Power quality optimization using a novel backstepping control of a three-phase grid-connected photovoltaic systems

Naddami, Salwa; Ababssi, Najib

doi:10.11591/ijece.v13i3.pp2517-2528

Cited by 5 publications

(3 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The nominal current generated by the cells is denoted by i P V −n in Relation (1), the current coefficient is K 1 ; the unit of electric charge is q; the solid-state semiconductor gap energy is denoted by E g , the number of cells connected in series is N c , the ideal cell coefficient is a; the ambient temperature is Tn, the nominal temperature is G, and the ambient nominal radiation intensity is G n . i 0−n represents the inverse saturation current under nominal conditions (Naddami, Ababssi, & Mokhlis, 2023). The PV cell current is a complicated function of temperature and radiation intensity, as shown in Eq.…”

Section: Modeling Of the Pv Arraymentioning

confidence: 99%

Optimizing Photovoltaic System Performance through the Design and Development of an Artificial Neural Network MPPT Control

Meddah,

Belarbi,

Negadi

et al. 2024

Preprint

View full text Add to dashboard Cite

This article conducts a thorough comparison of three Maximum Power Point Tracking control techniques for photovoltaic systems: Perturb and Observe, Incremental Conductance, and Artificial Neural Network. The study aims to identify the most effective MPPT method by subjecting each technique to numerical simulations. The article explores the performance, efficiency, and robustness of Perturb and Observe, Incremental Conductance and Artificial Neural Network in capturing the maximum power output from photovoltaic panels under varying environmental conditions. Following rigorous testing through numerical simulations, the superior technique is selected for implementation in a grid-connected photovoltaic power conversion chain. This research contributes valuable insights into the optimization of photovoltaic system performance through advanced MPPT control strategies, facilitating informed decisions for practical applications in renewable energy systems.

show abstract

Section: Modeling Of the Pv Arraymentioning

confidence: 99%

Optimizing Photovoltaic System Performance through the Design and Development of an Artificial Neural Network MPPT Control

Meddah,

Belarbi,

Negadi

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…The three-phase inverter is used to transform the voltage from DC to three-phase AC voltage [25]. The differential equations for each phase model the AC side of the conversion system.…”

Section: Modelling Of Voltage Source Inverter and Filtermentioning

confidence: 99%

Robust Control and Energy Management in Grid-connected Photovoltaic-battery Energy Storage Systems

2024

IJIES

View full text Add to dashboard Cite

This paper investigates the design of a robust non-linear backstepping controller for the DC-AC microgrid comprising a photovoltaic source and a battery energy storage system with grid integration, all feeding a non-linear load, to improve its power quality and dynamic stability. A unidirectional DC-DC boost converter and a bidirectional back boost converter are used on the DC side to connect the photovoltaic module and battery storage to the DC bus. The three phases of the voltage source inverter are connected to the electrical grid via an inductive filter on the AC side. The control objectives are fourfold: i) Produce the maximum power from the photovoltaic system regardless of variations in weather conditions (temperature and irradiation); ii) Keep the DC link voltage constant and close to a given reference value under various conditions to ensure a balance of power between the DC and AC sides; iii) Active power control by injecting excess power into the grid; iv) Propose an energy management strategy to optimize the energy consumption of each source: the solar source, the grid, and the battery storage. Simulation results under different operating conditions, parameter variations, and load disturbances are presented and discussed to verify the performance satisfaction of the proposed controllers. An improvement in the overall dynamic performance of the microgrid is demonstrated, with a reduction in voltage overshoot (4.8%) and settling time (5.6ms) on the DC bus, along with reduced total harmonic distortion (THD) in grid current (0.29%) when compared with the other controllers in this work: the proportional integral controller (voltage overshoot (5.8%), settling time (15.5ms); grid current THD (6.53%)) and sliding mode controller(voltage overshoot (4.6%), settling time (15ms), grid current THD (3.53%). finally, several tests have been conducted on the proposed microgrid to evaluate the controller's efficiency in maintaining power balance during irradiation distortion and non-linear load imbalance.

show abstract

“…Various PV architectures exists to realize a grid-connected inverter. These include single-stage, dual-stage and multi-stage power processing [11,12].…”

Section: Introductionmentioning

confidence: 99%

Maximum Power Point Tracking Algorithm of Photo-Voltaic Array through Determination of Boost Converter Conduction Mode

et al. 2023

View full text Add to dashboard Cite

In this paper, we present a new maximum power point tracking (MPPT) algorithm that can identify whether a boost converter is operating in continuous conduction mode (CCM) or discontinuous conduction mode (DCM). The conventional MPPT algorithm assumes that the converter is always in CCM mode, even though this is not always the case. The converter can enter DCM mode due to factors such as the inductor size, irradiance and temperature conditions, voltage step size of the algorithm, and operating point of the PV array. In the proposed work, the conduction mode of a boost converter is evaluated under different conditions. The region of the I–V curve where the converter is likely to operate in DCM mode is identified and a mathematical expression developed in this work is then used to detect the conduction mode of the converter. The proposed algorithm incorporates this expression into a modified perturb and observe (P&O) algorithm. In each iteration, the algorithm first detects the conduction mode of the converter. If the converter is in DCM mode, the algorithm takes a large voltage step to force the converter back into CCM mode, i.e., into the constant current region. The proposed MPPT algorithm was tested using simulation experiments, and the results show that the proposed algorithm can significantly improve the efficiency of the MPPT process.

show abstract

Power quality optimization using a novel backstepping control of a three-phase grid-connected photovoltaic systems

Cited by 5 publications

References 26 publications

Optimizing Photovoltaic System Performance through the Design and Development of an Artificial Neural Network MPPT Control

Optimizing Photovoltaic System Performance through the Design and Development of an Artificial Neural Network MPPT Control

Robust Control and Energy Management in Grid-connected Photovoltaic-battery Energy Storage Systems

Maximum Power Point Tracking Algorithm of Photo-Voltaic Array through Determination of Boost Converter Conduction Mode

Contact Info

Product

Resources

About