Prof. Dr. Marco Mussetta
Dept. of Energy, Politecnico di Milano, Milan, Lombardy, Italy
Politecnico di Milano, Dept. of Energy
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Academic Career Length
25Years (2002-Present)
(Calculated once Scilit publication data is synced: current year − year of first publication + 1)
Representative Publications
View All Publications1 January 2017 in Mathematics and Computers in Simulation
Journal Article
SLS. Leva
S. Leva
ADAlberto Dolara
Alberto Dolara
Francesco GrimacciaMarco MussettaEOE. Ogliari
E. Ogliari
10 June 2014 in IEEE Journal of Photovoltaics
Journal Article
PQPaolo Bellezza Quater
Paolo Bellezza Quater
FGFrancesco Grimaccia
Francesco Grimaccia
Sonia LevaMarco Mussetta
Mohammadreza AghaeiAfter a fast photovoltaic (PV) expansion in the past decade supported by many governments in Europe, in this postincentive era, one of the most significant open issues in the PV sector is to find appropriate inspection methods to evaluate real PV plant performance and failures. In this context, PV modules are surely the key components affecting the overall system performance; therefore, there is a main concern about the occurrence of any kind of failure in PV modules. This paper aims to propose a novel concept for monitoring PV plants by using light unmanned aerial vehicles (UAVs) or systems (UASs) during their operation and maintenance. The main objectives of this study are to explore and evaluate the use of different UAV technologies and to propose a reliable, cost-effective, and time-saving method for the inspection of PV plants. In this research, different UAVs were employed to inspect a PV array field. For this purpose, some thermal imaging cameras and a visual camera were chosen as monitoring tools to suitably scan PV modules. The first results show that the procedure of utilizing UAV was effective in the detection of different failures of PV modules. Moreover, such a process was much faster and cost effective than traditional methods.
Journal Article
ADAlberto Dolara
Alberto DolaraFrancesco GrimacciaSonia LevaMarco MussettaEOEmanuele Ogliari
Emanuele OgliariThe main purpose of this work is to lead an assessment of the day ahead forecasting activity of the power production by photovoltaic plants. Forecasting methods can play a fundamental role in solving problems related to renewable energy source (RES) integration in smart grids. Here a new hybrid method called Physical Hybrid Artificial Neural Network (PHANN) based on an Artificial Neural Network (ANN) and PV plant clear sky curves is proposed and compared with a standard ANN method. Furthermore, the accuracy of the two methods has been analyzed in order to better understand the intrinsic errors caused by the PHANN and to evaluate its potential in energy forecasting applications.
1 October 2014 in Renewable Energy
Journal Article
Quan Minh DuongFGFrancesco Grimaccia
Francesco Grimaccia
Sonia LevaMarco MussettaEOEmanuele Ogliari
Emanuele Ogliari
12 November 2014 in International Journal of Energy and Environmental Engineering
Journal Article
Francesco GrimacciaMohammadreza AghaeiMarco MussettaSonia LevaPQP. Bellezza Quater
P. Bellezza QuaterThe sustainable use of renewables will represent a key challenge in the near future, and relative energy management operations will play a crucial role in energy efficiency and savings for future generations. The operation and maintenance of energy systems are a very high valuable activity to prevent energy losses, and a correct monitoring can detect in advance equipment degradation guaranteeing good performance over time. Present research strives to find out possibility of unmanned aerial vehicle (UAV) use in monitoring applications for energy production sites and to investigate effects of this novel method on energy management procedures. Furthermore, investigation about novel approaches in cooperative inspection of real photovoltaic (PV) plants was carried out by light UAVs and utilize the global positioning system to find out the optimum route mapping during the solar PV modules monitoring. The purpose of this work is to propose a reliable, fast and cost effective method for PV plant planning and monitoring by means of UAS technology.
Journal Article
Mohamed LouzazniAKAhmed Khouya
Ahmed KhouyaKAKhalid Amechnoue
Khalid AmechnoueGAGandelli Alessandro
Gandelli AlessandroMarco MussettaACAurelian Craciunescu
Aurelian CraciunescuIn this paper, a Firefly algorithm is proposed for identification and comparative study of five, seven and eight parameters of a single and double diode solar cell and photovoltaic module under different solar irradiation and temperature. Further, a metaheuristic algorithm is proposed in order to predict the electrical parameters of three different solar cell technologies. The first is a commercial RTC mono-crystalline silicon solar cell with single and double diodes at 33 °C and 1000 W/m2. The second, is a flexible hydrogenated amorphous silicon a-Si:H solar cell single diode. The third is a commercial photovoltaic module (Photowatt-PWP 201) in which 36 polycrystalline silicon cells are connected in series, single diode, at 25 °C and 1000 W/m2 from experimental current-voltage. The proposed constrained objective function is adapted to minimize the absolute errors between experimental and predicted values of voltage and current in two zones. Finally, for performance validation, the parameters obtained through the Firefly algorithm are compared with recent research papers reporting metaheuristic optimization algorithms and analytical methods. The presented results confirm the validity and reliability of the Firefly algorithm in extracting the optimal parameters of the photovoltaic solar cell.
About Me
Marco Mussetta is Full Professor of Electrical Engineering in Politecnico di Milano, Italy.
He was born in 1979.
He received the M.S. degree in mechanical engineering and the Ph.D in electrical engineering from the Politecnico di Milano, Milan, Italy, in 2003 and 2007, respectively.
In 2008, he got a Postdoctoral Scholarship with the Department of Electronics in Politecnico di Torino, Turin, Italy, where he was involved in a project on the implementation of reflectarray antennas in aerospace application for global monitoring and surveillance.
In March 2011 he joined the Department of Energy, Politecnico di Milano, as an assistant professor.
Since 2015 he is an Associate Professor in the same Department.
Since 2022 he is the Coordinator of the PhD Program in Electrical Engineering at Politecnico di Milano.
Since 2023 he is Full Professor of Electrical Engineering.
His research activities include global evolutionary optimization techniques applied to reflectarray antennas design, printed antennas, FSS, wireless sensor networks, and modeling and optimization of renewable energy systems by means of advanced soft computing techniques.
Since 2001, Dr. Mussetta coauthored about 200 publications on WoS/Scopus-indexed journals and proceedings of international conferences.
He is a Senior Member of IEEE, CIS, PES, IES, and member of AEIT, and SPIE.