Second order sliding mode-based MPPT control for photovoltaic applications

Abbes

Int Trans Electr Energ Syst

et al. 2020

Summary This article is interested in a controlled three‐phased voltage source inverter (VSI) for a grid‐connected wind power generator. The objective consists of participating in auxiliary services by enhancing the grid stability. An active wind generator associated with a battery/super‐capacitor storage system composes the studied renewable generator. It is related to a power consumer (load) to form a microgrid (MG) that can work according to grid stability in two modes: standalone and grid‐tied. Thus, the principal objective of this study is to develop a robust control method for MG connection with the grid to ensure the grid voltage and frequency regulation. This control has two control tasks: First, an adaptive droop control is implemented to adjust power flows exchanged with the grid to ensure its stability. It regulates its voltage and frequency. Second, a sliding mode control (SMC) with super‐twisting algorithm is put forward for the VSI to improve the regulation of the grid voltage and frequency under uncertainties. It improves control results concerning the reduction of harmonics caused by sudden variations of the load and of powers exchanged with the grid. It allows preventing the phenomenon of chattering created by classical sliding mode technique. Extensive simulation studies are carried out under MATLAB/Simulink. They prove the effectiveness of the suggested droop and SMCs compared with the classical proportional‐integral controller.

“…By twice deriving the latter, we get the following relations:

\overset{•}{S_{i}} = \frac{\partial}{\partial t} S_{i} ((), t, x) + \frac{\partial}{\partial x} S_{i} ((), t, x) f ((), t, x, u)

\begin{matrix} lefttrue \\ \overset{• •}{S_{i}} = \frac{\partial}{\partial t} \overset{•}{S_{i}} (t, x, u) + \frac{\partial}{\partial x} \overset{•}{S_{i}} (t, x, u) f (t, x, u) \\ + \frac{\partial}{\partial x} \overset{•}{S_{i}} (t, x, u) \overset{•}{u} = φ (t, x, u) + γ (t, x, u) \overset{•}{u} \end{matrix}

where γ ( t , x , u ) and φ ( t , x , u ) are bounded functions. This leads to the fact that any solution relative to Equation (17) satisfies the following differential inclusion:

\overset{• •}{S_{i}} \in ([],, - ϕ, ϕ) + ([],, K_{m}, K_{M}) \overset{•}{u}

…”

Section: Proposed Control Design For Vsi Systemmentioning

confidence: 99%

STA and SOSM control‐based approach of a renewable power generator for adjusting grid frequency and voltage

Abbes

Int Trans Electr Energ Syst

et al. 2020

“…The main objective functions for the current and voltage loop are given in expressions (21) and (22), respectively, where f i (W) corresponds to the objective function of the current control loop and f v (W) to the objective function of the voltage control loop. The auxiliary functions are given by expressions (23) and (24), where φ i corresponds to the auxiliary function for the current loop and φ v the auxiliary function for the voltage loop. φ(W) indicates the distance of a particle in the favorable region of the original problem.…”

Section: W-psomentioning

confidence: 99%

“…In [21], Fuzzy Logic is used to continuously tune the gains of a PI controller applied to regulate the reactive power of a photovoltaic inverter. In relation to the sliding mode controller, the works in the literature use the method of Lyapunov [22][23][24] to guarantee the stability and project the controller; a process that requires a complex mathematical adjustment.…”

Section: Introductionmentioning

confidence: 99%

Weighted-PSO Applied to Tune Sliding Mode Plus PI Controller Applied to a Boost Converter in a PV System

et al. 2019

In this paper, a sliding mode plus proportional-integral (PI) controller for a boost converter in a photovoltaic system is proposed. The proposed controller is characterized by being easy to implement and by operating with constant switching frequency. The parameters of the proposed controller are calculated using the weighted particle swarm optimization technique, ensuring low percentage of overshoot and short setting time. The use of this optimization technique allows one to ensure the stability of the controller. A linear lead-leg controller is considered in order to compare the performance of the proposed controller. Finally, experimental results using a solar kit are presented to verify the performance of the proposed controller.

“…The main goal of this study is to design a new switching function based on Lyapunov stability, to overcome the drawbacks associated with control time and reduce the cost of the PV system. In this context, there are many approaches to mitigating the disadvantages of chattering in SMC, such as using a regular approximation of the switching element or using a higher order sliding mode control (HOSMC) strategy [32].…”

Section: Introductionmentioning

confidence: 99%

Practical Implementation of the Backstepping Sliding Mode Controller MPPT for a PV-Storage Application

et al. 2019

This study presents a design and an implementation of a robust Maximum Power Point Tracking (MPPT) for a stand-alone photovoltaic (PV) system with battery storage. A new control scheme is applied for the boost converter based on the combination of the adaptive perturb and observe fuzzy logic controller (P&O-FLC) MPPT technique and the backstepping sliding mode control (BS-SMC) approach. The MPPT controller design was used to accurately track the PV operating point to its maximum power point (MPP) under changing climatic conditions. The presented MPPT based on the P&O-FLC technique generates the reference PV voltage and then a cascade control loop type, based on the BS-SMC approach is used. The aims of this approach are applied to regulate the inductor current and then the PV voltage to its reference values. In order to reduce system costs and complexity, a high gain observer (HGO) was designed, based on the model of the PV system, to estimate online the real value of the boost converter’s inductor current. The performance and the robustness of the BS-SMC approach are evaluated using a comparative simulation with a conventional proportional integral (PI) controller implemented in the MATLAB/Simulink environment. The obtained results demonstrate that the proposed approach not only provides a near-perfect tracking performance (dynamic response, overshoot, steady-state error), but also offers greater robustness and stability than the conventional PI controller. Experimental results fitted with dSPACE software reveal that the PV module could reach the MPP and achieve the performance and robustness of the designed BS-SMC MPPT controller.