solution method according to claim 1, characterized in that: The controlled relationship includes a first association relationship and a second association relationship; the dependent parameter includes a first dependent parameter and a second dependent parameter; Determining the dependent parameters of the cluster system according to the controlled relationship between the subsystems includes: Expanding the output variables and controlled input variables of each subsystem to obtain output scalar elements corresponding to the output variables and controlled input scalar elements corresponding to the controlled input variables; For each subsystem, determining a first association relationship between an output scalar element and a controlled input scalar element in the subsystem, and generating a first dependency parameter according to the first association relationship; A second association relationship between the controlled input scalar element in the subsystem and the output scalar elements of other subsystems is determined, and a second dependent parameter is generated according to the second association relationship.

5. The cluster system simulation solution method according to claim 1, characterized in that: The determining, based on the dependent parameters, whether the cluster system has an algebraic ring includes: Constructing a dependency matrix of the cluster system according to the dependency parameters, and determining whether an element of the dependency matrix is 0; If so, determining that no algebraic ring exists in the cluster system; If not, it is determined that an algebraic ring exists in the cluster system.

6. The cluster system simulation solution method according to claim 5, characterized in that: The judgment formula of the dependency matrix includes: Where, and are all elements in the dependency matrix, representing the first dependency parameter and the second dependency parameter in the dependency parameters respectively; n represents the total number of subsystems in the cluster system; represents the i-th subsystem; represents the i-th controlled input scalar element in the controlled input variables; Represents the i-th output scalar element in the output variable.

7. The cluster system simulation solution method according to claim 1, characterized in that: The method also includes: After obtaining the state variables, controlled input variables, and output variables of each subsystem at the next moment, determining whether the next moment is the last step in the system simulation duration; If yes, then the simulation solution of the cluster system is terminated and the solution result is output; If not, continue to perform simulation and solve the cluster system at the next moment.

8. A cluster system simulation solution device, characterized in that: include: A system partitioning module is used to divide the cluster system into multiple independent subsystems according to the topological structure of the cluster system and to construct a digital model of each subsystem; wherein each subsystem is connected to the signal by a controlled source; A differential solution module is used to perform differential solution on the differential equations in each digital model based on the state variables of each subsystem at the current moment, and obtain the state variables of each subsystem at the next moment; an algebraic loop determination module, configured to determine dependent parameters of the cluster system according to the controlled relationships between the subsystems, and determine whether an algebraic loop exists in the cluster system based on the dependent parameters; A first algebraic solution module is configured to iteratively solve the algebraic equations in each digital model using a Newton-Raphson iterative algorithm when an algebraic loop exists in the cluster system, so as to obtain the controlled input variables and output variables of each subsystem at the next moment; The second algebraic solving module is used to sequentially solve the algebraic equations in each digital model using an explicit function when no algebraic loop exists in the cluster system, so as to obtain the controlled input variables and output variables of each subsystem at the next moment.

9. A storage medium, characterized in that: The storage medium stores computer-readable instructions, which, when executed by one or more processors, enable the one or more processors to execute the steps of the cluster system simulation solution method according to any one of claims 1 to 7.

10. A computer device, characterized in that: include: one or more processors, and memory; The memory stores computer-readable instructions, and when the one or more processors execute the computer-readable instructions, the steps of the cluster system simulation solution method according to any one of claims 1 to 7 are executed.

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