Design of a MPPT System Based on Modified Grey Wolf Optimization Algorithm in Photovoltaic System under Partially Shaded Condition

Conventional Maximum Potential Monitoring strategies such as perturbation and observation, incremental conduct, and climbing can effectively monitor the maximum power point in uniform shading, whereas failing in a partially shaded condition. Nevertheless, it is difficult to achieve optimal and reliable power by using photovoltaics. So, to solve this issue, this article proposes to monitor the photovoltaic system's global optimum powerpoint for partial shading with a Modified Gray Wolf Optimizer (MGWO) based maximum power point tracking algorithm. Under partial shadows, a mathematical model of the PV system is built with a single diode, EGWO is used to monitor global maximum power points.  A photovoltaic system includes deciding which converter is used to increase photovoltaic power generation. The MPPT architecture uses a modified gray wolf optimization algorithm to quickly track the output power and reduce photovoltaic oscillations. The efficiency of the maximum power tracker is better than the GWO algorithm of up to 0,4 s with the modified gray wolf optimization algorithm. Converters are used to resolve the power losses often occurring in PV systems with a soft-buck converter process.  The output of the power generator is greater than the soft-switching buck converter. The simulation and experimental results obtained suggest that both the P & O and IPSO MPPTs are superior to the proposed MPPT algorithm, the proposed algorithm increases the traceability efficiency. The suggested algorithm has the fastest follow-up speed since the α value decreases during the iteration exponentially

The use of mobile phone while driving: Behavior and determinant analysis in one of the largest metropolitan area of Italy

The use of mobile phones while driving is one of the main causes of road accidents and it is a phenomenon in continuous growth. The key aim of this study is to analyse simultaneously knowledge, attitudes, and behavior toward the use of mobile phones while driving in one of the largest and populous metropolitan areas of Italy, Naples. The data acquired from 774 questionnaires - administered to subjects evenly divided by gender and with an average age of 39 years - revealed that 69 % have used their mobile phone while driving at least once in their lifetime. Among those who used the phone, 63.6 % use it to make phone calls while 75.2 % only to answer them; 49.1 % read messages and only 33.3 % write them. It is also notable that 34.1 % do not stop to answer a call and only 10 % do not value the use of headsets while driving as fundamental. The results indicate that cell phone usage while driving is common in the study population, despite many having university-level education and satisfactory risks awareness. The multiple linear regression analysis shows how knowledge is not correlated to the behavior held. On the contrary, attitudes are strongly correlated to knowledge and behavior, meaning that good attitudes bring forth positive behavior. According to the collected data and statistical analysis, it is possible to identify factors that can greatly affect the use of mobile phone while driving and establish targeted prevention programs

Mobile Phone Usage Detection by ANN Trained with a Metaheuristic Algorithm †

Artificial neural networks (ANN) are widely used to classify high non-linear systems by using a set of input/output data. Moreover, they are trained using several optimization methodologies and this paper presents a novel algorithm for training ANN through an earthquake optimization method. Usually, gradient optimization method is implemented for the training process, with perhaps the large number of iterations leading to slow convergence, and not always achieving the optimal solution. Since metaheuristic optimization methods deal with searching for weight values in a broad optimization space, the training computational effort is reduced and ensures an optimal solution. This work shows an efficient training process that is a suitable solution for detection of mobile phone usage while driving. The main advantage of training ANN using the Earthquake Algorithm (EA) lies in its versatility to search in a fine or aggressive way, which extends its field of application. Additionally, a basic example of a linear classification is illustrated using the proposal-training method, so the number of applications could be expanded to nano-sensors, such as reversible logic circuit synthesis in which a genetic algorithm had been implemented. The fine search is important for the studied logic gate emulation due to the small searching areas for the linear separation, also demonstrating the convergence capabilities of the algorithm. Experimental results validate the proposed method for smart mobile phone applications that also can be applied for optimization applications