Search Results (10)

The growing use of proton-exchange membrane fuel cells (PEMFCs) in hybrid propulsion systems is aimed at replacing traditional internal combustion engines and reducing greenhouse gas emissions. Effective power distribution between the fuel cell and the energy storage system (ESS) is crucial and has led to a growing emphasis on developing energy management systems (EMSs) to efficiently implement this integration. To address this goal, this study examines the performance of a fuzzy logic rule-based strategy for a hybrid fuel cell propulsion system in a small hydrogen-powered passenger vessel. The primary objective is to optimize fuel efficiency, with particular attention on reducing hydrogen consumption. The analysis is carried out under typical operating conditions encountered during a river trip. Comparisons between the proposed strategy with other approaches—control based, optimization based, and deterministic rule based—are conducted to verify the effectiveness of the proposed strategy. Simulation results indicated that the EMS based on fuzzy logic mechanisms was the most successful in reducing fuel consumption. The superior performance of this method stems from its ability to adaptively manage power distribution between the fuel cell and energy storage systems. Full article

As the field of fault diagnosis in electrical machines has significantly attracted the interest of the research community in recent years, several methods have arisen in the literature. Also, raw data signals can be acquired easily nowadays, and, thus, machine learning (ML) and deep learning (DL) are candidate tools for effective diagnosis. At the same time, a challenging task is to identify the presence and type of a bearing fault under noisy conditions, especially when relevant faults are at their incipient stage. Since, in real-world applications and especially in industrial processes, electrical machines operate in constantly noisy environments, a key to an effective approach lies in the preprocessing stage adopted. In this work, an evaluation study is conducted to find the most suitable signal preprocessing techniques and the most effective model for fault diagnosis of 16 conditions/classes, from a low-workload (computational burden) perspective using a well-known dataset. More specifically, the reliability and resiliency of conventional ML and DL models is investigated here, towards rolling bearing fault detection, simulating data that correspond to noisy industrial environments. Diverse preprocessing methods are applied in order to study the performance of different training methods from the feature extraction perspective. These feature extraction methods include statistical features in time-domain analysis (TDA); wavelet packet decomposition (WPD); continuous wavelet transform (CWT); and signal-to-image conversion (SIC), utilizing raw vibration signals acquired under varying load conditions. The noise effect is examined and thoroughly commented on. Finally, the paper provides accumulated usual practices in the sense of preferred preprocessing methods and training models under different load and noise conditions. Full article

This paper examines the critical topic of load frequency control (LFC) in shipboard microgrids (SMGs), which face challenges due to low system inertia and the intermittent power injection of renewable energy sources. To maintain a constant frequency (even under system uncertainties), a robust and well-tuned controller is required. In this paper, a study was conducted first by examining the performance of three different controller architectures, in order to determine which is the most-appropriate for the multi-energy SMG system. The time delays that occur due to communication links between the sensors and the controller were also considered in the analysis. The controllers were tuned using a very recent bio-inspired optimization algorithm called the jellyfish search optimizer (JSO), which has not been used until recently in LFC problems. To assess the tuning efficiency of the proposed optimization algorithm, the SMG’s frequency response results were comprehensively compared to the results obtained with other bio-inspired optimization algorithms. The results showed that the controllers with gains provided by the JSO outperformed those tuned with other bio-inspired optimization counterparts, with improvements in performance ranging from 19.13% to 93.49%. Furthermore, the robustness of the selected controller was evaluated under various SMG operational scenarios. The obtained results clearly demonstrated that the controller’s gains established in normal conditions do not require retuning when critical system parameters undergo a significant variation. Full article

This paper deals with the analysis of the dynamic performance of a generator with a doubly salient exterior rotor excited by permanent magnets inserted in the stator yoke. The electrical generator works at low speed and is devoted to a wind energy conversion system. Indeed, the studied generator is a robust high-torque machine and can be directly coupled to the turbine blades. It must therefore assure the energy conversion for wind speeds lower or higher than its base speed. In fact, the control technique used in this work covers the two main operating zones: below the base speed and above it. In the first case, the maximum torque per ampere control law is developed; however, when the base speed is reached, the flux decay control law is implemented and, consequently, the system works above the nominal conditions. Fuzzy logic controllers are employed to regulate direct and quadrature machine currents and DC voltage in order to obtain satisfactory regulation performances. The ensemble of the wind turbine and electrical machine with technical control is performed in Matlab/Simulink software. The simulation results obtained show the capability of the machine to operate at variable speeds, ensuring efficient energy conversion under and over the nominal speed. Full article

Recent years include the world’s hottest year, while they have been marked mainly, besides the COVID-19 pandemic, by climate-related disasters, based on data collected by the Emergency Events Database (EM-DAT). Besides the human losses, disasters cause significant and often catastrophic socioeconomic impacts, including economic losses. Recent developments in artificial intelligence (AI) and especially in machine learning (ML) and deep learning (DL) have been used to better cope with the severe and often catastrophic impacts of disasters. This paper aims to provide an overview of the research studies, presented since 2017, focusing on ML and DL developed methods for disaster management. In particular, focus has been given on studies in the areas of disaster and hazard prediction, risk and vulnerability assessment, disaster detection, early warning systems, disaster monitoring, damage assessment and post-disaster response as well as cases studies. Furthermore, some recently developed ML and DL applications for disaster management have been analyzed. A discussion of the findings is provided as well as directions for further research. Full article

Mandatory regulations are published worldwide for the efficiency of line-operated electric motors. The small-sized single-phase induction motors (SPIMs) will not be off the hook in terms of efficiency, since new regulations are scheduled to be introduced regarding them no later than July 2023. By doing so, the efficiency of capacitor-run SPIMs will be forced to exceed the (currently) typical ratings and comply with the requirements of the IE3 (i.e., premium) efficiency class. Since this task is challenging, the already published research works investigated several design, control, and manufacturing aspects. Nevertheless, less attention has been devoted to the study of the rotor bar’s shape impact, both on the SPIMs’ efficiency and starting capability. This gap is filled in this work by examining rotor squirrel-cage configurations with eight different bar shapes for the case of a four-pole/1.0 HP capacitor-run SPIM. A sensitivity analysis, which involves the simultaneous variation of the bar’s cross-sectional area, run-capacitor value, and auxiliary to main winding turns ratio, is performed. The motor’s electromagnetic behavior is estimated through finite element analysis. Through the acquired results, useful directions toward the SPIMs’ efficiency enhancement are provided, while simultaneously conclusions—not found elsewhere—are drawn concerning performance quantities, such as the motor’s starting current, currents shift angle, particular losses, breakdown torque, etc. Full article

The low speed toothed doubly salient permanent-magnet (TDSPM) machine is an interesting candidate motor for electric ship applications because, of its high torque output, maintenance-free operation and flexible working modes, which gives the opportunity to increase system’s reliability, and decrease the system size, weight and noise which are key features for naval applications. However, particularly in the 3-phase configuration, the stator and rotor saliency of these machines leads to a high level of torque ripple. To overcome these drawbacks, the use of polyphase machines (with a number of phases greater than three) can be a relevant solution. In this paper, an optimal design of two kind of novel 4-phase motors is performed in order to fulfil the specifications of a high power naval ship propulsion. The designs aim to maximize the torque to mass ratio. The motors’ performances are directly linked to their structural parameters, so, the impact of the coil number in terms of mean torque, torque ripple, energy ratio values, and efficiency is also presented and analysed. The design of these two electromagnetic structures, as well as the determination of their electromagnetic performances, are carried out using a particle swarm optimization algorithm (PSO) with taking into account thermal constraint. The performance of the proposed machine in terms of mean torque, torque ripple, energy ratio, and efficiency values is presented and analysed. The results obtained reveal that the TDSPM machines with four poles/phase are good candidates to meet the requirements of high power direct-drive ship propulsion system. Full article

Wind turbine (WT) technology becomes more and more important due to the serious environmental and energy issues. The toothed poles outer rotor doubly salient permanent magnet (DSPM) generator with simple and durable design, high torque and high-power density has a great prospect in wind turbines application. The large diameter makes the construction of such a machine more convenient due to the installation of the turbine blades directly to the outer rotor generator surface. Nevertheless, the size of the generator must be increased to provide larger output power. This increases the generator’s mass. Thus, larger massive DSPM generators are undesirable in wind turbine design. In this paper, an optimization design procedure of the outer rotor doubly salient permanent magnet generator ORDSPMG is proposed for 10 kW WT application. The reduction of the generator weight is demonstrated and proofed. The considered machine version is characterized by having the same effective axial length and output torque imposed by the specifications relative to the 10 kW direct drive WT. An optimization procedure using a fast and effective method, namely the symbiotic organism search (SOS) algorithm coupled to a parametric two dimensional finite elements analysis (2D-FEA), is employed to optimize the machine parameters. The main parameters affecting the generator design are also analyzed. The results obtained reveal that the proposed generator topology presents low weight and thus high torque density among other satisfactory characteristics. Full article

The most frequent faults in rotating electrical machines occur in their rolling element bearings. Thus, an effective health diagnosis mechanism of rolling element bearings is necessary from operational and economical points of view. Recently, convolutional neural networks (CNNs) have been proposed for bearing fault detection and identification. However, two major drawbacks of these models are (a) their lack of ability to capture global information about the input vector and to derive knowledge about the statistical properties of the latter and (b) the high demand for computational resources. In this paper, short time Fourier transform (STFT) is proposed as a pre-processing step to acquire time-frequency representation vibration images from raw data in variable healthy or faulty conditions. To diagnose and classify the vibration images, the image classification transformer (ICT), inspired from the transformers used for natural language processing, has been suitably adapted to work as an image classifier trained in a supervised manner and is also proposed as an alternative method to CNNs. Simulation results on a famous and well-established rolling element bearing fault detection benchmark show the effectiveness of the proposed method, which achieved 98.3% accuracy (on the test dataset) while requiring substantially fewer computational resources to be trained compared to the CNN approach. Full article

The ongoing technological advancements of brushless DC motors (BLDCMs) have found a wide range of applications. For instance, ground-based electric vehicles, aerial drones and underwater scooters have already adopted high-performance BLDCMs. Nevertheless their adoption demands control systems to monitor torque, speed and other performance characteristics. Precise design structure and the particular motor functional characteristics are essential for the suitable configuration and implementation of an appropriate controller to suit a wide range of applications. Techniques which do not use Hall sensors should be used then. This paper deals with the analysis of hardware and software aspects during the development of such a microcontroller based and low cost speed controller for motors up to 500 W, along with its practical implementation. The sensorless method employed is based on the zero crossing point (ZCP) detection of the back-electromotive forces’ (back-EMF) differences, as the ZCPs of these quantities match to the time points at which the commutation sequence changes. Additionally, the study presents hardware and software details through calculations, figures, flowcharts and code, providing an insight of the practical issues that may arise in such a low cost prototype. Finally, results obtained by experiments validate the presented hardware/software architecture of the controller. Full article