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

Current Issue Vol. 18 Issue 1

PV-grid Performance improvement through Integrated Intelligent Water Drop Optimization with Neural Network for Maximum Power Point Tracking
A Digital Healthcare Monitoring System with Real-Time Analysis
Advancements in Smart Meter Design and Integration for Enhanced Energy Management and Efficiency
Electric Vehicles in Modern Transportation: Environmental Impacts, Configurations, and Future Trends – A Review
GTO Technique Based Hybrid Power System Controller Design

Most Cited

Volume 13 Issue 2 October - December 2019

Research Paper

Development of Improved Recursive Newton Type Algorithm to Estimate Power System Harmonics

Pratap Sekhar Puhan* , Pravat Kumar Ray**

* Department of Electrical and Electronics Engineering, Sreenidhi Institute of Science and Technology, Hyderabad, Telegana, India. ** Department of Electrical Engineering, National Institute of Technology, Rourkela, Odisha, India.

Puhan, P. S., & Ray, P. K. (2019). Development of Improved Recursive Newton Type Algorithm to Estimate Power System Harmonics.i-manager’s Journal on Electrical Engineering(JEE), 13(2), 1-10. https://doi.org/10.26634/jee.13.2.15525

Abstract

Estimation of power system parameters for generation of reference signal is one of the most essential requirements for switching action of the converters. This paper explores the measurement of power system harmonic components with a newly developed Recursive Newton Type algorithm. In the developed algorithm, first the parameters will be updated using Jacobian and Co-variance matrices. Implementation of the co-variance matrix in the proposed algorithm results in fast convergence, minimum computational and settling time, better accuracy of tracking the signals etc. The effect of the developed algorithm is observed in a power system signal through simulations. Effectiveness of the developed algorithm is also examined using Arduino micro-controller. A comparative analysis is made between the Newton Type Algorithm (NTA) and Improved Newton type algorithm to prove the effectiveness of the developed one.

References

[1]. Barros, J., & Pérez, E. (2006). Automatic detection and analysis of voltage events in power systems. IEEE Transactions on Instrumentation and Measurement, 55(5), 1487-1493. https://doi.org/10.1109/TIM.2006.881584

[2]. Begovic, M. M., Djuric, P. M., Dunlap, S. (1993). Frequency tracking in power networks in the presence of harmonics. IEEE Trans Power Delivery. 8(10), 480-486. https://doi.org/10.1109/61.216849

[3]. Chang, G. W., & Chen, C. I. (2010). An Accurate Time- Domain Procedure for Harmonics and Interharmonics Detection. IEEE Transactions on Power Delivery, 3(25), 1787- 1795. https:// doi.org/10.1109%2FTPWRD.2009.2037230

[4]. Jain, S. K., & Singh, S. N. (2012). Exact model order ESPRIT technique for harmonics and interharmonics estimation. IEEE Transactions on Instrumentation and Measurement, 61(7), 1915-1923. https://doi.org/10.1109/ TIM.2012.2182709

[5]. Joorabian, M., Mortazavi, S. S., & Khayyami, A. A. (2009). Harmonic estimation in a power system using a novel hybrid Least Squares-Adaline algorithm. Electric Power Systems Research, 79(1), 107-116. https://doi.org/10. 1016/j.epsr.2008.05.021

[6]. Panda, G., Ray, P. K., & Puhan, P. S. (2014). Harmonic estimation of distorted power system signals employing two hybrid strategies. International Journal of Modelling, Identification and Control, 22(1), 20-32. https://doi.org/10. 1504/IJMIC.2014.063873

[7]. Panda, G., Ray, P. K., Puhan, P. S., & Dash, S. K. (2013). Novel schemes used for estimation of power system harmonics and their elimination in a three-phase distribution system. International Journal of Electrical Power & Energy Systems, 53, 842-856. https://doi.org/10.1016/ j.ijepes.2013.05.037

[8]. Puhan, P. S., Sandeep, S. D., & Sutar, D. (2018). Analysis of Adaptive Linear Neural Network (adaline) in Power System Harmonics Signal. i-manager's Journal on Electrical Engineering, 11(4), 35-42. https://doi.org/10.26634/jee. 11.4.14656

[9]. Ray, P. K., & Subudhi, B. (2012). Ensemble-Kalman-filterbased power system harmonic estimation. IEEE Transactions on Instrumentation and Measurement, 61(12), 3216-3224. https://doi.org/10.1109/TIM.2012.2205515

[10]. Ray, P. K., Puhan, P. S., & Panda, G. (2015). Improved recursive Newton Type Algorithm based power system frequency estimation. International Journal of Electrical Power & Energy Systems, 65, 231-237. https://doi.org/10. 1016/j.ijepes.2014.10.017

[11]. Ray, P. K., Puhan, P. S., & Panda, G. (2016). Real time harmonics estimation of distorted power system signal. International Journal of Electrical Power & Energy Systems, 75, 91-98. https://doi.org/10.1016/j.ijepes.2015.08.017

[12]. Reddy, J. B. V., Dash, P. K., Samantaray, R., & Moharana, A. K. (2009). Fast tracking of power quality disturbance signals using an optimized unscented filter. IEEE Transactions on Instrumentation and Measurement, 58(12), 3943-3952. https://doi.org/10.1109/TIM.2009. 2020835

[13]. Seifossadat, S. G., Razzaz, M., Moghaddasian, M., & Monadi, M. (2007). Harmonic estimation in power systems using adaptive perceptrons based on a genetic algorithm. WSEAS Transactions On Power Systems, 11(2), 239-244.

[14]. Tao, C., Shanxu, D., Ting, R., & Fangrui, L. (2010). A robust parametric method for power harmonic estimation based on M-estimators. Measurement, 43(1), 67-77. https://doi.org/10.1016/j.measurement.2009.06.010

[15]. Tsao, T. P., Wu, R. C., & Ning, C. C. (2001). The optimization of spectral analysis for signal harmonics. IEEE Transactions on Power Delivery, 16(2), 149-153. https://doi.org/10.1109/61.915474

[16]. Yilmaz, A. S., Alkan, A., & Asyali, M. H. (2008). Applications of parametric spectral estimation methods on detection of power system harmonics. Electric Power Systems Research, 78(4), 683-693. https://doi.org/10.1016/ j.epsr.2007.05.011

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Research Paper

A New Design for Low Rating Three Phase Squirrel Cage Induction Motor with Improved Performance Operating Under Rated Voltage-A Design Consideration for Rural Areas

Raj Kumar Saini* , Devender Kumar Saini, Rajeev Gupta*, Piush Verma****

* Shoolini University, Solan, Himachal Pradesh, India. -* University of Petroleum and Energy Study, Dehradun, Uttarakhand, India. **** Bahra University, Vaknaghat, Solan,Himachal Prasesh, India.

Saini, R. K., Saini, D. K., Gupta, R., & Verma, P. (2019). A New Design for Low Rating Three Phase Squirrel Cage Induction Motor with Improved Performance Operating Under Rated Voltage-A Design Consideration for Rural Areas. i-manager’s Journal on Electrical Engineering, 13(2), 11-17. https://doi.org/10.26634/jee.13.2.16219

Abstract

This research article deals with modified and ideal plan parameters of 1HP three phase squirrel cage induction motor working under rated voltages. The simulated results are calculated with the assistance of JMAG Express (an electrical machine design software) under rated voltage. Based on the simulated results of stator and rotor design of 1 HP three phase squirrel cage induction motor is casted in the industry. The experimental results of a new 1 HP motor are compared with experimental results of standard motor of the same rating which was designed at rated voltage. The after effects of new planned motor was better when compared with old one. These types of low rating induction motors can be used in the villages for agribusiness and domestic purposes to show signs of improvement effectiveness.

References

[1]. ANSI C84.1- 2011 (n.d). American National Standard for Electric Power Systems and Equipment - Voltage Ratings. (60 Hertz). Retrieved from http://www.powerqualityworld. com/2011/04/ansi-c84-1-voltage-ratings-60-hertz. html

[2]. Anwari, M., & Hiendro, A. (2010). New unbalance factor for estimating performance of a three-phase induction motor with under and over voltage unbalance. IEEE Transactions on Energy Conversion, 25(3), 619-625. https://doi.org/10.1109/TEC.2010.2051548

[3]. Bhushan, B., & Singh, M. (2011). Adaptive control of DC motor using bacterial foraging algorithm. Applied Soft Computing, 11(8), 4913-4920. https://doi.org/10.1016/j. asoc.2011.06.008

[4]. Bollen, M. H. (2000). Understanding power quality problems. In Voltage Sags and Interruptions. IEEE press.

[5]. EN 50160 (2010). Voltage characteristics of electricity supplied by public distribution network. Retrieved from https://infostore.saiglobal.com/preview/98699522296.pdf ?sku=859794_saig_nsai_nsai_2045468

[6]. EN-ICE 61000-2-4 (2002). Electromagnetic Compatibility (EMC) – Part 2–4: Environment compatibility levels in industrial plants for low-frequency conducted disturbances. Retrieved from https://webstore.iec.ch/ publication/4135

[7]. Faiz, J., & Ebrahimpour, H. (2007). Precise derating of three phase induction motors with unbalanced voltages. Energy Conversion and Management, 48(9), 2579-2586. https://doi.org/10.1016/j.enconman.2007.03.023

[8]. Gnaci, P. (2008). Windings temperature and loss of life of an induction machine under voltage unbalance combined with over-or undervoltages. IEEE Transactions on Energy Conversion, 23(2), 363-371. https://doi.org/10. 1109/TEC.2008.918596

[9]. Gnacinski, P. (2008). Effect of unbalanced voltage on windings temperature, operational life and load carrying capacity of induction machine. Energy Conversion and Management, 49(4), 761-770. https://doi.org/10.1016/j. enconman.2007.07.033

[10]. Gnacinski, P. (2009). Derating of an induction machine under voltage unbalance combined with over or undervoltages. Energy Conversion and Management, 50(4), 1101-1107. https://doi.org/10.1016/j.enconman. 2008.12.006

[11]. Gnaciński, P. (2009). Effect of power quality on windings temperature of marine induction motors: Part I: Machine model. Energy Conversion and Management, 50(10), 2463-2476. https://doi.org/10.1016/j.enconman. 2009.05.011

[12]. Gnacinski, P. (2014). Thermal loss of life and loadcarrying capacity of marine induction motors. Energy Conversion and Management , 78, 574-583. https://doi.org/10.1016/j.enconman.2013.11.023

[13]. IEC Std. 60034-1 (2004). Rotating electrical machines. Part 1: Rating and performance. Retrieved from https://webstore.iec.ch/publication/12285

[14]. Kumar, S. R., Kumar, S. D., Rajeev, G., & Piush, V. (2016). Optimized design of three phase squirrel cage induction motor based on maximum efficiency operating under the rated voltage–based on software platform. Indian Journal of Science and Technology, 9, 21. https://doi.org/10.17485/ijst/2016/v9i21/92002

[15]. Lee, S. J., Kim, J. M., An, D. K., & Hong, J. P. (2014). Equivalent circuit considering the harmonics of core loss in the squirrel-cage induction motor for electrical power steering application. IEEE Transactions on Magnetics, 50(11), 1-4. https://doi.org/10.1109/TMAG.2014.2329316

[16]. Mangaraj, B. B., Misra, I. S., & Sanyal, S. K. (2011). Application of bacteria foraging algorithm for the design optimization of multi-objective Yagi-Uda array. International Journal of RF and Microwave Computer- Aided Engineering, 21(1), 25-35. https://doi.org/10.1002/ mmce.20483

[17]. Mendes, A. M. S., Quispe, E. C., Fernández, X. L., & Cardoso, A. M. (2010, September). Influence of the positive sequence voltage on the temperature of threephase induction motors. In XIX International Conference on Electrical Machines-ICEM 2010 (pp. 1-6). IEEE. https://doi. org/10.1109/ICELMACH.2010.5608011

[18]. Muñoz-Galeano, N., Alfonso-Gil, J. C., Orts-Grau, S., Segui-Chilet, S., & Gimeno-Sales, F. J. (2015). Instantaneous approach to IEEE Std. 1459 power terms and quality indices. Electric Power Systems Research, 125, 228- 234. https://doi.org/10.1016/j.epsr.2015.04.012

[19]. NEMA., MG.1-2009 (n.d). Motors and generators. Retrieved from https://webstore.ansi.org/standards/nema/ nemamg2009revision2010

[20]. Quispe, E. C., López-Fernández, X. M., Mendes, A. M. S., Cardoso, A. M., & Palacios, J. A. (2011, May). Experimental study of the effect of positive sequence voltage on the derating of induction motors under voltage unbalance. In 2011 IEEE International Electric Machines and Drives Conference (IEMDC) (pp. 908-912). IEEE. https://doi.org/10.1109/IEMDC.2011.5994936

[21]. Saini, R. K., Saini, D. K., Gupta, R., Dwivedi, R. P., & Verma, P. (2018). Design, analysis, and assessment of efficiency of three-phase squirrel-cage induction motors operating under the rated voltage: A design consideration for rural areas. In Intelligent Communication, Control and Devices (pp. 13-21). Singapore: Springer, https://doi.org/10. 1007/978-981-10-5903-2_2

[22]. Santos, V. S., Felipe, P. R. V., Sarduy, J. R. G., Lemozy, N. A., Jurado, A., & Quispe, E. C. (2014). Procedure for determining induction motor efficiency working under distorted grid voltages. IEEE Transactions on Energy Conversion, 30(1), 331-339. https://doi.org/10.1109/TEC. 2014.2335994

[23]. Subramanian, S., & Padma, S. (2011). Bacterial foraging algorithm based multiobjective optimal design of single phase transformer. Journal of Computer Science and Engineering, 6, 1-6.

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Research Paper

Grid Coupled Solar PV For Energy Feeding by using PSO-BFOA Based Incremental Conductance Method

Ch. Venkateswara Rao* , S. S. Tulsiram , B. Brahmaiah*, CH. Ramya****

* Department of Electrical and Electronics Engineering, Salalah College of Technology, Salalah, Sultanate of Oman. Department of Electrical and Electronics Engineering, G. Narayanamma Institute of Technology and Science, Hyderabad, India. * Department of Electrical and Electronics Engineering, St. Peter’s Engineering College, Hyderabad, India. **** Department of Electrical and Electronics Engineering, AIMS College of Engineering, Andhra Pradesh, India.

Rao, V, CH., Tulsiram, S. S., Brahmaiah, B., & Ramya, CH. (2019). Grid Coupled Solar PV For Energy Feeding by using PSO-BFOA Based Incremental Conductance Method. i-manager’s Journal on Electrical Engineering, 13(2), 18-32. https://doi.org/10.26634/jee.13.2.15918

Abstract

Most of the world's energy sources are come from conventional ones namely fossil fuels such as oil (petrol, diesel, etc.), coal, and of course natural gases. However, the alarming decline in the levels of fossil fuels is a clear sign of its rapid and early extinction. Hence demand for a renewable energy sources increases as it is environment friendly and pollution free, and reduces the greenhouse effect. Introducing renewable energy is one of the solution for the issue, among others PV and wind energy are clean and abundantly available in nature. The advantage of utilizing renewable resources over conventional methods lies in significant reduction in the level of pollution. The cost of conventional energy is mounting and solar energy has evolved to be a latent alternative. Solar energy is abundant, pollution free, distributed throughout the earth and is recyclable. The Photovoltaic (PV) systems output power fluctuates according to the irradiation and temperature (weather conditions). Irregular PV output power results in frequency variations in the power systems, especially when the penetration is high. The power generated through PVs or other dispersed power source has to be connected to conventional power grid. PV power is embedded to the grid at substation level, this helps to extend more reliable, and better grid quality power. The hybrid power system consists of DC-DC converters which offer high dynamic performance and frequency modulation transients, without quantization effects and low-complexity, the key findings of the present task. The main aim of this paper is to design, development of microgrid with Synchronous Reference Frame Control for PV module in MATLAB Simulink environment, and customized for accurate analysis by using PSO-BFOA based Incremental Conductance method to reduce sustained oscillations and fast searching of MPPT, generated FPGA coding for MPPT, and multilevel inverter.

References

[1]. Bhatnagar, P., & Nema, R. K. (2013). Maximum power point tracking control techniques: State-of-the-art in photovoltaic applications. Renewable and Sustainable Energy Reviews, 23, 224-241. https://doi.org/10.1016/j.rser. 2013.02.011

[2]. Bialasiewicz, J. T. (2008). Renewable energy systems with photovoltaic power generators: Operation and modeling. IEEE Transactions on Industrial Electronics, 55(7), 2752-2758. https://doi.org/10.1109/TIE.2008.920583

[3]. Chen, Y. M., Liu, Y. C., Hung, S. C., & Cheng, C. S. (2007). Multi-input inverter for grid-connected hybrid PV/wind power system. IEEE Transactions on Power Electronics, 22(3), 1070-1077. https://doi.org/10.1109/TPEL. 2007.897117

[4]. Chinnaiyan, V. K., Jerome, J., & Karpagam, J. (2013). An experimental investigation on a multilevel inverter for solar energy applications. International Journal of Electrical Power & Energy Systems, 47, 157-167. https://doi.org/10.1016/j.ijepes.2012.10.025

[5]. Das, S., Dasgupta, S., Biswas, A., Abraham, A., & Konar, A. (2009). On stability of the chemotactic dynamics in bacterial-foraging optimization algorithm. IEEE Transactions on Systems, Man, and Cybernetics-Part A: Systems and Humans, 39(3), 670-679. https://doi.org/10. 1109/TSMCA.2008.2011474

[6]. Eltawil, M. A., & Zhao, Z. (2010). Grid-Connected photovoltaic power systems: Technical and potential problems-A review. Renewable and Sustainable Energy Reviews, 14(1), 112-129. https://doi.org/10.1016/j.rser. 2009.07.015

[7]. Faranda, R., & Leva, S. (2008). Energy comparison of MPPT techniques for PV systems. WSEAS Transactions on Power Systems, 3(6), 446-455.

[8]. Grandi, G., Rossi, C., Ostojic, D., & Casadei, D. (2009). A new multilevel conversion structure for grid-connected PV applications. IEEE Transactions on Industrial Electronics, 56(11), 4416-4426. https://doi.org/10.1109/TIE.2009. 2029587

[9]. Haruni, A. M. O., Negnevitsky, M., Haque, M. E., & Gargoom, A. (2013). A novel operation and control strategy for a standalone hybrid renewable power system. IEEE Transactions on Sustainable Energy, 4(2), 402-413. http:// doi.org/10.1109/TDC.2010.5484293

[10]. Hodashinskii, I. A., Zemtsov, N. N., & Meshcheryakov, R. V. (2012). Construction of fuzzy approximators based on the bacterial foraging method. Russian Physics Journal, 55(3), 301-305. https://doi.org/10.1007/s11182-012-9811-8

[11]. Ishaque, K., Salam, Z., Amjad, M., & Mekhilef, S. (2012). An improved particle swarm optimization (PSO)–based MPPT for PV with reduced steady-state oscillation. IEEE Transactions on Power Electronics, 27(8), 3627-3638. https://doi.org/10.1109/TPEL.2012.2185713

[12]. Koutroulis, E., Kalaitzakis, K., & Tzitzilonis, V. (2009). Development of an FPGA-based system for real-time simulation of photovoltaic modules. Microelectronics Journal, 40(7), 1094-1102. https://doi.org/10.1016/j.mejo. 2008.05.014

[13]. Lohan, S. K., Dixit, J., Modasir, S., & Ishaq, M. (2012). Resource potential and scope of utilization of renewable energy in Jammu and Kashmir, India. Renewable Energy, 39(1), 24-29. https://doi.org/10.1016/j.renene.2011.08.033

[14]. Maharjan, L., Yamagishi, T., & Akagi, H. (2010). Activepower control of individual converter cells for a battery energy storage system based on a multilevel cascade PWM converter. IEEE Transactions on Power Electronics, 27(3), 1099-1107.https://doi.org/10.1109/TPEL.2010. 2059045

[15]. Majumder, R., Ghosh, A., Ledwich, G., & Zare, F. (2010). Power management and power flow control with back-to-back converters in a utility connected microgrid. IEEE Transactions on Power Systems, 25(2), 821-834. https://ui.adsabs.harvard.edu/link_gateway/2010ITPSy..25. .821M/doi:10.1109/TPWRS.2009.2034666

[16]. Nejabatkhah, F., Danyali, S., Hosseini, S. H., Sabahi, M., & Niapour, S. M. (2012). Modeling and control of a new three-input DC–DC boost converter for hybrid PV/FC/Battery power system. IEEE Transactions on Power Electronics, 27(5), 2309-2324. http://doi.org/10.1109%2FTPEL.2011. 2172465

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[18]. Rao, V., Tulasiram, S. S., & Brahmaiah, B. (2015). Digital controlled high power synchronous boost converter based MPPT charge controller for SPV system. International Journal of Innovations in Engineering and Technology (IJIET), 5(2), 104-110.

[19]. Rao, V., Tulaisram, S.S., Brahmaiah, B., & Ramya. (2019). Features of PSO – BFOA based increment conductance method with FPGA. i-manager's Journal on Circuits and Systems , 7(1), 14-23. https://doi.org/10.26634/ jcir.7.1.15391

[20]. Saggini, S., Garcea, G., Ghioni, M., & Mattavelli, P. (2005, March). Analysis of high-performance synchronousasynchronous digital control for DC-DC boost converters. In Twentieth Annual IEEE Applied Power Electronics Conference and Exposition, APEC 2005. (Vol. 2, pp. 892- 898). IEEE. https://doi.org/10.1109/APEC.2005.1453090

[21]. Singh, G. K. (2013). Solar power generation by PV (photovoltaic) technology: A review. Energy, 53, 1-13. https://doi.org/10.1016/j.energy.2013.02.057

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Research Paper

Neuro Fuzzy Gain Scheduler for Maximum Power Tracking of Wind Driven DFIG

G. Siva Kumar* , A. Lakshmi Devi**

*-** S. V. University College of Engineering, Tirupati, Andhra Pradesh, India.

Kumar, G. S., & Devi, A. L. (2019). Neuro Fuzzy Gain Scheduler for Maximum Power Tracking of Wind Driven DFIGi-manager’s Journal on Electrical Engineering, 13(2), 33-39. https://doi.org/10.26634/jee.13.2.15790

Abstract

In this paper, a new control strategy to capture most appropriate power from the wind system is presented with the help of Closed Loop Current Control (CLCC) and rotor speed regulation of Doubly Fed Induction Generator (DFIG). Firstly, wind turbine characteristics and reliable power curve are presented. Complex power decoupling control is achieved from the principle of rotor oriented field control. To maximize the power generation of a wind-driven DFIG, New control method is proposed considering the effect of saturation in both main and leakage flux paths. This method can be achieved by applying a vector control techniques with a neuro-fuzzy gain scheduler. The overall DFIG system performance using the proposed neuro-fuzzy gain tuner is compared with the conventional PI controllers.

References

[1]. Lazim, M. T., Al-khishali, M. J., & Al-Shawi, A. I. (2011). Space vector modulation direct torque speed control of induction motor. Procedia Computer Science, 5, 505-512. https://doi.org/10.1016/j.procs.2011.07.065

[2]. Nandakumar, V. N., Yadukumar, K., Sureshkumar, T., Ragupathi, S., & Hegde, R. K. (1993, April). A wind driven self-excited induction generator with terminal voltage controller and protection circuits. In Conference Record of the Power Conversion Conference-Yokohama 1993 (pp. 484-489). IEEE. https://doi.org/10.1109/PCCON.1993. 264207

[3]. Ozturk, S. B. (2008). Direct torque control of permanent magnet synchronous motors with non-sinusoidal back- EMF. (Doctoral dissertation), Texas A&M University, USA.

[4]. Busca, C., Stan, A. I., Stanciu, T., & Stroe, D. I. (2010, July). Control of permanent magnet synchronous generator for large wind turbines. In 2010 IEEE International Symposium on Industrial Electronics (pp. 3871-3876). IEEE. https://doi.org/10.1109/ISIE.2010.5637628

[5]. Huang, K., Huang, S., She, F., Luo, B., & Cai, L. (2008, October). A control strategy for direct-drive permanentmagnet wind-power generator using back-to-back PWM converter. In 2008 International Conference on Electrical Machines and Systems (pp. 2283-2288). IEEE.

[6]. Jurado, F., Caño, A., & Ortega, M. (2003). Neural networks and fuzzy logic in electrical engineering control courses. International Journal of Electrical Engineering Education, 40(1), 1-12. https://doi.org/10.7227%2FIJEEE. 40.1.1

[7]. Poorani, S., Priya, T. U., Kumar, K. U., & Renganarayanan, S. (2005). FPGA based fuzzy logic controller for electric vehicle. Journal of the Institution of Engineers, 45(5), 1-14.

[8]. Jang, J. S. (1993). ANFIS: Adaptive-network-based fuzzy inference system. IEEE Transactions on Systems, Man, and Cybernetics, 23(3), 665-685. https://doi.org/10.1109/21. 256541

[9]. Badoni, M., Singh, A., & Singh, B. (2016). Adaptive neurofuzzy inference system least-mean-square-based control algorithm for DSTATCOM. IEEE Transactions on Industrial Informatics, 12(2), 483-492. https://doi.org/10. 1109/TII.2016.2516823

[10]. Babu, U. R., Reddy, V. V. K., & Kalyani, S. T. (2015). Design of power system stabilizer with Neuro-Fuzzy UPFC controller. World Academy of Science, Engineering and Technology, 8(12), 1945-1948.

[11]. Babu, U. R., Reddy, V. V. K., & Tarakalani, S. (2015). Optimal DSTATCOM placement in radial distribution system using fuzzy-ANFIS. Power Research, 11(2), 305-310. https://doi.org/10.33686/pwj.v11i2.143175

[12]. Menghal, P. M., & Laxmi, A. J. (2013, July). Adaptive Neuro Fuzzy based dynamic simulation of induction motor drives. In 2013 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) (pp. 1-8). IEEE. https://doi.org/10.1109/ FUZZ-IEEE.2013.6622452

[13]. Mohan, S. L., & Bhim, S. (2014). Control strategies for DFIG based grid connected wind energy conversion system, International Journal of Grid Distribution Computing, 7(3), 49-60.

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Review Paper

A Review of the Power Source Used to Operate the Electrical Vehicles

Aditya Sahani* , Himanshu Singh, Anurag Dwivedi*, Nitesh Tiwari****

*-**** Department of Electrical Engineering, KIPM College of Engineering and Technology, GIDA, Gorakhpur, Uttar Pradesh, India.

Sahani, A., Singh, H., Dwivedi, A., & Tiwari, N. (2019). A Review of the Power Source Used to Operate the Electrical Vehicles. i-manager’s Journal on Electrical Engineering, 13(2), 40-49. https://doi.org/10.26634/jee.13.2.15894

Abstract

This paper reviews various types of electric vehicles such as Plugged-in Hybrid Electric Vehicles [PHEVs], Hybrid Electric Vehicles [HEVs] and Electric Vehicles [EVs]. These vehicles are operated by the source of electrical energy having very high torque electric motors. The electrical vehicles consist of various components to generate power. The source of power for these types of electric vehicles to generate power for the operation are batteries, ultra-capacitor, and the fuel cells. Various types of batteries are presently being used as the power source in the electric vehicles. The fuel cells and the ultra-capacitor have various advantages as they are cost effective and better when compared with conventional fuel. This review is very much important for the future study and various ongoing research on this topic.

References

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