i-manager Publications

i-manager's Journal on Power Systems Engineering (JPS)

Current Issue Vol. 12 Issue 1

Thermodynamic and Exergoeconomic Operation Optimization and Simulation of Steam Generation Solar Power Plant
Topology Transformation Approach for Optimal PMU Placement for Monitoring and Control of Power System
Performance Evaluation of Power System with HVDC Integration: Impact of SSSC and STATCOM on Power System Efficiency and Stability
Photovoltaic Systems: A Pollination-Based Optimization Approach for Critical Industrial Applications
Design of a Robust Controller for the Load Frequency Control of Interconnected Power System

Most Cited

Volume 11 Issue 3 October - December 2023

Research Paper

Voltage Stability and Power Loss Minimization with STATCOM using Particle Swarm Optimization and Hybrid Firefly Particle Swarm Optimization Algorithms

R. Siva Subramanyam Reddy*

Srikalahasteeswara Institute of Technology (SKIT), Andhra Pradesh, India.

Reddy, R. S. S. (2023). Voltage Stability and Power Loss Minimization with STATCOM using Particle Swarm Optimization and Hybrid Firefly Particle Swarm Optimization Algorithms. i-manager’s Journal on Power Systems Engineering, 11(3), 1-10. https://doi.org/10.26634/jps.11.3.20045

Abstract

This paper presents a hybrid algorithm using a hybrid firefly and particle swarm optimization (HFPSO) algorithm to determine the optimal placement of STATCOM devices. A multi-objective function is used to increase voltage stability, voltage profile and minimize overall power losses of the transmission system. At first, PSO algorithm is used to find the optimal STATCOM location and further more to reduce the power losses and voltage profile enhancement to overcome the sub-optimal operation of existing algorithms, the HFPSO algorithm is used to determine the optimal placement of STATCOM FACTS device and verification of the proposed algorithm was achieved on standard IEEE 14-bus and 30-bus transmission systems in the MATLAB environment. Comprehensive simulation results of two different cases show that the proposed HFPSO demonstrates significant improvements over other existing algorithms to enhance voltage stability and reducing active and reactive power losses in an electrical power transmission systems.

References

[1]. Abido, M. A. (2002). Optimal power flow using particle swarm optimization. International Journal of Electrical Power & Energy Systems, 24(7), 563-571.

[2]. Acha, E., Fuerte-Esquivel, C. R., Ambriz-Perez, H., & Angeles-Camacho, C. (2004). FACTS: Modelling and Simulation in Power Networks. John Wiley & Sons.

[3]. Ali, Q. W., & ul Asar, A. (2013). Smart power transmission system using FACTS device. International Journal of Applied Power Engineering (IJAPE), 2(2), 61-70.

[4]. Aydilek, I. B. (2018). A hybrid firefly and particle swarm optimization algorithm for computationally expensive numerical problems. Applied Soft Computing, 66, 232-249.

[5]. Bansal, R. C. (2005). Optimization methods for electric power systems: An overview. International Journal of Emerging Electric Power Systems, 2(1).

[6]. Chiang, C. L. (2005). Improved genetic algorithm for power economic dispatch of units with valve-point effects and multiple fuels. IEEE Transactions on Power Systems, 20(4), 1690-1699.

[7]. Durairaj, S., & Fox, B. (2008). Optimal placement of FACTS devices. Conference on Energy and Environment, (pp.407-411).

[8]. El Metwally, M. M., El Emary, A. A., El Bendary, F. M., & Mosaad, M. I. (2008, March). Optimal allocation of FACTS devices in power system using genetic algorithms. In 2008 12th International Middle-East Power System Conference, (pp. 1-4). IEEE.

[9]. Engelbrecht, A. P. (2007). Computational Intelligence: An Introduction. John Wiley & Sons.

[10]. Flatabo, N., Ognedal, R., & Carlsen, T. (1990). Voltage stability condition in a power transmission system calculated by sensitivity methods. IEEE Transactions on Power Systems, 5(4), 1286-1293.

[11]. Gerbex, S., Cherkaoui, R., & Germond, A. J. (2003, June). Optimal location of FACTS devices to enhance power system security. In 2003 IEEE Bologna Power Tech Conference Proceedings, 3, 7. IEEE.

[12]. Grigsby, L. L. (2007). Power System Stability and Control. CRC press, New York.

[13]. Hingorani, N. G., & Gyugi, L. (1999). Understanding FACTS – Concepts and Technology of Flexible AC Transmission Systems. Wiley-IEEE Press.

[14]. Jeong, H. M., Lee, H. S., & Park, J. H. (2009, October). Application of parallel particle swarm optimization on power system state estimation. In 2009 Transmission & Distribution Conference & Exposition: Asia and Pacific (pp. 1-4). IEEE.

[15]. Lee, K. Y., & El-Sharkawi, M. A. (2002). Modern Heuristic Optimization Techniques with Applications to Power Systems. Wiley-IEEE Press.

[16]. Li, N., Xu, Y., & Chen, H. (2000). FACTS-based power flow control in interconnected power system. IEEE Transactions on Power Systems, 15(1), 257-262.

[17]. Murali, D., & Rajaram, M. (2010). Active and reactive power flow control using FACTS devices. International Journal of Computer Applications, 9(8), 45-50.

[18]. Murali, D., Rajaram, M., & Reka, N. (2010). Comparison of FACTS devices for power system stability enhancement. International Journal of Computer Applications, 8(4), 30-35.

[19]. Povh, D. (2000). Modelling of FACTS in power system studies. In Proceedings of IEEE Power Engineering Society Winter Meeting, 2, 1435-1439.

[20]. Radman, G., & Raje, R. S. (2007). Power flow model/calculation for power systems with multiple FACTS controllers. Electric Power Systems Research, 77(12), 1521-1531.

[21]. Rao, S. S. (2019). Engineering Optimization: Theory and Practice. John Wiley & Sons.

[22]. Reddy, R. S. S. (2020). Optimal placement of UPFC using Heuristic Techniques. i-manager's Journal on Electrical Engineering, 14(2), 26-33.

[23]. Samy, M. M., & Barakat, S. (2019, December). Hybrid invasive weed optimization-particle swarm optimization algorithm for biomass/PV micro-grid power system. In 2019 21st international Middle East power systems conference (MEPCON) (pp. 377-382). IEEE.

[24]. Sharma, D., Gaur, P., & Mittal, A. P. (2014). Comparative analysis of hybrid GAPSO optimization technique with GA and PSO methods for cost optimization of an off-grid hybrid energy system. Energy Technology & Policy, 1(1), 106-114.

[25]. Song, Y. H., & Johns, A. T. (1999). Flexible Ac Transmission Systems (FACTS). Institution of Electric Engineers (IET), London.

Full Article (HTML)

Pdf

Research Paper

Load Frequency Control of Multi Area Power System Network with Soft Computing Algorithm Based Controller and Coordinated Strategy of TCSC and Battery Energy Storage

Ch. Naga Sai Kalyan*

Vasireddy Venkatadri Institute of Technology, Andhra Pradesh, India.

Kalyan, Ch. N. S. (2023). Load Frequency Control of Multi Area Power System Network with Soft Computing Algorithm Based Controller and Coordinated Strategy of TCSC and Battery Energy Storage. i-manager’s Journal on Power Systems Engineering, 11(3), 11-21. https://doi.org/10.26634/jps.11.3.20241

Abstract

This work proposes the possible implementation of a water cycle algorithm (WCA) optimized controller of tilt-integral derivative plus filter (TIDF) for the load frequency control (LFC) of a multi-area multi-fuel (MAMF) interconnected power system network (IPSN). The MAMF network has two areas, and the dynamic analysis of the IPSN is assessed pertaining to the step load perturbation (SLP) of 10% in area 1. However, the performance efficacy of the TIDF is demonstrated and validated with other recent control techniques reported in the literature. The IPSN is considered with the communication time delays (CTDs) as the realistic constraint, and their subsequent impact on the LFC performance is demonstrated and vindicated. Further, the IPSN model of the MAMF system is enacted with the battery energy storage (BES) in areas 1 and 2, and the thyristor-controlled series compensator (TCSC) with the tie-line as the territory regulator. Simulation results showcased that there will be an improvement in the LFC of MAMF system performance with the employment of a territory control scheme.

References

[1]. Abdelaziz, A. Y., & Ali, E. S. (2015). Cuckoo search algorithm based load frequency controller design for nonlinear interconnected power system. International Journal of Electrical Power & Energy Systems, 73, 632-643.

[2]. Alhelou, H. H., Nagpal, N., Kassarwani, N., & Siano, P. (2023). Decentralized optimized integral sliding modebased load frequency control for interconnected multiarea power systems. IEEE Access, 11, 32296-32307.

[3]. Ali, M., Kotb, H., AboRas, M. K., & Abbasy, H. N. (2022). Frequency regulation of hybrid multi-area power system using wild horse optimizer based new combined Fuzzy Fractional-Order PI and TID controllers. Alexandria Engineering Journal, 61(12), 12187-12210.

[4]. Asghar, R., Riganti Fulginei, F., Wadood, H., & Saeed, S. (2023). A review of load frequency control schemes deployed for wind-integrated power systems. Sustainability, 15(10), 8380.

[5]. Elgerd, O. I., & Fosha, C. E. (1970). Optimum megawatt-frequency control of multiarea electric energy systems. IEEE Transactions on Power Apparatus and Systems, (4), 556-563.

[6]. Eskandar, H., Sadollah, A., Bahreininejad, A., & Hamdi, M. (2012). Water cycle algorithm–A novel metaheuristic optimization method for solving constrained engineering optimization problems. Computers & Structures, 110, 151-166.

[7]. Fernández-Guillamón, A., Muljadi, E., & Molina-García, A. (2022). Frequency control studies: A review of power system, conventional and renewable generation unit modeling. Electric Power Systems Research, 211, 108191.

[8]. Guha, D., Roy, P. K., & Banerjee, S. (2016). Quasioppositional differential search algorithm applied to load frequency control. Engineering Science and Technology, an International Journal, 19(4), 1635-1654.

[9]. Jalendra, M., Reddy, K. S. G., Lavanya, M., Jaideep, N. V., Kalyan, C. N. S., & Suresh, C. V. (2022, December). Fruit fly optimization algorithm based TIDF controller for LFC of traditional hydro thermal system with SSSC and ultra-capacitors. In 2022 International Conference on Knowledge Engineering and Communication Systems (ICKES), (pp. 1-6). IEEE.

[10]. Kalyan, C. H., & Rao, G. S. (2020). Coordinated SMES and TCSC damping controller for load frequency control of multi area power system with diverse sources. International Journal on Electrical Engineering & Informatics, 12(4). 747-769.

[11]. Kalyan, C. N. S., Goud, B. S., Kishan, H., Ramineni, P., Kumar, B. P., & Kumar, T. A. (2022, December). Donkey and smuggler optimization algorithm-based degree of freedom controller for stability of two area power system with AC-DC links. In 2022 IEEE 7th International Conference on Recent Advances and Innovations in Engineering (ICRAIE), 7, 461-466. IEEE.

[12]. Kalyan, C. N. S., Goud, B. S., Kumar, M. K., Thakur, P., Bajaj, M., & Bansal, R. C. (2023). Squirrel search algorithm based intelligent controller for interconnected power system. International Journal of Modelling and Simulation, 1-21.

[13]. Kalyan, C. N. S. (2021, January). UPFC and SMES based coordinated control strategy for simultaneous frequency and voltage stability of an interconnected power system. In 2021 1st International Conference on Power Electronics and Energy (ICPEE), (pp. 1-6). IEEE.

[14]. Kalyan, C. N. S., & Rao, G. S. (2020). Performance comparison of various energy storage devices in combined LFC and AVR of multi area system with renewable energy integration. International Journal of Renewable Energy Research (IJRER), 10(2), 933-944.

[15]. Kalyan, C. N. S., & Rao, G. S. (2022). Coordinated control strategy for simultaneous frequency and voltage stabilisation of the multi-area interconnected system considering communication time delays. International Journal of Ambient Energy, 43(1), 5512-5524.

[16]. Kalyan, N. S., & Rao, G. S. (2020). Frequency and voltage stabilisation in combined load frequency control and automatic voltage regulation of multiarea system with hybrid generation utilities by AC/DC links. International Journal of Sustainable Energy, 39(10), 1009-1029.

[17]. Kumar, A., & Pan, S. (2022). Design of fractional order PID controller for load frequency control system with communication delay. ISA Transactions, 129, 138-149.

[18]. Mishra, S., Prusty, R. C., & Panda, S. (2021). Performance analysis of modified sine cosine optimized multistage FOPD PI controller for load frequency control of an islanded microgrid system. International Journal of Numerical Modelling: Electronic Networks, Devices and Fields, 34(6), e2923.

[19]. Mohapatra, G., Debnath, M. K., & Mohapatra, K. K. (2020). IMO-based novel adaptive dual-mode controller design for AGC investigation in different types of systems. Cogent Engineering, 7(1), 1711675.

[20]. Morsali, J., Zare, K., & Hagh, M. T. (2017). Modified group search optimisation based comparative performance evaluation of thyristor controlled series capacitor based fractional order damping controllers to improve load frequency control performance in restructured environment. IET Generation, Transmission & Distribution, 11(18), 4654-4669.

[21]. Padhy, S., & Panda, S. (2017). A hybrid stochastic fractal search and pattern search technique based cascade PI-PD controller for automatic generation control of multi-source power systems in presence of plug in electric vehicles. CAAI Transactions on Intelligence Technology, 2(1), 12-25.

[22]. Prakash, S., & Sinha, S. K. (2018). Automatic load frequency control of six areas' hybrid multi-generation power systems using neuro-fuzzy intelligent controller. IETE Journal of Research, 64(4), 471-481.

[23]. Raju, M., Saikia, L. C., & Sinha, N. (2016). Automatic generation control of a multi-area system using ant lion optimizer algorithm based PID plus second order derivative controller. International Journal of Electrical Power & Energy Systems, 80, 52-63.

[24]. Ranjan, M., & Shankar, R. (2022). A literature survey on load frequency control considering renewable energy integration in power system: Recent trends and future prospects. Journal of Energy Storage, 45, 103717.

[25]. Singh, S. S. (2022). Load frequency control of a threearea system with multiple-energy sources using exchange market algorithm optimized cascade IDD-PID controllers. Electric Power Components and Systems, 50(1-2), 27-37.

Full Article (HTML)

Pdf

Research Paper

Performance Analysis of Distribution System under Different Loading Conditions using Test Bench

Shekhappa G. Ankaliki * , Sanjeeth P. Aminabhavi, Manjula Sureban*, Pradeep Vibhuti****

*-**** Department of Electrical & Electronics Engineering, Shri Dharmasthala Manjunatheshwara College of Engineering and Technology, Karnataka, India.

Ankaliki, S. G., Aminabhavi, S. P., Sureban, M., and Vibhuti, P. (2023). Performance Analysis of Distribution System under Different Loading Conditions using Test Bench. i-manager’s Journal on Power Systems Engineering, 11(3), 22-29. https://doi.org/10.26634/jps.11.3.20399

Abstract

This paper presents performance analysis of power distribution system using distribution test bench. Distribution system is the part of power system in which significant portion of the power is utilized in it. The losses occur in distribution system mainly due to inherent characteristics of the number of components in the system. In this wok, we analyse the performance of distribution system having 4 feeders having different lengths supplying resistive and combination of resistive and inductive loads. The analysis includes the voltage regulation, power loss, efficiency for different loading conditions. Also shown are the improvement of terminal voltage of the feeders and receiving end power with compensation. Finally the inferences are drawn and suggestions are proposed to improve the performance.

References

[1]. Benedict, E., Collins, T., Gotham, D., Hoffman, S., Karipides, D., Pekarek, S., & Ramabhadran, R. (1992). Losses in electric power systems. Department of Electrical and Computer Engineering Technical Reports, Purdue University.

[2]. Chen, T. H., Chen, M. S., Inoue, T., Kotas, P., & Chebli, E. A. (1991). Three-phase co-generator and transformer models for distribution system analysis. IEEE Transactions on Power Delivery, 6(4), 1671-1681.

[3]. Gonen, T. (1986). Electric Power Distribution Engineering. CRC Press.

[4]. Gross, C.A. (1986). Power System Analysis. John Wiley.

[5]. Kerszenbaum, I., Mazur, A., Mistry, M., & Frank, J. (1991). Specifying dry-type distribution transformers for solid-state applications. IEEE Transactions on Industry Applications, 27(1), 173-178.

[6]. Mahendrabhai, M.N. (2013). Loss Minimization Techniques in Distribution System. Indian Journal of Applied Research, 3(8), 685-687.

[7]. Makino, A., Suzuki, K., Inoue, A., & Masumoto, T. (1991). Low core loss of a bcc Fe86Zr7B6Cu1 alloy with nanoscale grain size. Materials Transactions, JIM (Japan Institute of Metals), 32(6), 551-556.

[8]. Nasar, S.A. (1986). Electric Energy Conversion and Transmisson. Macmillan, USA.

[9]. Ng, H. W., Hasegawa, R. Y. U. S. U. K. E., Lee, A. C., & Lowdermilk, L. A. (1991). Amorphous alloy core distribution transformers. Proceedings of the IEEE, 79(11), 1608-1623.

[10]. Sun, D. I. H., Abe, S., Shoults, R. R., Chen, M. S., Eichenberger, P. A. E. P., & Farris, D. (1980). Calculation of energy losses in a distribution system. IEEE Transactions on Power Apparatus and Systems, (4), 1347-1356.

Full Article (HTML)

Pdf

Research Paper

Simulation of Solar PV with Different Control System of Boost Converter for Composite Climate

Roshni Rahangdale* , Deeksha Sahare**

*-** Department of Electrical & Electronics Engineering, Shri Shankaracharya Group of Institution, Durg, Chhattisgarh, India.

Rahangdale, R., and Sahare, D. (2023). Simulation of Solar PV with Different Control System of Boost Converter for Composite Climate. i-manager’s Journal on Power Systems Engineering, 11(3), 30-36. https://doi.org/10.26634/jps.11.3.20409

Abstract

This paper works on the modelling of solar PV with series parallel connection of the strings for the composite climate location and the detailed study has been done on the gate signal of the chopper used for boosting with three different conditions. As the climate conditions are very important factor for the installation of the project to fulfill the captive requirements, a study has also been done with the help of climate consultant software so that the radiations capacity and potential to develop the energy is with in its maximum output capacity. In the next phase an inverter has also been connected for the conversion into single phase AC output for the testing of the model for the usage in the load or grid. Various methods for the control of the booster has been analyzed and considered. Simulation is done on a Matlab software and the result is analyzed.

References

[1]. Abdel-Rahim, O., Orabi, M., & Ahmed, M. E. (2010, June). Development of high-gain high-efficiency gridnd connected inverter for PV Module. In The 2nd International Symposium on Power Electronics for Distributed Generation Systems, (pp. 368-373). IEEE.

[2]. Agorreta, J. L., Reinaldos, L., Gonzalez, R., Borrega, M., Balda, J., & Marroyo, L. (2009). Fuzzy switching technique applied to PWM boost converter operating in mixed conduction mode for PV systems. IEEE Transactions on Industrial Electronics, 56(11), 4363-4373.

[3]. Aranda, E. D., Galan, J. A. G., De Cardona, M. S., & Marquez, J. M. A. (2009). Measuring the IV curve of PV generators. IEEE Industrial Electronics Magazine, 3(3), 4-14.

[4]. Azab, M. (2008). A New Maximum Power Point Tracking for Photovoltaic Systems, In Proceedings of World Academy of Science, Engineering and Technology, 34, 73-79.

[5]. Azzouz, M. A., & Elshafei, A. L. (2010, September). An adaptive fuzzy regulation of the dc-bus voltage in wind energy conversion systems. In 2010 IEEE International Conference on Control Applications, (pp. 1193-1198). IEEE.

[6]. Azzouz, M., Elshafei, A. L., & Emara, H. (2010, September). Maximum-power tracking of wind energy conversion systems based on fuzzy algorithms. In 2010 IEEE International Symposium on Intelligent Control, (pp. 481-486). IEEE.

[7]. Bhasme, N. R., & Gandhare, D. W. (2012). Power converter for grid integration of wind power system. International Journal of Engineering Research and Applications, 2(4), 2181-2185.

[8]. Bhende, C. N., Mishra, S., & Malla, S. G. (2011). Permanent magnet synchronous generator-based standalone wind energy supply system. IEEE Transactions on Sustainable Energy, 2(4), 361-373.

[9]. Bijaykant, B.K., & Minz, S. (2016). Intelligent Control Algorithm for Maximum Power Point Tracking of PV Arrays. International Journal of Innovative Research in Science, Engineering and Technology, 5(9), 18913-18922.

[10]. Bouchafaa, F., Hamzaoui, I., & Hadjammar, A. (2011). Fuzzy Logic Control for the tracking of maximum power point of a PV system. Energy Procedia, 6, 633-642.

[11]. Dhanakodi, T., & Sivakumar, M. (2018). A Comparative Study on Efficiency Enhanced Solar Energy Harvesting Systems. International Journal of Applied Engineering Research, 13(20), 14757-14762.

[12]. Gow, J. A., & Manning, C. D. (1999). Development of a photovoltaic array model for use in power-electronics simulation studies. IEE Proceedings-Electric Power Applications, 146(2), 193-200.

[13]. Larbes, C., Cheikh, S. A., Obeidi, T., & Zerguerras, A. (2009). Genetic algorithms optimized fuzzy logic control for the maximum power point tracking in photovoltaic system. Renewable Energy, 34(10), 2093-2100.

[14]. Mihet-Popa, L., Blaabjerg, F., & Boldea, I. (2004). Wind turbine generator modeling and simulation where rotational speed is the controlled variable. IEEE Transactions on Industry Applications, 40(1), 3-10.

[15]. Muljadi, E., & Butterfield, C. P. (2001). Pitchcontrolled variable-speed wind turbine generation. IEEE Transactions on Industry Applications, 37(1), 240-246.

[16]. Salas, V., Olias, E., Barrado, A., & Lazaro, A. (2006). Review of the maximum power point tracking algorithms for stand-alone photovoltaic systems. Solar Energy Materials and Solar Cells, 90(11), 1555-1578.

[17]. Usta, M. A., Akyaszi, O., & Atlas, I. H. (2011, May). Design and performance of solar tracking system with fuzzy logic Controller. In Sixth International Advanced Technologies Symposium (IATS'11), Elazig, Turkey (pp. 331-336).

[18]. Verma, A., Thakur, B. N., Mishra, A., & Tamrakar, A. K. (2021). Design and Simulation of MPPT Control Strategy for Solar PV by Using Fuzzy Control. Journal of University of Shanghai for Science and Technology, 23(9), 1233-1240.

[19]. Verma, S., Verma, H. K., & Mohiddin, M. K. (2013). Modeling & analysis of standalone photovoltaic System. International Journal of Research in Engineering and Technology, 2(11), 259-265.

[20]. Villalva, M. G., Gazoli, J. R., & Ruppert Filho, E. (2009). Comprehensive approach to modeling and simulation of photovoltaic arrays. IEEE Transactions on Power Electronics, 24(5), 1198-1208.

[21]. World Health Organization. (2022). Tracking SDG7: The Energy Progress Report 2022.

[22]. Yang, L., Xu, Z., Ostergaard, J., Dong, Z. Y., & Wong, K. P. (2011). Advanced control strategy of DFIG wind turbines for power system fault ride through. IEEE Transactions on Power Systems, 27(2), 713-722.

Full Article (HTML)

Pdf

Research Paper

Enhancement of System Performance using PeSche Scheduling Algorithm on Multiprocessors

M. Sreenath* , P. A. Vijaya**

* Infosys Ltd., Bengaluru, India.

** Department of ECE., BNMIT, Bengaluru, Karnataka, India.

Sreenath, M., and Vijaya, P. A. (2023). Enhancement of System Performance using PeSche Scheduling Algorithm on Multiprocessors. i-manager’s Journal on Power Systems Engineering, 11(3), 37-54. https://doi.org/10.26634/jps.11.3.20277

Abstract

The scheduling techniques have been investigated by the job execution process in a system to maximize multiprocessor utilization. Dynamic Power Management (DPM) and Dynamic Voltage and Frequency Scaling (DVFS) represent two general strategies for lowering energy use. Performance enhanced Scheduling (PeSche) is a proposed scheduling algorithm designed for an optimal solution. CodeBlocks were utilized to run extensive simulations. In terms of computing performance (average waiting time and average turnaround time), the PeSche scheduling algorithm outperformed recently reported scheduling algorithms such as SJF, RR, FCFS, Priority, and SJF-LJF. The PeSche scheduling algorithm yielded better results by assigning priority in terms of energy-time ratio, programming running time, total energy, and total time than existing algorithms. In comparison to Minimum Energy Schedule (MES) and Slack Utilization for Reduced Energy (SURE), PeSche consumed less energy.

References

[1]. AL-Bakhrani, A. A., Hagar, A. A., Hamoud, A. A., & Kawathekar, S. (2020). Comparative analysis of cpu scheduling algorithms: Simulation and its applications. International Journal of Advanced Science and Technology, 29(3), 483-494.

[2]. Alworafi, M. A., Dhari, A., El-Booz, S. A., Nasr, A. A., Arpitha, A., & Mallappa, S. (2019). An enhanced task scheduling in cloud computing based on hybrid approach. In Data Analytics and Learning: Proceedings of DAL 2018 (pp. 11-25). Springer Singapore.

[3]. Alworafi, M. A., Dhari, A., El-Booz, S. A., Nasr, A. A., Arpitha, A., & Mallappa, S. (2019). An enhanced task scheduling in cloud computing based on hybrid approach. In Data Analytics and Learning: Proceedings of DAL 2018 (pp. 11-25). Springer Singapore.

[4]. Bhuiyan, A., Guo, Z., Saifullah, A., Guan, N., & Xiong, H. (2018). Energy-efficient real-time scheduling of DAG tasks. ACM Transactions on Embedded Computing Systems (TECS), 17(5), 1-25.

[5]. Bose, A., Biswas, T., & Kuila, P. (2018). A novel genetic algorithm based scheduling for multi-core systems. In Smart Innovations in Communication and Computational Sciences: Proceedings of ICSICCS-2018 (pp. 45-54). Singapore: Springer Singapore.

[6]. Burkimsher, A., Bate, I., & Indrusiak, L. S. (2013). Scheduling HPC workflows for responsiveness and fairness with networking delays and inaccurate estimates of execution times. In Euro-Par 2013 Parallel Processing: 19th International Conference, Aachen, Germany, August 26-30, 2013. Proceedings 19 (pp. 126-137). Springer Berlin Heidelberg.

[7]. Burkimsher, A., Bate, I., & Indrusiak, L. S. (2013). Scheduling HPC workflows for responsiveness and fairness with networking delays and inaccurate estimates of execution times. In Euro-Par 2013 Parallel Processing: 19th International Conference, Aachen, Germany, August 26-30, 2013. Proceedings 19 (pp. 126-137). Springer Berlin Heidelberg.

[8]. Butangen, A. K. G., Velasco, C. E., Codmos, J. C. B., Bayani, E. F., & Baquirin, R. B. (2020, January). Utilizing dynamic mean quantum time round robin to optimize the shortest job first scheduling algorithm. In Proceedings of 2020 the 6th International Conference on Computing and Data Engineering, (pp. 14-18).

[9]. Chandran, J., & Viswanatham, V. M. (2021, February). Evaluating the effectiveness of community detection algorithms for influence maximization in social networks. In 2021 International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT) (pp. 1-11). IEEE.

[10]. Chang, S., Bi, R., Sun, J., Liu, W., Yu, Q., Deng, Q., & Gu, Z. (2022). Toward minimum WCRT bound for DAG tasks under prioritized list scheduling algorithms. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 41(11), 3874-3885.

[11]. Chasapis, D., Moretó, M., Schulz, M., Rountree, B., Valero, M., & Casas, M. (2019, June). Power efficient job scheduling by predicting the impact of processor manufacturing variability. In Proceedings of the ACM International Conference on Supercomputing, (pp. 296-307).

[12]. Chen, J., Han, P., Liu, Y., & Du, X. (2023). Scheduling independent tasks in cloud environment based on modified differential evolution. Concurrency and Computation: Practice and Experience, 35(13), e6256.

[13]. Chen, Y., Goebel, R., Lin, G., Liu, L., Su, B., Tong, W., & Zhang, A. (2022). A local search 4/3-approximation algorithm for the minimum 3-path partition problem. Journal of Combinatorial Optimization, 44(5), 3595-3610.

[14]. Deng, Z., Cao, D., Shen, H., Yan, Z., & Huang, H. (2021). Reliability-aware task scheduling for energy efficiency on heterogeneous multiprocessor systems. The Journal of Supercomputing, 77, 11643-11681.

[15]. Duy, T. V. T., Sato, Y., & Inoguchi, Y. (2010, April). Performance evaluation of a green scheduling algorithm for energy savings in cloud computing. In 2010 IEEE International Symposium on Parallel & Distributed Processing, Workshops and PhD Forum (IPDPSW) (pp. 1-8). IEEE.

[16]. Duy, T. V. T., Sato, Y., & Inoguchi, Y. (2010, April). Performance evaluation of a green scheduling algorithm for energy savings in cloud computing. In 2010 IEEE International Symposium on Parallel & Distributed Processing, Workshops and Phd Forum (IPDPSW) (pp. 1-8). IEEE.

[17]. Gupta, A. K., Mathur, P., Travieso-Gonzalez, C. M., Garg, M., & Goyal, D. (2021, August). ORR: Optimized Round Robin CPU Scheduling Algorithm. In Proceedings of the International Conference on Data Science, Machine Learning and Artificial Intelligence, (pp. 296-304).

[18]. Hamayun, M., & Khurshid, H. (2015). An optimized shortest job first scheduling algorithm for CPU scheduling. Journal of Applied Environmental and Biological Sciences, 5(12), 42-46.

[19]. He, Q., Guan, N., & Guo, Z. (2019). Intra-task priority assignment in real-time scheduling of DAG tasks on multicores. IEEE Transactions on Parallel and Distributed Systems, 30(10), 2283-2295.

[20]. Hu, B., Cao, Z., & Zhou, M. (2021). Energy-minimized scheduling of real-time parallel workflows on heterogeneous distributed computing systems. IEEE Transactions on Services Computing, 15(5), 2766-2779.

[21]. Hu, B., Cao, Z., & Zhou, M. (2021). Energy-minimized scheduling of real-time parallel workflows on heterogeneous distributed computing systems. IEEE Transactions on Services Computing, 15(5), 2766-2779.

[22]. Ji, M., Zhang, W., Liao, L., Cheng, T. C. E., & Tan, Y. (2019). Multitasking parallel-machine scheduling with machine-dependent slack due-window assignment. International Journal of Production Research, 57(6), 1667-1684.

[23]. Krishnapura, R., Goddard, S., & Qadi, A. A. (2004). A dynamic real-time scheduling algorithm for reduced energy consumption. CSE Technical Reports, 72

[24]. Kumar, M., & Sharma, S. C. (2016, March). Priority Aware Longest Job First (PA-LJF) algorithm for utilization of the resource in cloud environment. In 2016 3rd International Conference on Computing for Sustainable Global Development (INDIACom) (pp. 415-420). IEEE.

[25]. Kumar, M., & Sharma, S. C. (2016, March). Priority aware longest job first (PA-LJF) algorithm for utilization of the resource in cloud environment. In 2016 3rd International Conference on Computing for Sustainable Global Development (INDIACom) (pp. 415-420). IEEE.

[26]. Lee, Z., Wang, Y., & Zhou, W. (2011, August). A dynamic priority scheduling algorithm on service request scheduling in cloud computing. In Proceedings of 2011 International Conference on Electronic & Mechanical Engineering and Information Technology, 9, 4665-4669. IEEE.

[27]. Lee, Z., Wang, Y., & Zhou, W. (2011, August). A dynamic priority scheduling algorithm on service request scheduling in cloud computing. In Proceedings of 2011 International Conference on Electronic & Mechanical Engineering and Information Technology, 9, 4665-4669. IEEE.

[28]. Legout, V., Jan, M., & Pautet, L. (2013, October). A scheduling algorithm to reduce the static energy consumption of multiprocessor real-time systems. In Proceedings of the 21st International Conference on Real-Time Networks and Systems, (pp. 99-108).

[29]. Legout, V., Jan, M., & Pautet, L. (2013, October). A scheduling algorithm to reduce the static energy consumption of multiprocessor real-time systems. In Proceedings of the 21st International Conference on Real-Time Networks and Systems, (pp. 99-108).

[30]. Lin, C. C., Shi, J., Ueter, N., Günzel, M., Reineke, J., & Chen, J. J. (2022). Type-aware federated scheduling for typed dag tasks on heterogeneous multicore platforms. IEEE Transactions on Computers, 72(5), 1286-1300.

[31]. Maia, C., Nogueira, L., & Pinho, L. M. (2013, June). Scheduling parallel real-time tasks using a fixed-priority work-stealing algorithm on multiprocessors. In 2013 8th IEEE International Symposium on Industrial Embedded Systems (SIES) (pp. 89-92). IEEE.

[32]. Mehalaine, R., & Boutekkouk, F. (2020). Energy consumption reduction in real time multiprocessor embedded systems with uncertain data. In Artificial Intelligence and Bioinspired Computational Methods: Proceedings of the 9th Computer Science On-line Conference 2020, 2(9), 46-55. Springer International Publishing.

[33]. Nayak, D., Malla, S. K., & Debadarshini, D. (2012). Improved round robin scheduling using dynamic time quantum. International Journal of Computer Applications, 38(5), 34-38.

[34]. Nayak, D., Malla, S. K., & Debadarshini, D. (2012). Improved round robin scheduling using dynamic time quantum. International Journal of Computer Applications, 38(5), 34-38.

[35]. Öztop, H., Fatih Tasgetiren, M., Türsel Eliiyi, D., & Pan, Q. K. (2018). Green permutation flowshop scheduling: A trade-off-between energy consumption and total flow time. In Intelligent Computing Methodologies: 14th International Conference, ICIC 2018, Wuhan, China, Procedings, Part III 14 (pp. 753-759). Springer International Publishing.

[36]. Paul, T., Hossain, R., & Samsuddoha, M. (2019). Improved round robin scheduling algorithm with progressive time quantum. International Journal of Computer Applications, 178(49), 30-36.

[37]. Prakash, K. B. (2020). A critical review on federated cloud consumer perspective of maximum resource utilization for optimal price using EM algorithm. Soft Computing for Problem Solving, 165-175.

[38]. Qin, Y., Zeng, G., Kurachi, R., Li, Y., Matsubara, Y., & Takada, H. (2019). Energy-efficient intra-task DVFS scheduling using linear programming formulation. IEEE Access, 7, 30536-30547.

[39]. Rizvi, N., Ramesh, D., Wang, L., & Basava, A. (2022). A workflow scheduling approach with modified fuzzy adaptive genetic algorithm in IaaS clouds. IEEE Transactions on Services Computing, 16(2), 872-885.

[40]. Sahoo, R. M., & Padhy, S. K. (2020). Improved crow search optimization for multiprocessor task scheduling: A novel approach. In Applications of Robotics in Industry Using Advanced Mechanisms: Proceedings of International Conference on Robotics and Its Industrial Applications 2019 (pp. 1-13). Springer International Publishing.

[41]. Santra, S., Dey, H., Majumdar, S., & Jha, G. S. (2014, July). New simulation toolkit for comparison of scheduling algorithm on cloud computing. In 2014 International Conference on Control, Instrumentation, Communication and Computational Technologies (ICCICCT) (pp. 466-469). IEEE.

[42]. Santra, S., Dey, H., Majumdar, S., & Jha, G. S. (2014, July). New simulation toolkit for comparison of scheduling algorithm on cloud computing. In 2014 International Conference on Control, Instrumentation, Communication and Computational Technologies (ICCICCT) (pp. 466-469). IEEE.

[43]. Singh, A., Goyal, P., & Batra, S. (2010). An optimized round robin scheduling algorithm for CPU scheduling. International Journal on Computer Science and Engineering, 2(07), 2383-2385.

[44]. Song, J., Xie, G., Li, R., & Chen, X. (2017, December). An efficient scheduling algorithm for energy consumption constrained parallel applications on heterogeneous distributed systems. In 2017 IEEE International Symposium on Parallel and Distributed Processing with Applications and 2017 IEEE International Conference on Ubiquito us Computing and Communications (ISPA/IUCC) (pp. 32-39). IEEE.

[45]. Song, J., Xie, G., Li, R., & Chen, X. (2017, December). An efficient scheduling algorithm for energy consumption constrained parallel applications on heterogeneous distributed systems. In 2017 IEEE International Symposium on Parallel and Distributed Processing with Applications and 2017 IEEE International Conference on Ubiquito us Computing and Communications (ISPA/IUCC) (pp. 32-39). IEEE.

[46]. Strohbach, M., Gellersen, H. W., Kortuem, G., & Kray, C. (2004). Cooperative artefacts: Assessing real world situations with embedded technology. In 6th Proceedings of Ubiquitous Computing 2004: Ubiquitous Computing: 6th International Conference, Nottingham, UK (pp. 250-267). Springer Berlin Heidelberg.

[47]. Tang, Q., Zhu, L. H., Lian, J., Zhou, L., & Wei, J. B. (2020). An efficient multi-functional duplication-based scheduling framework for multiprocessor systems. The Journal of Supercomputing, 76, 9142-9167.

[48]. Wei, H., Bao, H., & Ruan, X. (2020). Genetic algorithm-driven discovery of unexpected thermal conductivity enhancement by disorder. Nano Energy, 71, 104619.

[49]. Xie, G., Zeng, G., Xiao, X., Li, R., & Li, K. (2017). Energy-efficient scheduling algorithms for real-time parallel applications on heterogeneous distributed embedded systems. IEEE Transactions on Parallel and Distributed Systems, 28(12), 3426-3442.

i-manager's Journal on Power Systems Engineering (JPS)

Current Issue Vol. 12 Issue 1

Most Read

Most Cited

Volume 11 Issue 3 October - December 2023

Voltage Stability and Power Loss Minimization with STATCOM using Particle Swarm Optimization and Hybrid Firefly Particle Swarm Optimization Algorithms

R. Siva Subramanyam Reddy*

Srikalahasteeswara Institute of Technology (SKIT), Andhra Pradesh, India.

Reddy, R. S. S. (2023). Voltage Stability and Power Loss Minimization with STATCOM using Particle Swarm Optimization and Hybrid Firefly Particle Swarm Optimization Algorithms. i-manager’s Journal on Power Systems Engineering, 11(3), 1-10. https://doi.org/10.26634/jps.11.3.20045

Abstract

References

Full Article (HTML)

Pdf

Load Frequency Control of Multi Area Power System Network with Soft Computing Algorithm Based Controller and Coordinated Strategy of TCSC and Battery Energy Storage

Ch. Naga Sai Kalyan*

Vasireddy Venkatadri Institute of Technology, Andhra Pradesh, India.

Kalyan, Ch. N. S. (2023). Load Frequency Control of Multi Area Power System Network with Soft Computing Algorithm Based Controller and Coordinated Strategy of TCSC and Battery Energy Storage. i-manager’s Journal on Power Systems Engineering, 11(3), 11-21. https://doi.org/10.26634/jps.11.3.20241

Abstract

References

Full Article (HTML)

Pdf

Performance Analysis of Distribution System under Different Loading Conditions using Test Bench

Shekhappa G. Ankaliki * , Sanjeeth P. Aminabhavi**, Manjula Sureban***, Pradeep Vibhuti****

*-**** Department of Electrical & Electronics Engineering, Shri Dharmasthala Manjunatheshwara College of Engineering and Technology, Karnataka, India.

Ankaliki, S. G., Aminabhavi, S. P., Sureban, M., and Vibhuti, P. (2023). Performance Analysis of Distribution System under Different Loading Conditions using Test Bench. i-manager’s Journal on Power Systems Engineering, 11(3), 22-29. https://doi.org/10.26634/jps.11.3.20399

Abstract

References

Full Article (HTML)

Pdf

Simulation of Solar PV with Different Control System of Boost Converter for Composite Climate

Roshni Rahangdale* , Deeksha Sahare**

*-** Department of Electrical & Electronics Engineering, Shri Shankaracharya Group of Institution, Durg, Chhattisgarh, India.

Rahangdale, R., and Sahare, D. (2023). Simulation of Solar PV with Different Control System of Boost Converter for Composite Climate. i-manager’s Journal on Power Systems Engineering, 11(3), 30-36. https://doi.org/10.26634/jps.11.3.20409

Abstract

References

Full Article (HTML)

Pdf

Enhancement of System Performance using PeSche Scheduling Algorithm on Multiprocessors

M. Sreenath* , P. A. Vijaya**

* Infosys Ltd., Bengaluru, India. ** Department of ECE., BNMIT, Bengaluru, Karnataka, India.

Sreenath, M., and Vijaya, P. A. (2023). Enhancement of System Performance using PeSche Scheduling Algorithm on Multiprocessors. i-manager’s Journal on Power Systems Engineering, 11(3), 37-54. https://doi.org/10.26634/jps.11.3.20277

Abstract

References

Full Article (HTML)

Pdf

Shekhappa G. Ankaliki * , Sanjeeth P. Aminabhavi, Manjula Sureban*, Pradeep Vibhuti****

* Infosys Ltd., Bengaluru, India.

** Department of ECE., BNMIT, Bengaluru, Karnataka, India.