This paper proposes a cascade control structure for three-phase grid-connected Photovoltaic (PV) systems. The PV system consists of a PV Generator, DC/DC converter, a DC link, a DC/AC fully controlled inverter, and the main grid. For the control process, a new control strategy using nonlinear Backstepping technique is developed. This strategy comprises three targets, namely, DC/DC converter control; tight control of the DC link voltage; and delivering the desired output power to the active grid with unity power factor (PF). Moreover, the control process relies mainly on the formulation of stability based on Lyapunov functions. Maximizing the energy reproduced from a solar power generation system is investigated as well by using the Perturb and Observe (P&O) algorithm. The Energetic Macroscopic Representation (EMR) and its reverse Maximum Control Structure (MCS) are used to provide, respectively, an instantaneous average model and a cascade control structure. The robust proposed control strategy adapts well to the cascade control technique. Simulations have been conducted using Matlab/Simulink software in order to illustrate the validity and robustness of the proposed technique under different operating conditions, namely, abrupt changing weather condition, sudden parametric variations, and voltage dips, and when facing measurement uncertainties. The problem of controlling the grid-connected PV system is addressed and dealt by using the nonlinear Backstepping control.
Cascade control is one of the most efficient systems for improving the performance of the conventional single-loop control, especially in the case of disturbances. Usually, controller parameters in the inner and the outer loops are identified in a strict sequence. This paper presents a novel cascade control strategy for grid-connected photovoltaic (PV) systems based on fractional-order PID (FOPID). Here, simultaneous tuning of the inner and the outer loop controllers is proposed. Teaching-learning-based optimization (TLBO) algorithm is employed to optimize the parameters of the FOPID controller. The superiority of the proposed TLBO-based FOPID controller has been demonstrated by comparing the results with recently published optimization techniques such as genetic algorithm (GA), particle swarm optimization (PSO), and ant colony optimization (ACO). Simulations are conducted using MATLAB/Simulink software under different operating conditions for the purpose of verifying the effectiveness of the proposed control strategy. Results show that the performance of the proposed approach provides better dynamic responses and it outperforms the other control techniques.
This article concerns maximizing the energy reproduced from the photovoltaic (PV) system, ensured by using an efficient Maximum Power Point Tracking (MPPT) process. The process should be fast, rigorous and simple for implementation because the PV characteristics are extremely affected by fast changing conditions and Partial Shading (PS). PV systems are popularly known to have many peaks (one Global Peak (GP) and several local peaks). Therefore, the MPPT algorithm should be able to accurately detect the unique GP as the maximum power point (MPP), and avoid any other peak to mitigate the effect of (PS). Usually, with no shading, nearly all the conventional methods can easily reach the MPP with high efficiency. Nonetheless, they fail to extract the GP when PS occurs. To overcome this problem, Evolutionary Algorithms (AEs), namely the Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) are simulated and compared to the conventional methods (Perturb & Observe) under the same software.
In this paper, a novel cascade control technique is proposed in order to identify the parameters of cascade controllers in a grid-connected photovoltaic (PV) system. Here, tuning of the inner and outer loop controllers is done simultaneously by means of an optimized genetic algorithm-based fractional order PID (GA-FOPID) control. Simulations are conducted using Matlab/Simulink software under different operating conditions, namely under fast-changing weather conditions, sudden parametric variations, and voltage dip, for the purpose of verifying the effectiveness of the proposed control strategy. By comparing the results with recently published optimization techniques such as particle swarm optimization (PSO) and ant colony optimization (ACO), the superiority and effectiveness of the proposed GA-FOPID control have been proven.
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