Russian Electrical Engineering

Most diesel generators (DGs) in stand-alone electric power plants operate in accordance with a regulatory characteristic with a constancy of rotation frequency in a wide range of load changing. This has a number of negative consequences, including increased fuel and lubricant consumption. The traditional method of solving the problem, which consists in increasing the number of DGs or using generators with different power, increases the equipment cost and decreases weight–dimension factors. Another solution is related to regulation of DGs by a characteristic that is optimal in terms of the criterion of fuel and lubricant consumption, when the rotation frequency depends on load. DGs can operate with a variable rotation frequency when the generator is equipped with a semiconductor converter and the valve operation mode is implemented. Using valve DGs in stand-alone electric power plants makes it possible to apply generator sets with equal power and reduces their number. The results of experimental studies of a valve DG operating at a variable rotation frequency as part of stand-alone ship electric power plant are presented in the work.

This article considers adaptive electromechanical tracking systems intended for controlling multilink mechanical objects with actuating electric drives and synthesized taking and not taking into account electromagnetic behavior of electric drives. A bicascade mathematical model of a multilink mechanical object is developed taking account the electromagnetic behavior of actuating electric drives. Adaptive electromechanical tracking systems constructed using the Li–Slotine technique and a step-by-step procedure are synthesized. The construction of a special regressor matrix, taking into account the structure of actuating electric drives, is considered. The suggested technique of bicascade synthesis of adaptive electromechanical tracking systems, which was constructed taking into account the electromagnetic behavior of actuating electric drives, is illustrated by means of a detailed application of the developed synthesis techniques through the example of constructing adaptive electromechanical tracking systems for controlling a four-link manipulation robot made by Igus. The paper also provides the results of computer-aided modeling of the way in which the electromagnetic behavior of actuating electric drives influences the quality of transient processes and the program path reproduction accuracy treated as a singular perturbation or taken into account in synthesizing adaptive electromechanical tracking systems. The motion of the four-link manipulation robot is designed and modeled in the Mathcad and MATLAB/Simulink suites.

This paper considers the mathematical description of a servo drive system with a linear electric motor for the direct valve control system instead of the flapper–nozzle system. The description relies on the physical approach to determining electromagnetic parameters and electromechanical characteristics of electric motors. The obtained nonlinear traction characteristic and the resulting characteristic from the action of electromagnetic force and elasticity of the centering spring are used to determine the motor stroke limitations. The nonlinear action of counter-EMF against a rising electromagnetic time constant is substantiated. The solution proposed for the electrohydraulic tracking system is an adaptive control algorithm with an exomodel synthesized by the Lyapunov method that provides dissipativity of adaptation processes and expands the control range. The results of the practical tests of the adaptive control system are considered by the example of a servo system in the MATLAB/Simulink suite and on physical servo drive models.

It is proposed to apply a neural network observer in the problems of identification of an external disturbance in the form of a wind load acting on the main mirror of a large radio telescope. The mathematical description of the wind moment is analyzed taking into account its fluctuating component, which has a nonlinear random character. A method of constructing the structure of recurrent neural networks based on models of a nonlinear autoregressive with exogenous inputs for identification of nonlinear static and dynamic control objects is described. In computer simulation, the RMS error for different training algorithms is compared to study their identification accuracy. Design, training, and testing of recurrent neural networks is carried out in the MATLAB/Simulink software environment.

For modern electric installations to be competitive, they should be supported by numerical simulations of high quality and accuracy. In turn, to improve the accuracy of numerical simulation of electric installations, it is required to take into account the thermal-physical, electromagnetic, and mechanical properties of materials as much as possible. This article discusses the possibility of predicting the properties of carbon steels on the basis of their chemical composition for a wide temperature range using a set of artificial neural networks as exemplified by electrical resistivity. Training and verification of neural networks have been based on experimental data from reference books and databases. The methods have been applied so as to avoid retraining of neural networks, as well as to use initial data more completely. The training results of neural networks are given, the error level is evaluated, and the potential is determined of applying neural networks to production of material properties and, as a consequence, improving simulation quality.