Emmanouil Lioudakis

Electrical and Computer Engineer, M.Sc. Technical University of Crete

Biography

Emmanouil Lioudakis was born in Agios Nikolaos, Crete in 2000. He received the Diploma degree from the School of Electrical and Computer Engineering, Technical University of Crete, Chania, Greece, in 2023, achieving the highest grade among the school's graduates for the academic year 2022-2023. During his five-year undergraduate studies period, he was nominated with numerous excellence awards from the Technical University of Crete. In his diploma thesis, he developed an electronic system for the control of the power production of photovoltaic arrays under partial shading. In 2025 he received the M.Sc. degree on Electrical and Computer Engineering from the same school. In his M.Sc. thesis, he studied the regulation of the output power of grid-connected photovoltaic inverters. During his graduate studies, he also served as a laboratory teaching assistant at the Circuits, Sensors and Renewable Energy Sources Laboratory.

Education

September 2023 - February 2025: Master of Science (M.Sc.) on Electrical and Computer Engineering - Specialization: Electronics, Electric Power and Quantum Systems; School of Electrical and Computer Engineering, Technical University of Crete; M.Sc. thesis title: "Regulation of the power production of photovoltaic DC/AC converters"; M.Sc. thesis supervisor: Prof. Eftichios Koutroulis
September 2018 - June 2023: Diploma of Electrical and Computer Engineering (5-year Integrated Master Degree, 300 ECTS); School of Electrical and Computer Engineering, Technical University of Crete; GPA: 9.49/10; Diploma thesis title: "Electronic System for the Control of the Power Production of Photovoltaic Arrays under Partial Shading"; Diploma thesis supervisor: Prof. Eftichios Koutroulis
September 2015 - June 2018: High School Diploma; 2nd High School of Agios Nikolaos; GPA: 19.5/20

Academic Experience

March 2024 - August 2024: Laboratory Teaching Assistant for the course "Electric Measurements and Sensors" of the ECE school, during the spring semester of the academic year 2023-24; (Taught the laboratory exercises)
September 2023 - February 2024: Laboratory Teaching Assistant for the course "Basic Circuit Theory" of the ECE school, during the fall semester of the academic year 2023-24; (Participated in the laboratory teaching process)

Publications

Google Scholar profile: https://scholar.google.com/citations?user=OIFrrlgAAAAJ

ORCID profile: https://orcid.org/0009-0006-6968-2830

IEEEXplore author profile: https://ieeexplore.ieee.org/author/443998507140778

ResearchGate profile: https://www.researchgate.net/profile/Emmanouil-Lioudakis-2

Publications in journals

E. Lioudakis and E. Koutroulis, "Global Flexible Power Point Tracking Based on Reinforcement Learning for Partially Shaded PV Arrays", in IEEE Journal of Emerging and Selected Topics in Industrial Electronics, doi: 10.1109/JESTIE.2024.3476695.

Abstract

The penetration of Photovoltaic (PV) systems in modern electricity grids is continuously increasing during the last years. To support the electrical grid by eliminating frequency disturbances and also provide inertia to the power system, the output power of PV systems can be controlled by applying a Flexible Power Point Tracking (FPPT) technique, where the PV array output power is continuously regulated to the desired reference value. Since PV modules frequently operate under partial shading conditions (e.g. due to nearby objects in building-integrated PV applications, deposition of dust etc.), the FPPT algorithms designed for operation of the PV system under uniform incident solar irradiance cannot operate efficiently. Therefore, the concept of the Global FPPT (GFPPT) has been introduced. In this article, a novel GFPPT algorithm based on machine learning is proposed. By utilizing the proposed Q-learning algorithm, the GFPPT system is able to obtain knowledge over time, in order to achieve faster convergence. The experimental results demonstrated that the proposed GFPPT algorithm converged in significantly less time compared to past-proposed GFPPT methods, while achieving an almost same steady-state tracking error.
G. I. Orfanoudakis, E. Lioudakis, G. Foteinopoulos, E. Koutroulis and W. Wu, "Dynamic Global Maximum Power Point Tracking for Partially Shaded PV Arrays in Grid-Connected PV Systems", in IEEE Journal of Emerging and Selected Topics in Industrial Electronics, vol. 5, no. 4, pp. 1481-1492, Oct. 2024, doi: 10.1109/JESTIE.2024.3389686.

Abstract

Global Maximum Power Point Tracking (GMPPT) algorithms can extract the maximum available power from photovoltaic (PV) arrays even under partial shading conditions. The existing GMPPT algorithms originate from computationally-intensive optimization (heuristic) or artificial intelligence concepts which operate in discrete time-steps and impose intense variations to the demanded PV array voltage/current. These result in undesirable disturbances which increase the overall time required for the GMPPT process to complete and affect the quality of power injected to the grid. In this paper, a new GMPPT method with low computational complexity is presented, which exploits the dynamic response of the PV system. The proposed GMPPT technique can track the GMPP in significantly less time when applied to PV inverters with high PV-side capacitances, guarantees convergence to the GMPP even under complex partial shading conditions, while also avoiding the aforementioned disturbances. The performance of the proposed GMPPT method is evaluated using an experimental setup incorporating a 2kW single-phase grid-tied transformerless PV inverter and a rooftop PV array. The experimental results show that it can identify the GMPP in approximately 1 s under various operating conditions, which is more than 95% faster than the P-V curve scanning and Particle Swarm Optimization GMPPT algorithms.

Theses

E. Lioudakis, "Regulation of the power production of photovoltaic DC/AC converters", M.Sc. Thesis, School of Electrical and Computer Engineering, Technical University of Crete, Chania, Greece, 2025, https://doi.org/10.26233/heallink.tuc.102040.

Abstract

The continuously increasing demand of electric energy in modern societies, along with the increasing cost of fossil fuels are two of the main factors that led to a rapid increase on the share of photovoltaic (PV) systems in modern electricity generation and transmission grids. This may affect the grid frequency stability, since the energy production of PV systems varies over time, following the stochastic variation of solar irradiance. Grid frequency stability can be improved by regulating the output power of grid-connected PV systems to a specific reference value with the application of a Flexible Power Point Tracking (FPPT) algorithm. In cases where some of the modules of a PV array are shaded (e.g., due to neighboring buildings), the application of a Global FPPT (GFPPT) algorithm ensures efficient regulation of the PV systemβs power production. In this thesis a novel reinforcement learning-based GFPPT algorithm is presented. After examining its performance on a standalone PV battery charging system, it was modified to be applicable on a grid-connected PV inverter with Internet of Things (IoT) connectivity, which is remotely controlled via a Wi-Fi interface. In this case the algorithm had been trained before being applied to the real system, to ensure satisfying grid-injected power quality and quick convergence to the reference power from the first operation hours of the system. The experimental results of applying the proposed GFPPT algorithm on both systems showed that it is more efficient than the existing GFPPT algorithms that have been presented in the literature. A neural network-based GFPPT algorithm was also developed and compared with the aforementioned GFPPT method. The experimental results showed that while both algorithms had been trained on the same dataset, the neural network-based algorithm cannot guarantee the minimization of the PV inverter power losses during the process of regulating its power production.
E. Lioudakis, "Electronic System for the Control of the Power Production of Photovoltaic Arrays under Partial Shading", Diploma Thesis, School of Electrical and Computer Engineering, Technical University of Crete, Chania, Greece, 2023, https://doi.org/10.26233/heallink.tuc.96186.

Abstract

Photovoltaic (PV) systems constitute a significant percentage of the worldwide installed electrical power plants. They exploit a renewable energy source and they do not require frequent maintenance, since they do not contain mechanical parts. These, combined with the fact that their components get cheaper over time, explain the increasing penetration of PV power plants in modern electricity grids. To avoid frequency disturbance and to improve the grid's power quality, a way of controlling the output power of PV systems is the Flexible Power Point Tracking (FPPT), where a reference value is configured for the PV array's output power. Since PV modules are usually shaded, conventional FPPT algorithms cannot operate efficiently, thus the concept of the Global FPPT (GFPPT) algorithm was introduced. In this thesis, two novel GFPPT algorithms based on machine learning are proposed. To evaluate their effectiveness, an experimental system was developed. One GFPPT algorithm from the literature and the two new ones were tested experimentally on varying operating conditions, as it could happen in real world applications. The experimental results demonstrated that the two new algorithms developed within the framework of this thesis converged in significantly less time, while they achieved an almost same steady-state tracking error.

Scholarships and Awards

April 2024 - Award of Excellence (for achieving the highest grade among the school's graduates for the academic year 2022-2023): Issued by the State Scholarships Foundation (IKY)
December 2023 - Scholarship of Excellence of the Pancretan Endowment Fund: Issued by the Pancretan Association of America; https://www.tuc.gr/el/to-polytechneio/nea-anakoinoseis-syzitiseis/topic/47358/page
November 2023 - Excellence Award for the undergraduate studies (GPA: 9.49/10): Issued by the Technical University of Crete; https://www.tuc.gr/el/to-polytechneio/nea-anakoinoseis-syzitiseis/topic/46937/page
November 2022 - Excellence Award for the first 4 years of the undergraduate studies (GPA: 9.30/10): Issued by the Technical University of Crete; https://www.tuc.gr/el/to-polytechneio/nea-anakoinoseis-syzitiseis/topic/43683/page
November 2020 - Excellence Award for the 2nd year of the undergraduate studies (GPA: 9.17/10): Issued by the Technical University of Crete; https://www.tuc.gr/el/to-polytechneio/nea-anakoinoseis-syzitiseis/topic/36124/page
October 2019 - Excellence Award for the 1st year of the undergraduate studies (GPA: 9.33/10): Issued by the Technical University of Crete; https://www.tuc.gr/el/to-polytechneio/nea-anakoinoseis-syzitiseis/topic/32441/page
December 2018 - Award in memory of doctor Ioannis Christakis: Issued by the Pancretan Association of America; https://www.tuc.gr/el/to-polytechneio/nea-anakoinoseis-syzitiseis/topic/29567/page
March 2017 - 2nd Excellence Award (40th position) for the National High School Physics Competition "Aristotelis" on 2017: Issued by the Greek Physics Society for Science and Education / Physics Department of University of Athens; http://micro-kosmos.uoa.gr/gr/announcments/pdf/proteusantes_diag_2017.pdf

Certifications

On PV systems

Solar Design and Installation Training: 101, by the IEEE Power and Energy Society (December 2023 - January 2025): Part 1 - Introduction to Power Generation, Transmission, and Distribution (Certificate); Part 2 - Overview of Common Solar Energy and Solar PV System Concepts (Certificate); Part 3 - Solar Panels (Certificate); Part 4 - Solar Inverters (Certificate); Part 5 - Batteries for Solar PV Systems (Certificate); Part 6 - Racking of Solar Panels (Certificate); Part 7 - Solar System Design Guidelines; Part 8 - Solar PV System Installation Guide (Certificate); Part 9 - Testing, Performance Evaluation, Troubleshooting, and Maintenance (Certificate)
Huawei FusionSolar Installer Certifications: Huawei FusionSolar Certified Installer (valid from 01 February 2024 to 31 January 2025); Huawei FusionSolar Professional Installer (valid from 09 February 2024 to 08 February 2025)

On electrical installations

Hager Technical Training, a series of specialised technical courses for electrical engineers and installers (February 2024): Residual Current Devices (RCDs); Surge Protective Devices (SPDs); Arc Fault Detection Devices (AFDDs); Design Considerations for Consumer Units
ELEMKO e-learning, a series of technical courses on Earthing and Lightning Protection Systems (February 2024): E-learning 201 - Damages due to Ligthning and Risk Assessment; E-learning 202 - General Principles of external Lighting Protection System (LPS); E-learning 203 - Generation and transmission of surge overvoltages; E-learning 204 - Low voltage earthing systems

On research-related subjects

Elsevier Certified Peer Reviewer Course (January 2024)

Contact

Email address: elioudakis@tuc.gr

Last update: 10 February 2025