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All Journal International Journal of Electrical and Computer Engineering Bulletin of Electrical Engineering and Informatics Indonesian Journal of Electrical Engineering and Computer Science
Abdulrahim Thiab Humod
University of Technology
Computer Science & IT Electrical & Electronics Engineering

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Type 1 versus type 2 fuzzy logic speed controllers for brushless dc motors Hayder Yousif Abed; Abdulrahim Thiab Humod; Amjad J. Humaidi
International Journal of Electrical and Computer Engineering (IJECE) Vol 10, No 1: February 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (742.451 KB) | DOI: 10.11591/ijece.v10i1.pp265-274

Abstract

This work presented two fuzzy logic (FL) schemes for speed-controlled brushless DC motors. The first controller is a Type 1 FL controller (T1FLC), whereas the second controller is an interval Type 2 FL controller (IT2FLC). The two proposed controllers were compared in terms of system dynamics and performance. For a fair comparison, the same type and number of membership functions were used for both controllers. The effectiveness of the structures of the two FL controllers was verified through simulation in MATLAB/SIMULINK environment. Simulation result showed that IT2FLC exhibited better performance than T1FLC.
Optimum control for dynamic voltage restorer based on particle swarm optimization algorithm Saddam Subhi Salman; Abdulrahim Thiab Humod; Fadhil A. Hasan
Indonesian Journal of Electrical Engineering and Computer Science Vol 26, No 3: June 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v26.i3.pp1351-1359

Abstract

This article addresses a variety of power quality concerns, including voltage sag and swell, surges, harmonics, and so on, utilizing a dynamic voltage restorer (DVR). The proposed controller for DVR is proportional plus integral (PI) controller. Two methods are used for tuning the parameters of PI controller, trial and error and intelligent optimal method. The utilized optimal method is particle swarm optimization (PSO) method. Results depicted that DVR using PI controller tuned by PSO has improved performance than PI controller tuned by trial and error in term of rise time, maximum overshoot and settling time, as well as total harmonic distortion (THD). These improvements are applicable for voltage sag and swell conditions.
Injected power control for grid-connected converter based on particle swarm optimization Safa Sfoog Oleiwi; Abdulrahim Thiab Humod; Fadhil Abbas Hasan
Indonesian Journal of Electrical Engineering and Computer Science Vol 27, No 3: September 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v27.i3.pp1199-1211

Abstract

Inductance–capacitance–inductance (LCL) filters are very attractive candidates for renewable energy system applications due to their high efficiency, high harmonic reduction, small bulk, and improved harmonic distortion (THD). These papers take advantage of the capabilities of renewable energy sources and inject them into the network by using an inverter when it enters work at high loads at certain times. Therefore, it is necessary to control power with certain controllers. The proportional-integral controller (PI) is used; conventional methods for tuning the controller parameters cannot give satisfactory performance due to the high instability of the closed-loop system. This paper presents the particle swarm optimization (PSO) method for tuning the controller's parameters to achieve optimum performance associated with sufficient stability margin. The mathematical models for the LCL filter and the frequency response were investigated by using the bode-plot. The proposed approach shows effective results for both power control and harmonic reduction. The proposed PI-PSO controller gives overshoot (1.08%), settling time (0.03 sec), rise time (0.00035 sec) and improved THD from 10.29% to 1.67% with compared to using the trial and error method, which gives (1.035%), (0.015) and (0.003) and THD from 10.23% to 1.575%, respectively.
Dynamic voltage restorer based on particle swarm optimization algorithm and adaptive neuro-fuzzy inference system Saddam Subhi Salman; Abdulrahim Thiab Humod; Fadhil A. Hasan
Bulletin of Electrical Engineering and Informatics Vol 11, No 6: December 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i6.4023

Abstract

This article uses a dynamic voltage restorer to tackle a wide range of power quality issues, such as voltage drooping and swelling, spikes, distortions, and so on. The proportional controller, integrated controller (PI), and adaptive neuro-fuzzy inference system (ANFIS) are proposed dynamic voltage restorer (DVR) controllers. The control strategy's goal is to employ an injection transformer to mitigate for the needed voltage and keep the load voltage fixed. The settings of the PI controller are fine-tuned using two methods: trial and error and intelligent optimum. Particle swarm optimization (PSO) is now the most effective method. In terms of settling time, overshoot, undershoot, and disturbances around the final value, the PSO-tuned PI controller outperforms the trial-and-error PI controller. The ANFIS controller is used to regulate the DVR's responsiveness through the PI-PSO controller. The PI-PSO data is used as training data by the ANFIS controller. The results show that a DVR with an ANFIS controller outperforms a PI-PSO controller in terms of overshoot, undershoot spike voltage, steady state time, and settling time. In the case of a failure voltage, the DVR with an ANFIS controller has a 27% undershoot spike voltage while the PI-PSO controller has a 30% undershoot spike voltage.
Co-Authors Amjad J. Humaidi Fadhil A. Hasan Fadhil Abbas Hasan Hayder Yousif Abed Saddam Subhi Salman Saddam Subhi Salman Safa Sfoog Oleiwi