ORCID Profile
0000-0001-8367-4112
Current Organisation
Sunway University
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Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2021
Publisher: Elsevier BV
Date: 02-2022
Publisher: Elsevier BV
Date: 12-2015
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 05-2021
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 03-2022
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 11-2021
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2022
Publisher: IEEE
Date: 11-2016
Publisher: MDPI AG
Date: 03-11-2021
DOI: 10.3390/ELECTRONICS10212689
Abstract: In the last few years, intensive research has been done to enhance artificial intelligence (AI) using optimization techniques. In this paper, we present an extensive review of artificial neural networks (ANNs) based optimization algorithm techniques with some of the famous optimization techniques, e.g., genetic algorithm (GA), particle swarm optimization (PSO), artificial bee colony (ABC), and backtracking search algorithm (BSA) and some modern developed techniques, e.g., the lightning search algorithm (LSA) and whale optimization algorithm (WOA), and many more. The entire set of such techniques is classified as algorithms based on a population where the initial population is randomly created. Input parameters are initialized within the specified range, and they can provide optimal solutions. This paper emphasizes enhancing the neural network via optimization algorithms by manipulating its tuned parameters or training parameters to obtain the best structure network pattern to dissolve the problems in the best way. This paper includes some results for improving the ANN performance by PSO, GA, ABC, and BSA optimization techniques, respectively, to search for optimal parameters, e.g., the number of neurons in the hidden layers and learning rate. The obtained neural net is used for solving energy management problems in the virtual power plant system.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2022
Publisher: MDPI AG
Date: 19-02-2021
DOI: 10.3390/WEVJ12010031
Abstract: Electric vehicles are a leading candidate in the clean energy market. This paper aims to analyse the feasibility of the deployment of electric buses (EB) based on the existing bus routes in Brunei, by the use of life cycle cost analysis and the analysis of the parameters that influence the overall life cycle cost. The findings from the study revealed that EB are significantly more expensive than diesel buses (DB), with their acquisition and maintenance costs contributing substantially to their overall life cycle cost. In order to promote EB deployment, the government needs to look simultaneously into providing subsidies for EB and imposing taxes on DB, the provision of charging infrastructure, and ensuring maintenance capability, as well as increasing the current subsidised diesel price. It was also shown that increasing the cost of diesel to the average US diesel price of USD$3.101/L, an initial subsidy of USD$67,586 towards the purchase of EB, and a tax of USD$67,586 for the purchase of DB would allow EB to compete in the market, with the amount of tax and subsidy being gradually reducible over time, as EB and battery technology becomes more mature. From an environmental perspective, the emissions from EB come out higher than the emissions from DB. The efficiency of electric power generation needs to be enhanced, and renewable energy sources and the adoption of carbon capture technology need to be explored in order to exploit the full benefit of EB and ensure more environmentally sustainable bus operation.
Publisher: Elsevier BV
Date: 03-2014
Publisher: Elsevier BV
Date: 09-2013
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2021
Publisher: MDPI AG
Date: 12-11-2019
DOI: 10.3390/ELECTRONICS8111335
Abstract: This paper presents the development of fuzzy-based inverter controller for photovoltaic (PV) application to avoid the nonlinearity characteristic and fluctuations of PV inverter output. The fuzzy-based controller algorithm is employed in the PV inverter control system to optimize the duty cycles of the insulated-gate bipolar transistors (IGBTs) and to enhance the inverter outputs with lower harmonic contents and unity power factor. The developed fuzzy-based PV inverter controller is implemented in the MATLAB/Simulink models and experimentally tested in a dSPACE DS1104 process controller. The obtained simulation result of the developed fuzzy-based PV inverter controller is validated with experimental results under different performance conditions. It is seen that the experimental results of the switching signals, inverter voltage and current, control parameters, and total harmonic distortion (THD) of load current and output voltage of the PV inverter are closely matched with that of the simulation results. To validate the inverter performance, the proposed fuzzy-based PV inverter controller outperforms other studies with a voltage THD of 2.5% and a current THD of 3.5% with unity power factor.
Publisher: Elsevier BV
Date: 11-2021
Publisher: Elsevier BV
Date: 10-2021
Publisher: Public Library of Science (PLoS)
Date: 08-01-2016
Publisher: Elsevier BV
Date: 05-2017
Publisher: Science Publications
Date: 12-2013
Publisher: IEEE
Date: 09-2019
Publisher: IEEE
Date: 11-2013
Publisher: Elsevier BV
Date: 09-2021
Publisher: Hindawi Limited
Date: 2014
DOI: 10.1155/2014/549094
Abstract: Power oscillation d ing controller is designed in linearized model with heuristic optimization techniques. Selection of the objective function is very crucial for d ing controller design by optimization algorithms. In this research, comparative analysis has been carried out to evaluate the effectiveness of popular objective functions used in power system oscillation d ing. Two-stage lead-lag d ing controller by means of power system stabilizers is optimized using differential search algorithm for different objective functions. Linearized model simulations are performed to compare the dominant mode’s performance and then the nonlinear model is continued to evaluate the d ing performance over power system oscillations. All the simulations are conducted in two-area four-machine power system to bring a detailed analysis. Investigated results proved that multiobjective D-shaped function is an effective objective function in terms of moving unstable and lightly d ed electromechanical modes into stable region. Thus, D-shape function ultimately improves overall system d ing and concurrently enhances power system reliability.
Publisher: MDPI AG
Date: 27-05-2019
DOI: 10.3390/MOLECULES24102025
Abstract: The agricultural industry has made a tremendous contribution to the foundations of civilization. Basic essentials such as food, beverages, clothes and domestic materials are enriched by the agricultural industry. However, the traditional method in agriculture cultivation is labor-intensive and inadequate to meet the accelerating nature of human demands. This scenario raises the need to explore state-of-the-art crop cultivation and harvesting technologies. In this regard, optics and photonics technologies have proven to be effective solutions. This paper aims to present a comprehensive review of three photonic techniques, namely imaging, spectroscopy and spectral imaging, in a comparative manner for agriculture applications. Essentially, the spectral imaging technique is a robust solution which combines the benefits of both imaging and spectroscopy but faces the risk of underutilization. This review also comprehends the practicality of all three techniques by presenting existing ex les in agricultural applications. Furthermore, the potential of these techniques is reviewed and critiqued by looking into agricultural activities involving palm oil, rubber, and agro-food crops. All the possible issues and challenges in implementing the photonic techniques in agriculture are given prominence with a few selective recommendations. The highlighted insights in this review will hopefully lead to an increased effort in the development of photonics applications for the future agricultural industry.
Publisher: Elsevier BV
Date: 08-2022
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 05-2023
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2018
Publisher: Elsevier BV
Date: 10-2020
Publisher: Elsevier BV
Date: 11-2023
Publisher: Elsevier BV
Date: 02-2023
Publisher: Elsevier BV
Date: 04-2022
Publisher: Elsevier BV
Date: 04-2021
Publisher: MDPI AG
Date: 22-02-2023
DOI: 10.3390/SU15053986
Abstract: The use of renewable energy techniques is becoming increasingly popular because of rising demand and the threat of negative carbon footprints. Wind power offers a great deal of untapped potential as an alternative source of energy. The rising demand for wind energy typically results in the generation of high-quality output electricity through grid integration. More sophisticated contemporary generators, power converters, energy management, and controllers have been recently developed to integrate wind turbines into the electricity system. However, a comprehensive review of the role of converters in the wind system’s power conversion, control, and application toward sustainable development is not thoroughly investigated. Thus, this paper proposes a comprehensive review of the impact of converters on wind energy conversion with its operation, control, and recent challenges. The converters’ impact on the integration and control of wind turbines was highlighted. Moreover, the conversion and implementation of the control of the wind energy power system have been analyzed in detail. Also, the recently advanced converters applications for wind energy conversion were presented. Finally, recommendations for future converters use in wind energy conversions were highlighted for efficient, stable, and sustainable wind power. This rigorous study will lead academic researchers and industry partners toward the development of optimal wind power technologies with improved efficiency, operation, and costs.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2018
Publisher: Springer Science and Business Media LLC
Date: 13-03-2020
DOI: 10.1038/S41598-020-61464-7
Abstract: State of charge (SOC) is a crucial index used in the assessment of electric vehicle (EV) battery storage systems. Thus, SOC estimation of lithium-ion batteries has been widely investigated because of their fast charging, long-life cycle, and high energy density characteristics. However, precise SOC assessment of lithium-ion batteries remains challenging because of their varying characteristics under different working environments. Machine learning techniques have been widely used to design an advanced SOC estimation method without the information of battery chemical reactions, battery models, internal properties, and additional filters. Here, the capacity of optimized machine learning techniques are presented toward enhanced SOC estimation in terms of learning capability, accuracy, generalization performance, and convergence speed. We validate the proposed method through lithium-ion battery experiments, EV drive cycles, temperature, noise, and aging effects. We show that the proposed method outperforms several state-of-the-art approaches in terms of accuracy, adaptability, and robustness under erse operating conditions.
Publisher: AIP Publishing
Date: 11-2016
DOI: 10.1063/1.4967972
Abstract: This study presents a charging and discharging controller of a lithium-ion battery for charge equalization control of a battery storage system using the particle swarm optimization (PSO) algorithm. The charge equalization controller is designed using a bidirectional flyback DC–DC converter for exchanging the amount of energy from a battery series stack to an overdischarged cell to be charged and vice versa. The constant current–constant voltage charge proportional–integral (PI) control and discontinuous current mode control are applied to charge and discharge the lithium-ion battery on a flyback converter operation. This proposed system utilizes the PSO algorithm to optimize the values of the PI controller parameters. Optimization results produce the ideal values of the PI controller parameters with minimum error indices, thereby regulating the pulse-width modulation to the MOSFET switching drive of the flyback converter and upgrading the battery charge performance for charge equalization. The PSO algorithmic approach-based developed system is proven to be robust and competent for high-tech storage systems toward the advancement of sustainable electric vehicle technologies and renewable source of applications.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 05-2017
Publisher: Springer Science and Business Media LLC
Date: 30-07-2020
DOI: 10.1038/S41467-020-17623-5
Abstract: Three-phase induction motors (TIMs) are widely used for machines in industrial operations. As an accurate and robust controller, fuzzy logic controller (FLC) is crucial in designing TIMs control systems. The performance of FLC highly depends on the membership function (MF) variables, which are evaluated by heuristic approaches, leading to a high processing time. To address these issues, optimisation algorithms for TIMs have received increasing interest among researchers and industrialists. Here, we present an advanced and efficient quantum-inspired lightning search algorithm (QLSA) to avoid exhaustive conventional heuristic procedures when obtaining MFs. The accuracy of the QLSA based FLC (QLSAF) speed control is superior to other controllers in terms of transient response, d ing capability and minimisation of statistical errors under erse speeds and loads. The performance of the proposed QLSAF speed controller is validated through experiments. Test results under different conditions show consistent speed responses and stator currents with the simulation results.
Publisher: IEEE
Date: 11-2015
Publisher: Hindawi Limited
Date: 2014
DOI: 10.1155/2014/271087
Abstract: This paper presents an evaluation of an optimal DC bus voltage regulation strategy for grid-connected photovoltaic (PV) system with battery energy storage (BES). The BES is connected to the PV system DC bus using a DC/DC buck-boost converter. The converter facilitates the BES power charge/discharge to compensate for the DC bus voltage deviation during severe disturbance conditions. In this way, the regulation of DC bus voltage of the PV/BES system can be enhanced as compared to the conventional regulation that is solely based on the voltage-sourced converter (VSC). For the grid side VSC (G-VSC), two control methods, namely, the voltage-mode and current-mode controls, are applied. For control parameter optimization, the simplex optimization technique is applied for the G-VSC voltage- and current-mode controls, including the BES DC/DC buck-boost converter controllers. A new set of optimized parameters are obtained for each of the power converters for comparison purposes. The PSCAD/EMTDC-based simulation case studies are presented to evaluate the performance of the proposed optimized control scheme in comparison to the conventional methods.
Publisher: MDPI AG
Date: 23-11-2022
DOI: 10.3390/SU142315576
Abstract: The COVID-19 pandemic has affected every sector in the world, ranging from the education sector to the health sector, administration sector, economic sector and others in different ways. Multiple kinds of research have been performed by research centres, education institutions and research groups to determine the extent of how huge of a threat the COVID-19 pandemic poses to each sector. However, detailed analysis and assessment of its impact on every single target within the 17 Sustainable Development Goals (SDGs) have not been discussed so far. We report an assessment of the impact of COVID-19 effect towards achieving the United Nations SDGs. In assessing the pandemic effects, an expert elicitation model is used to show how the COVID-19 severity affects the positive and negative impact on the 169 targets of 17 SDGs under environment, society and economy groups. We found that the COVID-19 pandemic has a low positive impact in achieving only 34 (20.12%) targets across the available SDGs and a high negative impact of 54 targets (31.95%) in which the most affected group is the economy and society. The environmental group is affected less rather it helps to achieve a few targets within this group. Our elicitation model indicates that the assessment process effectively measures the mapping of the COVID-19 pandemic impact on achieving the SDGs. This assessment identifies that the COVID-19 pandemic acts mostly as a threat in enabling the targets of the SDGs.
Publisher: Springer Science and Business Media LLC
Date: 02-2016
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2022
Publisher: Elsevier BV
Date: 03-2016
Publisher: IEEE
Date: 12-2013
Publisher: Elsevier BV
Date: 06-2023
Publisher: Elsevier BV
Date: 05-2022
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2019
Publisher: Elsevier BV
Date: 2024
Publisher: IEEE
Date: 12-2012
Publisher: Elsevier BV
Date: 07-2023
Publisher: Elsevier BV
Date: 08-2023
Publisher: MDPI AG
Date: 08-08-2021
DOI: 10.3390/EN14164829
Abstract: High-voltage direct current (HVDC) has received considerable attention due to several advantageous features such as minimum transmission losses, enhanced stability, and control operation. An appropriate model of HVDC is necessary to assess the operating conditions as well as to analyze the transient and steady-state stabilities integrated with the AC networks. Nevertheless, the construction of an HVDC model is challenging due to the high computational cost, which needs huge ranges of modeling experience. Therefore, advanced dynamic modeling of HVDC is necessary to improve stability with minimum power loss. This paper presents a comprehensive review of the various dynamic modeling of the HVDC transmission system. In line with this matter, an in-depth investigation of various HVDC mathematical models is carried out including average-value modeling (AVM), voltage source converter (VSC), and line-commutated converter (LCC). Moreover, numerous stability assessment models of HVDC are outlined with regard to stability improvement models, current-source system stability, HVDC link stability, and steady-state rotor angle stability. In addition, the various control schemes of LCC-HVDC systems and modular multilevel converter- multi-terminal direct current (MMC-MTDC) are highlighted. This paper also identifies the key issues, the problems of the existing HVDC models as well as providing some selective suggestions for future improvement. All the highlighted insights in this review will hopefully lead to increased efforts toward the enhancement of the modeling for the HVDC system.
Publisher: Elsevier BV
Date: 03-2017
Publisher: Elsevier BV
Date: 11-2020
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 09-2022
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2018
Publisher: Elsevier BV
Date: 02-2021
Publisher: Institution of Engineering and Technology (IET)
Date: 12-2017
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 09-2023
Publisher: Elsevier BV
Date: 09-2023
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2022
Publisher: Elsevier BV
Date: 03-2023
Publisher: Elsevier BV
Date: 12-2018
Publisher: Elsevier BV
Date: 10-2018
Publisher: Elsevier BV
Date: 11-2021
Publisher: IEEE
Date: 10-2016
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 09-2022
Publisher: Elsevier BV
Date: 08-2017
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2020
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Publisher: Elsevier BV
Date: 06-2021
Publisher: AIP Publishing
Date: 03-2016
DOI: 10.1063/1.4944961
Abstract: Lithium-Ion (Li-Ion) batteries are commonly used as automobile energy storage systems for powering applications due to their lucrative features. However, a battery management system with in idual cell monitoring and balancing of Li-Ion batteries for long use and casualties' protection are still major issues in electric vehicle applications. This paper deals with the development of a voltage equalization control algorithm for in idual cell monitoring and balancing of series connected Li-Ion battery cells. The developed states and sequences of the control algorithm manage the whole processes of battery cell monitoring, charging, and discharging, respectively. A charge equalization model is implemented with series connected 10 Li-Ion battery cells utilizing the developed control algorithm. Results show that charging and discharging, and cell balancing performance of the control algorithm are capable of quickly responding to reach the state of charge difference of 2.5% among all cells, defending the existing anomaly, providing tolerable stress to components and operating at a higher efficiency of 84.9%.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 09-2020
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2018
Publisher: Penerbit UTM Press
Date: 29-12-2016
DOI: 10.11113/JT.V79.7344
Abstract: Mass commercialization of fuel cells (FC) and its usage in transportation requires that the FC technology to be competitive with regard to performance and cost, while meeting efficiency and emissions targets. Therefore, fuel cell output current ripple that may shorten FC lifespan, worsen FC efficiency and reduce the FC output capacity need to be addressed. In this paper, an improved multi-device interleaved boost converter (MDIBC) with novel multiplex controller topology is designed to further reduce the input current and output voltage ripples, without increasing the number of MDIBC switching devices. The Matlab/Simulink behaviour model of the improved MDIBC with novel multiplex controller and conventional MDIBC circuit are developed in the simulation studies. The proposed improved MDIBC design is then compared with the conventional MDIBC and its performance is verified.
Publisher: MDPI AG
Date: 13-09-2017
DOI: 10.3390/EN10091390
Publisher: Springer Science and Business Media LLC
Date: 10-2021
DOI: 10.1038/S41598-021-98915-8
Abstract: Accurate state of charge (SOC) estimation of lithium-ion (Li-ion) batteries is crucial in prolonging cell lifespan and ensuring its safe operation for electric vehicle applications. In this article, we propose the deep learning-based transformer model trained with self-supervised learning (SSL) for end-to-end SOC estimation without the requirements of feature engineering or adaptive filtering. We demonstrate that with the SSL framework, the proposed deep learning transformer model achieves the lowest root-mean-square-error (RMSE) of 0.90% and a mean-absolute-error (MAE) of 0.44% at constant ambient temperature, and RMSE of 1.19% and a MAE of 0.7% at varying ambient temperature. With SSL, the proposed model can be trained with as few as 5 epochs using only 20% of the total training data and still achieves less than 1.9% RMSE on the test data. Finally, we also demonstrate that the learning weights during the SSL training can be transferred to a new Li-ion cell with different chemistry and still achieve on-par performance compared to the models trained from scratch on the new cell.
Publisher: American Scientific Publishers
Date: 06-2017
Publisher: Elsevier BV
Date: 04-2020
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2020
Location: Bangladesh
No related grants have been discovered for M A Hannan.