Tidal supplementary control schemes-based load frequency regulation of a fully sustainable marine microgrid

The world is targeting fully renewable power generation by the middle of the century. Distributed generation is the way to increase the penetration level of renewable energies. This paper presents load frequency control of a hybrid tidal, wind, and wave microgrid to feed an isolated island. This research is a step towards 100% renewable energy communities in remote seas/oceans islands. The wave and tidal generation systems model are presented. The study presents load frequency control through three supplementary control strategies: conventional integrators, fractional order integrator, and non-linear fractional order integrator. All the controllers of the microgrid are designed by using a novel black widow optimization technique. The applied technique is compared to other existing state-of-the-art algorithms. The results show that the black widow non-linear fractional integrator has a better performance over other strategies. Coordination between the unloaded tidal system and blade pitch control of both wind and tidal systems are adopted in the microgrid to utilize the available reserve power for the frequency support. Simulation and optimization studies are performed using the MATLAB/SIMULINK 2017a software application

Power management optimization of hybrid power systems in electric ferries

The integration of more-electric technologies, such as energy storage systems (ESSs) and electric propulsion, has gained attention in recent years as a promising approach to reduce fuel consumption and emissions in the maritime industry. In this context, hybrid power systems (HPSs) with direct current (DC) distribution are currently gaining a commendable interest in research and industrial applications. This paper examines the impact of using HPS with DC distribution and a battery energy storage system (BESS) over a conventional AC power system for short haul roll-on/roll-off (RORO) ferries. An electric ferry with a HPS is modeled in this study and the power management system is simulated using the Matlab/Simulink software. The result is validated using measured load profile of a ferry. The performance of the DC HPS is compared with the conventional AC system based on fuel consumption and emission reductions. An approach to estimate the fuel consumption of the diesel engine through calculation of specific fuel oil consumption (SFOC) is also presented. This study uses two optimization techniques: a classical power management method namely Rule-Based control (RB) and a meta-heuristic power management method known as Grey Wolf Optimization (GWO) to optimally manage the power sharing of the proposed HPS. Fuel consumption and emission indicators are also used to assess the performance of the two power management methods. The simulation results show that the HPS provides a 2.91% and 7.48% fuel consumption reduction using RB method and GWO method respectively. It is apparent from the result that the HPS has more fuel savings while running the diesel generator sets (DGs) at higher operational efficiency. It is interesting that the proposed HPS using both power management methods provided a 100% emission reduction at berth. Finally, it was found that using a meta-heuristic optimization algorithm provides better fuel and emission reductions than a classical method

On the Traveling Salesman Problem in Nautical Environments: an Evolutionary Computing Approach to Optimization of Tourist Route Paths in Medulin, Croatia

The Traveling salesman problem (TSP) defines the problem of finding the optimal path between multiple points, connected by paths of a certain cost. This paper applies that problem formulation in the maritime environment, specifically a path planning problem for a tour boat visiting popular tourist locations in Medulin, Croatia. The problem is solved using two evolutionary computing methods – the genetic algorithm (GA) and the simulated annealing (SA) - and comparing the results (are compared) by an extensive search of the solution space. The results show that evolutionary computing algorithms provide comparable results to an extensive search in a shorter amount of time, with SA providing better results of the two

Data-driven solutions to enhance planning, operation and design tools in Industry 4.0 context

This thesis proposes three different data-driven solutions to be combined to state-of-the-art solvers and tools in order to primarily enhance their computational performances. The problem of efficiently designing the open sea floating platforms on which wind turbines can be mount on will be tackled, as well as the tuning of a data-driven engine's monitoring tool for maritime transportation. Finally, the activities of SAT and ASP solvers will be thoroughly studied and a deep learning architecture will be proposed to enhance the heuristics-based solving approach adopted by such software. The covered domains are different and the same is true for their respective targets. Nonetheless, the proposed Artificial Intelligence and Machine Learning algorithms are shared as well as the overall picture: promote Industrial AI and meet the constraints imposed by Industry 4.0 vision. The lesser presence of human-in-the-loop, a data-driven approach to discover causalities otherwise ignored, a special attention to the environmental impact of industries' emissions, a real and efficient exploitation of the Big Data available today are just a subset of the latter. Hence, from a broader perspective, the experiments carried out within this thesis are driven towards the aforementioned targets and the resulting outcomes are satisfactory enough to potentially convince the research community and industrialists that they are not just "visions" but they can be actually put into practice. However, it is still an introduction to the topic and the developed models are at what can be defined a "pilot" stage. Nonetheless, the results are promising and they pave the way towards further improvements and the consolidation of the dictates of Industry 4.0

Advances in power quality analysis techniques for electrical machines and drives: a review

The electric machines are the elements most used at an industry level, and they represent the major power consumption of the productive processes. Particularly speaking, among all electric machines, the motors and their drives play a key role since they literally allow the motion interchange in the industrial processes; it could be said that they are the medullar column for moving the rest of the mechanical parts. Hence, their proper operation must be guaranteed in order to raise, as much as possible, their efficiency, and, as consequence, bring out the economic benefits. This review presents a general overview of the reported works that address the efficiency topic in motors and drives and in the power quality of the electric grid. This study speaks about the relationship existing between the motors and drives that induces electric disturbances into the grid, affecting its power quality, and also how these power disturbances present in the electrical network adversely affect, in turn, the motors and drives. In addition, the reported techniques that tackle the detection, classification, and mitigations of power quality disturbances are discussed. Additionally, several works are reviewed in order to present the panorama that show the evolution and advances in the techniques and tendencies in both senses: motors and drives affecting the power source quality and the power quality disturbances affecting the efficiency of motors and drives. A discussion of trends in techniques and future work about power quality analysis from the motors and drives efficiency viewpoint is provided. Finally, some prompts are made about alternative methods that could help in overcome the gaps until now detected in the reported approaches referring to the detection, classification and mitigation of power disturbances with views toward the improvement of the efficiency of motors and drives.Peer ReviewedPostprint (published version