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135 results on '"Al-Sumaiti, Ameena Saad"'

9. Mathematical Models for Optimization of Grid-Integrated Energy Storage Systems

Author

Eyisi, Chiebuka, Al-Sumaiti, Ameena Saad, Turitsyn, Konstantin, and Li, Qifeng

Subjects
Mathematics - Optimization and Control
Abstract

Energy storage has been proven to yield positive effects on planning, operation and control of electric grids. It has become a crucial task to properly model the energy storage systems (ESS) under the framework of grid optimization on transmission and distribution networks including microgrids. This paper presents a review on mathematical models and test cases of ESSs used for grid optimization studies, where the network constraints of power systems are included. The existing ESS models are mainly classified into two categories; linear and nonlinear models. The two main categories are further divided into several subcategories respectively; such as mixed integer linear and convex nonlinear subcategories. Based on the review and discussions, this paper aims at providing suggestions for choosing proper ESS models for specific grid optimization studies considering the chosen power network model., Comment: Accepted for publication ahead of the PES General Meeting 2019

Published
2019

12. Optimal placement of electric vehicle charging infrastructures utilizing deep learning.

Author

Alansari, Mohamad, Al‐Sumaiti, Ameena Saad, and Abughali, Ahmed

Subjects
CONVOLUTIONAL neural networks, BOX-Jenkins forecasting, INFRASTRUCTURE (Economics), STANDARD deviations, ELECTRIC charge
Abstract

The increasing level of air pollution caused by the transport sector necessitates countries to adopt Electric Vehicles (EVs). To espouse EVs, the charging infrastructures' location should be optimal to fulfill the mass‐market consumer needs and reduce the governmental expenses. In this work, the placement of two categories of charging infrastructures, specifically Charging Station (CS) and Dynamic Wireless Charging (DWC) infrastructure is planned in Dubai, United Arab Emirates (UAE) as a case study. For this study, Dubai is divided into 14 districts as per its new addressing system, and the allocation of the two types of charging infrastructures is based on the projection of population growth, EVs adoption forecasting, and other factors with the objective of meeting the consumers' needs and minimizing the government's expenditure. The proposal introduces a novel hybrid model for forecasting, integrating the strengths of the Seasonal AutoRegressive Integrated Moving Average with eXogenous regressors (SARIMAX) model for capturing time‐series statistical characteristics, and the deep learning Attention‐based Convolutional Neural Network (ACNN) for modeling nonlinear relationships in time‐series data. The model's effectiveness was validated through comparative analyses against state‐of‐the‐art (SOTA) models on standard benchmarks, showing significant improvements: 29.70% reduction in Mean Absolute Error (MAE), and 19.15% reduction in Root Mean Square Error (RMSE). [ABSTRACT FROM AUTHOR]

Published
2024
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21. Contributors

Author

Abbaspour, Ali, primary, Agabalaye-Rahvar, Masoud, additional, Ahrabi, Masoumeh, additional, Akbari-Dibavar, Alireza, additional, Al-Sumaiti, Ameena Saad, additional, Ameli, Mohammad Taghi, additional, Ameli, Ali, additional, Ameli, Hossein, additional, Amin Mirzaei, Mohammad, additional, Amiri, Mohammad Mehdi, additional, Ansari, Meisam, additional, Ardashir, Jaber Fallah, additional, Asadi, Somayeh, additional, Asgharinejad Keisami, Mohammad Hosein, additional, Azad, Sasan, additional, Baghee, Hamid Reza, additional, Bagheri Tookanlou, Mahsa, additional, Fattaheian-Dehkordi, Sajjad, additional, Ghadim, Hadi Vatankhah, additional, Gharehpetian, Gevork B., additional, Habibi, Mahdi, additional, Heidari, Alireza, additional, Hemmati, Mohammad, additional, Hosseinnezhad, Vahid, additional, Jalilian, Faezeh, additional, Jalilpoor, Kamran, additional, Khounjahan, Hossein, additional, Lehtonen, Matti, additional, Mahajan, Vasundhara, additional, Mansour-Saatloo, Amin, additional, Marzband, Mousa, additional, Mohammadi-Ivatloo, Behnam, additional, Mohammadi, Fazel, additional, Moradzadeh, Arash, additional, Mousavi, Fariba, additional, Mudgal, Soumya, additional, Nazar, Mehrdad Setayesh, additional, Nazari-Heris, Morteza, additional, Parizad, Ali, additional, Pourebrahim, Roghayyeh, additional, Prajapati, Vijay, additional, Shahbazbegian, Vahid, additional, Strbac, Goran, additional, Tohidi, Sajjad, additional, Vahidinasab, Vahid, additional, Yadav, Atul Kumar, additional, and Zare, Kazem, additional

Published
2021
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23. A Common Grounded Voltage Quadrupler ASL/PSC Hybrid Converter With Reduced Voltage Stress

Author

Mohammed, Motiur Reza, Al-Sumaiti, Ameena Saad, Beig, Abdul R., Al Hosani, Khalifa, and Wang, Can

Abstract

Active switched inductor (ASL) dc–dc converters have very high step-up voltage gain but practically they suffer from high voltage stress across switches because of the resonance caused by the mismatch among the inductor values and switch parameters. This article presents a new hybrid dc-dc converter topology derived from ASL and passive switched capacitors (PSC). The proposed topology has high step-up voltage gain over wide range of duty cycle. The resonance due to mismatch of parameters is eliminated. The proposed converter has low voltage stress, low current stress on circuit elements, and common ground between input and output ports. The circuit configuration, operating principle and steady state analysis of the proposed converter are presented. The key performance parameters are compared with that of other high-gain dc–dc converters. A 300 W laboratory prototype is developed and experimental verification is carried out for the voltage step-up from 30 to 400 V. The closed-loop operation is also verified experimentally and results are presented.

Published
2024
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24. Dual Motor Power Sharing Control for Electric Vehicles With Battery Power Management

Author

Muduli, Utkal Ranjan, Beig, Abdul R., Al Jaafari, Khaled, Al Hosani, Khalifa, Al-Sumaiti, Ameena Saad, and Behera, Ranjan Kumar

Abstract

Induction motor powertrains are emerging as a cost-effective option for high-power electric vehicles (EVs). A differential four-wheel drive (D4WD) EV based on two open-end winding induction motor (OEWIM) propulsion is presented in this article. Each OEWIM is driven by two two-level voltage source inverters (VSIs) with two isolated battery packs as the source. The proposed drive control algorithm integrates the uniform state-of-charge (SoC) distribution and fault-tolerant operation. A two-stage lookup table (LUT)-based direct torque control (DTC) scheme is proposed to balance the SoC of batteries by selecting the suitable redundant VSI voltage vectors. The proposed LUT-DTC scheme is less sensitive to variations in motor parameters and provides excellent steady-state and transient performance. The results show the superiority of the proposed controller in terms of battery SoC balancing. The performance of the EV drive with the proposed LUT-DTC is validated by both simulation and laboratory experiments for the FTP75 and HFET driving cycles under different operating modes under normal and fault conditions. The dual-motor-driven D4WD with the proposed LUT-DTC is found to achieve 89.2% efficiency.

Published
2023
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38. Comparative Study between Cost Functions of Genetic Algorithm Used in Direct Torque Control of a Doubly Fed Induction Motor.

Author

Mahfud, Said, Derouich, Aziz, El Ouanjli, Najib, Mossa, Mahmoud A., Motahhir, Saad, El Mahfoud, Mohammed, and Al-Sumaiti, Ameena Saad

Subjects
TORQUE control, INDUCTION motors, GENETIC algorithms, COST functions, ERROR functions, INTELLIGENT control systems
Abstract

The proportional integral derivative (PID) regulator is the most often utilized controller in the industry due to its benefits. It permits linear systems to operate well, but it causes non-linear behavior when the system is subjected to physical variable circumstances, such as temperature and saturation. A PID controller is insufficient in this case. The proportional integral (PI) controller inside the direct torque control (DTC) regulates the speed of the doubly fed induction motor (DFIM). However, the system consisting of DTC and a DFIM is non-linear due to its multivariable parameters, resulting in undesirable overshoots and torque ripples. As a result, several approaches are used to improve the DTC's robustness. The integration of optimization methods was discovered. These algorithms are used to provide gains that are near-optimal, bringing the system closer to its ideal state in order to accomplish effective torque and speed control. This article focuses on a comparative study of the different objective functions, in order to have very effective DFIM behaviors, by using a genetic algorithm. Agenetic algorithm (GA) is presented in this study for adjusting the optimal PID parameters in DTC to control the DFIM, utilizing objective functions such as integral square error (ISE), integral time absolute error (ITAE), and integral absolute error (IAE), employed independently and in a weighted combination. This article offers a comparison of several objective functions inside the DTC and DFIM, which will be utilized in future research into another optimization technique for this control type. Matlab/Simulink was used to construct the novel hybrid structure based on the GA-DTC intelligent control. The simulation results demonstrated the efficiency of the GA-DTC intelligent control with a weighted combination, providing acceptable performance with respect to rapidity, precision, and stability, as well as an improvement of 14.53% in the rejection time reduction, fewer torque ripples and flux ripples on the stator and rotor by 27.88%, 15.13%, and 4.375%, respectively, and respective increases of 32.45% and 71% in the THDs of the stator and rotor currents, which are acceptable. [ABSTRACT FROM AUTHOR]

Published
2022
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39. Precise Voltage Regulation of Unbalanced Bipolar DC-Grid-Based Charging Stations: A New Multiobjective Predictive Control Approach

Author

Nguyen, Hoach The, Al Hosani, Khalifa, Al-Sumaiti, Ameena Saad, Alsawalhi, Jamal Yousuf, Al Jaafari, Khaled Ali, Nguyen, Thanh Hai, and El Moursi, Mohamed Shawky

Abstract

This article proposes a new multiobjective predictive control approach and an optimized switching scheme to precisely regulate the dc-voltages in bipolar dc-grid-based charging stations working under critical conditions. An angular region is preselected to reduce the size of control sets to seven candidates within minimized angle error of 30°. Then, two subregions are located to further simplify finite sets without losing options for multiobjective optimization. Lastly, a hybrid switching scheme is proposed to improve current regulation working under a reduced sampling frequency. Delayed-signal-cancellation technique and instantaneous power theory are also employed to ensure a resilient operation under grid-voltage distortions. Consequently, the proposed control strategy can precisely balance dc-voltages, track voltage references, and regulate grid-injected currents against unbalanced dc-loads and ac-grid distortions. MATLAB/Simulink and an experimental prototype are utilized to investigate the comparative performances between the proposed approach and the recent predictive control methods. The simulation and experiments show significant improvements in dc-link voltages including reference tracking, dc-voltage balance, fast dynamic response, and harmonic reduction of ac-currents under a reduced sampling frequency.

Published
2023
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