The capacity of photovoltaic solar power installations has been boosted last years by reaching a new record with 175 GWdc of newly installed solar power in 2021. To guarantee reliable performances of photovoltaic (PV) plants and maintain target requirements, faults have to be reliably detected and diagnosed. A method for an effective and reliable fault diagnosis of PV plants based on the behavioral model and performance analysis under the LabVIEW environment is presented in this paper. Specifically, the first phase of this study consists of the behavioral modeling of the PV array and the inverter in order to estimate the electricity production and analyze the performance of the 9.54 kWp Grid Connected PV System (GCPVS). Here, the results obtained from the empirical models were validated and calibrated by experimental data. Furthermore, a user interface for modeling and analyzing the performance of a PV system under LabVIEW has been designed. The second phase of this work is dedicated to the design of a simple and efficient diagnostic tool in order to detect and recognize faults occurring in the PV systems. Essentially, the residuals obtained using the parametric models are analyzed via the performance loss rates (PLR) of four electrical indicators (i.e., DC voltage, DC current, DC power, and AC power). To evaluate the proposed method, numerous environmental anomalies and electrical faults affecting the GCPVS were taken into account. Results demonstrated the satisfactory prediction performance of the considered empirical models to predict the considered variables, including DC current, DC power, and AC power with an $R^2$ of 0.99. Moreover, the obtained results show that the detection and recognition of faults were successfully achieved.

The research an autonomous miniature flying robots has intensified considerably, thanks to the recent growth of civil and military interest in Unmanned Aerial vehicles (UAV). In this paper a simple control algorithm to stabilize the attitude of a quadrotor aircraft subjected to various disturbances is presented. First, we present a dynamic model of the quadrotor followed by simulation and implementation of a classical PID controller; the results were inconclusive and show the stability of the quadrotor for different disturbances. Secondly, we present the navigation and control technology embedded in a recently commercialized micro Unmanned Aerial Vehicle (UAV) AR.Drone. Test flight stabilization will be conducted on the AR.Drone, experimental results are discussed and interpreted.

Maximum power point tracking (MPPT) is a technique used in extracting the maximum power from a photovoltaic panel (PV) under different weather conditions. The last decade has witnessed a wide variety of algorithm based on MPPT controllers, ranging from simple to more complexe ones. Each of them has its own advantage and disadvantage. Hence, it is crutial to propose methods that are both simple and effective to track and maintain the MPPT of a PV system, even under partial shading conditions. In this study, we propose a new bio inspired method namely seagull optimization algorithm (SOA) for solving the MPPT problem in a PV system. To evaluate the proposed SOA _MPPT performance in terms of accuracy, convergence and stability, a simulation methodology is used. First, by tunning the appropriate parameters, then, we consider the following scenarios: rapid change of solar irradiation, temperature, and three patterns to test partial shading effect. The results are compared with latest bio-inspired methods, namely, particle swarm optimization (PSO), Bat optimisation algorithm (BAT) and fire fly algorithm (FA). The obtained results confirm the effectiveness and robustness of the proposed controller compared to existing conventional and bio inspired controllers.