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In response to climate change, governments around the world have committed to reducing greenhouse gas emissions, which contribute to global warming, through the energy transition from fossil fuels to renewable energy sources and electrification of transportation. This article outlines the design procedure for the electrification of a railway vehicle used for maintenance services on the rail network. The proposed methodology consists of the design of both an all-electric propulsion system and storage system with the aim of zero emissions when the vehicle is operating in tunnels and to minimise noise during maintenance services in cities. After highlighting the characteristics of the railway vehicle under consideration, a simulation model of the propulsion and generation system was developed in order to calculate the energy consumption of the entire railway system. Finally, experimental tests carried out on the prototype proved the effectiveness of the design procedure adopted and the proposed mathematical model, showing a good matching with the simulated results. [ABSTRACT FROM AUTHOR]

An innovative variable-structure-systems-based approach for online training of neural network (NN) controllers as applied to the speed control of electric drives is presented. The proposed learning algorithm establishes an inner sliding motion in terms of the controller parameters, leading the command error towards zero. The outer sliding motion concerns the controlled electric drive, the state tracking error vector of which is simultaneously forced towards the origin of the phase space. The equivalence between the two sliding motions is demonstrated. In order to evaluate the performance of the proposed control scheme and its practical feasibility in industrial settings, experimental tests have been carried out with electric motor drives. Crucial problems such as adaptability, computational costs, and robustness are discussed. Experimental results illustrate that the proposed NN-based speed controller possesses a remarkable learning capability to control electric drives, virtually without requiring a priori knowledge of the plant dynamics and laborious startup procedures. Index Terms--Adaptive control, electric drives, neural networks (NNs), variable structure systems.

This paper presents some analysis techniques of the space vector of voltages induced in the stator windings after supply disconnection, to detect broken rotor bars in squirrel-cage induction machines. When the motor is disconnected from the supply no currents flow in the stator windings and the voltages measurable at its terminals are due to flux produced by rotor currents. When the rotor is healthy, the voltages measured at motor terminals are almost sinusoidal because of the symmetry of rotor windings. When there are broken rotor bars, the magnetomotive force generated by rotor windings is distorted, and some particular harmonics, contained in the voltages induced in the stator windings, increase their amplitudes. The diagnostic technique is based on monitoring these voltage harmonics and analyzing the space vector of the voltages induced in the stator windings via MUSIC pseudospectrum and short-time MUSIC (STMUSIC) time-frequency pseudorepresentation. The MUSIC algorithm is based on the eigen analysis of the autocorrelation matrix, and permits us to evidence the principal harmonic frequencies of the signal and decrease the noise influence, thus allowing a better detection of the broken rotor bars. The results obtained using MUSIC and STMUSIC algorithm have been compared experimentally with those obtained by fast Fourier transform (FFT) and short-time FFT, respectively, and two different sized induction motors have been tested, to demonstrate the superiority of the former approach. Differently from most of the diagnostic techniques already proposed in the technical literature, the proposed approach is effective regardless of the load condition of the machine, source characteristics, and iron saturation. Index Terms--Asynchronous rotating machines, discrete Fourier transforms, fault diagnosis, MUSIC, spectral analysis, transient analysis.