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Design and Analysis of Improved Mountain Gazelle Optimization Tuned PID and FOPID Controllers for PV MPPT System
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Volume 7 Issue 1 July - September 2013

Research Paper

A Multi-Cell Three-Level Five-phase Voltage Source Inverter

ATIF IQBAL* , Ihab Al-Jayyousi, Abdulhadi Al- Qahtani, Ebrahim Al-Agi, Mohammad Ali Ahmed

----* Department of Electrical Engineering, Qatar University, Doha, Qatar.

Al-Jayyousi, I.,Al-Qahtani, A., Al-Agi, E., Ahmed, M. A., and Iqbal, A. (2013). A Multi-Cell Three-Level Five-Phase Voltage Source Inverter. i-manager’s Journal on Electrical Engineering, 7(1),1-8. https://doi.org/10.26634/jee.7.1.2425

Abstract

The paper presents a modular multi-cell three-level five-phase voltage source inverter. The topology is an extension of a single-phase H-bridge inverter. Positive and negative voltage levels are obtained by operating two switches at one time while zero voltage level is obtained by shorting the load side. The source side shorting is avoided in order to protect the source. The major advantage is multi-level output without capacitor voltage or neutral voltage balancing in contrast to the more commonly used neutral point clamped type and flying capacitor type. The major disadvantage from the industrial application point of view is the requirement of five isolated dc sources. Mathematical model is derived and presented. Modulation is obtained using simple phase disposition and level shifted sinusoidal carrier based scheme. A five-phase induction machine is considered as load and excitation and loading transient is presented. The simulation model considered the effect of dead-band in the switching signals. Further the effect of change in DC link voltages of different cells are also investigated and reported. The simulation and experimental results are shown.

References

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[2]. S. Kouro, M. Malinowski, K. Gopakumar, L. G. Franquelo, J. Pou, J. Rodriguez, B. Wu, M. A. Perez and J. I. Leon,(2010) “ Recent Advances and Industrial Applications of Multilevel Converters,” IEEE Trans. Ind. Electron., Vol. 57, No. 8.

[3]. L. G. Franquelo, J. Rodriguez, J. I. Leon, S. Kouro, R. Portillo and M. M. Prats, (2008) “The age of multilevel converters arrives,” IEEE Trans. Ind. Electron. Magazine, Vol. 2, No. 2, pp. 28–39.

[4]. J. Rodrigues, S. Bernet, J. O. Pontt and S. Kouro, (2007). "Multilevel voltage sources converter topology for industrial medium voltage drives", IEEE Trans. Industrial Electronics, Vol.54, No. 6, pp. 2930- 2945.

[5]. S. Ho-Dong, P. Min-Young , P. Jong-Hyoung, K. Heung- Geun, C. Tae-Won, N. Eui-Cheol,(2011) “Novel H-bridge th multi-level inverter with Dclink switches”, 8 IEEE Int. conf. on Power Electronics and ECCE Asia (ICPE & ECCE), pp. 1734-1741.

[6]. F.P. Espino-cortes, P. Gomez, J.D.B. Ramirez, (2011). “Modeling of heat generated on stress grading coatings of motors fed by multilevel drives”, IEEE Trans. On Dielectrics and Electrical Insulation, Vol. 18, Issue. 4, pp. 1328-1333.

[7]. S. Kamel, S. Mark, A. Greg,(2012) “Sensorless control of induction motors using multi-level converters” IET Power Electronics, Vol. 5, Issue. 2, pp. 269-279

[8]. J. Ewanchuk, J. Salmon, A.M. Knight, “Performance of a High-Speed Motor Drive System Using a Novel Multilevel Inverter Topology” IEEE Trans. On Industry Applications, Vol. 45, Issue. 5, pp. 1706-1714.

[9]. E. Levi, R. Bojoi, F. Profumo, H.A. Toliyat and S. Williamson, (2007) “Multiphase induction motor drives-A technology status review”, IET Elect. Power Appl. Vol. 1, No. 4, pp. 489-516.

[10]. E. Levi, (2008) “Multi-phase Machines for variable speed applications”, IEEE Trans. On Ind. Elect. Vol. 55, No. 5, pp. 1893-1909.

[11]. O. Dordevic, M. Jones, and E. Levi, (2011) “A comparison of PWM techniques for three-level five-phase voltage source inverters”, Proc. 14th European Conf. on Power Elect. And Applications (EPE-2011), pp. 1-10.

[12]. O. Dordevic, M. Jones, and E. Levi, (2012). “A comparison of carrier-based and space vector PWM techniques for three-level five-phase voltage source inverters”, IEEE Trans. On Ind. Informatics, (accepted for publication in 2012).

[13]. Liliang Gao and John E. Fletcher, (2010). “A Space Vector Switching Strategy for Three-Level Five-Phase Inverter Drives”, IEEE Trans. On Industrial Electronics, Vol. 57, No. 7, pp. 2332-2343, July 2010.

[14]. O. Dordevic, E.Levi, M. Jones, (2013). “A Vector Space Decomposition Based Space Vector PWM Algorithm for a Three-Level Seven-Phase Voltage Source Inverter”, IEEE Trans. On Power Elect., Vol. 28, No. 2, pp. 637-649, 2013.

[15]. Q. Song, X. Zhang, F. Yu, and C. Zhang, ( “Research on space vector PWM of five-phase three-level inverter”, Proc. 8th ICEMS, Sep. 27– 29, 2005, Vol. 2, pp. 1418–1421.

[16]. O. Lopez, J. Alvarez, J. Doval-Gandoy, and F. D. Freijedo, (2008). “Multilevel multiphase space vector PWM algorithm”, IEEE Trans. Ind. Electron., Vol. 55, No. 5, pp. 1933–1942, May 2008.

[17]. G. Mahmoud, M. Masoud, I. El-Arabawy, ( “Inverter Faults In Variable Voltage Variable Frequency Induction Motor Drive” Compatibility in Power Electronics, 2007. CPE '07, pp. 1-6.

[18]. C. Choi, W. Lee, (2012). “Design and evaluation of voltage measurement-based sectoral diagnosis method for inverter open switch faults of permanent magnet synchronous motor drives” IET Electric Power Applications, Vol. 6, issue 8, pp. 526-532, 2012

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Research Paper

Palmprint Recognition Using 2-D Wavelet, Ridgelet, Curvelet and Contourlet

Hatem Elaydi* , Mohammed Alhanjouri, Mohanad A. M. Abukmeil*

* Associate Professor, Department of Electrical Engineering, Islamic University-Gaza, Palestine.

Associate Professor of AI & DSP, Director of Excellence and E-learning Center / Academic Affairs,IUG.

* ICT System Engineer, Department of Electrical Engineering-Communication Field, Islamic University-Gaza, Palestine.

Elaydi, H., Alhanjouri, M., and Abukmeil, M. A. M. (2013). Palmprint Recognition Using 2-D Wavelet, Ridgelet, Curvelet And Contourlet. i-manager’s Journal on Electrical Engineering, 7(1), 9-19. https://doi.org/10.26634/jee.7.1.2426

Abstract

Palmprint recognition is a promising biometric field which is used for forensic and commercial applications. This paper provides a comparative palmprint recognition approach using multi-scale transforms: 2D wavelets, ridgelets, curvelets, and contourlets for feature extraction phase, 2-D Principal Component Analysis (2-D PCA) for dimensionality reduction and artificial neural network for recognition phase. Finally, a comparative analysis has been done. The algorithms have been tested using PolyU hyperspectral palmprint database. The recognition rate accuracy was very good and is listed in this order curvelets, contourlets, ridgelets, and 2D discrete wavelets where the curvelets outperformed the others.

References

[1]. D. Zhang, Wai-Kin Kong, J. You and Michael Wong, (2003). "Online palmprint identification," on Pattern Analysis and Machine Intelligence, vol. 25, pp. 1041-1050, Sept. .

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[3]. A. Morales, M. Ferrer and A. Kumar, (2010). "Improved palmprint authentication using contactless imaging," 4th IEEE International Conference on Biometrics: Theory Applications and Systems (BTAS), pp. 1-6, Sep..

[4]. A. Kong, D. Zhang and M. Kamel, (2009). "A survey of palmprint recognition," Journal of Pattern Recognition, vol. 42, pp. 1408-1418, July..

[5]. Jiwen Lu, Erhu Zhang, Xiaobin Kang and Yanxue Xue, ( 2 0 0 6 ) . " Pa lmp r i n t r e c o g n i t i o n u s i n g w a v e l e t decomposition and 2D principal component analysis, "International conference on Communications, Circuits and Systems Proceedings, vol. 3, pp. 2133-2136, June. .

[6]. H. Masood, M. Asim, M. Mumtaz and A. Mansoor, (2009). "Combined contourlet and non-subsampled contourlet transforms based approach for personal identification using palmprint," Digital Image Computing: Techniques and Applications, DICTA '09, pp.408-415, Dec.

[7]. M. Sharkas, I. El-Rube and M.A. Mostafa, (2010). "The Contourlet transform with the principal component analysis for palmprint recognition," International C o n f e r e n c e o n C o m p u t a t i o n a l I n t e l l i g e n c e, Communication Systems and Networks (CICSyN), pp. 262-267, July .

[8]. H.B. Kekre, R. Vig and S. Bisani, (2012). "Identification of multi-spectral palmprints using energy compaction by hybrid wavelet," International Conference on Biometric (ICB), pp. 433-438, March.

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[11]. Samuel Foucher, Goz´e Bertin B´eni´e and Jean- Marc Boucher, (2001). "Multiscale map filtering of sar images," IEEE Transactions on Image Processing, vol. 10, no.1, 49-60, January.

[12]. E.J. Candues, Ridgelets: (1998). Theory and Applications, Ph.D. thesis, Department of Statistics, Stanford University.

[13]. E. J. Candues and D. L. Donoho, (1999). "Ridgelets: a key to higher- dimensional intermittency?" Phil. Trans. R. Soc. Lond. A. pp. 2495-2509.

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[18]. J. Starck, E. Candes and D. Donoho, (2002). "The curvelet transform for image denoising," IEEE Transactions on Image Processing, 11, pp. 670–684.

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[26]. Department of Computing, The Hong Kong Polytechnic University (PolyU), Hyperspectra Palmprint database, Polyu, accessed on Aug. 22, 2013, available at:http://www4.comp.polyu.edu.hk/~biometrics/Hypers pectral Palmprint/HSP.htm.

[27]. Zhenhua Guo, Lei Zhang, and David Zhang, ( "Feature band selection for multispectral palmprint 2010). recognition," International Conference on Pattern Recognition, pp. 1136-1139 .

[28]. Curvelet.org, accessed on July 14, 2013, available at: http://www.curvelet.org/.

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Research Paper

Harmonic Analysis Of Induction Motor Drive Using Neural Network

Prashant Menghal* , A. Jaya Laxmi**

* Faculty of Degree Engineering, Military College of Electronics & Mechanical Engineering, Secunderabad, &Research Scholar, EEE Dept., JNTU, Andhra Pradesh, India.

** Professor, Department of EEE, Jawaharlal Nehru Technological University, College of Engineering, Andhra Pradesh, India.

Menghal, P. M., and Laxmi, A. J., (2013). Harmonic Analysis Of Induction Motor Drive Using Neural Network. i-manager’s Journal on Electrical Engineering, 7(1), 20-28. https://doi.org/10.26634/jee.7.1.2427

Abstract

In Industries, because of its good self-starting capability, simple and rugged structure, low cost and reliability etc. more than 85 % of the motors are Induction Motors. By varying the stator frequency over a wide range, 3-phase squirrel cage Induction Motors can be used in variable frequency IGBT Inverter fed space vector control method to control the harmonics. The IGBT fed space vector control drive systems are widely preferred in Industries because of wide application of induction motors in industry, commercial and residential, and they constitute the largest component of the load. Due to harmonics, the power system gets polluted and creates hurdle to the operation of induction motors. Therefore, the effects of harmonics on the induction motors draws attention of many researchers. Recently, there is an increasing recognition of neural network based technology to meet the requirement of more efficient and compact variable speed drive systems for both AC and DC motor applications. Harmonic analysis of inverter fed induction motor drive using neural network is simulated in MATLAB/SIMULINK.

References

[1]. Tze Fun Chan., Keli Shi (2011). Applied Intelligent Control of Induction Motor Drives. IEEE Willey Press.

[2]. P.C. Krause (2000). Analysis of Electrical Machinery and Drives System. IEEE Willey Press.

[3]. Ned Mohan (2001). Advanced Electric Drives: Analysis, Control Modeling using Simulink. MNPERE Publication.

[4]. K.L . Shi, T . F. Chan, Y. K. Wong and S. L .HO (1999). Modeling and simulation of the three phase induction motor Using SIMULINK. IEEE Educ, 36, 163–172. doi. 10.1109/IEMDC.1997.604326.

[5]. P.M Menghal, and A.S Sindekar (2003). Harmonic Analysis for Some prominent PWM Strategies in Inverters. IE(I) Journal EL,84, 79 -85.

[6]. P M Menghal, A.Jaya Laxmi (2012). Artificial Intelligence Based Dynamic Simulation of Induction Motor Drives. IOSR Journal of Electrical and Electronics Engineering (IOSR-JEEE), 3(5),37-45.doi: 10.9790/1676- 0353745

[7]. P. M. Menghal, A. Jaya Laxmi (2013). Adaptive Neuro Fuzzy based Dynamic simulation of Induction Motor Drives. IEEE Int. conf. Fuzzy Systems, 1-8.

[8]. P. M. Menghal, A Jaya Laxmi (2013). Neural Network based Dynamic Simulation of Induction Motor Drives. IEEE International Conference. Power, Energy & Control,566- 571.doi :10.1109/ICPEC.2013.6527722.

[9]. P. M .Menghal,A Jaya Laxmi (2012). Adaptive Neuro Fuzzy Interference (ANFIS) based simulation of Induction motor drive. Int. Review on Modeling and Simulation (IRMOS), 5(5), 2007-2016.

[10]. M. Nasir Uddin, Muhammad Hafeez (2012). FLCBased DTC Scheme to Improve the Dynamic Performance of an IM Drive. IEEE Trans. Ind. Appl, 823- 831.doi: 10.1109/TIA.2011.2181287.

[11] . M. Nasir Uddin, Hao Wen (2007). Development of a Self-Tuned Neuro-Fuzzy Controller for Induction Motor D r i v e s. I E E E Tr a n s I n d . A p p , 1 1 0 8 - 1 1 1 6 . d o i : 10.1109/TIA.2007.900472.

[12] . M Nasir Uddin, Tawfik S. Radwan et al. (2002). Performance of Fuzzy logic based indirect vector control for induction motor drive, IEEE Trans. Ind. Appl, 38(5),1219- 1225.doi: 10.1109/TIA.2002.802990.

[13] . Besir Dandil, Muammer Gokbulut Fikrat Ata (2005). A PI Type Fuzzy –Neural Controller for Induction Motor Drives, Journal of Appl. Sci., 5(7), 1286.1291.doi: 10.3923/jas.2005.

[14]. Rajesh Kumar, R. A. Gupta Rajesh S. Surjuse (2009). Adaptive Neuro-Fuzzy Speed Controller for Vector Controlled Induction Motor Drive. Asian Power Electro. Journal,3(1), 8-14. doi:14.79e41505757a2a8cab.

[15]. Mouloud Azzedine Denai and Sid Ahmed Attia (2002). Fuzzy and Neural Control of an Induction Motor, International Journal on Applied Mathematics J. Appl. M a t h . C o m p u t e r. S c i e n c e , 1 2 ( 2 ) , 8 - 1 4 . doi:10.1.1.135.303.

[16]. Bimal K. Bose (2007). Neural Network Applications in Power Electronics and Motor Drives -An Introduction and Perspective. IEEE Trans. Industrial.Electronics,54(1),14- 33.doi: 10.1109/TIE.2006.888683.

[17]. Uddin, M.N.,Huang, Z.R. et al (2007). A Simplified Self-Tuned Neuro-Fuzzy Controller Based Speed Control of an Induction Motor Drive. IEEE Power Engineering Society General Meeting,1-8.doi: 10.1109/PES.2007.385720.

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Research Paper

Mathematical Modeling of Permanent Magnet Synchronous Generator used in Wind Turbines

Anbarasi Jebaselvi* , S.Paramasivam**

* Faculty of Electronics and Control Engineering, Sathyabama University, Rajiv Gandhi Salai, Jeppiaar Nagar, Chennai, Tamil Nadu,India.

** Head, Research and Development, Engineering Services Division, ESAB INDIA LIMITED, Sriperumpudur Taluk, Kanchipuram District, Chennai, Tamil Nadu, India.

Jebaselvi, G. D. A., and Paramasivam, S. (2013). Mathematical Modeling Of Permanent Magnet Synchronous Generator Used In Wind Turbines. i-manager’s Journal on Electrical Engineering, 7(1), 29-35. https://doi.org/10.26634/jee.7.1.2428

Abstract

The urgent need of electricity everywhere and anywhere prompt everyone to search for it with more benefits and profits. Simultaneously the same search should end up with good choice of energy production because the conventional source of getting electricity tends to reduce vigorously now a days. Sooner or later the options go for renewable sources by which less pollution encounters while more production dominates. Here is the discussion about the analysis of permanent magnet synchronous generator used in small scale wind generators with its suitable modeling in Simulink /Matlab.

References

[1]. Model Validation of Wind Turbine Generator, A dissertation report by National Renewable Energy Laboratory, A CIEE Report.

[2]. C.V. Nayar, S.M.Islam 'Power Electronics for Renewable Energy Sources'

[3]. Alejandro Rolan, Gustavo Azevedo (2009), “Modeling of a Variable Speed Wind Turbine with a Permanent Magnet Synchronous Generator”, IEEE International Symposium on Industrial Electronics (ISlE 2009), Seoul Olympic Parktel, Seoul, Korea.

[4]. S. Vijayalakshmi et al (2011), 'Modeling and control of a Wind Turbine using Permanent Magnet Synchronous Generator', International Journal of Engineering Science and Technology, ISSN: 0975-5462, Volume 3, Issue 3, March Edition.

[5]. Jan Vergauwe, André Martinez, Alberto Ribas, 'Optimization of a Wind Turbine using Permanent Magnet Synchronous Generator (PMSG)”, KAHO St Lieven Gebroeders Desmetstraat 1 9000 GENT – Belgium, EIGSI La Rochelle 26, rue de Vaux de Foletier – 17041 LA ROCHELLE

[6]. Emna Mahersi et al (2013), International Journal of Renewable Energy Research, Vol.3, No.1.

[7]. Ahmed M. Hemeida, Wael A. Farag, and Osama A. Mahgoub (2011). 'Modeling and Control of Direct Driven PMS for Ultra Large Wind Turbines', World Academy of Science, Engineering and Technology,59

[8]. Zhonghui Zhang, Jiao Shu (2010). 'Matlab-based Permanent Magnet Synchronous Motor Vector Control Simulation', 978-1-4244-5539-3/10/$26.00 © IEEE

[9]. Mayouf. Messaoud, Rachid. Abdessamed (2011). Modeling and Optimization of Wind Turbine Driving Permanent Magnet Synchronous Generator, Jordan Journal of Mechanical and Industrial Engineering, Volume 5, Number 6, ISSN 1995-6665, Pages 489 - 494.

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Research Paper

Detection of Saturation Level in the Magnetic Core of a Welding Transformer by Artificial Neural Network Detector

S. Rama Subbanna* , Suryakalavarthi, M**

* Department of Electrical and Electronics Engineering, St. Martins Engineering College, JNTU Hyderabad.

** Department of Electrical and Electronics Engineering, Jawaharlal Nehru Technological University, Hyderabad.

Subbanna, S. R.,and Suryakalavarthi, M. (2013). Detection Of Saturation Level In The Magnetic Core Of A Welding Transformer By Artificial Neural Network Detector. i-manager’s Journal on Electrical Engineering, 7(1), 36-40. https://doi.org/10.26634/jee.7.1.2429

Abstract

This paper aims to develop an Artificial Neural Network based controller to detect the saturation level in the magnetic core of a welding transformer. The magnetization level detector is a substantial component of a Middle-Frequency Direct Current (MFDC) Resistance Spot Welding System (RSWS). The basic circuit of a resistance spot welding system consists of an input rectifier, an inverter, a welding transformer, and a full-wave rectifier which is mounted on the output of the welding transformer. The presence of unbalanced resistances of the transformer secondary windings and the difference in characteristics of output rectifier diodes can cause the transformers magnetic core to become saturated. This produces spikes in the primary current and finally leads to the over-current protection switch-off of the entire system. To prevent the occurrence of such a phenomena, the welding system control must detect that the magnetic core is approaching saturation. In this paper,an Artificial Neural Network is designed whose only input is the primary current of the welding transformer. The proposed ANN is based on the dynamic model of the Resistance Spot Welding System. Before the ANN can be applied, its structure must be defined and an appropriate learning method must be adopted for its training. The ANN implemented in this paper is a three layered ANN and is trained using the resilient back-propagation algorithm. The ANN is trained so as to recognize the waveform of the current spikes in the primary current caused by the magnetic core saturation, which is then used for magnetization level detection.

References

[1]. K. Deželak, J. Pihler, G. Štumberger, B. Klopcicand D. Dolinar, (2010). “Artificial Neural Network Applied for Detection of Magnetization Level in the Magnetic Core of a Welding Transformer,” IEEE Transactions on Magnetics, Vol. 46, No. 2, pp. 634–637, February.

[2]. B. M. Brown, (1987) “A comparison of AC and DC resistance welding of automotive steels,” Welding J., Vol. 66, No. 1, pp. 18-23, Jan.

[3]. W. Li, E. Feng, D. Cerjanec, and G. A. Grzazinski, (2004). “A comparison of AC and DC resistance welding of automotive steels,” in Sheet Metal Welding Conf. XI, Sterling Heights, MI, May.

[4]. Miyachi Electronic Company, Chiba-ken, Japan, (2000). “Method and apparatus for controlling resistance welding,” U.S. Patent 6 011 235, Jan.

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[6]. W. Li, D. Cerjanec, and G. A. Grzadzinski, (2005). “A comparative study of single AC and multiphase DC resistance spot welding,” ASME Trans. J. Manuf. Sci. Eng., Vol. 127, No. 3,pp. 583-889.

[7]. A.Canova, G. Gruosso, and B. Vusini, (2007). “Electromagnetic modelling of resistance spot welding system,” in ISEF 2007, XIII Int. Symp. Electromagnetic Fields in Mechatronics, Czech Republic.

[8]. B. Klopcic, G. Štumberger, and D. Dolinar, (2007). “Iron core saturation of a welding transformer in a medium frequency resistance spot welding system caused by the asymmetric output rectifier characteristics,” in Conf. Rec. IAS 2007 Annu. Meeting, New Orleans, LA, Sep. 2007, pp. 2319–2326.

[9]. K. Deželak, B. Klopcic, G. Štumberger and D. Dolinar, (2008). “Detecting saturation level in the iron core of a welding transformer in a resistance spot-welding system,” J. Magn. Magn. Mater. Vol. 320, No. 20, pp. 878–883, Oct.

[10]. K. Deželak, G. Štumberger, B. Klopcic, D. Dolinar, and J. Pihler, (2008). “Iron core saturation detector supplemented by an artificial neural network,” Prz. Elektrotech., Vol. 84, No. 12, pp. 157–159.

[11]. B. Klopcic, D. Dolinar, and G. Štumberger, (2008). “Advanced control of a resistance spot welding system,” IEEE Trans. Power Electron., Vol. 23, No. 1, pp. 144–152, Jan.

[12]. D. J. Ramboz, (1996). “Machinable Rogowski coil, design, and calibration,” IEEE Trans. Instrum. Meas., Vol. 45, No. 2, Apr.

[13]. J. Pihler, B.Grcar, and D. Dolinar, “Improved operation of power transformer protection using artificial neural network,” IEEE Trans. PowerDel., Vol. 12, No. 3, pp. 1128–1136.

[14]. M. El-Banna, D. Filev, and R. B. Chinnam, “Online qualitative nugget classification by using a linear vector quantization neural network for resistance spot welding,” Int. J. Adv. Manuf. Technol., Vol. 36, No. 2–4, pp. 237–248.

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Research Paper

Control of Active and Reactive Power Flow in Multiple Lines through UPFC&HPFC

Rammurthy* , Satheesh Babu**

*IEEE Student Member, Department of EEE, Madanapalle Institute of Technology and Science, Andhra Pradesh, India.

** Assistant Professor, Madanapalle Institute of Technology and Science, Andhra Pradesh, India.

Ramamurthy, B. M., and Babu, K. V. S. (2013). Control Of Active And Reactive Power Flow In Multiple Lines Through UPFC And HPFC. i-manager’s Journal on Electrical Engineering, 7(1), 41-48. https://doi.org/10.26634/jee.7.1.2430

Abstract

This paper is based on the practical contingency condition of a transmission line, over which a study using HPFC is done without sacrificing the available generation capacity. The Hybrid Power Flow Controller (HPFC) is the most versatile and complex power electronic equipment that has emerged for the control and optimization of power flow in electrical power transmission system. Installing the HPFC makes it possible to control an amount of real and reactive power flow through the line. This paper presents the control of power flow through multiple overloaded/under loaded lines under study. Simulation is carried out using MATLAB software to overcome the contingency condition by using HPFC.

References

[1]. N.G. Hingorani and L. Gyugyi, (1999). “Understanding FACTS: concepts and technology of flexible ac transmission systems”, IEEE Press, NY.

[2]. Y.H. Song and A.T. Johns, (1999). “Flexible ac transmission systems (FACTS)”, The Institute of Electrical Engineers, London.

[3]. M. Noroozian, L. Angquist, M. Ghandhari, and G. Andersson, (1997). “Use of UPFC for optimal power flow control”, IEEE Trans. on Power Delivery, Vol. 12, No. 4, pp. 1629-1634.

[4]. L. Gyugyi, “Dynamic compensation of ac transmission line by solid-state synchronous voltage sources”, IEEE Trans. Power Delivery, Vol. 9, pp. 904-911, Apr.

[5]. R. Mihali et.al, “Improvement of Transient Stability Using Unified Power Flow Controller”, IEEE Transactions on Power Delivery. Vol.11,No1. January.

[6]. A. Edris, A.S. Mehraban, L. Gyugyi, S. Arabi, T. Reitman, (1998). "Controlling the Flow of Real and Reactive Power ", IEEE Computer Applications in Power, pp. 20-25, January.

[7]. L.Gyugyi, C.D. Schauder, S.L. Williams, T.R. Reitman, D. R. Torgerson, A. Edris, (1995). "The Unified Power Flow Controller: A New Approach to Power Transmission Control,” IEEE Trans. on Power Delivery, Volume 10, No. 2, pp. 1085-1097, April .

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[9]. Farhangfar, A.; Sajjadi, S.J.; Afsharnia, S.; (2004). “Power flow control and loss minimization with Unified Power Flow Controller (UPFC)”, IEEE Conference on Electrical and Computer Engineering, 2004, Page(s): 385 - 388 Vol.1.

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

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Volume 7 Issue 1 July - September 2013

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Harmonic Analysis Of Induction Motor Drive Using Neural Network

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* Faculty of Degree Engineering, Military College of Electronics & Mechanical Engineering, Secunderabad, &Research Scholar, EEE Dept., JNTU, Andhra Pradesh, India. ** Professor, Department of EEE, Jawaharlal Nehru Technological University, College of Engineering, Andhra Pradesh, India.

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Mathematical Modeling of Permanent Magnet Synchronous Generator used in Wind Turbines

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* Faculty of Electronics and Control Engineering, Sathyabama University, Rajiv Gandhi Salai, Jeppiaar Nagar, Chennai, Tamil Nadu,India. ** Head, Research and Development, Engineering Services Division, ESAB INDIA LIMITED, Sriperumpudur Taluk, Kanchipuram District, Chennai, Tamil Nadu, India.

Jebaselvi, G. D. A., and Paramasivam, S. (2013). Mathematical Modeling Of Permanent Magnet Synchronous Generator Used In Wind Turbines. i-manager’s Journal on Electrical Engineering, 7(1), 29-35. https://doi.org/10.26634/jee.7.1.2428

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Detection of Saturation Level in the Magnetic Core of a Welding Transformer by Artificial Neural Network Detector

S. Rama Subbanna* , Suryakalavarthi, M**

* Department of Electrical and Electronics Engineering, St. Martins Engineering College, JNTU Hyderabad. ** Department of Electrical and Electronics Engineering, Jawaharlal Nehru Technological University, Hyderabad.

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Control of Active and Reactive Power Flow in Multiple Lines through UPFC&HPFC

Rammurthy* , Satheesh Babu**

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ATIF IQBAL* , Ihab Al-Jayyousi, Abdulhadi Al- Qahtani, Ebrahim Al-Agi, Mohammad Ali Ahmed

----* Department of Electrical Engineering, Qatar University, Doha, Qatar.

Hatem Elaydi* , Mohammed Alhanjouri, Mohanad A. M. Abukmeil*

* Faculty of Degree Engineering, Military College of Electronics & Mechanical Engineering, Secunderabad, &Research Scholar, EEE Dept., JNTU, Andhra Pradesh, India.

** Professor, Department of EEE, Jawaharlal Nehru Technological University, College of Engineering, Andhra Pradesh, India.

* Faculty of Electronics and Control Engineering, Sathyabama University, Rajiv Gandhi Salai, Jeppiaar Nagar, Chennai, Tamil Nadu,India.

** Head, Research and Development, Engineering Services Division, ESAB INDIA LIMITED, Sriperumpudur Taluk, Kanchipuram District, Chennai, Tamil Nadu, India.

* Department of Electrical and Electronics Engineering, St. Martins Engineering College, JNTU Hyderabad.

** Department of Electrical and Electronics Engineering, Jawaharlal Nehru Technological University, Hyderabad.

*IEEE Student Member, Department of EEE, Madanapalle Institute of Technology and Science, Andhra Pradesh, India.

** Assistant Professor, Madanapalle Institute of Technology and Science, Andhra Pradesh, India.