Sonia Leva mainly investigates Photovoltaic system, Renewable energy, Artificial neural network, Maximum power point tracking and Control engineering. Her Photovoltaic system research includes elements of Automotive engineering, Electronic engineering and Grid-connected photovoltaic power system. Her Renewable energy research incorporates themes from Electrical network, Production, Simulation and Systems design.
Her Artificial neural network study combines topics from a wide range of disciplines, such as Real-time computing and Smart grid. Her Maximum power point tracking research is multidisciplinary, incorporating perspectives in Solar irradiance and Nonlinear system. Her studies deal with areas such as Systems engineering, Global Positioning System, Energy management and Benchmark as well as Control engineering.
Her primary scientific interests are in Photovoltaic system, Renewable energy, Control theory, Electronic engineering and Automotive engineering. Her study in Photovoltaic system is interdisciplinary in nature, drawing from both Artificial neural network, Reliability engineering, Maximum power point tracking, Grid-connected photovoltaic power system and Control engineering. The study incorporates disciplines such as Solar irradiance, Solar energy and Nonlinear system in addition to Maximum power point tracking.
Her studies examine the connections between Renewable energy and genetics, as well as such issues in Electric power system, with regards to Electrical engineering and Voltage drop. Sonia Leva has researched Control theory in several fields, including Power factor, Wind power, Induction generator, Converters and AC power. Her Electronic engineering research also works with subjects such as
Her scientific interests lie mostly in Photovoltaic system, Renewable energy, Artificial neural network, Grid and Reliability engineering. She is interested in Photovoltaics, which is a branch of Photovoltaic system. Her Renewable energy study integrates concerns from other disciplines, such as Simulation and Industrial engineering.
Her studies in Artificial neural network integrate themes in fields like Bidding, Weather forecasting, Smart grid and Sensitivity. Her Grid study incorporates themes from Islanding, Distributed generation, Electricity, Control engineering and Mathematical optimization. Her research investigates the connection between Reliability engineering and topics such as Electricity generation that intersect with problems in Production, Fault, Track, Solar micro-inverter and Benchmark.
Her primary areas of study are Photovoltaic system, Renewable energy, Artificial neural network, Reliability engineering and Real-time computing. Sonia Leva combines Photovoltaic system and Range in her studies. In general Renewable energy study, her work on Microgrid often relates to the realm of Management system, thereby connecting several areas of interest.
Her Artificial neural network research is multidisciplinary, relying on both Bidding, Simulation and Smart grid. Her work in Reliability engineering covers topics such as Electricity generation which are related to areas like Solar micro-inverter, Track, Fault, Electricity market and Operations research. The Real-time computing study combines topics in areas such as Field, Series, Micro grid and Constant.
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Energy comparison of MPPT techniques for PV Systems
Roberto Faranda;Sonia Leva;Piazza Leonardo da Vinci.
Modeling Guidelines and a Benchmark for Power System Simulation Studies of Three-Phase Single-Stage Photovoltaic Systems
A Yazdani;A R Di Fazio;H Ghoddami;M Russo.
IEEE Transactions on Power Delivery (2011)
MPPT techniques for PV Systems: Energetic and cost comparison
R. Faranda;S. Leva;V. Maugeri.
power and energy society general meeting (2008)
Energy Comparison of Seven MPPT Techniques for PV Systems
Alberto Dolara;Roberto Sebastiano Faranda;Sonia Leva.
Journal of Electromagnetic Analysis and Applications (2009)
Comparison of different physical models for PV power output prediction
Alberto Dolara;Sonia Leva;Giampaolo Manzolini.
Solar Energy (2015)
Analysis and validation of 24 hours ahead neural network forecasting of photovoltaic output power
S. Leva;A. Dolara;F. Grimaccia;M. Mussetta.
Mathematics and Computers in Simulation (2017)
EXPERIMENTAL INVESTIGATION OF PARTIAL SHADING SCENARIOS ON PV (PHOTOVOLTAIC) MODULES
Alberto Dolara;George Cristian Lazaroiu;Sonia Leva;Giampaolo Manzolini.
Light Unmanned Aerial Vehicles (UAVs) for Cooperative Inspection of PV Plants
Paolo Bellezza Quater;Francesco Grimaccia;Sonia Leva;Marco Mussetta.
IEEE Journal of Photovoltaics (2014)
Day-Ahead Photovoltaic Forecasting: A Comparison of the Most Effective Techniques
Alfredo Nespoli;Emanuele Ogliari;Sonia Leva;Alessandro Massi Pavan.
A Physical Hybrid Artificial Neural Network for Short Term Forecasting of PV Plant Power Output
Alberto Dolara;Francesco Grimaccia;Sonia Leva;Marco Mussetta.
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