Methods for increasing the energy efficiency of induction motors by an appropriate control strategy have been a subject of research during the last years. Several methods for loss minimization have been developed for induction motors operated in a steady state. In recent years, some solutions for the dynamic case have been given as well either using an online or offline optimization approach, implying a certain computational burden, which is undesired in practice. This paper shows that the appropriate application of steady state techniques during transients due to a changing motor torque is a suboptimal strategy with an acceptable performance for efficiency optimization given an induction machine where saturation effects of the main inductance must be considered. The optimization problem is simplified such that a simple suboptimal solution is possible and the quality of the suboptimal solution is investigated by simulations and measurements. The proposed solution is simple, easy to implement, and does not require an online optimization. In addition, the influence of magnetizing induction saturation is considered.

Condition monitoring (CM) is crucial for ensuring equipment reliability. Yet, its practical implementation encounters several challenges. On the one hand, these include hurdles in big data collection/storage, sensor selection/calibration/installation. On the other hand, developing effective CM techniques, especially for anomaly detection throughout the degradation process, requires extracting and selecting appropriate health indicators (HIs) and ensuring accurate anomaly detection. While bearings and gears have been intensively studied in the past, only little attention has been given to roller chain (RC) systems. Therefore, this study introduces an innovative approach leveraging a sensorless strategy and deep weighted K-nearest neighborhood (DWKNN) for detecting the abnormal status in RC systems. First, by utilizing readily available motor driver data, the need for expensive sensor selection/installation and data management is eliminated, enhancing cost-effectiveness and applicability across diverse industrial applications. Second, leveraging position information from the motor, the raw data are segmented, transformed into the frequency domain, and fused to provide a comprehensive understanding of the system’s behavior, thus improving CM performance. Subsequently, DWKNN entails intelligent indicator extraction and anomaly detection. Intelligent indicator extraction utilizes the deep sparse autoencoder (DSAE) method to dig hidden information in the acquired data and represent the degradation process. Meanwhile, anomaly detection is achieved through a weighted K-nearest neighborhood (WKNN), ensuring effectiveness and robustness. Through validation on multiple chains, the developed methodology demonstrates its effectiveness in addressing real-world CM challenges in industrial environments, offering a cost-effective and reliable solution compared with other methods.

The current paper illustrates a method for sensorless control of a switched reluctance motor. The detection of the time instants for switching between the working phases is determined based on the evaluation of the switching frequency of the hysteresis current controllers for appropriately selected sensing phases. This enables a simple and cost efficient implementation. The method is compared to a pulse injection method in terms of efficiency and resolution.

This article addresses the problem of increasing the energy efficiency of electromechanical systems driven by asynchronous electric drives. In this context, one of the promising areas is the application of a predictive control strategy that allows for reducing energy losses in dynamic modes of electric drives. This paper proposes a computerised method for the multiparameter optimisation of predictive control algorithms for asynchronous electric drives. A computer model was designed in MATLAB and Simulink R2024a based on the gradient-based model predictive control strategy. A series of simulation experiments were carried out by varying the sampling step, number of iterations, prediction horizon, loss function parameters, and maximum linear search step to identify their impact on the control quality indicators. A taxonomic approach was used for multi-criteria optimisation. The study results show that the optimal setting of the algorithmic parameters improves the accuracy of task processing, reduces energy consumption, and reduces computation time. The results obtained can be used to design and operate energy-efficient control systems for asynchronous electric drives in industrial and transport applications. Prospects for further research will focus on hybrid intelligent architectures to enhance adaptability and integration into automated systems.