My name is Alejandra Obando, and I am from Ecuador. I am a responsible and proactive person. As a results-oriented worker, I enjoy conducting research and collaborating in multidisciplinary groups. In my spare time, I like to go out for walks and discover new places.
Degrees
Master in Robotics and Automation Engineering (2024) – Università della Calabria (UNICAL), Italy
Master in Quality and Productivity (2021) – Pontificia Universidad Católica del Ecuador (PUCE), Ecuador
Electronic and Control Engineer (2020) – Escuela Politécnica Nacional (EPN), Ecuador
Research history
Dual-Mode based Sliding Mode Control approach for Non-Linear Chemical Processes (2023), Journal of American Chemical Society ACS Omega, URL: https://pubs.acs.org/doi/10.1021/acsomega.2c08201
Sliding Mode Controller Based on a Hybrid Surface for Tracking Improvement of Non-Linear Processes (2020), 21st IFAC World Congress, URL: https://www.sciencedirect.com/science/article/pii/S2405896320309915?via%253Dihub
Hardware in the Loop Simulation for Sliding Mode Control Schemes for Dead time Systems (2019), International Conference on Information Systems and Computer Science, URL: https://ieeexplore.ieee.org/document/9052242
Internships and work experience
Consulting Engineer Internship, Italy, (Oct/2024 – Dec/2024)
Research Internship in Academic Excellence Path program at UNICAL Laboratories, Italy, (Jan/2024 – Jul/2024)
Consultant for ISO certifications in Integrated Management Systems, Ecuador, (Jan/2022 – Sep/2022)
Technical and Commercial Automation Engineer, Ecuador, (Jan/2021 –Dec/2021)
Topic of Doctoral Thesis
Data-driven control of power converters for modern power networks
Research objectives:
This thesis aims to develop data-driven controllers to be implemented in VSC-based grid-connected power converters. The transition of the power networks towards a system of black-boxed systems interconnected together with their highly variable nature driven by renewable energy variation, requires new technology capable of operating in such an environment.
Optimization-based data-driven-based predictive controllers thrive in such an environment as they are capable of construct high performance, reliable and adaptable controllers fully based on network captured data, without requiring detailed models.
The thesis will provide the methodology to synthetize such controllers, starting from the system data capture process, model identification (if needed), construction of the optimization-based controller and then simulation verification in simulation benchmark power systems implemented in Simulink and PSCAD. As an example of this class of controllers, the thesis will further develop and expand the concept of Data-enabled Predictive Control (DeePC) further.
The key applications for the developed techniques will be HVDC, FACTS and renewable energy systems controllers.
Secondment:
Academic: ETH Zürich, Florian Dörfler (PhD co-supervisor), 3-6 months, purpose: further development of the data-driven control
Industrial: SIEMENS ENERGY, Dr. Rodrigo Alvarez, 3-6 months, purpose: Industrial application of the developed controllers
Motivation to work in Inter-oPEn
My primary motivation for working on the Inter-oPEn project is the opportunity to establish a professional network in an international context and gain experience in developing activities from a more pragmatic perspective that aligns with the industry’s actual needs. Furthermore, given my skill set and experience, I am able to offer contributions to the project, working from a multidisciplinary approach as needed.