7In this paper an artificial neural network for photovoltaic plant energy fore-8 casting is proposed and analyzed in term of its sensitivity with respect to the 9 input data sets.

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. Index Terms-Photovoltaic (PV) module, PV system control and monitoring, thermal imaging camera, unmanned aerial vehicle (UAV).Paolo Bellezza Quater received the M.Sc. degree in mechanical engineering.He is currently the CEO of Nimbus S.r.l., Lombardore, Italy, where he is the ARIS SpA Board Member with R&D responsibility. He was the Technical Director in ARIS SpA, in charge of the direction and coordination of all the working teams related to the design, computational fluid dynamics, computeraided engineering, prototyping, and testing of vehicles and complete systems. Moreover, he holds, as Inventor or Leading Inventor, more than 20 national and international patents. He is currently in the Board of Directors of API Torino SME Association, chairing the Commission for Innovation.

In the past, wind power penetration was extremely limited compared to the total power production. As a result, the interconnection requirements for wind farms were not included in the grid codes. However, in the recent years, the significant amount of energy injected by wind farms has already impacted the power system, both from a technical and a regulatory point of view in the recent years. Large wind plants have a significant influence on power system operation since they are related to unpredictability of the primary source. Thus wind turbines must improve their quality production to ensure the stability and reliability of the power system as conventional power plants. Wind energy is not constant and, since wind turbines output is proportional to the cube of wind speed, this causes the power output of Squirrel-Cage Induction Generator Wind Turbine (SCIG WT) to fluctuate. In order to improve power quality and maintain the stable output generated from SCIG wind farm, this paper presents a hybrid controller based on PI and fuzzy technique for the pitch angle controller which has been one of the most common methods for smoothing output power fluctuations. All models as well as controllers here presented are developed using Matlab-Simulink software

Abstract:The main purpose of this work is to lead an assessment of the day ahead forecasting activity of the power production by photovoltaic plants. Forecasting methods can play a fundamental role in solving problems related to renewable energy source (RES) integration in smart grids. Here a new hybrid method called Physical Hybrid Artificial Neural Network (PHANN) based on an Artificial Neural Network (ANN) and PV plant clear sky curves is proposed and compared with a standard ANN method. Furthermore, the accuracy of the two methods has been analyzed in order to better understand the intrinsic errors caused by the PHANN and to evaluate its potential in energy forecasting applications.

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The particle swarm optimization (PSO) method has been successfully applied to different electromagnetic optimization problems. Because of the complexity of this kind of problems, the associated cost function is in general computationally expensive. A fast convergence of the optimization algorithm is hence required to attain results in short time. Here few variations over the standard algorithm, referred to as differentiated meta-PSO, aimed to enhance the global search capability, and to improve the algorithm convergence, are introduced. In order to verify their effectiveness the different techniques have been first applied to benchmark test functions and then used for the optimization of a planar array

A new effective optimization algorithm suitably developed for electromagnetic applications called genetical swarm optimization (GSO) is presented. This is a hybrid algorithm developed in order to combine in the most effective way the properties of two of the most popular evolutionary optimization approaches now in use for the optimization of electromagnetic structures, the particle swarm optimization (PSO) and genetic algorithms (GAs). The algorithm effectiveness has been tested here with respect to both its "ancestors," GA and PSO, dealing with an electromagnetic application, the optimization of a linear array. The here proposed method shows itself as a general purpose tool able to effectively adapt itself to different electromagnetic optimization problems

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.