Prof.-Dr. Marco Rivera received the Electronic Civil Engineering degree and the M.Sc. degree in Engineering, with specialization in Electrical Engineering, from the Universidad de Concepción. Later he obtained a PhD. degree in Electronic Engineering from the Universidad Técnica Federico Santa María, and was awarded the “Premio Tesis de Doctorado Academia Chilena de Ciencias 2012”, for the best PhD Thesis developed in 2011 for national and foreign students in any exact or natural sciences program, that is member of the Academia Chilena de Ciencias, Chile. Through the last years, he has been a visiting professor at several international universities. He has directed and participated in several projects financed by the National Fund for Scientific and Technological Development (Fondo Nacional de Desarrollo Científico y Tecnológico, FONDECYT), the Chilean National Agency for Research and Development (Agencia Nacional de Investigación y Desarrollo, ANID), and the Paraguayan Program for the Development of Science and Technology (Proyecto Paraguayo para el Desarrollo de la Ciencia y Tecnología, PROCIENCIA), among others. He has been the responsible researcher of basal financed projects whose objective is to enhance, through substantial and long-term financing, Chile’s economic development through excellence and applied research.
He is the Director of the Laboratory of Energy Conversion and Power Electronics (Laboratorio de Conversión de Energías y Electrónica de Potencia, LCEEP) at Universidad de Talca, Chile. He was a full professor at the Department of Electrical Engineering at the Universidad de Talca. Since April 2023 he joined the Power Electronics, Machines and Control (PEMC) Research Institute of the University of Nottingham as a Professor. His main research areas are power electronics, renewable energies, advanced control of power converters, microgrids, among others. He has published more than 520 academic publications in leading international conferences and journals.
Trends and Challenges for the Practical Implementation of Model Predictive Control Techniques for Power Converters and Drives
In the last decades, the application of fast modern microcontrollers has been continuously growing, allowing the development and implementation of new and more intelligent control strategies as an alternative to conventional techniques for power converters and drives. Model Predictive Control is one of these powerful and attractive alternatives that has received a lot of attention in recent years. The use of predictive control offers several interesting advantages: it is an intuitive control approach, it does not need linear controllers and modulators, and it is possible to include nonlinearities and restrictions in the control law easily. The advantages of predictive control are expected to lead to industrial applications very shortly. This presentation will present new advances and trends in the application of model predictive control for power electronics and electrical drives focusing on some tips and considerations for the real implementation of this control technique.
Mehmet Sevimler is the CEO of Inelso Energy, a renowned company in Antalya known for its innovative energy solutions. With a strong foundation in electrical and electronics engineering, Mehmet has a wealth of experience in the energy and automotive industries. Since 2013, he has made significant contributions as a Research & Design Engineer at the Mediterranean Electricity Distribution Company, specializing in improving electrical reliability, enhancing power quality, and leading complex projects like SCADA/DMS/OMS adaptation and the “Recloser – Sectionalizer” R&D project. Prior to this, he worked at Ford Motor Company as a Project Process Engineer, where he played a key role in optimizing production processes for the Ford Transit V363 project. Mehmet holds a Bachelor’s degree in Electrical and Electronics Engineering from Middle East Technical University and is pursuing a Master’s in Engineering Management at Istanbul Technical University. His expertise, strategic thinking, and commitment to technological innovation have positioned him as a leader in the energy sector, driving Inelso Energy’s mission to deliver cutting-edge solutions.
Weekly Failure Prediction and Prioritized Maintenance Forecasting System
Machine learning is revolutionizing the power system industry, particularly in predictive maintenance and failure forecasting. By harnessing the power of vast datasets, machine learning models can predict equipment failures before they occur, allowing utilities to schedule maintenance more efficiently and reduce unexpected downtimes. The ability to make data-driven predictions is crucial for maintaining the reliability and efficiency of power grids, where any failure can lead to significant operational and financial setbacks. This presentation explores the development of a Weekly Failure Prediction and Prioritized Maintenance Forecasting System, which integrates machine learning with OMS-based failure statistics and geographic data to create accurate failure predictions and prioritize maintenance tasks. Through a structured approach—spanning Business Understanding, Data Understanding, Data Preparation, Modeling, Evaluation, and Deployment—the system improves asset management by proactively identifying maintenance needs and enabling informed decision-making. The outcome is a more reliable, cost-effective, and optimized maintenance strategy that enhances the sustainability of power system operations.