Convolutional Neural Network for Dust and Hotspot Classification in PV Modules
20th IEEE International Conference on Environment and Electrical Engineering, EEEIC 2020, online, 9 Jun 2020 - 12 Jun 2020; Energies : open-access journal of related scientific research, technology development and studies in policy and management 13(23), 6357 (2020). doi:10.3390/en13236357 special issue: "Special Issue "Selected Papers from 20 IEEE International Conference on Environment and Electrical Engineering (EEEIC 2020)" / Special Issue Editor: Prof. Dr. Rodolfo Araneo, Guest Editor"
Axial Flux Permanent Magnet Synchronous Generators for Pico Hydropower Application: A Parametrical Study
A pico hydropower plant is an energy harvesting system that allows energy production using the power of the water flowing in small watercourses, and in water distribution network. Axial Flow Flux Permanent Magnet Synchronous Generator (AFPMSG) are particularly suitable for this application, being efficient machines that achieve high power with small dimensions. This paper presents a parametrical study of several configurations and topologies of three-phase and single-phase AFPMSG, for pico hydropower application, to assess the most suitable dimensional characteristics for the most energy production using a safe voltage of 25 V. The AFPMSGs here considered has a simple single stator and roto…
Automatic detection of thermal anomalies in induction motors
The paper proposes a methodology based on Artificial Intelligence techniques for the automatic detection of abnormal thermal distributions in electric motors, to rapidly identify pre-faults or fault conditions. The proposed approach, applied to induction motors of different sizes, installed in waterworks plants, is based on the execution of Thermographic Non-Destructive Tests, which allow identifying abnormal operating conditions without interrupting the ordinary working conditions of the system. Thermographic images of induction motors are acquired at the installation site and with perspectives visible to the operator, which are sometimes partially obstructed. These thermographic images ar…
Deep learning strategies for automatic fault diagnosis in photovoltaic systems by thermographic images
Abstract Losses of electricity production in photovoltaic systems are mainly caused by the presence of faults that affect the efficiency of the systems. The identification of any overheating in a photovoltaic module, through the thermographic non-destructive test, may be essential to maintain the correct functioning of the photovoltaic system quickly and cost-effectively, without interrupting its normal operation. This work proposes a system for the automatic classification of thermographic images using a convolutional neural network, developed via open-source libraries. To reduce image noise, various pre-processing strategies were evaluated, including normalization and homogenization of pi…
Thermal anomalies detection in a photovoltaic plant using artificial intelligence: Italy case studies
This paper proposes the application of artificial intelligence techniques for the identification of thermal anomalies that occur in a photovoltaic system due to malfunctions or faults, with the aim to limit the energy production losses by detecting faults at an early stage. The proposed approach is based on a Thermographic Non-Destructive Test conducted with Unmanned Aerial Vehicles equipped with a thermal imaging camera, which allows the detection of abnormal operating conditions without interrupting the normal operation of the PV system rapidly and cost-effectively. The thermographic images and videos are automatically inspected using a Convolutional Neural Network, developed by an open-s…