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Fluorescent penetrant inspection (FPI) is a well-assessed non-destructive test method used in manufacturing for detecting cracks and other flaws of the product under test. This is a critical phase in the mechanical and aerospace industrial sector. The purpose of this work was to present the implementation of an automated inspection system, developing a vision-based expert system to automate the inspection phase of the FPI process in an aerospace manufacturing line. The aim of this process was to identify the defectiveness status of some mechanical parts by the means of images. This paper will present, test and compare different machine learning architectures to perform the automated defect detection on a given dataset. For each test sample, several images at different angles were captured to properly populate the input dataset. In this way, the defectiveness status should be found combining the information contained in all the pictures. In particular, the system was designed for increasing the reliability of the evaluations performed on the airplane part, by implementing proper artificial intelligence (AI) techniques to reduce current human operators’ effort. The results show that, for applications in which the dataset available is quite small, a well-designed feature extraction process before the machine learning classifier is a very important step for achieving high classification accuracy.
Alessandro Niccolai; Davide Caputo; Leonardo Chieco; Francesco Grimaccia; Marco Mussetta. Machine Learning-Based Detection Technique for NDT in Industrial Manufacturing. Mathematics 2021, 9, 1251 .
AMA StyleAlessandro Niccolai, Davide Caputo, Leonardo Chieco, Francesco Grimaccia, Marco Mussetta. Machine Learning-Based Detection Technique for NDT in Industrial Manufacturing. Mathematics. 2021; 9 (11):1251.
Chicago/Turabian StyleAlessandro Niccolai; Davide Caputo; Leonardo Chieco; Francesco Grimaccia; Marco Mussetta. 2021. "Machine Learning-Based Detection Technique for NDT in Industrial Manufacturing." Mathematics 9, no. 11: 1251.
One of the common problems faced by Telecommunication (TLC) companies is the lack of power supply, usually for those appliances with scarce chances of grid connection often placed in remote zones. This issue is more and more critical if the radio network has the specific task of guaranteeing the so-called “mission-critical communications”. This manuscript aims to propose and assess a viable solution to optimize the power supply and maintenance operations required to assure the proper functionality in such critical and remote sites. In particular, the main goals are defining a method to select the critical sites in an extensive and composite radio system and designing the hybrid power system in a way to improve the service availability and technical-economic benefits of the whole mission-critical TLC system. Finally, the proposed method and related procedures are tested and validated in a real scenario.
Sonia Leva; Francesco Grimaccia; Marco Rozzi; Matteo Mascherpa. Hybrid Power System Optimization in Mission-Critical Communication. Electronics 2020, 9, 1971 .
AMA StyleSonia Leva, Francesco Grimaccia, Marco Rozzi, Matteo Mascherpa. Hybrid Power System Optimization in Mission-Critical Communication. Electronics. 2020; 9 (11):1971.
Chicago/Turabian StyleSonia Leva; Francesco Grimaccia; Marco Rozzi; Matteo Mascherpa. 2020. "Hybrid Power System Optimization in Mission-Critical Communication." Electronics 9, no. 11: 1971.
Our energy scenario is nowadays shaped by progressive electrification of energy final use. In this context, electricity networks are seeing a growing multitude of distributed assets entering from the edges of the grid and acquiring new ICT capabilities that were limited before to a restricted number of major players. Particularly, assets like Photovoltaic Inverter (PvI), Electric Vehicle (EV) chargers, wind turbines controllers, programmable loads, storage systems, and other Distributed Energy Resources (DER) are now able to communicate through different technologies and make conscious choices under human-decisions or even independently. This is leading to a decentralization of the system’s view by increasing single actor independence. Notwithstanding, a problem arises when current centrally-managed electricity networks struggle to coordinate massive amounts of new figures and adapt to this new decentralized paradigm. Therefore, a decentralized coordination-and-control framework will ensure better integration of s and new figures as prosumers, while allowing higher exploitation of their potential compared to centrally managed systems. This article seeks in Blockchains the enabling technology for designing and supporting such a grid infrastructure. It develops a first framework to address this need by envisioning a grid-system based on the direct participation of nowadays-used embedded-energy-devices within a decentralized platform hosting specific coordination procedures. The platform was developed in an experimental research campaign performed at ABB Laboratories basing on embedded-devices currently designed as control-connectivity boards for smart-inverters . Therefore this article introduces the background theory and reasons behind this proposed system. The intent here is not to give all the specific details of the implementation, but introduce the supporting reason, high-level design, and required characteristic of the Blockchain-based platform for coordinating grid operations. Blockchain technology is seen here as the appropriate technology to enable the realization of a multi-actor energy-management system and enable distributed coordination in power grids.
Alvise Baggio; Francesco Grimaccia. Blockchain as Key Enabling Technology for Future Electric Energy Exchange: A Vision. IEEE Access 2020, 8, 205250 -205271.
AMA StyleAlvise Baggio, Francesco Grimaccia. Blockchain as Key Enabling Technology for Future Electric Energy Exchange: A Vision. IEEE Access. 2020; 8 ():205250-205271.
Chicago/Turabian StyleAlvise Baggio; Francesco Grimaccia. 2020. "Blockchain as Key Enabling Technology for Future Electric Energy Exchange: A Vision." IEEE Access 8, no. : 205250-205271.
The proper monitoring and operation and maintenance (O&M) of solar photovoltaic (PV) systems are an integral part of the service tasks required to ensure long-term reliability and prolonged lifetime of the installation. In the recent years, with the end of the feed-in-tariff legislation, the PV stakeholders and operators shifted their focus from the design and construction of utility-scale grid-connected PV plants to the development of monitoring and more efficient O&M practices. Service technical personnel have been asked to investigate and resolve unexpected failures of PV systems, whose repair may require specific strategies not initially planned during the design stage. Reliability and, consequently, availability of PV systems are dependent upon a full understanding of failure dynamics, which requires suitable practices to either mitigate or eliminate repeated components failures. Currently, guidelines or technical standards that include PV systems-specific data for reliability assessments are not available, which has caused nonstandardized fault analyses. These differences in practices have somehow compromised the understanding of PV systems reliability at industry level. Based on information gathered from more than 80 PV plants located in Italy, this article outlines remote monitoring techniques that may be used for a general standardization and as a common basis for reliability assessments and an effective O&M. By discussing the most widespread issues, major failures and unexpected events that can occur in PV systems, the authors identify novel remote monitoring techniques to improve both failure reporting and corrective action systems.
Gianfranco Di Lorenzo; Rodolfo Araneo; Massimo Mitolo; Alessandro Niccolai; Francesco Grimaccia. Review of O&M Practices in PV Plants: Failures, Solutions, Remote Control, and Monitoring Tools. IEEE Journal of Photovoltaics 2020, 10, 914 -926.
AMA StyleGianfranco Di Lorenzo, Rodolfo Araneo, Massimo Mitolo, Alessandro Niccolai, Francesco Grimaccia. Review of O&M Practices in PV Plants: Failures, Solutions, Remote Control, and Monitoring Tools. IEEE Journal of Photovoltaics. 2020; 10 (4):914-926.
Chicago/Turabian StyleGianfranco Di Lorenzo; Rodolfo Araneo; Massimo Mitolo; Alessandro Niccolai; Francesco Grimaccia. 2020. "Review of O&M Practices in PV Plants: Failures, Solutions, Remote Control, and Monitoring Tools." IEEE Journal of Photovoltaics 10, no. 4: 914-926.
This article presents a novel method for boundary extraction of photovoltaic (PV) plants using a fully convolutional network (FCN). Extracting the boundaries of PV plants is essential in the process of aerial inspection and autonomous monitoring by aerial robots. This method provides a clear delineation of the utility-scale PV plants’ boundaries for PV developers, operation and maintenance service providers for use in aerial photogrammetry, flight mapping, and path planning during the autonomous monitoring of PV plants. For this purpose, as a prerequisite, the “Amir” dataset consisting of aerial imagery of PV plants from different countries, has been collected. A Mask-RCNN architecture is employed as a deep network with VGG16 as a backbone to detect the boundaries precisely. As comparison, the results of another framework based on classical image processing are compared with the FCN performance in PV plants boundary detection. The results of the FCN demonstrate that the trained model is able to detect the boundaries of PV plants with an accuracy of 96.99% and site-specific tuning of boundary parameters is no longer required.
Amir Mohammad Moradi Sizkouhi; Mohammadreza Aghaei; Sayyed Majid Esmailifar; Mohammad Reza Mohammadi; Francesco Grimaccia. Automatic Boundary Extraction of Large-Scale Photovoltaic Plants Using a Fully Convolutional Network on Aerial Imagery. IEEE Journal of Photovoltaics 2020, 10, 1061 -1067.
AMA StyleAmir Mohammad Moradi Sizkouhi, Mohammadreza Aghaei, Sayyed Majid Esmailifar, Mohammad Reza Mohammadi, Francesco Grimaccia. Automatic Boundary Extraction of Large-Scale Photovoltaic Plants Using a Fully Convolutional Network on Aerial Imagery. IEEE Journal of Photovoltaics. 2020; 10 (4):1061-1067.
Chicago/Turabian StyleAmir Mohammad Moradi Sizkouhi; Mohammadreza Aghaei; Sayyed Majid Esmailifar; Mohammad Reza Mohammadi; Francesco Grimaccia. 2020. "Automatic Boundary Extraction of Large-Scale Photovoltaic Plants Using a Fully Convolutional Network on Aerial Imagery." IEEE Journal of Photovoltaics 10, no. 4: 1061-1067.
The correct design of a Wireless Sensor Network (WSN) is a very important task because it can highly influence its installation and operational costs. An important aspect that should be addressed with WSN is the routing definition in multi-hop networks. This problem is faced with different methods in the literature, and here it is managed with a recently developed swarm intelligence algorithm called Social Network Optimization (SNO). In this paper, the routing definition in WSN is approached with two different problem codifications and solved with SNO and Particle Swarm Optimization. The first codification allows the optimization algorithm more degrees of freedom, resulting in a slower and in many cases sub-optimal solution. The second codification reduces the degrees of freedom, speeding significantly the optimization process and blocking in some cases the convergence toward the real best network configuration.
Alessandro Niccolai; Francesco Grimaccia; Marco Mussetta; Alessandro Gandelli; Riccardo Zich. Social Network Optimization for WSN Routing: Analysis on Problem Codification Techniques. Mathematics 2020, 8, 583 .
AMA StyleAlessandro Niccolai, Francesco Grimaccia, Marco Mussetta, Alessandro Gandelli, Riccardo Zich. Social Network Optimization for WSN Routing: Analysis on Problem Codification Techniques. Mathematics. 2020; 8 (4):583.
Chicago/Turabian StyleAlessandro Niccolai; Francesco Grimaccia; Marco Mussetta; Alessandro Gandelli; Riccardo Zich. 2020. "Social Network Optimization for WSN Routing: Analysis on Problem Codification Techniques." Mathematics 8, no. 4: 583.
In this paper, the authors propose an UAV-based automatic inspection method for photovoltaic plants analyzing and testing a vision-based guidance method developed to this purpose. The maintenance of PV plants represents a key aspect for the profitability in energy production and autonomous inspection of such systems is a promising technology especially for large utility-scale plants where manned techniques have significant limitations in terms of time, cost and performance. In this light, an ad hoc flight control solution is investigated to exploit available UAV sensor data to enhance flight monitoring capability and correct GNSS position errors with respect to final target needs. The proposed algorithm has been tested in a simulated environment with a software-in-the loop (SITL) approach to show its effectiveness and final comparison with state of the art solutions.
Gabriele Roggi; Alessandro Niccolai; Francesco Grimaccia; And Marco Lovera. A Computer Vision Line-Tracking Algorithm for Automatic UAV Photovoltaic Plants Monitoring Applications. Energies 2020, 13, 838 .
AMA StyleGabriele Roggi, Alessandro Niccolai, Francesco Grimaccia, And Marco Lovera. A Computer Vision Line-Tracking Algorithm for Automatic UAV Photovoltaic Plants Monitoring Applications. Energies. 2020; 13 (4):838.
Chicago/Turabian StyleGabriele Roggi; Alessandro Niccolai; Francesco Grimaccia; And Marco Lovera. 2020. "A Computer Vision Line-Tracking Algorithm for Automatic UAV Photovoltaic Plants Monitoring Applications." Energies 13, no. 4: 838.
Photovoltaic (PV) plant monitoring and maintenance has become an often critical activity: the high efficiency requirements of the new European policy have often been in contrast with the many low-quality plants installed in several countries over the past few years. In actual industrial practices, heterogeneous information is produced, and they are often managed in a fragmented way. Several software tools have been developed for obtaining reliable and valuable information from the PV plant’s raw data. With the aim of gathering and managing all these data in a more complex and integrated manner, an information managing system is proposed in this work—it is composed of a structured database, called the Photovoltaic Indexed Database, and a user interface, called the Digital Map, that allows for easy access and completion of the information present in the database. This information managment system and PV plant digitalization process is able to analyze and properly index the IR in the database, as well as the visual images obtained in photovoltaic plant monitoring.
Alessandro Niccolai; Francesco Grimaccia; Sonia Leva. Advanced Asset Management Tools in Photovoltaic Plant Monitoring: UAV-Based Digital Mapping. Energies 2019, 12, 4736 .
AMA StyleAlessandro Niccolai, Francesco Grimaccia, Sonia Leva. Advanced Asset Management Tools in Photovoltaic Plant Monitoring: UAV-Based Digital Mapping. Energies. 2019; 12 (24):4736.
Chicago/Turabian StyleAlessandro Niccolai; Francesco Grimaccia; Sonia Leva. 2019. "Advanced Asset Management Tools in Photovoltaic Plant Monitoring: UAV-Based Digital Mapping." Energies 12, no. 24: 4736.
Wireless Sensor Networks (WSN) have been widely adopted for years, but their role is growing significantly currently with the increase of the importance of the Internet of Things paradigm. Moreover, since the computational capability of small-sized devices is also increasing, WSN are now capable of performing relevant operations. An optimal scheduling of these in-network processes can affect both the total computational time and the energy requirements. Evolutionary optimization techniques can address this problem successfully due to their capability to manage non-linear problems with many design variables. In this paper, an evolutionary algorithm recently developed, named Social Network Optimization (SNO), has been applied to the problem of task allocation in a WSN. The optimization results on two test cases have been analyzed: in the first one, no energy constraints have been added to the optimization, while in the second one, a minimum number of life cycles is imposed.
Alessandro Niccolai; Francesco Grimaccia; Marco Mussetta; Riccardo Zich. Optimal Task Allocation in Wireless Sensor Networks by Means of Social Network Optimization. Mathematics 2019, 7, 315 .
AMA StyleAlessandro Niccolai, Francesco Grimaccia, Marco Mussetta, Riccardo Zich. Optimal Task Allocation in Wireless Sensor Networks by Means of Social Network Optimization. Mathematics. 2019; 7 (4):315.
Chicago/Turabian StyleAlessandro Niccolai; Francesco Grimaccia; Marco Mussetta; Riccardo Zich. 2019. "Optimal Task Allocation in Wireless Sensor Networks by Means of Social Network Optimization." Mathematics 7, no. 4: 315.
Asset optimization has recently become a crucial issue in Oil&Gas industry, considering oil price conjuncture and an increased awareness on environmental aspects. In this paper, an Artificial Intelligence (AI) technique is presented, which is able to manage big dataset to automatically match the entire production model against measured field data. The tool is based on a hybrid in-house developed AI technique, integrating deep neural networks, biogenetical algorithms, commercial simulators and real-time data. The workflow starts with the modeling of the production system through physics-based commercial simulators. A sensitivity analysis identifies the critical variables, which are then randomly varied with a Sobol distribution, exploring the entire solution domain. With these data, a proxy model to the commercial software is generated using an artificial neural network. Finally, the AI tool fed by real-time data is used to match the field behavior: uncertain parameters are modified through a differential evolution algorithm that minimizes the error between calculated and measured variables. The matching parameters are, then, passed to the simulators achieving a field representative model. The tool has been developed considering an operating field in offshore western Africa. The typical uncertain parameters in this kind of field are related to the fluid characteristics, in particular densities and compositions, but also to the physical characterization of the pipelines such as roughness and heat transfer characteristics. The matching process has been performed coupling the proxy model, which is a neural network able to replicate the field behavior, and a differential evolution algorithm as the optimization algorithm. The fitness function to be minimized is a Mean Absolute Percentage Error (MAPE) that represents the distance between the actual field production parameters and the modelled ones. The best configuration of both the neural network and the differential evolution algorithm required a computational time of 6 seconds with a MAPE equal to 2.6%. These results are compared to the one obtained coupling the same differential evolution algorithm with the commercial simulator to perform the matching. The required computational time is equal to about 20 hours (70400s) and a MAPE equal to 2.2%. The big gain with the novel approach is clearly the knocking down of computational time with a comparable error. In this paper, it has been shown how substituting the physical model with a proxy one can give substantial advantages in terms of computational time. In principle, with the velocity of the tool implemented, the matching procedure could be done on a daily basis. This is a breakthrough because it allows having the simulator model always tuned and ready to be utilized.
Marco Giuliani; Luca Cadei; Marco Montini; Amalia Bianco; Alessandro Niccolai; Marco Mussetta; Francesco Grimaccia. Hybrid Artificial Intelligence Techniques for Automatic Simulation Models Matching with Field Data. Day 3 Wed, November 14, 2018 2018, 1 .
AMA StyleMarco Giuliani, Luca Cadei, Marco Montini, Amalia Bianco, Alessandro Niccolai, Marco Mussetta, Francesco Grimaccia. Hybrid Artificial Intelligence Techniques for Automatic Simulation Models Matching with Field Data. Day 3 Wed, November 14, 2018. 2018; ():1.
Chicago/Turabian StyleMarco Giuliani; Luca Cadei; Marco Montini; Amalia Bianco; Alessandro Niccolai; Marco Mussetta; Francesco Grimaccia. 2018. "Hybrid Artificial Intelligence Techniques for Automatic Simulation Models Matching with Field Data." Day 3 Wed, November 14, 2018 , no. : 1.
Application of Machine Learning in forecasting renewable energy sources (RES) is increasing: in particular, several neural networks have been employed to perform the day-ahead photo-voltaic output power forecast. The aim of this paper is to consider different training approaches in order to improve the accuracy of the PV power prediction, with particular attention to day-ahead and intra-day forecasts. Additionally, novel error metrics, specifically proposed for the defined task, are compared with traditional ones, showing the best approach for the different considered cases. The results will be validated over a 1-year time range of experimentally measured data, for a PV module installed in the Solar Tech Lab in the department of Energy at Politecnico di Milano.
Alfredo Nespoli; Emanuele Ogliari; Alberto Dolara; Francesco Grimaccia; Sonia Leva; Marco Mussetta. Validation of ANN Training Approaches for Day-Ahead Photovoltaic Forecasts. 2018 International Joint Conference on Neural Networks (IJCNN) 2018, 1 -6.
AMA StyleAlfredo Nespoli, Emanuele Ogliari, Alberto Dolara, Francesco Grimaccia, Sonia Leva, Marco Mussetta. Validation of ANN Training Approaches for Day-Ahead Photovoltaic Forecasts. 2018 International Joint Conference on Neural Networks (IJCNN). 2018; ():1-6.
Chicago/Turabian StyleAlfredo Nespoli; Emanuele Ogliari; Alberto Dolara; Francesco Grimaccia; Sonia Leva; Marco Mussetta. 2018. "Validation of ANN Training Approaches for Day-Ahead Photovoltaic Forecasts." 2018 International Joint Conference on Neural Networks (IJCNN) , no. : 1-6.
In this paper the effect of the introduction of some hypotheses in the optimization of a thinned array will be analysed. The results are analysed using three different evolutionary optimization algorithms: the Stud-Genetic Algorithm, a very effective implementation of Genetic Algorithm, the binary Particle Swarm Optimization and the binary Social Network Optimization.
Francesco Grimaccia; Marco Mussetta; Alessandro Niccolai; Paola Pirinoli; Riccardo E. Zich. Effect of introduction of hypotheses in Antenna Optimization: Thinned array test case. 2018 IEEE International Symposium on Antennas and Propagation & USNC/URSI National Radio Science Meeting 2018, 1587 -1588.
AMA StyleFrancesco Grimaccia, Marco Mussetta, Alessandro Niccolai, Paola Pirinoli, Riccardo E. Zich. Effect of introduction of hypotheses in Antenna Optimization: Thinned array test case. 2018 IEEE International Symposium on Antennas and Propagation & USNC/URSI National Radio Science Meeting. 2018; ():1587-1588.
Chicago/Turabian StyleFrancesco Grimaccia; Marco Mussetta; Alessandro Niccolai; Paola Pirinoli; Riccardo E. Zich. 2018. "Effect of introduction of hypotheses in Antenna Optimization: Thinned array test case." 2018 IEEE International Symposium on Antennas and Propagation & USNC/URSI National Radio Science Meeting , no. : 1587-1588.
Binary problems are common in engineering and they can be suitably faced with Evolutionary Optimization. In the antenna field, these problems are quite common and they are characterized to be often multi-modal and non-convex, so they cannot be easily solved by means of standard optimization techniques. In particular, three different Evolutionary Algorithms have been frequently considered in recent years in the field of antenna arrays optimization, namely Stud-Genetic Algorithm (Stud-GA), binary Particle Swarm Optimization (bPSO) and Social Network Optimization (SNO). The aim of this paper is to extensively compare these three heuristics over standard benchmark functions and on a well-known antenna problem, i.e. the optimization of a thinned array. Numerical simulation will be conducted on an array of 121 elements and performances of the different approaches will be compared and validated over this real-world electromagnetic application.
Francesco Grimaccia; Marco Mussetta; Alessandro Niccolai; Riccardo E. Zich. Comparison of Binary Evolutionary Algorithms for Optimization of Thinned Array Antennas. 2018 IEEE Congress on Evolutionary Computation (CEC) 2018, 1 -8.
AMA StyleFrancesco Grimaccia, Marco Mussetta, Alessandro Niccolai, Riccardo E. Zich. Comparison of Binary Evolutionary Algorithms for Optimization of Thinned Array Antennas. 2018 IEEE Congress on Evolutionary Computation (CEC). 2018; ():1-8.
Chicago/Turabian StyleFrancesco Grimaccia; Marco Mussetta; Alessandro Niccolai; Riccardo E. Zich. 2018. "Comparison of Binary Evolutionary Algorithms for Optimization of Thinned Array Antennas." 2018 IEEE Congress on Evolutionary Computation (CEC) , no. : 1-8.
Nowadays wireless and networking technologies represent a crucial research area which involves communication and digital electronics and integrate a large amount of distributed sensing capabilities. The recent improved integration in sensing, communication and computing technologies in mobile phone and similar devices contributed to increase the penetration of Wireless Sensor Networks (WSN) concept in everyday life. In particular, in this work the computational capability of a WSN is used to perform a distributed run of the recently presented optimization Evolutionary Algorithm called Social Network Optimization (SNO). Additionally, the aim of this paper is to present a distributed implementation of SNO (called d-SNO), suitably developed for real-world distributed application problems. The distributed computation capability of the WSN is then optimized in order to have an optimally spread computational time, taking into account the different computational speed of each sensor of the network.
Francesco Grimaccia; Marco Mussetta; Alessandro Niccolai; Riccardo E. Zich. Optimal Computational Distribution of Social Network Optimization in Wireless Sensor Networks. 2018 IEEE Congress on Evolutionary Computation (CEC) 2018, 1 -7.
AMA StyleFrancesco Grimaccia, Marco Mussetta, Alessandro Niccolai, Riccardo E. Zich. Optimal Computational Distribution of Social Network Optimization in Wireless Sensor Networks. 2018 IEEE Congress on Evolutionary Computation (CEC). 2018; ():1-7.
Chicago/Turabian StyleFrancesco Grimaccia; Marco Mussetta; Alessandro Niccolai; Riccardo E. Zich. 2018. "Optimal Computational Distribution of Social Network Optimization in Wireless Sensor Networks." 2018 IEEE Congress on Evolutionary Computation (CEC) , no. : 1-7.
The increase of renewable energy usage in the last two decades, in particular photovoltaic (PV) systems, has opened up different solar plant configurations that need to operate and properly perform in terms of efficient power transfer with respect to all of the involved components, such as inverters, grid interface, storage, and other electrical loads. In such applications, the power characteristics of the plant modules all together represent the main components that are responsible for power extraction, depending on both external and internal factors. Conventional maximum power point tracking techniques may not have a proper conversion efficiency under particular external dynamic conditions. This paper proposes an evolutionary-based maximum power point tracking algorithm suitable to operate under dynamic partial shading conditions and compares its performance with classical maximum power point tracking methods in order to evaluate their conversion efficiency in partial shading scenarios with relevant and dynamic changes in the environmental conditions. Simulations taking into account the different dynamic shading conditions were carried out to prove the effectiveness and limitations of the proposed approach with respect to classical algorithms.
Alberto Dolara; Francesco Grimaccia; Marco Mussetta; Emanuele Ogliari; Sonia Leva. An Evolutionary-Based MPPT Algorithm for Photovoltaic Systems under Dynamic Partial Shading. Applied Sciences 2018, 8, 558 .
AMA StyleAlberto Dolara, Francesco Grimaccia, Marco Mussetta, Emanuele Ogliari, Sonia Leva. An Evolutionary-Based MPPT Algorithm for Photovoltaic Systems under Dynamic Partial Shading. Applied Sciences. 2018; 8 (4):558.
Chicago/Turabian StyleAlberto Dolara; Francesco Grimaccia; Marco Mussetta; Emanuele Ogliari; Sonia Leva. 2018. "An Evolutionary-Based MPPT Algorithm for Photovoltaic Systems under Dynamic Partial Shading." Applied Sciences 8, no. 4: 558.
The relevance of forecasting in renewable energy sources (RES) applications is increasing, due to their intrinsic variability. In recent years, several machine learning and hybrid techniques have been employed to perform day-ahead photovoltaic (PV) output power forecasts. In this paper, the authors present a comparison of the artificial neural network’s main characteristics used in a hybrid method, focusing in particular on the training approach. In particular, the influence of different data-set composition affecting the forecast outcome have been inspected by increasing the training dataset size and by varying the training and validation shares, in order to assess the most effective training method of this machine learning approach, based on commonly used and a newly-defined performance indexes for the prediction error. The results will be validated over a one-year time range of experimentally measured data. Novel error metrics are proposed and compared with traditional ones, showing the best approach for the different cases of either a newly deployed PV plant or an already-existing PV facility.
Alberto Dolara; Francesco Grimaccia; Sonia Leva; Marco Mussetta; Emanuele Ogliari. Comparison of Training Approaches for Photovoltaic Forecasts by Means of Machine Learning. Applied Sciences 2018, 8, 228 .
AMA StyleAlberto Dolara, Francesco Grimaccia, Sonia Leva, Marco Mussetta, Emanuele Ogliari. Comparison of Training Approaches for Photovoltaic Forecasts by Means of Machine Learning. Applied Sciences. 2018; 8 (2):228.
Chicago/Turabian StyleAlberto Dolara; Francesco Grimaccia; Sonia Leva; Marco Mussetta; Emanuele Ogliari. 2018. "Comparison of Training Approaches for Photovoltaic Forecasts by Means of Machine Learning." Applied Sciences 8, no. 2: 228.
This paper is proposing and analyzing an electric energy storage system fully integrated with a photovoltaic PV module, composed by a set of lithium-iron-phosphate (LiFePO4) flat batteries, which constitutes a generation-storage PV unit. The batteries were surface-mounted on the back side of the PV module, distant from the PV backsheet, without exceeding the PV frame size. An additional low-emissivity sheet was introduced to shield the batteries from the backsheet thermal irradiance. The challenge addressed in this paper is to evaluate the PV cell temperature increase, due to the reduced thermal exchanges on the back of the module, and to estimate the temperature of the batteries, verifying their thermal constraints. Two one-dimensional (1D) thermal models, numerically implemented by using the thermal library of Simulink-Matlab accounting for all the heat exchanges, are here proposed: one related to the original PV module, the other related to the portion of the area of the PV module in correspondence of the proposed energy-storage system. Convective and radiative coefficients were then calculated in relation to different configurations and ambient conditions. The model validation has been carried out considering the PV module to be at the nominal operating cell temperature (NOCT), and by specific experimental measurements with a thermographic camera. Finally, appropriate models were used to evaluate the increasing cell batteries temperature in different environmental conditions.
Manel Hammami; Simone Torretti; Francesco Grimaccia; Gabriele Grandi. Thermal and Performance Analysis of a Photovoltaic Module with an Integrated Energy Storage System. Applied Sciences 2017, 7, 1107 .
AMA StyleManel Hammami, Simone Torretti, Francesco Grimaccia, Gabriele Grandi. Thermal and Performance Analysis of a Photovoltaic Module with an Integrated Energy Storage System. Applied Sciences. 2017; 7 (11):1107.
Chicago/Turabian StyleManel Hammami; Simone Torretti; Francesco Grimaccia; Gabriele Grandi. 2017. "Thermal and Performance Analysis of a Photovoltaic Module with an Integrated Energy Storage System." Applied Sciences 7, no. 11: 1107.
New Renewable Energy sources are changing the way the Electric Grid is conceived: new challenges are given to Distribution Network Operators (DNOs) and to Transmission Systems Operators (TSOs). These challenges regards business models adopted and reliability, efficiency and controllability of the system. In this new framework, it is important to give to Decision Makers tools able to help them in facing these new challenges. In this paper, ad hoc Neural Networks have been designed and tested in order to provide a useful tool as part of a larger Decision Making Support System (DSS) for the management of a real run-of-the-river hydroelectric power plant.
Francesco Grimaccia; Marco Mussetta; Alessandro Niccolai; Riccardo E. Zich. Neural networks as decision making support system for hydroelectric power plant. 2017 International Conference on Smart Systems and Technologies (SST) 2017, 217 -221.
AMA StyleFrancesco Grimaccia, Marco Mussetta, Alessandro Niccolai, Riccardo E. Zich. Neural networks as decision making support system for hydroelectric power plant. 2017 International Conference on Smart Systems and Technologies (SST). 2017; ():217-221.
Chicago/Turabian StyleFrancesco Grimaccia; Marco Mussetta; Alessandro Niccolai; Riccardo E. Zich. 2017. "Neural networks as decision making support system for hydroelectric power plant." 2017 International Conference on Smart Systems and Technologies (SST) , no. : 217-221.
The optimization of electrical machines can be managed using advanced computational intelligence algorithms. These algorithms can speed up the design phase and can improve the performances, being able to find out the optimal design also in problems involving a large number of physical and geometric parameters. In this paper, a new population based metaheuristic algorithms, named Social Network Optimization (SNO), has been used to find the optimal design of a tubular permanent magnet linear generator (TPMLG), in the context of a vehicular energy harvesting system.
Francesco Grimaccia; Giambattista Gruosso; Marco Mussetta; Alessandro Niccolai; Riccardo E. Zich. Optimized linear generator for vehicle energy harvesting by social network optimization algorithm. IECON 2017 - 43rd Annual Conference of the IEEE Industrial Electronics Society 2017, 7818 -7823.
AMA StyleFrancesco Grimaccia, Giambattista Gruosso, Marco Mussetta, Alessandro Niccolai, Riccardo E. Zich. Optimized linear generator for vehicle energy harvesting by social network optimization algorithm. IECON 2017 - 43rd Annual Conference of the IEEE Industrial Electronics Society. 2017; ():7818-7823.
Chicago/Turabian StyleFrancesco Grimaccia; Giambattista Gruosso; Marco Mussetta; Alessandro Niccolai; Riccardo E. Zich. 2017. "Optimized linear generator for vehicle energy harvesting by social network optimization algorithm." IECON 2017 - 43rd Annual Conference of the IEEE Industrial Electronics Society , no. : 7818-7823.
The optimization of electrical machines and energy-harvesting devices has been one of the hot topics of research in recent years. In this context, advanced computational intelligence algorithms are suitable design tools, since they are able to optimize complex systems with a large number of constraints and free design parameters. This paper presents the social network optimization (SNO): a new population-based algorithm developed to guarantee an effective and faster exploration of the solution domain with respect to traditional optimization techniques. The proposed technique will be described and validated with a benchmark case study. Finally, the optimization problem of a tubular permanent magnet linear generator will be formulated in the context of vehicular energy-harvesting systems.
Francesco Grimaccia; Giambattista Gruosso; Marco Mussetta; Alessandro Niccolai; Riccardo E. Zich. Design of Tubular Permanent Magnet Generators for Vehicle Energy Harvesting by Means of Social Network Optimization. IEEE Transactions on Industrial Electronics 2017, 65, 1884 -1892.
AMA StyleFrancesco Grimaccia, Giambattista Gruosso, Marco Mussetta, Alessandro Niccolai, Riccardo E. Zich. Design of Tubular Permanent Magnet Generators for Vehicle Energy Harvesting by Means of Social Network Optimization. IEEE Transactions on Industrial Electronics. 2017; 65 (2):1884-1892.
Chicago/Turabian StyleFrancesco Grimaccia; Giambattista Gruosso; Marco Mussetta; Alessandro Niccolai; Riccardo E. Zich. 2017. "Design of Tubular Permanent Magnet Generators for Vehicle Energy Harvesting by Means of Social Network Optimization." IEEE Transactions on Industrial Electronics 65, no. 2: 1884-1892.