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Dr. Md Alamgir Hossain
UNSW Canberra

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Journal article
Published: 29 August 2021 in Electronics
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The design of a solar PV system and its performance evaluation is an important aspect before going for a mass-scale installation and integration with the grid. The parameter evaluation of a solar PV model helps in accurate modeling and consequently efficient designing of the system. The parameters appear in the mathematical equations of the solar PV cell. A Chaos Induced Coyote Algorithm (CICA) to obtain the parameters in a single, double, and three diode model of a mono-crystalline, polycrystalline, and a thin-film solar PV cell has been proposed in this work. The Chaos Induced Coyote Algorithm for extracting the parameters incorporates the advantages of the conventional Coyote Algorithm by employing only two control parameters, making it easier to include the unique strategy that balances the exploration and exploitation in the search space. A comparison of the Chaos Induced Coyote Algorithm with some recently proposed solar photovoltaic cell parameter extraction algorithms has been presented. Analysis shows superior curve fitting and lesser Root Mean Square Error with the Chaos Induced Coyote Algorithm compared to other algorithms in a practical solar photovoltaic cell.

ACS Style

Shoeb Ahmad Khan; Shafiq Ahmad; Adil Sarwar; Mohd Tariq; Javed Ahmad; Mohammed Asim; Ahmed T. Soliman; Alamgir Hossain. Chaos Induced Coyote Algorithm (CICA) for Extracting the Parameters in a Single, Double, and Three Diode Model of a Mono-Crystalline, Polycrystalline, and a Thin-Film Solar PV Cell. Electronics 2021, 10, 2094 .

AMA Style

Shoeb Ahmad Khan, Shafiq Ahmad, Adil Sarwar, Mohd Tariq, Javed Ahmad, Mohammed Asim, Ahmed T. Soliman, Alamgir Hossain. Chaos Induced Coyote Algorithm (CICA) for Extracting the Parameters in a Single, Double, and Three Diode Model of a Mono-Crystalline, Polycrystalline, and a Thin-Film Solar PV Cell. Electronics. 2021; 10 (17):2094.

Chicago/Turabian Style

Shoeb Ahmad Khan; Shafiq Ahmad; Adil Sarwar; Mohd Tariq; Javed Ahmad; Mohammed Asim; Ahmed T. Soliman; Alamgir Hossain. 2021. "Chaos Induced Coyote Algorithm (CICA) for Extracting the Parameters in a Single, Double, and Three Diode Model of a Mono-Crystalline, Polycrystalline, and a Thin-Film Solar PV Cell." Electronics 10, no. 17: 2094.

Journal article
Published: 22 June 2021 in IEEE Transactions on Applied Superconductivity
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Accurate prediction of wind power generation is complex due to stochastic component, but can play a significant role in minimizing operating costs, and improving reliability and security of a power system. This paper proposes a hybrid deep learning model to accurately forecast the very-short-term (5-min and 10-min) wind power generation of the Boco Rock Wind Farm in Australia. The model consists of a convolutional neural network, gated recurrent units (GRU) and a fully connected neural network. To improve performance, the hyper-parameters of the model are tuned using the Harris Hawks Optimization algorithm. The effectiveness of the proposed model is evaluated against other advanced models, including multilayer feedforward neural network (NN), recurrent neural network (RNN), long short-term memory (LSTM) and GRU. The forecasting model demonstrates around 38% and 24% higher accuracy as compared to the 5- and 10-min forecasting of the NN model, respectively.

ACS Style

Alamgir Hossain; Ripon K. Chakrabortty; Sondoss Elsawah; Evan Mac A Gray; Michael J. Ryan. Predicting Wind Power Generation using Hybrid Deep Learning with Optimization. IEEE Transactions on Applied Superconductivity 2021, PP, 1 -1.

AMA Style

Alamgir Hossain, Ripon K. Chakrabortty, Sondoss Elsawah, Evan Mac A Gray, Michael J. Ryan. Predicting Wind Power Generation using Hybrid Deep Learning with Optimization. IEEE Transactions on Applied Superconductivity. 2021; PP (99):1-1.

Chicago/Turabian Style

Alamgir Hossain; Ripon K. Chakrabortty; Sondoss Elsawah; Evan Mac A Gray; Michael J. Ryan. 2021. "Predicting Wind Power Generation using Hybrid Deep Learning with Optimization." IEEE Transactions on Applied Superconductivity PP, no. 99: 1-1.

Journal article
Published: 18 June 2021 in Journal of Cleaner Production
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In the last decade, there have been significant developments in the field of intelligent energy management systems (IEMSs), with various methods and new solutions proposed for managing the energy resources intelligently. An important issue related to finding the desired outcomes remains unexplored, i.e., how to determine key insights from the sparse academic literature in the age of digital publishing. To mitigate the issue, this study proposes a novel strategy to systematically survey the relevant studies by converting the sparse literature into visual presentations. We first apply a systematic approach called a PRISMA (Preferred Reporting Items for Systematic reviews and Meta-analyses) statement to provide the insights from the published literature of the past decade (2010–2020). Then, VOSviewer experiments are conducted to transform these sparse scholarly data into visual representations. In total, eighty-one papers published in high-impact journals are identified based on their scientific soundness and relevance, and a VOSviewer analysis is applied. The analysis revealed the existence of three research clusters focused on the following main thematic areas: the energy management in smart homes and smart grids (35 journal papers); the emerging concept of context-awareness (26 journal papers); and the role of privacy preservation in IEMSs (20 journal papers). This analysis uncovers the current state of IEMSs and explores existing issues, methods, findings, and gaps. Thus, future research directions have been recommended to fill the existing gaps. This systematic literature review is to assist both researchers and industry practitioners to understand the research gaps of previous studies.

ACS Style

Muhammad Ali; Krishneel Prakash; Alamgir Hossain; Hemanshu R. Pota. Intelligent energy management: Evolving developments, current challenges, and research directions for sustainable future. Journal of Cleaner Production 2021, 314, 127904 .

AMA Style

Muhammad Ali, Krishneel Prakash, Alamgir Hossain, Hemanshu R. Pota. Intelligent energy management: Evolving developments, current challenges, and research directions for sustainable future. Journal of Cleaner Production. 2021; 314 ():127904.

Chicago/Turabian Style

Muhammad Ali; Krishneel Prakash; Alamgir Hossain; Hemanshu R. Pota. 2021. "Intelligent energy management: Evolving developments, current challenges, and research directions for sustainable future." Journal of Cleaner Production 314, no. : 127904.

Journal article
Published: 21 May 2021 in IEEE Access
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In this work, the evaluation of the design and optimization of proposed offgrid hybrid microgrid systems for different load dispatch strategies is presented by assessing the component sizes, system responses and different cost analyses of the proposed system. This study optimizes the sizing of the Barishal and Chattogram (two popular divisions in Bangladesh) hybrid microgrid systems consisting of wind turbine, storage unit, solar PV, diesel generator and a load profile of 27.31 kW for five dispatch techniques: (i) generator order, (ii) cycle charging, (iii) load following, (iv) HOMER predictive dispatch and (v) combined dispatch strategy. The considered microgrids are optimized for the least CO 2 gas emission, Net Present Cost, and Levelized Cost of Energy. The two microgrids are analyzed for the five dispatch techniques using HOMER software, and subsequently, the power system performance and feasibility study of the microgrids are performed in MATLAB Simulink. The results in this research provide a guideline to estimate different component sizes and probable costing for the optimal operation of the proposed microgrids under various load dispatch conditions. The simulation results suggest that ‘Load Following’ is the best dispatch strategy for the proposed microgrids having a stable power system response with the lowest net present cost, levelized cost of energy, operating cost, and CO 2 emission rate. Additionally, the combined dispatch strategy is determined to be the worst dispatch technique for the proposed off-grid hybrid microgrid design having the maximum levelized cost of energy, net present cost, operating cost and CO 2 emission.

ACS Style

Fatin Ishraque; Sk. A. Shezan; M. M. Rashid; Ananta Bijoy Bhadra; Alamgir Hossain; Ripon K. Chakrabortty; Michael J. Ryan; Shahriar Rahman Fahim; Subrata K. Sarker; Sajal K. Das. Techno-Economic and Power System Optimization of a Renewable Rich Islanded Microgrid Considering Different Dispatch Strategies. IEEE Access 2021, 9, 77325 -77340.

AMA Style

Fatin Ishraque, Sk. A. Shezan, M. M. Rashid, Ananta Bijoy Bhadra, Alamgir Hossain, Ripon K. Chakrabortty, Michael J. Ryan, Shahriar Rahman Fahim, Subrata K. Sarker, Sajal K. Das. Techno-Economic and Power System Optimization of a Renewable Rich Islanded Microgrid Considering Different Dispatch Strategies. IEEE Access. 2021; 9 ():77325-77340.

Chicago/Turabian Style

Fatin Ishraque; Sk. A. Shezan; M. M. Rashid; Ananta Bijoy Bhadra; Alamgir Hossain; Ripon K. Chakrabortty; Michael J. Ryan; Shahriar Rahman Fahim; Subrata K. Sarker; Sajal K. Das. 2021. "Techno-Economic and Power System Optimization of a Renewable Rich Islanded Microgrid Considering Different Dispatch Strategies." IEEE Access 9, no. : 77325-77340.

Journal article
Published: 17 April 2021 in Energy Conversion and Management
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The optimal operation of solar cells depends on the accurate determination of parameters in the Photovoltaic (PV) models, such as resistance and currents, which may vary due to unstable weathers conditions and equipment aging. The precise selection of these parameters resembles a multi-variable, nonlinear and multi-modal problem. Despite a few parameter extraction techniques being available to solve such a problem, more-accurate and advanced solutions still present a challenging research question. This paper therefore proposes an improved gaining-sharing knowledge (IGSK) algorithm to accurately and precisely extract the parameters of PV models. The improvement in the classical GSK algorithm is incorporated by introducing an adaptive mechanism to automatically adjust the value of the knowledge rate parameter. This adaptive mechanism ensures the balance between the number of dimensions updated by the junior gaining-sharing phase and the number of dimensions updated by the senior gaining-sharing phase. A bound-constraint handling method is also presented and a linear population size reduction technique is used to boost the speed of convergence and to maintain a tared-off between the exploration and exploitation properties. The efficacy of the proposed IGSK has been demonstrated by considering three different PV modules models, i.e., single diode, double diode, and PV modules and two other commercial ones (Thin Film ST40 and Mono-crystalline SM55). For those modules, the proposed IGSK receptively produces the following outcomes: 0.00098602188, 0.0009827277, 0.0024250749, 0.0017298137, and 0.016600603. The statistical obtained results demonstrate that the IGSK indicates competitive or even better performance on convergence speed, accuracy and reliability compared with other competing techniques. Therefore, the proposed approach is believed to be an effective and efficient alternative for parameter extraction of PV models.

ACS Style

Karam M. Sallam; Alamgir Hossain; Ripon K. Chakrabortty; Michael J. Ryan. An improved gaining-sharing knowledge algorithm for parameter extraction of photovoltaic models. Energy Conversion and Management 2021, 237, 114030 .

AMA Style

Karam M. Sallam, Alamgir Hossain, Ripon K. Chakrabortty, Michael J. Ryan. An improved gaining-sharing knowledge algorithm for parameter extraction of photovoltaic models. Energy Conversion and Management. 2021; 237 ():114030.

Chicago/Turabian Style

Karam M. Sallam; Alamgir Hossain; Ripon K. Chakrabortty; Michael J. Ryan. 2021. "An improved gaining-sharing knowledge algorithm for parameter extraction of photovoltaic models." Energy Conversion and Management 237, no. : 114030.

Journal article
Published: 23 March 2021 in Energy Conversion and Management
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Providing an accurate and precise photovoltaic model is a vital stage prior to the system design, therefore, this paper proposes a novel algorithm, enhanced marine predators algorithm (EMPA), to identify the unknown parameters for different photovoltaic (PV) models including the static PV models (single-diode and double-diode) and dynamic PV model. In the proposed EMPA, the differential evolution operator (DE) is incorporated into the original marine predators algorithm (MPA) to achieve stable, and reliable performance while handling that nonlinear optimization problem of PV modeling. Three different real datasets are used to show the effectiveness of the proposed algorithm. In the first case study, the proposed algorithm is used to identify the unknown parameters of a single-diode and double-diode PV models. The root-mean-square error (RMSE) and standard deviation (STD) values for a single-diode are 7.7301e-04 and 5.9135e-07. Similarly for double diode are 7.4396e-04 and 3.1849e-05, respectively. In addition, the second case study is used to test the proposed model in identifying the unknown parameters of a double-diode PV model. Here, the proposed algorithm is compared with classical MPA in five scenarios at different operating conditions. In this case study, the RMSE and STD of the proposed algorithm are less than that obtained by the MPA algorithm. Moreover, the third case study is utilized to test the ability of the proposed model in identifying the parameters of a dynamic PV model. In this case study, the performance of the proposed algorithm is compared with the one obtained by MAP and heterogeneous comprehensive learning particle swarm optimization (HCLPSO) algorithms in terms of RMSE ± STD. The obtained value of RMSE ± STD by the proposed algorithm is 0.0084505±1.0971e-17, which is too small compared with that obtained by MPA and HCLPSO algorithms (0.0084505±9.6235e-14 and 0.0084505±2.5235e-9). The results show the proposed model’s superiority over the MPA and other recent proposed algorithms in data fitting, convergence rate, stability, and consistency. Therefore, the proposed algorithm can be considered as a fast, feasible, and a reliable optimization algorithm to identify the unknown parameters in static and dynamic PV models. The code of the dynamic PV models is available via this link: https://github.com/DAyousri/Identifying-the-parameters-of-the-integer-and-fractional-order-dynamic-PV-models?_ga=2.104793926.732834951.1616028563-1268395487.1616028563.

ACS Style

Mohamed Abd Elaziz; Sudhakar Babu Thanikanti; Ibrahim Anwar Ibrahim; Songfeng Lu; Benedetto Nastasi; Majed A. Alotaibi; Alamgir Hossain; Dalia Yousri. Enhanced Marine Predators Algorithm for identifying static and dynamic Photovoltaic models parameters. Energy Conversion and Management 2021, 236, 113971 .

AMA Style

Mohamed Abd Elaziz, Sudhakar Babu Thanikanti, Ibrahim Anwar Ibrahim, Songfeng Lu, Benedetto Nastasi, Majed A. Alotaibi, Alamgir Hossain, Dalia Yousri. Enhanced Marine Predators Algorithm for identifying static and dynamic Photovoltaic models parameters. Energy Conversion and Management. 2021; 236 ():113971.

Chicago/Turabian Style

Mohamed Abd Elaziz; Sudhakar Babu Thanikanti; Ibrahim Anwar Ibrahim; Songfeng Lu; Benedetto Nastasi; Majed A. Alotaibi; Alamgir Hossain; Dalia Yousri. 2021. "Enhanced Marine Predators Algorithm for identifying static and dynamic Photovoltaic models parameters." Energy Conversion and Management 236, no. : 113971.

Journal article
Published: 04 March 2021 in Journal of Cleaner Production
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Accurate forecasting of wind power generation plays a key role in improving the operation and management of a power system network and thereby its reliability and security. However, predicting wind power is complex due to the existence of high non-linearity in wind speed that eventually relies on prevailing weather conditions. In this paper, a novel hybrid deep learning model is proposed to improve the prediction accuracy of very short-term wind power generation for the Bodangora wind farm located in New South Wales, Australia. The hybrid model consists of convolutional layers, gated recurrent unit (GRU) layers and a fully connected neural network. The convolutional layers have the ability to automatically learn complex features from raw data while the GRU layers are capable of directly learning multiple parallel sequences of input data. The data sets of 5-min intervals from the wind farm are used in case studies to demonstrate the effectiveness of the proposed model against other advanced existing models, including long short-term memory, GRU, autoregressive integrated moving average and support vector machine, which are tuned to optimise outcome. To further evaluate the efficacy of the proposed model, another data set taken from the Capital wind farm in Australia is used. It is observed that the hybrid deep learning model exhibits superior performance in both the data sets over other forecasting models to improve the accuracy of wind power forecasting, numerically for the Bodangora wind farm, up to 1.59 per cent in mean absolute error, 3.73 per cent in root mean square error and 8.13 per cent in mean absolute percentage error.

ACS Style

Alamgir Hossain; Ripon K. Chakrabortty; Sondoss Elsawah; Michael J. Ryan. Very short-term forecasting of wind power generation using hybrid deep learning model. Journal of Cleaner Production 2021, 296, 126564 .

AMA Style

Alamgir Hossain, Ripon K. Chakrabortty, Sondoss Elsawah, Michael J. Ryan. Very short-term forecasting of wind power generation using hybrid deep learning model. Journal of Cleaner Production. 2021; 296 ():126564.

Chicago/Turabian Style

Alamgir Hossain; Ripon K. Chakrabortty; Sondoss Elsawah; Michael J. Ryan. 2021. "Very short-term forecasting of wind power generation using hybrid deep learning model." Journal of Cleaner Production 296, no. : 126564.

Journal article
Published: 22 February 2021 in Electronics
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The high-level penetration of renewable energy sources (RESs) is the main reason for shifting the conventional centralized power system control paradigm into distributed power system control. This massive integration of RESs faces two main problems: complex controller structure and reduced inertia. Since the system frequency stability is directly linked to the system’s total inertia, the renewable integrated system frequency control is badly affected. Thus, a fractional order controller (FOC)-based superconducting magnetic energy storage (SMES) is proposed in this work. The detailed modeling of SMES, FOC, wind, and solar systems, along with the power network, is introduced to facilitate analysis. The FOC-based SMES virtually augments the inertia to stabilize the system frequency in generation and load mismatches. Since the tuning of FOC and SMES controller parameters is challenging due to nonlinearities, the whale optimization algorithm (WOA) is used to optimize the parameters. The optimized FOC-based SMES is tested under fluctuating wind and solar powers. The extensive simulations are carried out using MATLAB Simulink environment considering different scenarios, such as light and high load profile variations, multiple load profile variations, and reduced system inertia. It is observed that the proposed FOC-based SMES improves several performance indices, such as settling time, overshoot, undershoot compared to the conventional technique.

ACS Style

Alam; Majed Alotaibi; Alam; Alamgir Hossain; Shafiullah; Fahad Al-Ismail; Mamun Ur Rashid; Mohammad Abido. High-Level Renewable Energy Integrated System Frequency Control with SMES-Based Optimized Fractional Order Controller. Electronics 2021, 10, 511 .

AMA Style

Alam, Majed Alotaibi, Alam, Alamgir Hossain, Shafiullah, Fahad Al-Ismail, Mamun Ur Rashid, Mohammad Abido. High-Level Renewable Energy Integrated System Frequency Control with SMES-Based Optimized Fractional Order Controller. Electronics. 2021; 10 (4):511.

Chicago/Turabian Style

Alam; Majed Alotaibi; Alam; Alamgir Hossain; Shafiullah; Fahad Al-Ismail; Mamun Ur Rashid; Mohammad Abido. 2021. "High-Level Renewable Energy Integrated System Frequency Control with SMES-Based Optimized Fractional Order Controller." Electronics 10, no. 4: 511.

Journal article
Published: 18 February 2021 in Energies
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From a residential point of view, home energy management (HEM) is an essential requirement in order to diminish peak demand and utility tariffs. The integration of renewable energy sources (RESs) together with battery energy storage systems (BESSs) and central battery storage system (CBSS) may promote energy and cost minimization. However, proper home appliance scheduling along with energy storage options is essential to significantly decrease the energy consumption profile and overall expenditure in real-time operation. This paper proposes a cost-effective HEM scheme in the microgrid framework to promote curtailing of energy usage and relevant utility tariff considering both energy storage and renewable sources integration. Usually, the household appliances have different runtime preferences and duration of operation based on user demand. This work considers a simulator designed in the C++ platform to address the domestic customer’s HEM issue based on usages priorities. The positive aspects of merging RESs, BESSs, and CBSSs with the proposed optimal power sharing algorithm (OPSA) are evaluated by considering three distinct case scenarios. Comprehensive analysis of each scenario considering the real-time scheduling of home appliances is conducted to substantiate the efficacy of the outlined energy and cost mitigation schemes. The results obtained demonstrate the effectiveness of the proposed algorithm to enable energy and cost savings up to 37.5% and 45% in comparison to the prevailing methodology.

ACS Style

Mamun Ur Rashid; Majed Alotaibi; Abdul Chowdhury; Muaz Rahman; Shafiul Alam; Alamgir Hossain; Mohammad Abido. Home Energy Management for Community Microgrids Using Optimal Power Sharing Algorithm. Energies 2021, 14, 1060 .

AMA Style

Mamun Ur Rashid, Majed Alotaibi, Abdul Chowdhury, Muaz Rahman, Shafiul Alam, Alamgir Hossain, Mohammad Abido. Home Energy Management for Community Microgrids Using Optimal Power Sharing Algorithm. Energies. 2021; 14 (4):1060.

Chicago/Turabian Style

Mamun Ur Rashid; Majed Alotaibi; Abdul Chowdhury; Muaz Rahman; Shafiul Alam; Alamgir Hossain; Mohammad Abido. 2021. "Home Energy Management for Community Microgrids Using Optimal Power Sharing Algorithm." Energies 14, no. 4: 1060.

Journal article
Published: 25 December 2020 in Sustainable Cities and Society
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Although several energy management schemes are developed in the literature, they are not thoroughly examined in order to reduce the impact of uncertain power generation, demand and electricity prices to minimise the operating cost of a small-scale power system. This paper investigates energy management schemes under uncertain environments and proposes a scheduling scheme to minimise the operating cost of a grid-connected microgrid. Optimisation problems are formulated as real-time scheduling approaches, and are solved by developing modified particle swarm optimisation (MPSO) algorithms. The modification in the MPSO algorithm is performed through structural change in incorporating a mechanism that regulates the selection procedure for decision variables. The effectiveness of the proposed algorithm is justified by comparing the results with other recent algorithms. All the algorithms are separately tuned using the Taguchi technique to demonstrate a fair comparison in solving the scheduling problem. The scheduling program demonstrates superior performance in all cases, including when there is uncertainty in prediction, as compared to other energy management approaches, although solutions have significant deviations due to prediction errors. It is also shown that the proposed MPSO algorithm for the scheduling program can save 16.80 per cent operational cost as compared to the PSO algorithm.

ACS Style

Alamgir Hossain; Ripon K. Chakrabortty; Michael J. Ryan; Hemanshu Roy Pota. Energy management of community energy storage in grid-connected microgrid under uncertain real-time prices. Sustainable Cities and Society 2020, 66, 102658 .

AMA Style

Alamgir Hossain, Ripon K. Chakrabortty, Michael J. Ryan, Hemanshu Roy Pota. Energy management of community energy storage in grid-connected microgrid under uncertain real-time prices. Sustainable Cities and Society. 2020; 66 ():102658.

Chicago/Turabian Style

Alamgir Hossain; Ripon K. Chakrabortty; Michael J. Ryan; Hemanshu Roy Pota. 2020. "Energy management of community energy storage in grid-connected microgrid under uncertain real-time prices." Sustainable Cities and Society 66, no. : 102658.

Original research paper
Published: 09 December 2020 in IET Generation, Transmission & Distribution
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The main goal of an interconnected power system is to transfer power from one area to another while the network frequency and tie‐line flow remain within the prescribed limits. However, both of these quantities may violate their desired values during this transfer due to disturbances in the network. This paper proposes a stratagem for choosing the right feedback path for an interconnected power system to maintain the system frequency and tie‐line flows within the prescribed limits while external disturbances exist. Area control error (ACE), a combination of frequency error and tie‐flow deviations, is used as the performance indicator. In the proposed approach, feedback control is designed using active disturbance rejection controller (ADRC) based load frequency control to tackle ACE. It is observed that the individual load change monitoring is sufficient for selecting the right feedback paths rather than the consideration of simultaneous load changes of all load centres. The effectiveness of the proposed controller for selecting the feedback paths has been tested by conducting several case studies. The results demonstrate that the proposed controller can reduce transient magnitude around 57% for ACE, 55% for frequency error and 72% for tie‐line error as compared to the PID controller.

ACS Style

A. Hasib Chowdhury; Mijanur Rahman; Alamgir Hossain. Optimal feedback path selection for interconnected power systems using load frequency control strategy. IET Generation, Transmission & Distribution 2020, 15, 619 -630.

AMA Style

A. Hasib Chowdhury, Mijanur Rahman, Alamgir Hossain. Optimal feedback path selection for interconnected power systems using load frequency control strategy. IET Generation, Transmission & Distribution. 2020; 15 (4):619-630.

Chicago/Turabian Style

A. Hasib Chowdhury; Mijanur Rahman; Alamgir Hossain. 2020. "Optimal feedback path selection for interconnected power systems using load frequency control strategy." IET Generation, Transmission & Distribution 15, no. 4: 619-630.

Journal article
Published: 07 September 2020 in Electronics
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Several efforts have been taken to promote clean energy towards a sustainable and green economy. Existing sources of electricity present some complications concerning consumers, utility owners, and the environment. Utility operators encourage household applicants to employ residential energy management (REM) systems. Renewable energy sources (RESs), energy storage systems (ESS), and optimal energy allocation strategies are used to resolve these difficulties. In this paper, the development of a cluster-based energy management scheme for residential consumers of a smart grid community is proposed to reduce energy use and monetary cost. Normally, residential consumers deal with household appliances with various operating time slots depending on consumer preferences. A simulator is designed and developed using C++ software to resolve the residential consumer’s REM problem. The benefits of the RESs, ESS, and optimal energy allocation techniques are analyzed by taking in account three different scenarios. Extensive case studies are carried out to validate the effectiveness of the proposed cluster-based energy management scheme. It is demonstrated that the proposed method can save energy and costs up to 45% and 56% compared to the existing methods.

ACS Style

Mamun Ur Rashid; Fabrizio Granelli; Alamgir Hossain; Shafiul Alam; Fahad Al-Ismail; Rakibuzzaman Shah. Development of Cluster-Based Energy Management Scheme for Residential Usages in the Smart Grid Community. Electronics 2020, 9, 1462 .

AMA Style

Mamun Ur Rashid, Fabrizio Granelli, Alamgir Hossain, Shafiul Alam, Fahad Al-Ismail, Rakibuzzaman Shah. Development of Cluster-Based Energy Management Scheme for Residential Usages in the Smart Grid Community. Electronics. 2020; 9 (9):1462.

Chicago/Turabian Style

Mamun Ur Rashid; Fabrizio Granelli; Alamgir Hossain; Shafiul Alam; Fahad Al-Ismail; Rakibuzzaman Shah. 2020. "Development of Cluster-Based Energy Management Scheme for Residential Usages in the Smart Grid Community." Electronics 9, no. 9: 1462.

Journal article
Published: 19 August 2020 in Energies
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The steady increase in energy demand for residential consumers requires an efficient energy management scheme. Utility organizations encourage household applicants to engage in residential energy management (REM) system. The utility’s primary goal is to reduce system peak load demand while consumer intends to reduce electricity bills. The benefits of REM can be enhanced with renewable energy sources (RESs), backup battery storage system (BBSS), and optimal power-sharing strategies. This paper aims to reduce energy usages and monetary cost for smart grid communities with an efficient home energy management scheme (HEMS). Normally, the residential consumer deals with numerous smart home appliances that have various operating time priorities depending on consumer preferences. In this paper, a cost-efficient power-sharing technique is developed which works based on priorities of appliances’ operating time. The home appliances are sorted on priority basis and the BBSS are charged and discharged based on the energy availability within the smart grid communities and real time energy pricing. The benefits of optimal power-sharing techniques with the RESs and BBSS are analyzed by taking three different scenarios which are simulated by C++ software package. Extensive case studies are carried out to validate the effectiveness of the proposed energy management scheme. It is demonstrated that the proposed method can save energy and reduce electricity cost up to 35% and 45% compared to the existing methods.

ACS Style

Mamun Ur Rashid; Fabrizio Granelli; Alamgir Hossain; Shafiul Alam; Fahad Saleh Al-Ismail; Ashish Kumar Karmaker; Mijanur Rahaman. Development of Home Energy Management Scheme for a Smart Grid Community. Energies 2020, 13, 4288 .

AMA Style

Mamun Ur Rashid, Fabrizio Granelli, Alamgir Hossain, Shafiul Alam, Fahad Saleh Al-Ismail, Ashish Kumar Karmaker, Mijanur Rahaman. Development of Home Energy Management Scheme for a Smart Grid Community. Energies. 2020; 13 (17):4288.

Chicago/Turabian Style

Mamun Ur Rashid; Fabrizio Granelli; Alamgir Hossain; Shafiul Alam; Fahad Saleh Al-Ismail; Ashish Kumar Karmaker; Mijanur Rahaman. 2020. "Development of Home Energy Management Scheme for a Smart Grid Community." Energies 13, no. 17: 4288.

Preprint
Published: 16 June 2020
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Although energy management of a microgrid is generally performed using a day-ahead scheduling method, its effectiveness has been questioned by the research community due to the existence of high uncertainty in renewable power generation, power demand and electricity market. As a result, real-time energy management schemes are recently developed to minimise the operating cost of a microgrid while high uncertainty presents in the network. This paper develops modified particle swarm optimisation (MPSO) algorithms to solve optimisation problems of energy management schemes for a community microgrid and proposes a scheduling approach after taking into consideration high uncertainty to effectively minimise the operational cost of the microgrid. The optimisation problems are formulated for real-time and scheduling approaches, and solution methods are developed to solve the problems. It is observed that the scheduling program demonstrates superior performance in all the cases, including uncertainty in prediction, as compared to the other energy management approaches, although solutions have significant deviations due to prediction errors.

ACS Style

Alamgir Hossain; Ripon Kumar Chakrabortty; Michael Ryan; Hemanshu Roy Pota. Energy Management of Community Microgrids Considering Uncertainty using Particle Swarm Optimisation. 2020, 1 .

AMA Style

Alamgir Hossain, Ripon Kumar Chakrabortty, Michael Ryan, Hemanshu Roy Pota. Energy Management of Community Microgrids Considering Uncertainty using Particle Swarm Optimisation. . 2020; ():1.

Chicago/Turabian Style

Alamgir Hossain; Ripon Kumar Chakrabortty; Michael Ryan; Hemanshu Roy Pota. 2020. "Energy Management of Community Microgrids Considering Uncertainty using Particle Swarm Optimisation." , no. : 1.

Journal article
Published: 04 April 2020 in Applied Sciences
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Audio equalization is an active research topic aiming at improving the audio quality of a loudspeaker system by correcting the overall frequency response using linear filters. The estimation of their coefficients is not an easy task, especially in binaural and multipoint scenarios, due to the contribution of multiple impulse responses to each listening point. This paper presents a deep learning approach for tuning filter coefficients employing three different neural networks architectures—the Multilayer Perceptron, the Convolutional Neural Network, and the Convolutional Autoencoder. Suitable loss functions are proposed for each architecture, and are formulated in terms of spectral Euclidean distance. The experiments were conducted in the automotive scenario, considering several loudspeakers and microphones. The obtained results show that deep learning techniques give superior performance compared to baseline methods, achieving almost flat magnitude frequency response.

ACS Style

Giovanni Pepe; Leonardo Gabrielli; Stefano Squartini; Luca Cattani. Designing Audio Equalization Filters by Deep Neural Networks. Applied Sciences 2020, 10, 2483 .

AMA Style

Giovanni Pepe, Leonardo Gabrielli, Stefano Squartini, Luca Cattani. Designing Audio Equalization Filters by Deep Neural Networks. Applied Sciences. 2020; 10 (7):2483.

Chicago/Turabian Style

Giovanni Pepe; Leonardo Gabrielli; Stefano Squartini; Luca Cattani. 2020. "Designing Audio Equalization Filters by Deep Neural Networks." Applied Sciences 10, no. 7: 2483.

Journal article
Published: 25 March 2020 in Sustainability
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The growing popularity of electric vehicles (EV) is creating an increasing burden on the power grid in Bangladesh due to massive energy consumption. Due to this uptake of variable energy consumption, environmental concerns, and scarcity of energy lead to investigate alternative energy resources that are readily available and environment friendly. Bangladesh has enormous potential in the field of renewable resources, such as biogas and biomass. Therefore, this paper proposes a design of a 20 kW electric vehicle charging station (EVCS) using biogas resources. A comprehensive viability analysis is also presented for the proposed EVCS from technological, economic, and environmental viewpoints using the HOMER (Hybrid Optimization of Multiple Energy Resources) model. The viability result shows that with the capacity of 15–20 EVs per day, the proposed EVCS will save monthly $16.31 and $29.46, respectively, for easy bike and auto-rickshaw type electric vehicles in Bangladesh compare to grid electricity charging. Furthermore, the proposed charging station can reduce 65.61% of CO2 emissions than a grid-based charging station.

ACS Style

Ashish Kumar Karmaker; Alamgir Hossain; Nallapaneni Manoj Kumar; Vishnupriyan Jegadeesan; ArunKumar Jayakumar; Biplob Ray. Analysis of Using Biogas Resources for Electric Vehicle Charging in Bangladesh: A Techno-Economic-Environmental Perspective. Sustainability 2020, 12, 2579 .

AMA Style

Ashish Kumar Karmaker, Alamgir Hossain, Nallapaneni Manoj Kumar, Vishnupriyan Jegadeesan, ArunKumar Jayakumar, Biplob Ray. Analysis of Using Biogas Resources for Electric Vehicle Charging in Bangladesh: A Techno-Economic-Environmental Perspective. Sustainability. 2020; 12 (7):2579.

Chicago/Turabian Style

Ashish Kumar Karmaker; Alamgir Hossain; Nallapaneni Manoj Kumar; Vishnupriyan Jegadeesan; ArunKumar Jayakumar; Biplob Ray. 2020. "Analysis of Using Biogas Resources for Electric Vehicle Charging in Bangladesh: A Techno-Economic-Environmental Perspective." Sustainability 12, no. 7: 2579.

Journal article
Published: 20 January 2020 in Applied Acoustics
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This paper presents a novel multichannel audio equalization technique based on evolutionary computation algorithms for tuning the filters coefficients. Specifically, two distinct evolutionary algorithms are used on purpose, i.e. the Particle Swarm Optimization (PSO) and Gravitational Search Algorithm (GSA). Two alternative solutions for the definition of evolutionary particles have been devised and tested with both techniques. Given the desired frequency response, the fitness function is formulated in terms of amplitude spectral distance. These techniques have been assessed by computer experiments, conducted on a in-car binaural equalization scenario considering 7 loudspeakers and a binaural microphone. The obtained results show that the proposed solutions achieve a remarkably superior performance compared to the baseline methods, with a 5 times reduction of the mean square error in the amplitude spectral domain.

ACS Style

Giovanni Pepe; Leonardo Gabrielli; Stefano Squartini; Luca Cattani. Evolutionary tuning of filters coefficients for binaural audio equalization. Applied Acoustics 2020, 163, 107204 .

AMA Style

Giovanni Pepe, Leonardo Gabrielli, Stefano Squartini, Luca Cattani. Evolutionary tuning of filters coefficients for binaural audio equalization. Applied Acoustics. 2020; 163 ():107204.

Chicago/Turabian Style

Giovanni Pepe; Leonardo Gabrielli; Stefano Squartini; Luca Cattani. 2020. "Evolutionary tuning of filters coefficients for binaural audio equalization." Applied Acoustics 163, no. : 107204.

Journal article
Published: 05 November 2019 in IEEE Transactions on Emerging Topics in Computational Intelligence
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In the recent years, several supervised and unsupervised approaches to fall detection have been presented in the literature. These are generally based on a corpus of examples of human falls that are, though, hard to collect. For this reason, fall detection algorithms should be designed to gather as much information as possible from the few available data related to the type of events to be detected. The one-shot learning paradigm for expert systems training seems to naturally match these constraints, and this inspired the novel Siamese Neural Network (SNN) architecture for human fall detection proposed in this contribution. Acoustic data are employed as input, and the twin convolutional autoencoders composing the SNN are trained to perform a suitable metric learning in the audio domain and, thus, extract robust features to be used in the final classification stage. A large acoustic dataset has been recorded in three real rooms with different floor types and human falls performed by four volunteers, and then adopted for experiments. Obtained results show that the proposed approach, which only relies on two real human fall events in the training phase, achieves a F $_1$ -Measure of 93.58% during testing, remarkably outperforming the recent supervised and unsupervised state-of-art techniques selected for comparison.

ACS Style

Diego Droghini; Stefano Squartini; Emanuele Principi; Leonardo Gabrielli; Francesco Piazza. Audio Metric Learning by Using Siamese Autoencoders for One-Shot Human Fall Detection. IEEE Transactions on Emerging Topics in Computational Intelligence 2019, 5, 108 -118.

AMA Style

Diego Droghini, Stefano Squartini, Emanuele Principi, Leonardo Gabrielli, Francesco Piazza. Audio Metric Learning by Using Siamese Autoencoders for One-Shot Human Fall Detection. IEEE Transactions on Emerging Topics in Computational Intelligence. 2019; 5 (1):108-118.

Chicago/Turabian Style

Diego Droghini; Stefano Squartini; Emanuele Principi; Leonardo Gabrielli; Francesco Piazza. 2019. "Audio Metric Learning by Using Siamese Autoencoders for One-Shot Human Fall Detection." IEEE Transactions on Emerging Topics in Computational Intelligence 5, no. 1: 108-118.

Journal article
Published: 30 September 2019 in Journal of Cleaner Production
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Greenhouse gas emission is increasing alarmingly due to bulk electricity generation from fossil fuel such as coal, gas and oil with its limited stock. While researchers are struggling to minimize the emission using suitable mitigation techniques including renewable energy sources, the Government of Bangladesh is implementing several Coal based power plants. This paper presents mathematical model of parameters related to Greenhouse gas emission and demonstrates its emission rates resulting from various fuels used in Bangladeshi power plants. The Greenhouse gas emission from the existing fossil fuel power plants using HOMER (Hybrid Optimization of Multiple Energy Resources) software is analyzed in this research. The result shows that power plants of coal, diesel and natural gas emit 0.90 kg, 0.76 kg and 0.566 kg of CO2 per kWh, respectively. Furthermore, several Greenhouse gas mitigating procedures are proposed for fossil fuel-based power generating stations.

ACS Style

Ashish Kumar Karmaker; Mijanur Rahman; Alamgir Hossain; Raju Ahmed. Exploration and corrective measures of greenhouse gas emission from fossil fuel power stations for Bangladesh. Journal of Cleaner Production 2019, 244, 118645 .

AMA Style

Ashish Kumar Karmaker, Mijanur Rahman, Alamgir Hossain, Raju Ahmed. Exploration and corrective measures of greenhouse gas emission from fossil fuel power stations for Bangladesh. Journal of Cleaner Production. 2019; 244 ():118645.

Chicago/Turabian Style

Ashish Kumar Karmaker; Mijanur Rahman; Alamgir Hossain; Raju Ahmed. 2019. "Exploration and corrective measures of greenhouse gas emission from fossil fuel power stations for Bangladesh." Journal of Cleaner Production 244, no. : 118645.

Journal article
Published: 10 September 2019 in IFAC-PapersOnLine
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An energy storage system is a key element of renewable-based power generation. Its flexible operational capabilities reduce not only the impact of intermittent power generation but also operational costs. In this paper, a dynamic penalty function is proposed to the charging term of the cost function to efficiently manage the battery energy and thereby reducing operational costs. The charging/discharging periods of the battery are effectively controlled based on the solar power generation and residential real-time electricity prices (RRTP). The optimisation problem formulated for the application of real-time energy management is solved with the help of particle swarm optimisation (PSO). It is shown that the proposed cost function can reduce operational costs over a time horizon of 96 hours by 4.2 per cent as compared to the cost function reported in the literature. Simulation studies are carried out to demonstrate the effectiveness of the proposed cost function over the existing cost function.

ACS Style

Alamgir Hossain; Hemanshu Roy Pota; Carlos Macana Moreno. Real-time Battery Energy Management for Residential Solar Power System. IFAC-PapersOnLine 2019, 52, 407 -412.

AMA Style

Alamgir Hossain, Hemanshu Roy Pota, Carlos Macana Moreno. Real-time Battery Energy Management for Residential Solar Power System. IFAC-PapersOnLine. 2019; 52 (4):407-412.

Chicago/Turabian Style

Alamgir Hossain; Hemanshu Roy Pota; Carlos Macana Moreno. 2019. "Real-time Battery Energy Management for Residential Solar Power System." IFAC-PapersOnLine 52, no. 4: 407-412.