Traffic congestion is one of the most notable urban transport problems, as it causes high energy consumption and air pollution. Unavailability of free parking spaces is one of the major reasons for traffic jams. Congestion and parking are interrelated because searching for a free parking spot creates additional delays and increase local circulation. In the center of large cities, 10% of the traffic circulation is due to cruising, as drivers nearly spend 20 min searching for free parking space. Therefore, it is necessary to develop a parking space availability prediction system that can inform the drivers in advance about the location-wise, day-wise, and hour-wise occupancy of parking lots. In this paper, we proposed a framework based on a deep long short term memory network to predict the availability of parking space with the integration of Internet of Things (IoT), cloud technology, and sensor networks. We use the Birmingham parking sensors dataset to evaluate the performance of deep long short term memory networks. Three types of experiments are performed to predict the availability of free parking space which is based on location, days of a week, and working hours of a day. The experimental results show that the proposed model outperforms the state-of-the-art prediction models.

In 2015, Romania was the first country in Europe that achieved EU targets regarding the share of renewables in the generation mix, far ahead of the 2020 deadline. Starting with the energy structure in Romania, the paper: (1) analyses the evolution of the main indicators in the renewable energy sector, (2) discloses the perspectives of renewable energy in Romania synthesizing the main trends of development in the field, and (3) analyses the challenges facing with the development of renewable energy in Romania. Based on analyzing the exploratory data, the paper makes a preliminary prediction of the development of the sector for future decades and proposes targeted countermeasures and suggestions. Romania still has unexploited potential concerning renewable energy sources. Since Romania registered continuous economic growth, the demand for electricity is steadily growing, and this trend is expected to continue. Additionally, Romania could introduce a support mechanism for developing the potential of unexploited existing resources. The results of the present study may be useful for further research regarding public policies for the development of renewable energy. Furthermore, it can represent a useful analysis in order to identify future trends of renewable energy in Romania.

The analysis of electrical machine faults during start-up, and variable speed and load conditions offers numerous advantages for fault detection and diagnosis. In this context, diagnosing localized bearing faults through vibration signals remains challenging, particularly in developing physically meaningful, simple, and resampling-free techniques to monitor fault characteristic components throughout machine start-up. This study introduces a straightforward method for qualitatively identifying the time-frequency evolutions of localized bearing faults during the start-up of an inverter-fed machine. The proposed technique utilizes the time-frequency representation of the envelope spectrum, effectively highlighting characteristic fault frequencies during transient operation. The method is tested in an open-source dataset including transient vibration signals. In addition, the work studies the method limitations induced by the mechanical transfer path, when the bearing surroundings are not directly accessible for vibration acquisition. The proposed methodology efficiently identifies incipient localized bearing faults during inverter-fed machine start-up when the fault signature is not highly attenuated.

This article presents and validates a method for early estimation of the high-frequency impedance response for a real permanent magnet synchronous machine. The main contribution of this work lies in the simplicity of the data required to tackle the prediction, which do not require any experimental tuning or 3-D simulations. The required inputs for the method are the 2-D cross section of the machine and the material characteristics of the conductors, insulation, and iron laminations. An approach using the finite element method coupled with a circuit simulator is utilized to obtain the machine common-mode and differential-mode impedances over a broad frequency range. Experimental measurements are performed up to the resonance band to validate the methodology. In addition, the influence of the machine rotor and housing on the impedance curves is experimentally assessed. A machine with concentrated winding is used as a case study machine to test the model.

Designing an electrical motor is a complex process that needs to deal with the non-linearity phenomena caused by the saturation of the iron at high magnetic field strength, the multi-physical nature of the investigated system and with requirements that may come into conflict. This paper proposes to use geometric parametric models to evaluate the multi-physical performances of electrical machines and build a machine learning model that is able to predict multi-physical characteristics of electrical machines from input geometrical parameters. The focus of this work is to accurately estimate the electromagnetic characteristics, motor losses and stator natural frequencies, using the developed machine learning model, at the early-design stage of the electrical motor, when the information about the housing is not available and to include the model in optimisation loops, to speed-up the computational time. Three individual machine learning models are built for each physics analysed, a model for the torque and back electromotive force harmonic orders, one model for motor losses and another one for natural frequencies of the mode-shapes. The necessary data is obtained by varying the geometrical parameters of 2D electromagnetic and 3D structural motor parametric models. The accuracy of different machine learning regression algorithms are compared to obtain the best model for each physics involved. Ultimately, the developed multi-attribute model is integrated in an optimisation routine, to compare the computational time with the classical finite element analysis (FEA) optimisation approach.

This article presents the optimal design from the electromagnetic and structural point of view of a fault-tolerant modular outer rotor synchronous reluctance machine, a not previously constructed topology. Two new topologies with the rotor in modular construction, one with each pole of the rotor considered as a separate module and a segmented one where each rib of a module can be considered as independent, are compared with a classic structure. The electromagnetic analysis is carried out using FLUX 2D software. The main studied parameters are electromagnetic torque, torque ripple content, saliency ratio, and power factor. In order to optimize the power factor of the machine, a cosimulation procedure is carried out for each structure, by employing a MATLAB and FLUX 2D coupling. Based on the obtained performances, the best topology is selected. Furthermore, a mechanical analysis is performed to determine if the new proposed rotor meets the structural integrity at rated speed and to establish the maximum speed value that can be attained such that the structural failure of the rotor is prevented. A modular outer rotor synchronous reluctance machine is designed and built. Experimental results of testing the prototype are given to confirm the validity of the proposed machine.