i-manager Publications

i-manager's Journal on Electrical Engineering (JEE)

Current Issue Vol. 17 Issue 2

Most Cited

Volume 14 Issue 2 October - December 2020

Research Paper

Adaptive PI and Optimized PI Controllers to Enhance Switched Reluctance Motor Performance

Mahmoud Abdallah Attia* , Mohamed Mokhtar **

*-** Department of Electric Power and Machines, Ain Shams University, Cairo, Egypt.

Attia, M. A., and Mokhtar, M. (2020). Adaptive PI and Optimized PI Controllers to Enhance Switched Reluctance Motor Performance. i-manager's Journal on Electrical Engineering, 14(2), 1-8. https://doi.org/10.26634/jee.14.2.17813

Abstract

Switched Reluctance Motor (SRM) suffers from current ripples and speed oscillations due to variations in loading conditions. Most of researches focus on enhancing the performance of SRM through speed and current controllers. This paper proposes two relatively new strategies to enhance the performance of the SRM. First one is optimizing the gain of classical proportional integral (PI) controller using sine-cosine optimization (SCO). The other strategy is to use adaptive PI controller. The proposed techniques are evaluated under several loading conditions with the proposed strategies applied in the current control system. The results deduced the superiority of adaptive controller under all loading conditions presented.

References

[1]. Bian, H., Chen, H., & Wang, R. (2020, November). Modeling and simulation of three-phase 6/4 switched reluctance motor speed control system. In Journal of Physics: Conference Series (Vol. 1684, No. 1). IOP Publishing.

[2]. Hamouda, M., & Számel, L. (2020). Modeling and simulation of switched reluctance machines. In R. E., Araujo & J. Camacho (Eds.), Modelling and Control of Switched Reluctance Machines. Retrieved from https://doi.org/ 10.5772/intechopen.89851

[3]. Hussien, A. M., & Attia, M. A. (2017). Comparison between HS and TLBO to optimize PIA speed controller and current controller for switched reluctance motor. imanager's Journal on Circuits & Systems, 5(1), 1-10. https://doi.org/10.26634/jcir.5.1.13542

[4]. Johan, K. (2002). Control system design (Lecture Notes). University of California, Santa Barbara.

[5]. Kondelaji, M. A. J., Farahani, E. F., & Mirsalim, M. (2020). Performance analysis of a new switched reluctance motor with two sets of embedded permanent magnets. IEEE Transactions on Energy Conversion, 35(2), 818-827.

[6]. Miller, T. J. E. (2001). Electronic Control of Switched Reluctance Machines. NewNes Power Engineering Series, Elsevier.

[7]. Mokhtar, M., Marei, M. I., & El-Sattar, A. A. (2017). An adaptive droop control scheme for DC microgrids integrating sliding mode voltage and current controlled boost converters. IEEE Transactions on Smart Grid, 10(2), 1685-1693.

[8]. Mostafa Elsaied, M., Abdallah Attia, M., Abdelhamed Mostafa, M., & Fouad Mekhamer, S. (2018). Application of different optimization techniques to load frequency control with WECS in a multi-area system. Electric Power Components and Systems, 46(7), 739-756. https://doi.org /10.1080/15325008.2018.1509913

[9]. Sasmal, M., & Mula, D. (2015). Simulation of speed controlled switched reluctance motor drive system using MATLAB Simulink. International Journal of Engineering Research and General Science, 3(3).

[10]. Wang, H. (2014). ECE 900 simulation of switched reluctance motor and control based on MATLAB environment (Doctoral dissertation), University of Alberta.

[11]. Wang, X., Palka, R., & Wardach, M. (2020). Nonlinear digital simulation models of switched reluctance motor drive. Energies, 13(24), 6715-6732. https://doi.org/10.3 390/en13246715

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Research Paper

Automatic Generation Control of Hybrid Two Area Power System using Whale Optimization Algorithm

V. S. R. Pavan Kumar* , Nerella Sameera**

* Department of Electrical and Electronics Engineering, Sir C. R. Reddy College of Engineering, Eluru, Andhra Pradesh, India.

** Department of Information Technology, Sir C. R. Reddy College of Engineering, Eluru, Andhra Pradesh, India.

Neeli, V. S. R. P. K., and Sameera, N. (2020). Automatic Generation Control of Hybrid Two Area Power System using Whale Optimization Algorithm. i-manager's Journal on Electrical Engineering, 14(2), 9-18. https://doi.org/10.26634/jee.14.2.17774

Abstract

The objective of the work is to design a load frequency controller for a two area Thermal-Thermal power system interconnected with a hybrid distributed generation system in area-1. A novel nature inspired meta-heuristic optimization algorithm, called Whale Optimization Algorithm (WOA) is used for obtaining the gain values of Proportional-Integral- Derivative (PID) controller. The dynamic system performance has been studied and the results obtained are compared with other techniques like Particle swarm optimization (PSO), Harmony search (HS), Flower pollination algorithm (FPA). The results demonstrate the robustness of the proposed algorithm in terms of settling time in the profile of frequency deviations. The simulation process is carried out in MATLAB R2010a environment.

References

[1]. Behera, S. P., Biswal, A., Samantray, S. S., Swain, B. (2018a). Hybrid power systems frequency regulation using hybrid PIDF based robust controller design and Differential Evolution (DE) algorithm. In International Conference on Technologies for Smart-City Energy Security and Power (ICSESP). https://doi.org/10.1109/ICSESP.2018.8376698

[2]. Behera, S. P., Biswal, A., Swain, B., & Samantray, S. S. (2018b, March). Hybrid power systems frequency regulation using TID based robust controller design and differential evolution (DE) algorithm. In 2018 Technologies for Smart-City Energy Security and Power (ICSESP) (pp. 1-6). IEEE. https://doi.org/10.1109/ICSESP.2018.8376728

[3]. Bhatti, T. S., Al-Ademi, A. A. F., & Bansal, N. K. (1997). Load frequency control of isolated wind diesel hybrid power systems. Energy Conversion and Management, 38(9), 829-837. https://doi.org/10.1016/S0196-8904(96) 00108-2

[4]. Chatterjee, A., Ghoshal, S. P., & Mukherjee, V. (2012). Solution of combined economic and emission dispatch problems of power systems by an opposition-based harmony search algorithm. International Journal of Electrical Power & Energy Systems, 39(1), 9-20. https://doi.o rg/10.1016/j.ijepes.2011.12.004

[5]. Das, D. C., Roy, A. K., & Sinha, N. (2011a). PSO optimized for wind–solar thermal-diesel hybrid energy generation system: A study. International Journal of Wisdom Based Computing, 1(3), 128–133.

[6]. Das, D. C., Roy, A. K., & Sinha, N. (2011b, December). PSO based frequency controller for wind-solar-diesel hybrid energy generation/energy storage system. In 2011 International Conference on Energy, Automation and Signal (pp. 1-6). IEEE. https://doi.org/10.1109/ICEAS.20 11.6147150

[7]. Das, D. C., Roy, A. K., & Sinha, N. (2012). GA based frequency controller for solar thermal–diesel–wind hybrid energy generation/energy storage system. International Journal of Electrical Power & Energy Systems, 43(1), 262- 279. https://doi.org/10.1016/j.ijepes.2012.05.025

[8]. Das, D. C., Sharma, A., Dema, D., & Modi, A. (2015, November). Performance analysis of solar PV-diesel based autonomous hybrid power system using FFA and CSA optimized controller. In TENCON 2015-2015 IEEE Region 10 Conference (pp. 1-6). IEEE. https://doi.org/10.1109/TEN CON.2015.7373073

[9]. Das, D. C., Sinha, N., & Roy, A. K. (2014a). Automatic generation control of an organic rankine cycle solar– thermal/wind–diesel hybrid energy system. Energy Technology, 2(8), 721-731. https://doi.org/10.1002/ente.2 01402024

[10]. Das, D. C., Sinha, N., & Roy, A. K. (2014b). Small signal stability analysis of dish-Stirling solar thermal based autonomous hybrid energy system. International Journal of Electrical Power & Energy Systems, 63, 485-498. https://doi.org/10.1016/j.ijepes.2014.06.006

[11]. Debnath, M. K., Sinha, S., & Mallick, R. K. (2018). Automatic generation control including solar themal power generation with fuzzy-PID controller with derivative filter. International Journal of Renewable Energy Research (IJRER), 8(1), 26-35.

[12]. El-Amary, N. H., Balbaa, A., Swief, R. A., & Abdel- Salam, T. S. (2018). A reconfigured whale optimization technique (RWOT) for renewable electrical energy optimal scheduling impact on sustainable development applied to Damietta seaport, Egypt. Energies, 11(3), 535. https://doi.org/10.3390/en11030535

[13]. Elazab, O. S., Hasanien, H. M., Elgendy, M. A., & Abdeen, A. M. (2017). Whale optimization algorithm for photovoltaic model identification. The Journal of Engineering, 2017(13), 1906-1911.

[14]. Kumar, R. H., & Ushakumari, S. (2014, March). Biogeography based tuning of PID controllers for Load Frequency Control in microgrid. In 2014 International Conference on Circuits, Power and Computing Technologies [ICCPCT-2014] (pp. 797-802). IEEE. https://d oi.org/10.1109/ICCPCT.2014.7054992

[15]. Lal, D. K., & Barisal, A. K. (2017, August). Load frequency control of AC microgrid interconnected thermal power system. In IOP Conference Series: Materials Science and Engineering (Vol. 225, No. 1, p. 012090). IOP Publishing.

[16]. Lee, D. J., & Wang, L. (2008). Small-signal stability analysis of an autonomous hybrid renewable energy power generation/energy storage system part I: Timedomain simulations. IEEE Transactions on Energy Conversion, 23(1), 311-320. https://doi.org/10.1109/TEC.2 007.914309

[17]. Mirjalili, S., & Lewis, A. (2016). The whale optimization algorithm. Advances in Engineering Software, 95, 51-67. https://doi.org/10.1016/j.advengsoft.2016.01.008

[18]. Mohanty, B., Panda, S., & Hota, P. K. (2014a). Controller parameters tuning of differential evolution algorithm and its application to load frequency control of multi-source power system. International Journal of Electrical Power & Energy Systems, 54, 77-85. https://do i.org/10.1016/j.ijepes.2013.06.029

[19]. Mohanty, S. R., Kishor, N., & Ray, P. K. (2014b). Robust H-infinite loop shaping controller based on hybrid PSO and harmonic search for frequency regulation in hybrid distributed generation system. International Journal of Electrical Power & Energy Systems, 60, 302-316. https://doi.org/10.1016/j.ijepes.2014.03.012

[20]. Pan, I., & Das, S. (2016). Fractional order fuzzy control of hybrid power system with renewable generation using chaotic PSO. ISA Transactions, 62, 19-29. https://doi.org/ 10.1016/j.isatra.2015.03.003

[21]. Pandey, S. K., Mohanty, S. R., & Kishor, N. (2013). A literature survey on load–frequency control for conventional and distribution generation power systems. Renewable and Sustainable Energy Reviews, 25, 318-334. https://doi.o rg/10.1016/j.rser.2013.04.029

[22]. Pandey, S. K., Mohanty, S. R., Kishor, N., & Catalão, J. P. (2014). Frequency regulation in hybrid power systems using particle swarm optimization and linear matrix inequalities based robust controller design. International Journal of Electrical Power & Energy Systems, 63, 887-900. https://doi.org/10.1016/j.ijepes.2014.06.062

[23]. Senjyu, T., Nakaji, T., Uezato, K., & Funabashi, T. (2005). A hybrid power system using alternative energy facilities in isolated island. IEEE Transactions on Energy Conversion, 20(2), 406-414. https://doi.org/10.1109/TEC.2004.837275

[24]. Shankar, G., & Mukherjee, V. (2016). Load frequency control of an autonomous hybrid power system by quasioppositional harmony search algorithm. International Journal of Electrical Power & Energy Systems, 78, 715-734. https://doi.org/10.1016/j.ijepes.2015.11.091

[25]. Vakula, V. S., & Ramesh, N. (2017). Frequency regulation in hybrid power systems. International Journal of Engineering Development and Research, 5(3), 790-803.

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Research Paper

Realtime Data Analysis and Controlling of an Induction Motor using PLCC

Devkinandan Sahu * , Naveen Goel **

*-** Department of Electrical Engineering, Shri Shankaracharya Group of Institutions, Bhilai, Chhattisgarh, India.

Sahu, D., and Goel, N. (2020). Realtime Data Analysis and Controlling of an Induction Motor using PLCC. i-manager's Journal on Electrical Engineering, 14(2), 19-25. https://doi.org/10.26634/jee.14.2.17803

Abstract

This paper presents a technique by which we will get realtime data of an induction motor and controlling its speed using PLCC (Power Line Carrier Communication). The main aim of using power line carrier communication is to utilize the already existing power cable for data transferring and communication. We can apply this system usually in small areas such as house, offices, etc., by which we can control various kinds of system remotely and this will help to make work efficiently through automation. In this project we are controlling the speed of single phase induction motor and collecting its realtime data through PLCC system. In order to control speed of induction motor, TRIAC circuitry is used.

References

[1]. Akarte, V., Punse, N., & Dhanorkar, A. (2014). Power line communication systems. International Journal of Innovative Research in Electrical, Electronics, Instrumentation and Control Engineering, 2(1), 709-713.

[2]. Basha, C. K., Jilani, S. A. K., & Sultana, S. S. (2016). Power line data transmission based remote control with status feedback. International Journal of Advance Technology and Innovative Research, 8(8), 1582-1585.

[3]. Jangir, N. K. (2012). PLCC with sensors and microcontroller networking for power management. International Journal of Computer and Electrical Engineering, 4(2), 223- 226. https://doi.org/10.7763/IJCEE.2012.V4.483

[4]. Palpankar, P. M., Harle, S., Karade, T., & Lekurwale, S. (2015). Speed control of induction motor using TRIAC. International Journal of Industrial Electronics and Electrical Engineering, 3(3), 36-38.

[5]. Patil, N. S. P., Abhilasha, P., & Kumar, N. (2016). RF based data communication between industrials PLC's using KQ330 module. International Journal of Industrial Electronics and Electrical Engineering, Special Issue (2016), 41-44.

[6]. Sathishbaboo, M., Chandru, R., & Vanitha, K. (2017, March). Microcontroller based consumer end energy consumption control from substation using PLCC. In 2017 Third International Conference on Science Technology Engineering & Management (ICONSTEM) (pp. 659-663). IEEE. https://doi.org/10.1109/ICONSTEM.2017.8261405

[7]. Serrao, J., Fakih, A., Khatik, R., Afzal, S., & Ravindra, C. (2012). Transmission of data using power line carrier communication system. International Journal of Electronics Communication and Computer Technology (IJECCT), 2(6), 280-283.

[8]. Sharma, K., Gupta, B., Gupta, I., & Gupta, N. (2016). Speed control of single phase induction motor using TRIAC & reversal of direction. Journal of Emerging Technologies and Innovative Research (JETIR), 3(4), 152-156.

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Research Paper

Optimal Placement of UPFC using Heuristic Techniques

R. Siva Subramanyam Reddy*

Department of Electrical and Electronics Engineering, Srikalahasteeswara Institute of Technology, Srikalahasti, Andhra Pradesh, India.

Reddy, R. S. S. (2020). Optimal Placement of UPFC using Heuristic Techniques. i-manager's Journal on Electrical Engineering, 14(2), 26-33. https://doi.org/10.26634/jee.14.2.17865

Abstract

Present-day power system has highly complex and stressed operating condition owing to insufficient reactive power for meeting the required power demand. This increases the real power and reactive power losses and voltage instability that occurs in a power system. To attain the malleable operation of the power system, FACTS (Flexible Alternating Current Transmission System) devices have been used. Optimal location of FACTS devices influences the performance of the system and majorly affects the line/bus reactive power flows and hence with this effect, the line/bus voltage profiles has been improved and further the power losses are reduced. Genetic Algorithm (GA), Particle Swarm Optimization (PSO) and GAPSO Hybrid methods have been used for identifying the weakest bus/branch for suitable placement of UPFC device to improve the voltage stability. More significant reduction of the power losses in a MATLAB environment have been discussed in this paper.

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]. Ambriz-Perez, H., Acha, E., & Fuerte-Esquivel, C. R. (2000). Advanced SVC models for Newton-Raphson load flow and Newton optimal power flow studies. IEEE Transactions on Power Systems, 15(1), 129-136.

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

[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]. 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 (Vol. 3, pp. 7-pp). IEEE.

[12]. Hingorani, N. G., & Gyugi, L. (2001). Understanding FACTS – Concepts and technology of flexible AC transmission systems. Wiley-IEEE Press.

[13]. Jeong, H. M., Lee, H. S., &. Park, J. H. (2009). Application of parallel particle swarm optimization on power system state estimation. In Proceedings of Transmission & Distribution Conference & Exposition: Asia and Pacific.

[14]. Kundur, P. (1994). Power system stability and control. New York: McGraw Hill.

[15]. Lee, K. Y., & El-Sharkawi, M. A. (2008). 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]. Nabavi-Niaki, A., & Iravani, M. R. (1996). Steady-state and dynamic models of unified power flow controller (UPFC) for power system studies. IEEE Transactions on Power Systems, 11(4), 1937-1943.

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

[21]. 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.

[22]. Rao, S. S. (2019). Engineering optimization: Theory and practice (4th Ed.). John Wiley & Sons.

[23]. Sahoo, A. K., Dash, S. S., & Thyagarajan, T. (2007). Modeling of UPFC and UPFC for power system steady state operation and control. In International Conference on Information and Communication Technology in Electrical Sciences (ICTES 2007).

[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.

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Research Paper

The Study of Load form Effect in Radial Delivery Networks on Loss Allocation

S. Sankar* , Anna Gina Perri**

* Department of Electrical and Electronics Engineering, AMET University, Chennai, Tamil Nadu, India.

** Department of Electronics and Instrumentation Engineering, Erode Sengunthar Engineering College, Erode, Tamil Nadu, India.

Sankar, S., and Kumar, M. N. S. (2020). The Study of Load form Effect in Radial Delivery Networks on Loss Allocation. i-manager's Journal on Electrical Engineering, 14(2), 34-41. https://doi.org/10.26634/jee.14.2.17769

Abstract

This paper discusses the effects of the distribution of energy losses for different load forms in radial distribution networks. Three types of characteristic load are taken into consideration: steady energy, constant current, and steady imbalance load. On the basis of the current flowing through network branches, deficit distribution has been calculated. All fair allocation parameters are considered by the calculation algorithm. The graphs obtained define the topology of the network. Formulas are given in each branch of the network for calculation and loss distribution. It is shown that real power losses are hardly influenced by the type of load and must therefore be considered for loss allocation estimation.

References

[1]. Almabsout, E. A., El-Sehiemy, R. A., An, O. N. U., & Bayat, O. (2020). A hybrid local Search-Genetic algorithm for simultaneous placement of DG units and shunt capacitors in radial distribution systems. IEEE Access, 8, 54465-54481. https://doi.org/10.1109/ACCESS.2020.2981406

[2]. Andrade, J., Ochoa, L. F., & Freitas, W. (2020). Regional-scale allocation of fast charging stations: Travel times and distribution system reinforcements. IET Generation, Transmission & Distribution, 14(19), 4225-4233.

[3]. Daryani, N., Zare, K., & Tohidi, S. (2019). Design for independent and self-adequate microgrids in distribution systems considering optimal allocation of DG units. IET Generation, Transmission & Distribution, 14(5), 728-734. https://doi.org/10.1049/iet-gtd.2019.0379

[4]. Elkadeem, M. R., Abd Elaziz, M., Ullah, Z., Wang, S., & Sharshir, S. W. (2019). Optimal planning of renewable energy-integrated distribution system considering uncertainties. IEEE Access, 7, 164887-164907. https://doi. org/10.1109/ACCESS.2019.2947308

[5]. Elsayed, A. M., Mishref, M. M., & Farrag, S. M. (2019). Optimal allocation and control of fixed and switched capacitor banks on distribution systems using grasshopper optimisation algorithm with power loss sensitivity and rough set theory. IET Generation, Transmission & Distribution, 13(17), 3863-3878. https://doi.org/10.1049/iet-gtd.2018.5 494

[6]. Gholami, M., Abbaspour, A., Fattaheian-Dehkordi, S., Lehtonen, M., Moeini-Aghtaie, M., & Fotuhi, M. (2020). Optimal allocation of PMUs in active distribution network considering reliability of state estimation results. IET Generation, Transmission & Distribution, 14(18), 3641-3651.

[7]. Moghaddam, M. J. H., Kalam, A., Shi, J., Nowdeh, S. A., Gandoman, F. H., & Ahmadi, A. (2020). A new model for reconfiguration and distributed generation allocation in distribution network considering power quality indices and network losses. IEEE Systems Journal, 14(3), 3530-3538. https://doi.org/10.1109/JSYST.2019.2963036

[8]. Muruganantham, B., Selvam, M. M., Gnanadass, R., & Padhy, N. P. (2017, December). Energy loss reduction and load balancing through network reconfiguration in practical LV distribution feeder using GAMS. In 2017 7th International Conference on Power Systems (ICPS) (pp. 509- 513). IEEE. https://doi.org/10.1109/ICPES.2017.8387346

[9]. Nunna, H. K., Sesetti, A., Rathore, A. K., & Doolla, S. (2020). Multiagent-based energy trading platform for energy storage systems in distribution systems with interconnected microgrids. IEEE Transactions on Industry Applications, 56(3), 3207-3217. https://doi.org/10.1109/ TIA.2020.2979782

[10]. Sadeghian, O., Oshnoei, A., Khezri, R., & Hagh, M. T. (2019). Data clustering-based approach for optimal capacitor allocation in distribution systems including wind farms. IET Generation, Transmission & Distribution, 13(15), 3397-3408. https://doi.org/10.1049/iet-gtd.2018.6326

i-manager's Journal on Electrical Engineering (JEE)

Current Issue Vol. 17 Issue 2

Most Read

Most Cited

Volume 14 Issue 2 October - December 2020

Adaptive PI and Optimized PI Controllers to Enhance Switched Reluctance Motor Performance

Mahmoud Abdallah Attia* , Mohamed Mokhtar **

*-** Department of Electric Power and Machines, Ain Shams University, Cairo, Egypt.

Attia, M. A., and Mokhtar, M. (2020). Adaptive PI and Optimized PI Controllers to Enhance Switched Reluctance Motor Performance. i-manager's Journal on Electrical Engineering, 14(2), 1-8. https://doi.org/10.26634/jee.14.2.17813

Abstract

References

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Automatic Generation Control of Hybrid Two Area Power System using Whale Optimization Algorithm

V. S. R. Pavan Kumar* , Nerella Sameera**

* Department of Electrical and Electronics Engineering, Sir C. R. Reddy College of Engineering, Eluru, Andhra Pradesh, India. ** Department of Information Technology, Sir C. R. Reddy College of Engineering, Eluru, Andhra Pradesh, India.

Neeli, V. S. R. P. K., and Sameera, N. (2020). Automatic Generation Control of Hybrid Two Area Power System using Whale Optimization Algorithm. i-manager's Journal on Electrical Engineering, 14(2), 9-18. https://doi.org/10.26634/jee.14.2.17774

Abstract

References

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Realtime Data Analysis and Controlling of an Induction Motor using PLCC

Devkinandan Sahu * , Naveen Goel **

*-** Department of Electrical Engineering, Shri Shankaracharya Group of Institutions, Bhilai, Chhattisgarh, India.

Sahu, D., and Goel, N. (2020). Realtime Data Analysis and Controlling of an Induction Motor using PLCC. i-manager's Journal on Electrical Engineering, 14(2), 19-25. https://doi.org/10.26634/jee.14.2.17803

Abstract

References

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Optimal Placement of UPFC using Heuristic Techniques

R. Siva Subramanyam Reddy*

Department of Electrical and Electronics Engineering, Srikalahasteeswara Institute of Technology, Srikalahasti, Andhra Pradesh, India.

Reddy, R. S. S. (2020). Optimal Placement of UPFC using Heuristic Techniques. i-manager's Journal on Electrical Engineering, 14(2), 26-33. https://doi.org/10.26634/jee.14.2.17865

Abstract

References

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The Study of Load form Effect in Radial Delivery Networks on Loss Allocation

S. Sankar* , Anna Gina Perri**

* Department of Electrical and Electronics Engineering, AMET University, Chennai, Tamil Nadu, India. ** Department of Electronics and Instrumentation Engineering, Erode Sengunthar Engineering College, Erode, Tamil Nadu, India.

Sankar, S., and Kumar, M. N. S. (2020). The Study of Load form Effect in Radial Delivery Networks on Loss Allocation. i-manager's Journal on Electrical Engineering, 14(2), 34-41. https://doi.org/10.26634/jee.14.2.17769

Abstract

References

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* Department of Electrical and Electronics Engineering, Sir C. R. Reddy College of Engineering, Eluru, Andhra Pradesh, India.

** Department of Information Technology, Sir C. R. Reddy College of Engineering, Eluru, Andhra Pradesh, India.

* Department of Electrical and Electronics Engineering, AMET University, Chennai, Tamil Nadu, India.

** Department of Electronics and Instrumentation Engineering, Erode Sengunthar Engineering College, Erode, Tamil Nadu, India.