Hybrid Predictive Models for Accurate Forecasting in PV Systems

The accurate forecasting of energy production from renewable sources represents an important topic also looking at different national authorities that are starting to stimulate a greater responsibility towards plants using non-programmable renewables. In this paper the authors use advanced hybrid evolutionary techniques of computational intelligence applied to photovoltaic systems forecasting, analyzing the predictions obtained by comparing different definitions of the forecasting error

Light Unmanned Aerial Vehicles (UAVs) for cooperative inspection of PV plants

After 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

A Machine Learning-Based Method for Modelling a Proprietary SO2 Removal System in the Oil and Gas Sector

The aim of this study is to develop a model for a proprietary SO2 removal technology by using machine learning techniques and, more specifically, by exploiting the potentialities of artificial neural networks (ANNs). This technology is employed at the Eni oil and gas treatment plant in southern Italy. The amine circulating in this unit, that allows for a reduction in the SO2 concentration in the flue gases and to be compliant with the required specifications, is a proprietary solvent; thus, its composition is not publicly available. This has led to the idea of developing a machine learning (ML) algorithm for the unit description, with the objective of becoming independent from the licensor and more flexible in unit modelling. The model was developed in MatLab® by implementing ANNs and the aim was to predict three targets, namely the flow rate of SO2 that goes to the Claus unit, the emissions of SO2, and the flow rate of steam sent to the regenerator reboiler. These represent, respectively, the two physical outputs of the unit and a proxy variable of the amine quality. Three different models were developed, one for each target, that employed the Levenberg–Marquardt optimization algorithm. In addition, the ANN topology was optimized case by case. From the analysis of the results, it emerged that with a purely data-driven technique, the targets can be predicted with good accuracy. Therefore, this model can be employed to better manage the SO2 removal system, since it allows for the definition of an optimal control strategy and the maximization of the plant’s productivity by not exceeding the process constraints

Analysis and validation of 24 hours ahead neural network forecasting of photovoltaic output power

In this paper an artificial neural network for photovoltaic plant energy forecasting is proposed and analyzed in terms of its sensitivity with respect to the input data sets. Furthermore, the accuracy of the method has been studied as a function of the training data sets and error definitions. The analysis is based on experimental activities carried out on a real photovoltaic power plant accompanied by clear sky model. In particular, this paper deals with the hourly energy prediction for all the daylight hours of the following day, based on 48 hours ahead weather forecast. This is very important due to the predictive features requested by smart grid application: renewable energy sources planning, in particular storage system sizing, and market of energy

Integration of rule-based ‘Expert Systems’ on RPAS capable of specific category operations within the U-space: An original mitigation strategy for operational safety risks

The use of RPAS for civil purposes is spreading across Europe and worldwide; Aviation Authorities are working to layout regulations to assure a safe and secure integration of RPAS with manned aircraft across both controlled and uncontrolled (below 500 Feet of altitude) airspace. Following the identification of a selection of safety risks potentially associated to RPAS Specific Category of operations, an original strategy of risks mitigation focused on rule-based ‘Expert Systems’, has been conceived and it is discussed in this work. The article recalls the main components of rule-based ‘Expert Systems’ that is the knowledge basis and the rules to instruct the ‘Expert system’. Then the work describes the implementation of the rules as statements derived from a safety risk matrix associated to RPAS capable of performing Specific Category operations within the U-space. Finally, the idea of integrating the ‘Expert System’ as a software module within RPAS functional architecture is presented and discussed. Such solution is deemed to be a valuable novelty for future implementations of advanced RPAS autopilots capable of recognizing and solving in flight/on ground operational safety risks in such a way to speed up the integration of RPAS into not segregated airspace and their market development

Performance analysis of grid-connected wind turbines

The development of wind turbines (WT) and the capacities of wind power plants have increased significantly in the last years. Wind power plants (WPP) must provide the power quality required by new regulations and the reliability of the power system that is interconnected to. It is very important to analyze and understand the sources of disturbances that affect the power quality. In this paper is analyzed the performance of three different popular wind generators that are connected to the power system. Based on this analysis was made a comparison for the three wind turbines studied that are: The squirrel-cage induction generator (SCIG), the doubly-fed induction generator (DFIG), and the permanent-magnet synchronous generator (PMSG). The fixed speed system is more simple and reliable, but severely limits the energy production of a wind turbine and power quality. In case of variable speed systems, comparisons shows that generator of similar rating can significantly enhance energy capture as well as power quality. Moreover, performance of their output power leveling is validated by a new method numerically as maximum energy function and leveling function. The performances of these wind turbines and their characteristics are analysed in steady-state. Wind turbines systems are modeled in Matlab/Simulink environment. Simulation results matched well with the theoretical turbines operation

Advanced monitoring systems for biological applications in marine environments

The increasing need to manage complex environmental problems demands a new approach and new technologies to provide the information required at a spatial and temporal resolution appropriate to the scales at which the biological processes occur. In particular sensor networks, now quite popular on land, still poses many difficult problems in underwater environments. In this context, it is necessary to develop an autonomous monitoring system that can be remotely interrogated and directed to address unforeseen or expected changes in such environmental conditions. This system, at the highest level, aims to provide a framework for combining observations from a wide range of different in-situ sensors and remote sensing instruments, with a long-term plan for how the network of sensing modalities will continue to evolve in terms of sensing modality, geographic location, and spatial and temporal density. The advances in sensor technology and digital electronics have made it possible to produce large amount of small tag-like sensors which integrate sensing, processing, and communication capabilities together and form an autonomous entity. To successfully use this kind of systems in under water environments2 , it becomes necessary to optimize the network lifetime and face the relative hindrances that such a field imposes, especially in terms of underwater information exchange

Evaluations on hydrogen fuel cells as a source of energy for specific operations category civil RPAS systems

This paper is on the evaluation of hydrogen fuel cells as a mean to enhance RPAS systems performances in terms of reachable range and endurance to integrate them into controlled airspaces operatively and safely. Main steps to size a fuel cell system to feed electrical motors of a fixed wing RPAS capable of specific operations category are described in this article. Then, a more extensive parametric model of a fuel cell power line based on operative and safety requirements for medium range/medium endurance RPAS systems is presented and discussed

Adaptive wavelet neural network for wind speed and solar power forecasting for Italian data

Conventional energy sources are nowadays exhausting and that is the reason why renewable energy sources are so important in current situation. In addition renewables are non-pollutant and freely available in nature. Wind and solar power are the fastest growing renewable energy sources for the past few decades, especially according to the 2020 energy strategy in Europe. They are having enough scope in the power market. The main problem with these renewable energy sources is their unpredictability and, in this context, issues like power quality and power system grid stability arise. In order to limit the effects of these issues, power market needs information about power generation at least one day in advance. This problem can be addressed by proper forecasting of Renewable Energy Sources (RES). Forecasting helps to schedule power properly. Adaptive Wavelet Neural Network (AWNN), a technique already assessed in literature for wind speed forecasting, is here applied also to solar power prediction. After forecasting each individual signal, the Mean Absolute Percentage Error (MAPE) is calculated in different time horizons