In this work, we have developed two new intelligent maximum power point tracking (MPPT) techniques for photovoltaic (PV) solar systems. To optimize the PWM duty cycle driving the DC/DC boost converter, we have used two optimization algorithms namely the whale optimization algorithm (WOA) and grey wolf optimization (GWO) so we can tune the PID controller gains. The oscillation around the MPP and the fail accuracy under fast variable isolation are among the well-known drawbacks of conventional MPPT algorithms. To overcome these two drawbacks, we have formulated a new objective fitness function that includes WOA/GWO based accuracy, ripple, and overshoot. To provide the most relevant variable step size, this objective fitness function was optimized using the two aforementioned optimization algorithms (i.e., WOA and GWO). We have carried out several tests on Solarex MSX-150 panel and DC/DC boost converter based PV systems. In the simulation results section, we can clearly see that the two proposed algorithms perform better than the conventional ones in term of power overshoot, ripple and the response time.
The control of the doubly-fed induction motor is a complex operation because of this motor characterised by a non-linear multivariable dynamics, having settings that change over time and a significant link between the mechanical component and magnetic behavior (flux) (speed and couple). This article then proposes a new strategy of a robust control of this motor, which is decoupled due to the stator flux's direction. The proposed control is integrated with the backstepping control which based on Lyapunov theory; this approach consists in constructively designing a control law of nonlinear systems by considering some state variables as being virtual commands, and the important branch of artificial intelligence type-2 fuzzy logic. The hybrid control backstepping-fuzzy logic consists in replacing the regulators applied to the backstepping control by regulators based on type-2 fuzzy logic. This control will be evaluated by numerous simulations where there is a parametric and non-parametric variation.
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