The estimation of the photovoltaic (PV) cell/module model parameters could lead to accomplish a diagnostic tool and to estimate several factors which affect the health state of a PV generator. In this context, it is crucial to look for an extraction technique which performs this evaluation precisely and quickly. Due to the nonlinear and implicit nature of the PV cell/module, significant computational effort is required to obtain all the parameters; therefore, in this context different metaheuristic algorithms are proposed. For the identification of the meaningful parameters of PV cell/module models, illuminated current-voltage (I–V) curves, under real conditions of PV cells temperature and incident irradiance, are employed. Considering several PV cell/module models, the goodness of the proposed algorithms is analyzed by means of statistical errors, convergence speed, and unknown parameters precision. Then these algorithms are tested and validated using a daily set of measured I–V curves, specifically for each one both the whole set of measured data and a reduced set around the maximum power point are used.

Early detection of faults in wind energy systems can reduce downtime, operating, and maintenance costs while increasing productivity. This paper proposes a method based on the analysis of generator stator current signals to detect faults in a wind turbine gearbox equipped with a doubly fed induction generator (DFIG). A localized parameter model was established to simulate the vibratory response of a two-stage gear system under healthy and faulty conditions. The simulation was performed in the MATLAB/Simulink environment. The results include a detailed analysis of the mechanical part of the gearbox, highlighting mesh stiffness, output speed, and accelerations. Additionally, the electrical part was evaluated based on the current supplied by the doubly fed induction generator. The results were presented in the case of healthy gears and in the presence of faults such as a broken or cracked tooth. Fast Fourier transform (FFT) analysis was employed to detect gear defects in the stator current signal. The presence of a crack or broken tooth in the gearbox induces modulation of the DFIG stator current signals according to the shaft frequencies corresponding to the faulty gear. These findings provide a preliminary basis for the detection and diagnosis of this type of failure.

Energy Management and Control Strategy of Hybrid Energy Storage System for Fuel Cell Power Sources