Publications Scientifiques

Permanent URI for this communityhttps://dspace.univ-boumerdes.dz/handle/123456789/10

Browse

Filters

2019 - 201912020 - 20251

Settings

Author: search.filters.author.Haddouche, Rezki

Search Results

Now showing 1 - 2 of 2
  • Thumbnail Image
    Item
    Effects of Direct-Quadrature Rotor Current Sensors’ Faults in Wind Energy Conversion System based on Doubly Fed Induction Generator
    (Istanbul University, 2025) Chouider, Naziha; Beddek, Karim; Haddouche, Rezki; Zerrougui, Mohamed; Ramdani, Omar
    This paper investigates the dynamic performance of a grid-connected wind energy conversion system (WECS) based on a doubly fed induction generator (DFIG) in the presence of additive and multiplicative faults in directquadrature rotor current sensors. The system architecture includes direct current-link voltage regulation, via the grid side converter and active/reactive power control, through the rotor side converter. Unlike prior studies that mainly consider symmetrical grid faults, this work systematically introduces various sensor faults specifically in stator current, stator voltage, and rotor current measurements into the rotor current control loop. The impact of these faults is assessed by examining key electrical parameters such as current, voltage, active, and reactive power across the grid, stator, and rotor. Simulation results reveal that rotor current sensor faults significantly degrade system stability and power regulation. These findings highlight the importance of developing robust fault detection and isolation and fault-tolerant control strategies and provide a foundation for enhancing the reliability of DFIG-based WECS under sensor fault conditions
  • No Thumbnail Available
    Item
    Neural network ARX model for gas conditioning tower
    (Taylor and Francis Online, 2019) Haddouche, Rezki; Boukhemis, Chetate; Mohand Said, Boumedine
    This work focuses on the identification of the gas conditioning tower (GCT) operating in a cement plant. It is an important element in the cement production line. Mathematical modeling of such a process proves to be very complex. This is due to the phenomena that occur during the operation of the system. An artificial neural network-based auto-regressive with exogenous inputs (NNARX) model is constructed with the aim to study the system as well as used to control the process. Resulted models are tested and validated using data extracted on a GCT operating at Chlef cement plant in Algeria.