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Dr. Irfan Khan
Texas A&M University

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Journal article
Published: 10 August 2021 in Electronics
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Critical infrastructures (e.g., energy and transportation systems) are essential lifelines for most modern sectors and have utmost significance in our daily lives. However, these important domains can fail to operate due to system failures or natural disasters. Though the major disturbances in such critical infrastructures are rare, the severity of such events calls for the development of effective resilience assessment strategies to mitigate relative losses. Traditional critical infrastructure resilience approaches consider that the available critical infrastructure agents are resource-sufficient and agree to exchange local data with the server and other agents. Such assumptions create two issues: (1) uncertainty in reaching convergence while applying learning strategies on resource-constrained critical infrastructure agents, and (2) a huge risk of privacy leakage. By understanding the pressing need to construct an effective resilience model for resource-constrained critical infrastructure, this paper aims at leveraging a distributed machine learning technique called Federated Learning (FL) to tackle an agent’s resource limitations effectively and at the same time keep the agent’s information private. Particularly, this paper is focused on predicting the probable outage and resource status of critical infrastructure agents without sharing any local data and carrying out the learning process even when most of the agents are incapable of accomplishing a given computational task. To that end, an FL algorithm is designed specifically for a resource-constrained critical infrastructure environment that could facilitate the training of each agent in a distributed fashion, restrict them from sharing their raw data with any other external entities (e.g., server, neighbor agents), choose proficient clients by analyzing their resources, and allow a partial amount of computation tasks to be performed by the resource-constrained agents. We considered a different number of agents with various stragglers and checked the performance of FedAvg and our proposed FedResilience algorithm with prediction tasks for a probable outage, as well as checking the agents’ resource-sharing scope. Our simulation results show that if the majority of the FL agents are stragglers and we drop them from the training process, then the agents learn very slowly and the overall model performance is negatively affected. We also demonstrate that the selection of proficient agents and allowing them to complete only parts of their tasks can significantly improve the knowledge of each agent by eliminating the straggler effects, and the global model convergence is accelerated.

ACS Style

Ahmed Imteaj; Irfan Khan; Javad Khazaei; Mohammad Amini. FedResilience: A Federated Learning Application to Improve Resilience of Resource-Constrained Critical Infrastructures. Electronics 2021, 10, 1917 .

AMA Style

Ahmed Imteaj, Irfan Khan, Javad Khazaei, Mohammad Amini. FedResilience: A Federated Learning Application to Improve Resilience of Resource-Constrained Critical Infrastructures. Electronics. 2021; 10 (16):1917.

Chicago/Turabian Style

Ahmed Imteaj; Irfan Khan; Javad Khazaei; Mohammad Amini. 2021. "FedResilience: A Federated Learning Application to Improve Resilience of Resource-Constrained Critical Infrastructures." Electronics 10, no. 16: 1917.

Journal article
Published: 03 August 2021 in IEEE Access
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Optimum Photovoltaic (PV) system integration in power grid depend upon the total of power accessible from the PV. To figure the PV systems highest power yield, PV panels must be positioned at an optimal tilt angle (OPTA) to absorb maximum solar radiation (SR). This OPTA is a function of the latitude, clearness index, diffuse SR, global SR, direct SR and optimum PV size. Therefore OPTA has an impact on maximum power generation and optimal PV system sizing. The PV is not installed at OPTA for most of the sites in India which is important for maximum power generation and optimum sizing of standalone PV systems. This results in variation of OPTA from site to site and its effect on PV sizing needs to be investigated. The innovative aspect of this work is the calculation of OPTA, which are employed as sensitive factors in Hybrid Optimization of Multiple Energy Resources (HOMER), to determine their impact on maximum optimum sizing and power generation for 26 cities in India’s various climate zones. This methodology can be applied all over the world to determine the impact of OPTA on maximum power generation and size. It is found that OPTA varies from 63° to 0° throughout year in India and it is maximum for December in India. The results indicates that Net Present Cost varies from $\$ $ 1105 to $\$ $ 1280 and Cost of Energy (COE) variation is 0.041 to 0.048 $\$ $ /kWh throughout India cities and low temperature sites are good for photovoltaic (PV) power generation. Two axis tracking system produces more power in comparison to other tracking systems. This research is beneficial for researcher and industry to install PV system in different climatic zones of India to generate maximum power at minimum cost of energy.

ACS Style

Amit Kumar Yadav; Hasmat Malik; S. S. Chandel; Irfan Ahmad Khan; Sattam Al Otaibi; Hend I. Alkhammash. Novel Approach to Investigate the Influence of Optimum Tilt Angle on Minimum Cost of Energy-Based Maximum Power Generation and Sizing of PV Systems: A Case Study of Diverse Climatic Zones in India. IEEE Access 2021, 9, 110103 -110115.

AMA Style

Amit Kumar Yadav, Hasmat Malik, S. S. Chandel, Irfan Ahmad Khan, Sattam Al Otaibi, Hend I. Alkhammash. Novel Approach to Investigate the Influence of Optimum Tilt Angle on Minimum Cost of Energy-Based Maximum Power Generation and Sizing of PV Systems: A Case Study of Diverse Climatic Zones in India. IEEE Access. 2021; 9 ():110103-110115.

Chicago/Turabian Style

Amit Kumar Yadav; Hasmat Malik; S. S. Chandel; Irfan Ahmad Khan; Sattam Al Otaibi; Hend I. Alkhammash. 2021. "Novel Approach to Investigate the Influence of Optimum Tilt Angle on Minimum Cost of Energy-Based Maximum Power Generation and Sizing of PV Systems: A Case Study of Diverse Climatic Zones in India." IEEE Access 9, no. : 110103-110115.

Journal article
Published: 25 July 2021 in Electronics
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This paper presents a novel, scalable, and modular multiport power electronic topology for the integration of multiple resources. This converter is not only scalable in terms of the integration of multiple renewable energy resources (RES) and storage devices (SDs) but is also scalable in terms of output ports. Multiple dc outputs of a converter are designed to serve as input to the stacking modules (SMs) of the modular multilevel converter (MMC). The proposed multiport converter is bidirectional in nature and superior in terms of functionality in a way that a modular universal converter is responsible for the integration of multiple RES/SDs and regulates multiple dc output ports for SMs of MMC. All input ports can be easily integrated (and controlled), and output ports also can be controlled independently in response to any load variations. An isolated active half-bridge converter with multiple secondaries acts as a central hub for power processing with multiple renewable energy resources that are integrated at the primary side. To verify the proposed converter, a detailed design of the converter-based system is presented along with the proposed control algorithm for managing power on the individual component level. Additionally, different modes of power management (emulating the availability/variability of renewable energy sources (RES)) are exhibited and analyzed here. Finally, detailed simulation results are presented in detail for the validation of the proposed concepts and design process.

ACS Style

Syed Rahman; Irfan Khan; Khaliqur Rahman; Sattam Al Otaibi; Hend Alkhammash; Atif Iqbal. Scalable Multiport Converter Structure for Easy Grid Integration of Alternate Energy Sources for Generation of Isolated Voltage Sources for MMC. Electronics 2021, 10, 1779 .

AMA Style

Syed Rahman, Irfan Khan, Khaliqur Rahman, Sattam Al Otaibi, Hend Alkhammash, Atif Iqbal. Scalable Multiport Converter Structure for Easy Grid Integration of Alternate Energy Sources for Generation of Isolated Voltage Sources for MMC. Electronics. 2021; 10 (15):1779.

Chicago/Turabian Style

Syed Rahman; Irfan Khan; Khaliqur Rahman; Sattam Al Otaibi; Hend Alkhammash; Atif Iqbal. 2021. "Scalable Multiport Converter Structure for Easy Grid Integration of Alternate Energy Sources for Generation of Isolated Voltage Sources for MMC." Electronics 10, no. 15: 1779.

Review
Published: 20 June 2021 in Electronics
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The development of offshore wind farms (WF) is inevitable as they have exceptional resistance against climate change and produce clean energy without hazardous wastes. The offshore WF usually has a bigger generation capacity with less environmental impacts, and it is more reliable too due to stronger and consistent sea winds. The early offshore WF installations are located near the shore, whereas most modern installations are located far away from shore, generating higher power. This paradigm shift has forced the researchers and industry personnel to look deeper into transmission options, namely, high voltage AC transmission (HVAC) and high voltage DC transmission (HVDC). This evaluation can be both in terms of power carrying capability as well as cost comparisons. Additionally, different performance requirements such as power rating, onshore grid requirements, reactive power compensation, etc., must be considered for evaluation. This paper elaborately reviews and explains the offshore wind farm structure and performance requirements for bulk offshore power transfer. Based on the structure and performance requirements, both HVDC and HVAC transmission modes are compared and analyzed critically. Finally, a criterion for selection and increasing popularity of HVDC transmission is established.

ACS Style

Syed Rahman; Irfan Khan; Hend Alkhammash; Muhammad Nadeem. A Comparison Review on Transmission Mode for Onshore Integration of Offshore Wind Farms: HVDC or HVAC. Electronics 2021, 10, 1489 .

AMA Style

Syed Rahman, Irfan Khan, Hend Alkhammash, Muhammad Nadeem. A Comparison Review on Transmission Mode for Onshore Integration of Offshore Wind Farms: HVDC or HVAC. Electronics. 2021; 10 (12):1489.

Chicago/Turabian Style

Syed Rahman; Irfan Khan; Hend Alkhammash; Muhammad Nadeem. 2021. "A Comparison Review on Transmission Mode for Onshore Integration of Offshore Wind Farms: HVDC or HVAC." Electronics 10, no. 12: 1489.

Research article
Published: 10 June 2021 in International Transactions on Electrical Energy Systems
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The continuous increase of the distributed energy resources (DERs) penetration levels leads to voltage stability problems in the distribution system. One of the approaches for the mentioned emerging challenge is the proper placement of automatic voltage regulators (AVRs). This paper investigates the optimal placement and sizing of AVRs in a distribution network by presenting a new modification of the teaching-learning-based optimization (TLBO) algorithm. The objective functions consist of minimizing the distribution system voltage deviation, energy generation cost, and electrical losses. The modification improves the convergence velocity and accuracy of the TLBO algorithm using the combination of mutation technique and quasi-opposition-based-learning concept. This paper compares the performance of the proposed algorithm with other famous evolutionary algorithms. The test distribution system contains installed DERs that work more efficiently after the placement of AVRs based on the mentioned objective functions by the proposed optimization algorithm. The simulation results display the best optimization algorithms for AVRs placement with a significant level of less than 0.10 (ie, probability-value). The proposed multiobjective optimization algorithm's considerable merit is the accuracy and convergence velocity in solving this specific optimization problem.

ACS Style

Seyed Iman Taheri; Mauricio B.C. Salles; Irfan Ahmad Khan. Supporting distributed energy resources with optimal placement and sizing of voltage regulators on the distribution system by an improved teaching‐learning‐based optimization algorithm. International Transactions on Electrical Energy Systems 2021, 31, e12974 .

AMA Style

Seyed Iman Taheri, Mauricio B.C. Salles, Irfan Ahmad Khan. Supporting distributed energy resources with optimal placement and sizing of voltage regulators on the distribution system by an improved teaching‐learning‐based optimization algorithm. International Transactions on Electrical Energy Systems. 2021; 31 (8):e12974.

Chicago/Turabian Style

Seyed Iman Taheri; Mauricio B.C. Salles; Irfan Ahmad Khan. 2021. "Supporting distributed energy resources with optimal placement and sizing of voltage regulators on the distribution system by an improved teaching‐learning‐based optimization algorithm." International Transactions on Electrical Energy Systems 31, no. 8: e12974.

Journal article
Published: 21 May 2021 in Energies
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The efficiency of PV systems can be improved by accurate estimation of PV parameters. Parameter estimation of PV cells and modules is a challenging task as it requires accurate operation of PV cells and modules followed by an optimization tool that estimates their associated parameters. Mostly, population-based optimization tools are utilized for PV parameter estimation problems due to their computational intelligent behavior. However, most of them suffer from premature convergence problems, high computational burden, and often fall into local optimum solution. To mitigate these limitations, this paper presents an improved variant of particle swarm optimization (PSO) aiming to reduce shortcomings offered by conventional PSO for estimation of PV parameters. PSO is improved by introducing two strategies to control inertia weight and acceleration coefficients. At first, a sine chaotic inertia weight strategy is employed to attain an appropriate balance between local and global search. Afterward, a tangent chaotic strategy is utilized to guide acceleration coefficients in search of an optimal solution. The proposed algorithm is utilized to estimate the parameters of the PWP201 PV module, RTC France solar cell, and a JKM330P-72 PV module-based practical system. The obtained results indicate that the proposed technique avoids premature convergence and local optima stagnation of conventional PSO. Moreover, a comparison of obtained results with techniques available in the literature proves that the proposed methodology is an efficient, effective, and optimal tool to estimate PV modules and cells’ parameters.

ACS Style

Arooj Kiani; Muhammad Nadeem; Ali Ahmed; Irfan Khan; Hend Alkhammash; Intisar Sajjad; Babar Hussain. An Improved Particle Swarm Optimization with Chaotic Inertia Weight and Acceleration Coefficients for Optimal Extraction of PV Models Parameters. Energies 2021, 14, 2980 .

AMA Style

Arooj Kiani, Muhammad Nadeem, Ali Ahmed, Irfan Khan, Hend Alkhammash, Intisar Sajjad, Babar Hussain. An Improved Particle Swarm Optimization with Chaotic Inertia Weight and Acceleration Coefficients for Optimal Extraction of PV Models Parameters. Energies. 2021; 14 (11):2980.

Chicago/Turabian Style

Arooj Kiani; Muhammad Nadeem; Ali Ahmed; Irfan Khan; Hend Alkhammash; Intisar Sajjad; Babar Hussain. 2021. "An Improved Particle Swarm Optimization with Chaotic Inertia Weight and Acceleration Coefficients for Optimal Extraction of PV Models Parameters." Energies 14, no. 11: 2980.

Journal article
Published: 01 May 2021 in Energies
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The power quality of the Electrical Power System (EPS) is greatly affected by electrical harmonics. Hence, accurate and proper estimation of electrical harmonics is essential to design appropriate filters for mitigation of harmonics and their associated effects on the power quality of EPS. This paper presents a novel statistical (Least Square) and meta-heuristic (Grey wolf optimizer) based hybrid technique for accurate detection and estimation of electrical harmonics with minimum computational time. The non-linear part (phase and frequency) of harmonics is estimated using GWO, while the linear part (amplitude) is estimated using the LS method. Furthermore, harmonics having transients are also estimated using proposed harmonic estimators. The effectiveness of the proposed harmonic estimator is evaluated using various case studies. Comparing the proposed approach with other harmonic estimation techniques demonstrates that it has a minimum mean square error with less complexity and better computational efficiency.

ACS Style

Muhammad Abdullah; Tahir Malik; Ali Ahmed; Muhammad Nadeem; Irfan Khan; Rui Bo. A Novel Hybrid GWO-LS Estimator for Harmonic Estimation Problem in Time Varying Noisy Environment. Energies 2021, 14, 2587 .

AMA Style

Muhammad Abdullah, Tahir Malik, Ali Ahmed, Muhammad Nadeem, Irfan Khan, Rui Bo. A Novel Hybrid GWO-LS Estimator for Harmonic Estimation Problem in Time Varying Noisy Environment. Energies. 2021; 14 (9):2587.

Chicago/Turabian Style

Muhammad Abdullah; Tahir Malik; Ali Ahmed; Muhammad Nadeem; Irfan Khan; Rui Bo. 2021. "A Novel Hybrid GWO-LS Estimator for Harmonic Estimation Problem in Time Varying Noisy Environment." Energies 14, no. 9: 2587.

Review
Published: 12 April 2021 in International Journal of Energy Research
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With the growth of forecasting models, energy forecasting is used for better planning, operation, and management in the electric grid. It is important to improve the accuracy of forecasting for a faster decision‐making process. Big data can handle large scale of datasets and extract the patterns fed to the deep learning models that improve the accuracy than the traditional models and hence, recently started its application in energy forecasting. In this study, an in‐depth insight is initially derived by investigating artificial intelligence (AI) and machine learning (ML) techniques with their strengths and weaknesses, enhancing the consistency of renewable energy integration and modernizing the overall grid. However, Deep learning (DL) algorithms have the capability to handle big data by capturing the inherent non‐linear features through automatic feature extraction methods. Hence, an extensive and exhaustive review of generative, hybrid, and discriminative DL models is being examined for short‐term, medium‐term, and long‐term forecasting of renewable energy, energy consumption, demand, and supply etc. This study also explores the different data decomposition strategies used to build forecasting models. The recent success of DL is being investigated, and the insights of paradoxes in parameter optimization during the training of the model are identified. The impact of weather prediction in the wind and solar energy forecasting is examined in detail. From the existing literatures, it has seen that the average mean absolute percentage error (MAPE) value of solar and wind energy forecasting is 10.29% and 6.7% respectively. Current technology barriers involved in implementing these models for energy forecasting and the recommendations to overcome the existing system barriers are identified. An in‐depth analysis, discussions of the results, and the scope for improvement are provided in this study including the potential directions for future research in the energy forecasting.

ACS Style

Jayanthi Devaraj; Rajvikram Madurai Elavarasan; Gm Shafiullah; Taskin Jamal; Irfan Khan. A holistic review on energy forecasting using big data and deep learning models. International Journal of Energy Research 2021, 45, 13489 -13530.

AMA Style

Jayanthi Devaraj, Rajvikram Madurai Elavarasan, Gm Shafiullah, Taskin Jamal, Irfan Khan. A holistic review on energy forecasting using big data and deep learning models. International Journal of Energy Research. 2021; 45 (9):13489-13530.

Chicago/Turabian Style

Jayanthi Devaraj; Rajvikram Madurai Elavarasan; Gm Shafiullah; Taskin Jamal; Irfan Khan. 2021. "A holistic review on energy forecasting using big data and deep learning models." International Journal of Energy Research 45, no. 9: 13489-13530.

Journal article
Published: 30 March 2021 in Applied Sciences
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The attractive features of the direct AC-AC converters increase their use in many applications such as voltage control for a heavy-duty load that has a high time constant, AC machine drives, and heating systems based on the induction process. These converters process power in single-stage having simple circuit topologies with reduced switching devices and circuit components. These characteristics lead to the efficient power conversion process. The use of a low-frequency input transformer with multiple output tapping for the regulation of output voltage and frequency is one of the major sources of cost, size, and conversion losses. The complication in the switching algorithms is also the main concern in these converters. The preceding deficiencies lower their potency to be used in daily life. The costly controllers or processors are to be employed to realize the complex control techniques or algorithms. That increases the overall cost and circuit complication. This paper introduces the simple control techniques employed to a novel transformer less multi converter to have the various ac outputs for voltage and frequency regulation. The validation of power circuit and control schemes is tested through the simulation and practical results obtained in Simulink and practical setup respectively.

ACS Style

Naveed Ashraf; Ghulam Abbas; Irfan Khan; Ali Raza; Nasim Ullah. A Transformer-Less Multiconverter Having Output Voltage and Frequency Regulation Characteristics, Employed with Simple Switching Algorithms. Applied Sciences 2021, 11, 3075 .

AMA Style

Naveed Ashraf, Ghulam Abbas, Irfan Khan, Ali Raza, Nasim Ullah. A Transformer-Less Multiconverter Having Output Voltage and Frequency Regulation Characteristics, Employed with Simple Switching Algorithms. Applied Sciences. 2021; 11 (7):3075.

Chicago/Turabian Style

Naveed Ashraf; Ghulam Abbas; Irfan Khan; Ali Raza; Nasim Ullah. 2021. "A Transformer-Less Multiconverter Having Output Voltage and Frequency Regulation Characteristics, Employed with Simple Switching Algorithms." Applied Sciences 11, no. 7: 3075.

Journal article
Published: 09 March 2021 in IEEE Access
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The development of highly efficient models of Photovoltaic (PV) cells and modules is essential for optimized performance, evaluation and control of solar PV systems. The accurate estimation of PV cells parameters is a challenging task because of their non-linear characteristics. In this paper, an improved variant of Flower Pollination Algorithm (FPA) is proposed for accurate estimation of PV cells and modules parameters. The proposed algorithm involves double exponential based dynamic switch probability and a dynamic step size function that mitigate the limitations of conventional FPA. The dynamic switch probability improves the overall performance of algorithm by maintaining a balance between local and global search, while dynamic step function controls the search speed which avoids premature convergence and local optima stagnation. Moreover, Newton Raphson Method is utilized for accurate computation of estimated current for optimum set of estimated parameters. The proposed methodology is evaluated using seven benchmark functions and three case studies; 1- RTC France silicon PV cell, 2- Photo-watt PWP-201 PV module and 3- a practical solar PV system (EAGLE PERC 60M 310W monocrystalline PV module) under different environmental conditions by estimating parameters for single and double diode models. The analysis of results indicates that, the proposed approach improves the convergence speed, precision, avoids premature convergence and stagnation in local optima of conventional FPA. Furthermore, comparative analysis of results illustrates that, the proposed approach is more reliable and efficient than many other techniques in literature.

ACS Style

Mehar-Un-Nisa Khursheed; Mohammed A. Alghamdi; Muhammad Faisal Nadeem Khan; Ahmed Khalil Khan; Irfan Khan; Ali Ahmed; Arooj Tariq Kiani. PV Model Parameter Estimation Using Modified FPA With Dynamic Switch Probability and Step Size Function. IEEE Access 2021, 9, 42027 -42044.

AMA Style

Mehar-Un-Nisa Khursheed, Mohammed A. Alghamdi, Muhammad Faisal Nadeem Khan, Ahmed Khalil Khan, Irfan Khan, Ali Ahmed, Arooj Tariq Kiani. PV Model Parameter Estimation Using Modified FPA With Dynamic Switch Probability and Step Size Function. IEEE Access. 2021; 9 ():42027-42044.

Chicago/Turabian Style

Mehar-Un-Nisa Khursheed; Mohammed A. Alghamdi; Muhammad Faisal Nadeem Khan; Ahmed Khalil Khan; Irfan Khan; Ali Ahmed; Arooj Tariq Kiani. 2021. "PV Model Parameter Estimation Using Modified FPA With Dynamic Switch Probability and Step Size Function." IEEE Access 9, no. : 42027-42044.

Journal article
Published: 02 March 2021 in Energies
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This paper presents Nyström minimum kernel risk-sensitive loss (NysMKRSL) based control of a three-phase four-wire grid-tied dual-stage PV-hybrid energy storage system, under varying conditions such as irradiation variation, unbalanced load, and abnormal grid voltage. The Voltage Source Converter (VSC) control enables the system to perform multifunctional operations such as reactive power compensation, load balancing, power balancing, and harmonics elimination while maintaining Unity Power Factor (UPF). The proposed VSC control delivers more accurate weights with fewer oscillations, hence reducing overall losses and providing better stability to the system. The seamless control with the Hybrid Energy Storage System (HESS) facilitates the system’s grid-tied and isolated operation. The HESS includes the battery, fuel cell, and ultra-capacitor to accomplish the peak shaving, managing the disturbances of sudden and prolonged nature occurring due to load unbalancing and abnormal grid voltage. The DC link voltage is regulated by tuning the PI controller gains utilizing the Salp Swarm Optimization (SSO) algorithm to stabilize the system with minimum deviation from the reference voltage, during various simulated dynamic conditions. The optimized DC bus control generates the accurate loss component of current, which further enhances the performance of the proposed VSC control. The presented system was simulated in the MATLAB 2016a environment and performed satisfactorily as per IEEE 519 standards.

ACS Style

Mukul Chankaya; Ikhlaq Hussain; Aijaz Ahmad; Irfan Khan; S.M. Muyeen. Nyström Minimum Kernel Risk-Sensitive Loss Based Seamless Control of Grid-Tied PV-Hybrid Energy Storage System. Energies 2021, 14, 1365 .

AMA Style

Mukul Chankaya, Ikhlaq Hussain, Aijaz Ahmad, Irfan Khan, S.M. Muyeen. Nyström Minimum Kernel Risk-Sensitive Loss Based Seamless Control of Grid-Tied PV-Hybrid Energy Storage System. Energies. 2021; 14 (5):1365.

Chicago/Turabian Style

Mukul Chankaya; Ikhlaq Hussain; Aijaz Ahmad; Irfan Khan; S.M. Muyeen. 2021. "Nyström Minimum Kernel Risk-Sensitive Loss Based Seamless Control of Grid-Tied PV-Hybrid Energy Storage System." Energies 14, no. 5: 1365.

Journal article
Published: 06 February 2021 in Journal of Energy Storage
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Lithium-ion (Li-ion) based batteries are commonly used in many applications such as electric vehicles, utility-scale storage, and consumer electronics. To maximize power utilization and optimize runtime voltage-current (V-I) characteristics, it is imperative to predict these batteries’ behavior for varying load profiles accurately. In this work, we first perform electrical modeling of two commonly used Li-ion cell chemistries, i.e., Nickel Manganese Cobalt (NMC) and Lithium Iron Phosphate (LiFePO4), using the modified Shepherd equation. Secondly, we evaluate the operation of NMC based Li-ion chemistry's operation for a stationary uninterruptible power supply (UPS) application commonly used in developing countries to cater to utility grid intermittency. The evaluation of cells is done through the 2RC model to ascertain the V-I behavior of these cells in a much more frequent charge-discharge regime in UPSs compared to standard EV applications. A UPS system's real charge/discharge pattern is taken with an average daily outage of up to 6 hrs. Finally, a comparison between the life and cost of Li-ion intervention is made and compared to a conventionally used lead-acid battery, which dominates over 90% of stationery UPS markets in developing countries.

ACS Style

Muhammad U. Tahir; Muhammad Anees; Hassan A. Khan; Irfan Khan; Nauman Zaffar; Taha Moaz. Modeling and evaluation of nickel manganese cobalt based Li-ion storage for stationary applications. Journal of Energy Storage 2021, 36, 102346 .

AMA Style

Muhammad U. Tahir, Muhammad Anees, Hassan A. Khan, Irfan Khan, Nauman Zaffar, Taha Moaz. Modeling and evaluation of nickel manganese cobalt based Li-ion storage for stationary applications. Journal of Energy Storage. 2021; 36 ():102346.

Chicago/Turabian Style

Muhammad U. Tahir; Muhammad Anees; Hassan A. Khan; Irfan Khan; Nauman Zaffar; Taha Moaz. 2021. "Modeling and evaluation of nickel manganese cobalt based Li-ion storage for stationary applications." Journal of Energy Storage 36, no. : 102346.

Journal article
Published: 19 January 2021 in IEEE Access
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Double-stator switched reluctance motors (DSSRMs) with single-tooth winding topology possesses high torque density when compared to conventional switched reluctance motors (SRMs). However, their inherent high torque ripple is still an issue for industrial applications. In SRMs, the torque shared by the outgoing phase reduces significantly in the commutation region. However, at the same time, the incoming phase does not achieve sufficient torque generation. This results in a high torque ripple in this region. In this paper, several design procedures are discussed to improve the performance of the radial flux DSSRM with single-tooth winding topology. Firstly, the pole arc equations of stator pole and rotor segments for the higher difference between aligned and unaligned inductance are derived for high output torque and based on this, the selection of the number of stator slots/rotor segments is discussed. Furthermore, the influence of winding polarities on the core loss and output torque of DSSRM is discussed. Finally, the design modification in rotor structure is proposed with an angular shift in the alternate rotor segments in the direction of rotation to mitigate the torque ripple. To investigate the effectiveness of the proposed design modification, a finite-element model of a 3-phase 12/10/12 pole radial flux DSSRM is developed in ANSYS/MAXWELL software, and simulation results are presented. It is observed that a 40% reduction in the torque ripple is achieved in the case of the proposed motor. The proposed design modification improves the torque generating capability of the incoming phase in the commutation region, which reduces the torque dip in this region and subsequently reduces the torque ripple.

ACS Style

Tripurari Das Gupta; Kalpana Chaudhary; Rajvikram Madurai Elavarasan; R. K. Saket; Irfan Khan; Eklas Hossain. Design Modification in Single-Tooth Winding Double-Stator Switched Reluctance Motor for Torque Ripple Mitigation. IEEE Access 2021, 9, 19078 -19096.

AMA Style

Tripurari Das Gupta, Kalpana Chaudhary, Rajvikram Madurai Elavarasan, R. K. Saket, Irfan Khan, Eklas Hossain. Design Modification in Single-Tooth Winding Double-Stator Switched Reluctance Motor for Torque Ripple Mitigation. IEEE Access. 2021; 9 ():19078-19096.

Chicago/Turabian Style

Tripurari Das Gupta; Kalpana Chaudhary; Rajvikram Madurai Elavarasan; R. K. Saket; Irfan Khan; Eklas Hossain. 2021. "Design Modification in Single-Tooth Winding Double-Stator Switched Reluctance Motor for Torque Ripple Mitigation." IEEE Access 9, no. : 19078-19096.

Journal article
Published: 13 January 2021 in IEEE Access
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Distributed Generation (DG) based on Renewable Energy Sources (RES) are considered as an effective and economical technology for the advancement of an Electric Power System (EPS) to fulfill the load demand. Mostly, studies pertaining to DG planning are performed while considering constant load demand and DG generation. However, these considerations may provide misleading and inconsistent values for loss reduction, voltage profile, power quality, and other operational parameters. Therefore, this paper proposes a novel framework to determine the impact of different Time Varying Voltage Dependent (TVVD) load models on wind DG planning study. Firstly, wind DG optimal allocation is performed using Salp Swarm Algorithm (SSA) for different TVVD load models. Afterwards, impact of different TVVD load models on wind DG planning is investigated. Comparative evaluation of various impact indices, real and reactive power (losses and intakes), penetration level, and apparent power support provided due to integration of wind DG are discussed for various TVVD loads. The analysis of results indicates that TVVD loads have a significant impact on performance of distribution system and DG planning studies.

ACS Style

Ali Ahmed; Muhammad Faisal Nadeem Khan; Irfan Khan; Hani Alquhayz; Arooj Tariq Kiani. A Novel Framework to Determine the Impact of Time Varying Load Models on Wind DG Planning. IEEE Access 2021, 9, 11342 -11357.

AMA Style

Ali Ahmed, Muhammad Faisal Nadeem Khan, Irfan Khan, Hani Alquhayz, Arooj Tariq Kiani. A Novel Framework to Determine the Impact of Time Varying Load Models on Wind DG Planning. IEEE Access. 2021; 9 ():11342-11357.

Chicago/Turabian Style

Ali Ahmed; Muhammad Faisal Nadeem Khan; Irfan Khan; Hani Alquhayz; Arooj Tariq Kiani. 2021. "A Novel Framework to Determine the Impact of Time Varying Load Models on Wind DG Planning." IEEE Access 9, no. : 11342-11357.

Journal article
Published: 12 September 2020 in Journal of Energy Storage
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Reasonable energy storage capacity in a high source-to-charge ratio local power grid can not only reduce system costs but also improve local power supply reliability. This paper introduces the capacity sizing of energy storage system based on reliable output power. The proposed model is formulated to determine the relationship between the power capacity and wind energy loss, considering the wind curtailment loss and traditional energy power uncertain reserve. The non-parametric kernel density estimation is adopted to estimate the confidence intervals of wind power prediction error and fluctuation ranges of the actual output of a wind farm under different confidence degrees. The actual historical data of scenery resources in a certain area is used to verify the feasibility of the proposed method. The simulation shows the large-capacity energy storage, the reliable output power of the microgrid wind ensures the feasibility of day-ahead generation plans.

ACS Style

Muhammad Shahzad Nazir; Ahmad N. Abdalla; Yeqin Wang; Zhang Chu; Ji Jie; Peng Tian; Mingxin Jiang; Irfan Khan; P. Sanjeevikumar; Yongfeng Tang. Optimization configuration of energy storage capacity based on the microgrid reliable output power. Journal of Energy Storage 2020, 32, 101866 .

AMA Style

Muhammad Shahzad Nazir, Ahmad N. Abdalla, Yeqin Wang, Zhang Chu, Ji Jie, Peng Tian, Mingxin Jiang, Irfan Khan, P. Sanjeevikumar, Yongfeng Tang. Optimization configuration of energy storage capacity based on the microgrid reliable output power. Journal of Energy Storage. 2020; 32 ():101866.

Chicago/Turabian Style

Muhammad Shahzad Nazir; Ahmad N. Abdalla; Yeqin Wang; Zhang Chu; Ji Jie; Peng Tian; Mingxin Jiang; Irfan Khan; P. Sanjeevikumar; Yongfeng Tang. 2020. "Optimization configuration of energy storage capacity based on the microgrid reliable output power." Journal of Energy Storage 32, no. : 101866.

Journal article
Published: 14 August 2020 in Energies
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Tracking performance and stability play a major role in observer design for speed estimation purpose in motor drives used in vehicles. It is all the more prevalent at lower speed ranges. There was a need to have a tradeoff between these parameters ensuring the speed bandwidth remains as wide as possible. This work demonstrates an improved static and dynamic performance of a sliding mode state observer used for speed sensorless 3 phase induction motor drive employed in electric vehicles (EVs). The estimated torque is treated as a model disturbance and integrated into the state observer while the error is constrained in the sliding hyperplane. Two state observers with different disturbance handling mechanisms have been designed. Depending on, how they reject disturbances, based on their structure, their performance is studied and analyzed with respect to speed bandwidth, tracking and disturbance handling capability. The proposed observer with superior disturbance handling capabilities is able to provide a wider speed range, which is a main issue in EV. Here, a new dimension of model based design strategy is employed namely the Processor-in-Loop. The concept is validated in a real-time model based design test bench powered by RT-lab. The plant and the controller are built in a Simulink environment and made compatible with real-time blocksets and the system is executed in real-time targets OP4500/OP5600 (Opal-RT). Additionally, the Processor-in-Loop hardware verification is performed by using two adapters, which are used to loop-back analog and digital input and outputs. It is done to include a real-world signal routing between the plant and the controller thereby, ensuring a real-time interaction between the plant and the controller. Results validated portray better disturbance handling, steady state and a dynamic tracking profile, higher speed bandwidth and lesser torque pulsations compared to the conventional observer.

ACS Style

Mohan Krishna Srinivasan; Febin Daya John Lionel; Umashankar Subramaniam; Frede Blaabjerg; Rajvikram Madurai Elavarasan; G. M. Shafiullah; Irfan Khan; Sanjeevikumar Padmanaban. Real-Time Processor-in-Loop Investigation of a Modified Non-Linear State Observer Using Sliding Modes for Speed Sensorless Induction Motor Drive in Electric Vehicles. Energies 2020, 13, 4212 .

AMA Style

Mohan Krishna Srinivasan, Febin Daya John Lionel, Umashankar Subramaniam, Frede Blaabjerg, Rajvikram Madurai Elavarasan, G. M. Shafiullah, Irfan Khan, Sanjeevikumar Padmanaban. Real-Time Processor-in-Loop Investigation of a Modified Non-Linear State Observer Using Sliding Modes for Speed Sensorless Induction Motor Drive in Electric Vehicles. Energies. 2020; 13 (16):4212.

Chicago/Turabian Style

Mohan Krishna Srinivasan; Febin Daya John Lionel; Umashankar Subramaniam; Frede Blaabjerg; Rajvikram Madurai Elavarasan; G. M. Shafiullah; Irfan Khan; Sanjeevikumar Padmanaban. 2020. "Real-Time Processor-in-Loop Investigation of a Modified Non-Linear State Observer Using Sliding Modes for Speed Sensorless Induction Motor Drive in Electric Vehicles." Energies 13, no. 16: 4212.

Review
Published: 14 August 2020 in Sustainability
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A strong energy mix of Renewable Energy Sources (RESs) is needed for sustainable development in the electricity sector. India stands as one of the fastest developing countries in terms of RES production. In this framework, the main objective of this review is to critically scrutinize the Maharashtra state energy landscape to discover the gaps, barriers, and challenges therein and to provide recommendations and suggestions for attaining the RES target by 2022. This work begins with a discussion about the RES trends in various developing countries. Subsequently, it scrutinizes the installed capacity of India, reporting that Maharashtra state holds a considerable stake in the Indian energy mix. A further examination of the state energy mix is carried out by comparing the current and future targets of the state action plan. It is found that the installed capacity of RESs accounts for about 22% of the state energy mix. Moreover, the current installed capacity trend is markedly different from the goals set out in the action plan of the state. Notably, the installed capacity of solar energy is four times less than the target for 2020. Importantly, meeting the targeted RES capacity for 2022 presents a great challenge to the state. Considering this, an analysis of the state’s strengths, barriers, and challenges is presented. Moreover, strong suggestions and recommendations are provided to clear the track to reach the desired destination. This can be useful for the government agencies, research community, private investors, policymakers, and stakeholders involved in building a sustainable energy system for the future.

ACS Style

Rajvikram Madurai Elavarasan; Leoponraj Selvamanohar; Kannadasan Raju; Raghavendra Rajan Vijayaraghavan; Ramkumar Subburaj; Mohammad Nurunnabi; Irfan Ahmad Khan; Syed Afridhis; Akshaya Hariharan; Rishi Pugazhendhi; Umashankar Subramaniam; Narottam Das. A Holistic Review of the Present and Future Drivers of the Renewable Energy Mix in Maharashtra, State of India. Sustainability 2020, 12, 6596 .

AMA Style

Rajvikram Madurai Elavarasan, Leoponraj Selvamanohar, Kannadasan Raju, Raghavendra Rajan Vijayaraghavan, Ramkumar Subburaj, Mohammad Nurunnabi, Irfan Ahmad Khan, Syed Afridhis, Akshaya Hariharan, Rishi Pugazhendhi, Umashankar Subramaniam, Narottam Das. A Holistic Review of the Present and Future Drivers of the Renewable Energy Mix in Maharashtra, State of India. Sustainability. 2020; 12 (16):6596.

Chicago/Turabian Style

Rajvikram Madurai Elavarasan; Leoponraj Selvamanohar; Kannadasan Raju; Raghavendra Rajan Vijayaraghavan; Ramkumar Subburaj; Mohammad Nurunnabi; Irfan Ahmad Khan; Syed Afridhis; Akshaya Hariharan; Rishi Pugazhendhi; Umashankar Subramaniam; Narottam Das. 2020. "A Holistic Review of the Present and Future Drivers of the Renewable Energy Mix in Maharashtra, State of India." Sustainability 12, no. 16: 6596.

Journal article
Published: 04 August 2020 in Energies
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Parameters associated with electrical equivalent models of the photovoltaic (PV) system play a significant role in the performance enhancement of the PV system. However, the accurate estimation of these parameters signifies a challenging task due to the higher computational complexities and non-linear characteristics of the PV modules/panels. Hence, an effective, dynamic, and efficient optimization technique is required to estimate the parameters associated with PV models. This paper proposes a double exponential function-based dynamic inertia weight (DEDIW) strategy for the optimal parameter estimation of the PV cell and module that maintains an appropriate balance between the exploitation and exploration phases to mitigate the premature convergence problem of conventional particle swarm optimization (PSO). The proposed approach (DEDIWPSO) is validated for three test systems; (1) RTC France solar cell, (2) Photo-watt (PWP 201) PV module, and (3) a practical test system (JKM330P-72, 310 W polycrystalline PV module) which involve data collected under real environmental conditions for both single- and double-diode models. Results illustrate that the parameters obtained from proposed technique are better than those from the conventional PSO and various other techniques presented in the literature. Additionally, a comparison of the statistical results reveals that the proposed methodology is highly accurate, reliable, and efficient.

ACS Style

Arooj Tariq Kiani; Muhammad Faisal Nadeem; Ali Ahmed; Irfan Khan; Rajvikram Madurai Elavarasan; Narottam Das. Optimal PV Parameter Estimation via Double Exponential Function-Based Dynamic Inertia Weight Particle Swarm Optimization. Energies 2020, 13, 4037 .

AMA Style

Arooj Tariq Kiani, Muhammad Faisal Nadeem, Ali Ahmed, Irfan Khan, Rajvikram Madurai Elavarasan, Narottam Das. Optimal PV Parameter Estimation via Double Exponential Function-Based Dynamic Inertia Weight Particle Swarm Optimization. Energies. 2020; 13 (15):4037.

Chicago/Turabian Style

Arooj Tariq Kiani; Muhammad Faisal Nadeem; Ali Ahmed; Irfan Khan; Rajvikram Madurai Elavarasan; Narottam Das. 2020. "Optimal PV Parameter Estimation via Double Exponential Function-Based Dynamic Inertia Weight Particle Swarm Optimization." Energies 13, no. 15: 4037.

Journal article
Published: 03 August 2020 in Energies
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Photovoltaic (PV) is a highly promising energy source because of its environment friendly property. However, there is an uncertainty present in the modeling of PV modules owing to varying irradiance and temperature. To solve such uncertainty, the fuzzy logic control-based intelligent maximum power point tracking (MPPT) method is observed to be more suitable as compared with conventional algorithms in PV systems. In this paper, an isolated PV system using a push pull converter with the fuzzy logic-based MPPT algorithm is presented. The proposed methodology optimizes the output power of PV modules and achieves isolation with high DC gain. The DC gain is inverted into a single phase AC through a closed loop fuzzy logic inverter with a low pass filter to reduce the total harmonic distortion (THD). Dynamic simulations are developed in Matlab/Simulink by MathWorks under linear loads. The results show that the fuzzy logic algorithms of the proposed system efficiently track the MPPT and present reduced THD.

ACS Style

Tehzeeb-Ul Hassan; Rabeh Abbassi; Houssem Jerbi; Kashif Mehmood; Muhammad Tahir; Khalid Cheema; Rajvikram Elavarasan; Farman Ali; Irfan Khan. A Novel Algorithm for MPPT of an Isolated PV System Using Push Pull Converter with Fuzzy Logic Controller. Energies 2020, 13, 4007 .

AMA Style

Tehzeeb-Ul Hassan, Rabeh Abbassi, Houssem Jerbi, Kashif Mehmood, Muhammad Tahir, Khalid Cheema, Rajvikram Elavarasan, Farman Ali, Irfan Khan. A Novel Algorithm for MPPT of an Isolated PV System Using Push Pull Converter with Fuzzy Logic Controller. Energies. 2020; 13 (15):4007.

Chicago/Turabian Style

Tehzeeb-Ul Hassan; Rabeh Abbassi; Houssem Jerbi; Kashif Mehmood; Muhammad Tahir; Khalid Cheema; Rajvikram Elavarasan; Farman Ali; Irfan Khan. 2020. "A Novel Algorithm for MPPT of an Isolated PV System Using Push Pull Converter with Fuzzy Logic Controller." Energies 13, no. 15: 4007.

Journal article
Published: 16 July 2020 in International Journal of Electrical Power & Energy Systems
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Power systems are confronted with prodigious challenges of scheduling and operation incurred by the high penetration of distributed renewable energy with intermittency and uncertainty. Therefore, this paper proposes an improved distributed robust optimization approach with self-adaptive step-sizes based on the line search method and a polynomial filter, to minimize the overall costs of flexible resources including conventional generators, energy storage systems, renewable energy curtailments, deferrable loads and tie-line power exchanges, while considering various constraints, such as supply-demand power balance, line congestion constraints and power output limits. Numerical case studies conducted in a modified IEEE 14-bus system and a modified IEEE 118-bus system demonstrate the reliability, robustness and extensibility of the proposed approach. In addition, the effectiveness and accuracy of the proposed distributed robust optimization approach are validated through comparisons with the traditional centralized gradient method and the convergence performance is better in contrast to other distributed optimization algorithm.

ACS Style

Xinyue Chang; Yinliang Xu; HongBin Sun; Irfan Khan. A distributed robust optimization approach for the economic dispatch of flexible resources. International Journal of Electrical Power & Energy Systems 2020, 124, 106360 .

AMA Style

Xinyue Chang, Yinliang Xu, HongBin Sun, Irfan Khan. A distributed robust optimization approach for the economic dispatch of flexible resources. International Journal of Electrical Power & Energy Systems. 2020; 124 ():106360.

Chicago/Turabian Style

Xinyue Chang; Yinliang Xu; HongBin Sun; Irfan Khan. 2020. "A distributed robust optimization approach for the economic dispatch of flexible resources." International Journal of Electrical Power & Energy Systems 124, no. : 106360.