Giorgio Sulligoi

TL;DR: In this article, a fault diagnostic technique for photovoltaic systems based on Artificial Neural Networks (ANN) is proposed for a given set of working conditions -i.e., solar irradiance and PV module's temperature -a number of attributes such as current, voltage, and number of peaks in the current voltage characteristics of the PV strings are calculated using a simulation model.

TL;DR: In this article, a series of advanced methods in control, management, and objective-oriented optimization that would establish the technical interface enabling future applications in multiple industrial areas, such as smart buildings, electric vehicles, aerospace/aircraft power systems, and maritime power systems.

TL;DR: A control method based on a Linearization via State Feedback (LSF), is proposed to face the CPL destabilizing effect and to ensure the MVDC bus voltage stability.

TL;DR: The need for research in the design methods area is demonstrated through an overview of the latest results of technological research, including early stage design, dependable-oriented design, and the improvements achievable through software simulators and hardware-in-the-loop.

TL;DR: The design and the validation of an innovative online-trained artificial neural network-based control system for a hybrid microgrid that tracks the maximum power point of renewable energy generators and to control the power exchanged between the front-end converter and the electrical grid is proposed.