Electromechanical systems are inherently hybrid in nature, combining electrical and mechanical processes, and their increasing complexity requires the development of universal and computationally efficient mathematical models. In this study, we propose a macromodeling approach that represents the electromechanical system as a “black box,” in which internal physical processes are disregarded and the system behavior is defined solely by the relationship between input and output signals. The identification of such macromodels is reduced to solving a nonlinear optimization problem. To address this challenge, the directed cone method is applied, which searches for the global minimum of the objective function through stochastic movement across the hyperplane defined by the optimization problem. Several algorithmic improvements of the directed cone method are investigated, including step-size adaptation, simultaneous adaptation of step size and hypercone opening angle, and a tunneling procedure. Their effectiveness is evaluated using the construction of a macromodel of a single-phase asynchronous motor as a case study. Performance was assessed according to computational complexity (measured as the number of objective function evaluations until convergence), relative modeling accuracy, and the dynamics of progression toward the global minimum. The experimental results show that the tunneling-based algorithm provides the highest modeling accuracy with the lowest computational cost, whereas the step-size-only adaptation was found to be the least effective. The proposed approach demonstrates the feasibility of constructing accurate macromodels of electromechanical systems that can be integrated into computer-aided modeling environments such as MATLAB/Simulink R2023b. Future work will focus on extending the approach to a broader class of electromechanical systems and developing hybrid algorithms to enhance robustness with respect to model nonlinearity.

The article deals with the problem of formation of the tariff policy of the state concerning generation and consumption of electric energy. An analysis of the structure of the electric power generating industry has been carried out. The order of formation of tariffs for power generating companies and their consumers is determined. Conflict of the situation in tariff policy is revealed. The theoretical-game model of the solution of a conflict situation is proposed. An example of the formation of optimal electricity tariffs under fuzzy conditions is considered. Recommendations for setting tariffs are developed.

The paper is devoted to the development of approaches to the application of parallel algorithms in modelling complex dynamic objects. An overview of the existing principles of computer modelling based on parallel computing procedures is given. It is proposed to describe complex dynamic objects in the form of macromodels. An algorithm for parallelising computations when constructing a nonlinear macromodel of a dynamic object with a separate linear part is described. An iterative algorithm for constructing a macromodel that describes heterogeneous dynamic characteristics of an object is formulated

The mathematical description of the behavior of many complex technical objects or systems involves building models in the form of a system of equations with a high degree of nonlinearity. The variables in these equations are the observed indicators of the object behavior. In the case of a “black box” model, such indicators are the signals observed at the input and output of a complex object. The form of the equations in the model, which is the essence of structural identification, is often determined by the analysis of the nature of the general object behavior. The model will adequately reflect this behavior by selecting the appropriate coefficients in the equations, which are the parameters of this model. This stage of model building is called parametric identification. In many cases, the problem of parametric identification is solved by applying optimization methods. It is important for the researcher to choose the most effective method for solving a certain class of such problems. There can be several criteria for the effectiveness of the method, namely the accuracy of reaching the global extremum point, the number of iterations required to find this point, the number of calculations of the objective function and each iteration, the ability of the method to leave the zone of attraction of the local extremum, the speed of directed movement towards the global extremum point. Determining the advantages of various optimization methods with regard to these criteria requires detailed research. This article considers the application of several optimization methods for a parametric model of the “black box” type of an electromechanical object, in particular, several modifications of the directional cone method, as well as methods implemented in the scipy.optimize library of the Python language. To study the efficiency of these methods, appropriate software was developed. A comparative analysis of their efficiency was conducted according to several criteria, which allowed for concluding that it is appropriate to use a specific method to solve the optimization problem for building parametric models of the “black box” type of electromechanical objects.

In this paper, we consider methods of simulating complex dynamic systems that have a variable structure. Modeling concept is formulated. Models classes are defined. It is proposed to describe the dynamic properties of a complex system by dynamic macromodels. The methods of modification the macromodel when changing the structure of the system are proposed. The conditions of application of theoretic-game models are determined. Examples of application of models for displaying of distribution processes in the computer market are given.