Browsing by Author "Fellag, Sid Ali"

Filter results by typing the first few letters
Now showing 1 - 2 of 2
  • No Thumbnail Available
    Item
    Fuzzy logic control of a 400 KV transmission line by a statcom based on NPC multilevel voltage inverters
    (Centre for Info Bio Technology (CIBTech) (Regd, 2024) Boukarana, Leila; Fellag, Sid Ali; Habi, Idir
    Recent developments in power systems mean that it will be increasingly difficult to reliably control power transfers in highly interconnected systems using traditional slow response time control systems such as phase- shifting transformers and passives shunts or series compensators. This has recently led to studies of a new generation of FACTS (Flexible AC Transmission System) control devices, using new power static components that can control both opening and closing: a FACTS device. This paper introduces the STATCOM, a power device reliant on a semiconductor power switches for controlling electrical networks. It serves to enhance the regulation of both active and reactive power transmitted through power lines. Specifically, the STATCOM compensates the reactive energy of the lines, maintains the voltage levels at the bus bars and enables the power source to operate with a unity power factor. After a concise introduction to the operating principle of the STATCOM, a model is presented featuring key control loops including Phase-Locked Loop (PLL), DC bus voltage control, and power control. Linear controllers (Proportional Integral PI) are used and an approach using Fuzzy Logic Controllers (FLC) is tested. Simulation results for a 400 kV line conducted within the Matlab/Simulink environment are presented, providing a comparative analysis and discussion on the effectiveness of the adopted control strategies in addressing both dynamic and static aspects of the
  • No Thumbnail Available
    Item
    A medical comparative study evaluating electrocardiogram signal-based blood pressure estimation
    (IGI Global, 2024) Moussaoui, Siham; Fellag, Sid Ali; Chebi, Hocine
    In general, blood pressure (BP) is measured using standard methods (medical monitors), which are widely used, or from physiological sensor data, which is a difficult task usually solved by combining several signals. In recent research, electrocardiogram (ECG) signals alone have been used to estimate blood pressure. The authors present a comparative study that evaluates ECG signal-based blood pressure estimation using complexity analysis to extract features, comparing the results obtained with a random forest regression model as well as with the combination of a stacking-based classification module and a regression module. It was determined that the best result obtained is a mean absolute error range of 3.73 mmHg with a standard deviation of 5.19 mmHg for diastolic blood pressure (DBP) and 5.92 mmHg with a standard deviation of 7.23 mmHg for systolic blood pressure (PAS).