Técnicas de control para el motor de corriente continua: Una revisión sistemática de literatura

La ingeniería de control se especializa en desarrollar procesos de alta calidad mediante el modelamiento matemático de diversos sistemas y el diseño de control que permite regular el comportamiento de un sistema utilizando condiciones deseadas. Las técnicas de control que se utilizan para el motor de corriente continua son de mucha utilidad al momento de llevar a cabo una estabilización de la velocidad o el par, algunas de ellas pertenecen a técnicas de control inteligente (lógica difusa y redes neuronales), pero la mayoría se centra en las técnicas de control clásicas (PI, PID) logrando resultados satisfactorios. Las técnicas de modelamiento matemático facilitan la representación de las ecuaciones diferenciales, dependiendo del tipo del motor DC se han utilizado diferentes técnicas (transformada de Laplace, espacio de estados). El software y hardware tienen una fuerte relación con lo que se refiere a las simulaciones y experimentaciones que se usan para validar el funcionamiento de un sistema complejo como lo es el motor CC. En este trabajo se presenta una revisión sistemática de literatura sobre técnicas de control, técnicas de modelamiento matemático, software y hardware que se aplican en un motor de corriente continua, para ello se analizó y resumió 75 artículos científicos de los últimos 4 años provenientes de cinco bases bibliográficas (IEEE Xplore, Digital Library, ScienceDirect, SpringerLink, ResearchGate, Preprints). Los documentos responden a tres preguntas de investigación planteadas en este estudio. Por medio de los resultados obtenidos se identificaron grandes ventajas y desventajas de las técnicas de control y modelamiento matemático, con respecto al software y hardaware se demostró su gran utilidad para la realización de sistemas automatizados

Running Dynamics of Rail Vehicles

The investigation of rail vehicle running dynamics plays an important role in the more than 200 year development of railway vehicles and infrastructure. Currently, there are a number of new requirements for rail transport associated with the reduced environmental impact, energy consumption and wear, whilst increasing train speed and passenger comfort. Therefore, the running dynamics of rail vehicles is still a research topic that requires improved simulation tools and experimental procedures. The book focuses on the current research topics in railway vehicles running dynamics. Special attention is given to high-speed railway transport, acoustic and vibrational impact of railway transport to the surroundings, optimization of energy supply systems for railway transport, traction drives optimization and wear of wheels and rails

Advanced Signal Processing Techniques Applied to Power Systems Control and Analysis

The work published in this book is related to the application of advanced signal processing in smart grids, including power quality, data management, stability and economic management in presence of renewable energy sources, energy storage systems, and electric vehicles. The distinct architecture of smart grids has prompted investigations into the use of advanced algorithms combined with signal processing methods to provide optimal results. The presented applications are focused on data management with cloud computing, power quality assessment, photovoltaic power plant control, and electrical vehicle charge stations, all supported by modern AI-based optimization methods

Advanced Modeling, Control, and Optimization Methods in Power Hybrid Systems - 2021

The climate changes that are becoming visible today are a challenge for the global research community. In this context, renewable energy sources, fuel cell systems and other energy generating sources must be optimally combined and connected to the grid system using advanced energy transaction methods. As this reprint presents the latest solutions in the implementation of fuel cell and renewable energy in mobile and stationary applications such as hybrid and microgrid power systems based on the Energy Internet, blockchain technology and smart contracts, we hope that they will be of interest to readers working in the related fields mentioned above

Intelligent Circuits and Systems

ICICS-2020 is the third conference initiated by the School of Electronics and Electrical Engineering at Lovely Professional University that explored recent innovations of researchers working for the development of smart and green technologies in the fields of Energy, Electronics, Communications, Computers, and Control. ICICS provides innovators to identify new opportunities for the social and economic benefits of society.　 This conference bridges the gap between academics and R&D institutions, social visionaries, and experts from all strata of society to present their ongoing research activities and foster research relations between them. It provides opportunities for the exchange of new ideas, applications, and experiences in the field of smart technologies and finding global partners for future collaboration. The ICICS-2020 was conducted in two broad categories, Intelligent Circuits & Intelligent Systems and Emerging Technologies in Electrical Engineering

Reinforcement learning for power scheduling in a grid-tied pv-battery electric vehicles charging station

Grid-tied renewable energy sources (RES) based electric vehicle (EV) charging stations are an example of a distributed generator behind the meter system (DGBMS) which characterizes most modern power infrastructure. To perform power scheduling in such a DGBMS, stochastic variables such as load profile of the charging station, output profile of the RES and tariff profile of the utility must be considered at every decision step. The stochasticity in this kind of optimization environment makes power scheduling a challenging task that deserves substantial research attention. This dissertation investigates the application of reinforcement learning (RL) techniques in solving the power scheduling problem in a grid-tied PV-powered EV charging station with the incorporation of a battery energy storage system. RL is a reward-motivated optimization technique that was derived from the way animals learn to optimize their behavior in a new environment. Unlike other optimization methods such as numerical and soft computing techniques, RL does not require an accurate model of the optimization environment in order to arrive at an optimal solution. This study developed and evaluated the feasibility of two RL algorithms, namely, an asynchronous Q-learning algorithm and an advantage actor-critic (A2C) algorithm, in performing power scheduling in the EV charging station under static conditions. To assess the performances of the proposed algorithms, the conventional Q-learning and actor-critic algorithm were implemented to compare their global cost convergence and their learning characteristics. First, the power scheduling problem was expressed as a sequential decision-making process. Then an asynchronous Q-learning algorithm was developed to solve it. Further, an advantage actor-critic (A2C) algorithm was developed and was used to solve the power scheduling problem. The two algorithms were tested using a 24-hour load, generation and utility grid tariff profiles under static optimization conditions. The performance of the asynchronous Q-learning algorithm was compared with that of the conventional Q-learning method in terms of the global cost, stability and scalability. Likewise, the A2C was compared with the conventional actor-critic method in terms of stability, scalability and convergence. Simulation results showed that both the developed algorithms (asynchronous Q-learning algorithm and A2C) converged to lower global costs and displayed more stable learning characteristics than their conventional counterparts. This research established that proper restriction of the action-space of a Q-learning algorithm improves its stability and convergence. It was also observed that such a restriction may come with compromise of computational speed and scalability. Of the four algorithms analyzed, the A2C was found to produce a power schedule with the lowest global cost and the best usage of the battery energy storage system

ReSCon '12, Research Student Conference: Book of Abstracts

The fifth SED Research Student Conference (ReSCon2012) was hosted over three days, 18-20 June 2012, in the Hamilton Centre at Brunel University. The conference consisted of 130 oral and 70 poster presentations, based on the high quality and diverse research being conducted within the School of Engineering and Design by postgraduate research students. The conference is held annually, and ReSCon plays a key role in contributing to research and innovations within the School