This review paper looks briefly at conventional approaches and examines the intelligent means for fault diagnosis (FD) and condition monitoring (CM) of electrical drives in detail, especially the ones that are common in Industry 4.0. After giving an overview on fault statistics, standard methods for the FD and CM of rotating machines are first visited, and then its orientation towards intelligent approaches is discussed. Major diagnostic procedures are addressed in detail together with their advancements to date. In particular, the emphasis is given to motor current signature analysis (MCSA) and digital signal processing techniques (DSPTs) mostly used for feature engineering. Consequently, the statistical procedures and machine learning techniques (stemming from artificial intelligence—AI) are also visited to describe how FD is carried out in various systems. The effectiveness of the amalgamation of the model, signal, and data-based techniques for the FD and CM of inductions motors (IMs) is also highlighted in this review. It is worth mentioning that a variety of neural- and non-neural-based approaches are discussed concerning major faults in rotating machines. Finally, after a thorough survey of the diagnostic techniques based on specific faults for electrical drives, several open problems are identified and discussed. The paper concludes with important recommendations on where to divert the research focus considering the current advancements in the FD and CM of rotating machines.

In this paper, a hybrid storage system solution consisting of flywheels and batteries with a Lithium-manganese oxide cathode and a graphite anode is proposed, for supporting the electrical network primary frequency regulation. The aim of the paper is to investigate the benefits of flywheels in mitigation of the accelerating aging that li-ion batteries suffer during the grid frequency regulation operation. For this purpose, experimental aging tests have been performed on a lithium-manganese oxide battery module and an electrical battery model which takes into account the battery aging has been developed in a Simulink environment. Then, a flywheel electrical model has been implemented, taking into account the thermal and the electromechanical phenomena governing the electrical power exchange. This more complete model of a hybrid storage system enables us to simulate the same aging cycles of the battery-based storage system and to compare the performances of the latter with the hybrid storage system. The simulation results suggest that suitable control of the power shared between the batteries and the flywheels could effectively help in countering Li-ion battery accelerated aging due to the grid frequency regulation service.

This paper presents a two-stage fault detection and classification scheme specifically designed for rotating electrical machines. The approach involves the use of new condition indicators that are specific to the frequency domain. The paper proposes two distinct features: one based on the extraction of peaks by using the prominence measure, a technique originating from the topology of mountains, and other based on the calculation of the occupied band power ratio for specific characteristic fault frequencies. A linear based feature reduction technique, the principal component analysis (PCA) has been employed to represent all the data. Afterwards, shallow neural networks have been used to detect and classify the three-phase current signals online. The effectiveness of the proposed scheme has been validated experimentally by using signals obtained with grid and inverter fed induction motors.

An 8-pole 48-slot dual three-phase synchronous reluctance motor has been tested under half control mode, that is, when only a single three-phase system is supplied while the other is not operating. Changing the terminal connections of the motor prototype available, two different winding configurations have been tested. This allows their performances to be experimentally compared. At first, the cross coupling effect between the axis in $dq$ reference is analyzed, by measuring the flux linkages for different current combinations. The torque characteristics are compared in terms of torque map, maximum torque capability and torque ripple. Then the finite element analysis has been used to explain the experimental results and to investigate the radial forces on the rotor. Finally, the impact of a lack of symmetry between the two three-phase system is investigated.

Detection of stator-based faults in Induction Machines (IMs) can be carried out in numerous ways. In particular, the shorted turns in stator windings of IM are among the most common faults in the industry. As a matter of fact, most IMs come with pre-installed current sensors for the purpose of control and protection. At this aim, using only the stator current for fault detection has become a recent trend nowadays as it is much cheaper than installing additional sensors. The three-phase stator current signatures have been used in this study to observe the effect of stator inter-turn fault with respect to the healthy condition of the IM. The pre-processing of the healthy and faulty current signatures has been done via the in-built DSP module of dSPACE after which, these current signatures are passed into the MATLAB® software for further analysis using AI techniques. The authors present a Growing Curvilinear Component Analysis (GCCA) neural network that is capable of detecting and follow the evolution of the stator fault using the stator current signature, making online fault detection possible. For this purpose, a topological manifold analysis is carried out to study the fault evolution, which is a fundamental step for calibrating the GCCA neural network. The effectiveness of the proposed method has been verified experimentally.

The paper presents a novel configuration of an axial hybrid magnetic bearing (AHMB) for the suspension of steel flywheels applied in power-intensive energy storage systems. The combination of a permanent magnet (PM) with excited coil enables one to reduce the power consumption, to limit the system volume, and to apply an effective control in the presence of several types of disturbances. The electromagnetic design of the AHMB parts is carried out by parametric finite element analyses with the purpose to optimize the force performances as well as the winding inductance affecting the electrical supply rating and control capability. Such investigation considers both the temperature dependence of the PM properties and the magnetic saturation effects. The electrical parameters and the force characteristics are then implemented in a control scheme, reproducing the electromechanical behavior of the AHMB-flywheel system. The parameter tuning of the controllers is executed by a Matlab/Simulink code, examining the instantaneous profiles of both the air-gap length and the winding ampere-turns. The results of different dynamic tests are presented, evidencing the smooth air-gap changes and the optimized coil utilization, which are desirable features for a safe and efficient flywheel energy storage.