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

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PV-grid Performance improvement through Integrated Intelligent Water Drop Optimization with Neural Network for Maximum Power Point Tracking
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Volume 9 Issue 2 October - December 2015

Research Paper

Performance Analysis of Model Reference Adaptive Control Using Lyapunov Approach for A Dynamical System.

Eshita Rastogi* , L. B. Prasad**

* Post Graduate, Department of Electrical Engineering, Madan Mohan Malaviya University of Technology, Gorakhpur, India

** Assistant Professor, Department of Electrical Engineering, Madan Mohan Malaviya University of Technology, Gorakhpur, India.

Rastogi, E., and Prasad, L. B. (2015). Performance Analysis of Model Reference Adaptive Control Using Lyapunov Approach for A Dynamical System. i-manager’s Journal on Electrical Engineering,9(2), 1-7. https://doi.org/10.26634/jee.9.2.3715

Abstract

In this paper, a review on adaptive control scheme and the performance analysis of model reference adaptive control is presented. This paper deals with the application of Model Reference Adaptive Control (MRAC) using Lyapunov stability criteria for a dynamical system. The control scheme is implemented for the first order dynamical system considering different values of adaptation gain. Simulation result and analysis were presented for different values of adaptation gain and reference signals. The tracking error and parameter estimation error were also analyzed

References

[1]. A. S. Morse, (1980). “Global Stability of Parameter Adaptive Control System”, IEEE Trans. Automat Contr., Vol. 25, No.3, pp.433-439.

[2]. S. Sastry, and M. Badson, (2011). Adaptive Control: Stability, Convergence and Robustness, Courier Corporation.,

[3]. H. Kaufman, I. Barakana, and K Sobel, Direct adaptive control algorithm: Theory and application, Springer.

[4]. K. S. Narendra, and A. M. Annaswamy, (2012). Stable Adaptive System, Courier Corporation.

[5]. C. E. Rohrs, L. Valavani, M. Athans, and G. Stein, (1985). “Robustness of continuous-time adaptive control algorithms in the presence of unmodelled dynamics,” IEEE Trans. Automat. Contr., Vol. AC-30, No. 9, pp. 881- 889.

[6]. P. A. Loannou,and P. V. Kokotovic, ( “Instability analysis and the improvement of robustness of adaptive control,” Automaticu, Vol. 20, No. 5.

[7]. P. A. Loannou and K. S. Tsakalis, (1986). “A robust direct adaptive controller,” IEEE Trans. Automat. Contr., Vol. AC- 31, No. 11, pp. 1033-1043.

[8]. M. K. Ciliz, and C Cezayirli, (2006). “Increased transient performance of adaptive control of feedback linearizable system using multi models”, International Journal of Control, Taylor and Francis. Vol 79, No.10.

[9]. P. A. Iaonnou, and J. Sun, (2012). Robust adaptive control, Courier Corporation.

[10]. L. Fan, (2009). “Review of robust feedback control application in power system”, IEEE Power System Conf. and Exposition, pp. 1-7.

[11]. H. Li, Z. Sun, M. Chow, and F. Sun, (2011). “Gain Scheduling based state feedback integral control system for networked control system”, IEEE Trans. on Industrial Electronics, Vol. 58, No. 6, pp 2465-2472.

[12]. F. Piltan, N. Sulaiman, M Rashidi, Z. Tajpeikar and P. Ferdosali, (2011). “Design and implementation of sliding mode algorithm: applied to robotic manipulator- a review”, International Journal of Robotic and Automation, Vol. 2, No. 5, pp. 265-282.

[13]. H. Butler, (1992). Model Reference Adaptive Control: from theory to practice, Prentice Hall. New York, London.

[14]. K. B. Pathak, and D. M. Adhyaru, (2012). “Survey of model reference adaptive control”, International Conf. (NUiCONE), pp. 1-6.

[15]. S. H. Huh, and Z. Bien, (2007). “Robust sliding mode control of a roboti manipulator based on variable structure using model reference adaptive control approach”, IET Control Theory and Application, Vol. 1, No. 5, pp. 1355-1363.

[16]. P.Swarnkar, S. Jain, and R. K. Nema, (2014). “Adaptive control scheme for improving the control system dynamics: a review”, IETE, Vol. 31, No. 1, pp 17-33.

[17]. S. Pankaj, J. S. Kumar, and R. K. Nema, ( “Comparative analysis of MIT and Lyapunov rule in model reference adaptive control scheme”, Innovative System Design and Engineering, Vol. 2, No. 4, pp. 154-162.

[18]. P. C. Parks, (1966). “Lyapunov redesign of Model Reference Adaptive Controller systems,” IEEE trans. Automat. Contr., Vol. 11, No. 3, pp. 362-367.

[19]. P. Swarnkar, S. Jain, and R. K. Nema, (2010). “Effect of adaptation gain on system performance for model reference adaptive control scheme using MIT rule”, World Academy of Science Engineering and Technology, Vol. 40, pp. 621-626.

[20]. P. Swarnkar, S. Jain, and R. K. Nema, ( “Effect of adaptation gain on system performance for model reference adaptive controlled second order system”, ETASR, Engineering, Technology and Applied Science, Vol. 1, No. 3, pp. 70-75.

[21]. C. F. Price, (1970). “An accelerated gradient method for adaptive control,” Proc 9th IEEE on Adaptive Process Decision and Control.

[22]. K. J. Astrom, Bjorn Wittenmark, (2013). Adaptive nd Control, 2 Ed. Pearson Education Asia.

[23]. J. J. Slotine and W.P. Li (1991). “Applied linear control”, Prentice Hall Publication, Second Edition.

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Research Paper

Effect Of Resonance on the Performance Of Single phase Two Winding Self Excited Induction Generator.

M. Rizwan Khan* , M. Faisal Khan**

* Associate Professor, Department of Electrical Engineering, Aligarh Muslim University, Aligarh, India.

** Assistant Professor, Faculty of Engineering & Technology, University Polytechnic, Aligarh Muslim University, Aligarh, India.

Khan, M. R., and Khan, M. F. (2015). Effect of Resonance on the Performance of Single phase Two Winding Self Excited Induction Generator. i-manager’s Journal on Electrical Engineering,9(2), 8-15. https://doi.org/10.26634/jee.9.2.3716

Abstract

Due to an inherently poor voltage regulation, the Self Excited Induction Generators (SEIGs) are incapable of supplying RL loads. One way of improving their voltage regulation is by series capacitance compensation. However, as the steady state load-ability limit of a compensated SEIG is increased, it is more likely to experience resonance due to change in either inductive loading or frequency or both. In this paper, a detailed analysis of resonating behavior of a single phase, two winding SEIG is carried out. The effects of resonance on the performance of SEIG are evaluated through performance characteristics obtained at the loads of different power factors. A steady state model of series compensated single phase, double winding SEIG is developed through MATLAB M file. The optimum excitation and compensation capacitances are selected by performing experimental tests on a 1 phase, double winding, 220/230 V SEIG.

References

[1]. O. Ojo and I. Bhat, (1995), “An analysis of single-phase selfexcited induction generators: Model development and steady state calculations”, IEEE Trans. on Energy Conversion, Vol. 10, No. 2, pp. 254-260.

[3]. R. C. Bansal (2005). “Three-Phase Self-Excited Induction Generators: An Overview”, IEEE Transactions on Energy Conversion, Vol. 20, No. 2, pp. 292-299.

[4] R.C. Bansal, T. S. Bhatti, and D. P. Kothari, (2003). “A bibliographical survey in induction generators for application of non-conventional energy systems,” IEEE Trans. Energy Convers., Vol.18, No.3, pp. 433-439.

[5]. B. Singh, and L. B. Shilpkar, (1999). “Steady-state analysis of singlephase self-excited induction generator”, IEE Proc.-Gener. Transm. Dislrib., Vol. 146, No. 5, pp. 421- 427.

[6]. L. Shridhar, B. Singh, C. S. Jha, B. P. Singh and S. S. Murthy (1995). “Selection of capacitors for the self regulated short shunt self excited induction generator”, IEEE Trans. on Energy Conversion, Vol. 10, No. 1, pp.10-16.

[7]. E. Bim, J. Szajner and Y. Burian, (1989). “Voltage compensation of an induction generator with long-shunt connection”, IEEE Trans. on Energy Conversion, Vol. 4, No. 3, pp. 526-530.

[8]. M. H. Haque, (2009). “Characteristics of Shunt, Short- Shunt and Long-Shunt Single-Phase Induction Generators”, Power & Energy Society General Meeting, PES’09, IEEE, pp.1-7.

[10]. S.S Murthy, (1993). “A novel self-excited selfregulated single phase induction generator part-i: basic system and theory ”, IEEE Transactions on Energy Conversion, Vol. 8, No. 3, pp. 377-382.

[11]. M.Faisal Khan and M. Rizwan Khan, (2014)., "Voltage control of single-phase two winding self excited induction generator for isolated loads," Advances in Energy Conversion Technologies (ICAECT), 2014 International Conference on, pp.209-214.

[12]. D. Joshi, K. Sandhu, M. Soni, ( “Constant voltage constant frequency operation of a self-excited induction generator”, IEEE Transactions on Energy Conversion, Vol. 21, No. 1, pp.228-234.

[13] J. Upendar, G. K. Singh and C. P. Gupta (2010). “Steady state analysis of a wind driven single-phase SEIG using particle swarm optimization”, World Journal of Modelling and Simulation, Vol. 6 No. 4, pp. 310-320.

[14]. S.S.Murthy, Bhim Singh, and Vuddanti Sandeep, (2012). “A novel and comprehensive performance analysis of a single-phase two-winding self-excited induction generator”, IEEE Transactions On Energy Conversion, Vol. 27, No. 1, pp. 117-127.

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Research Paper

Permanent Magnet Synchronous Motor As Traction Motors For Diesel-Electric Traction in India.

C. Nagamani* , R. Somanatham, U. Kusuma Kumari*, U. Chaitanya Kumar****

* Research Scholar, Department of Electrical Engineering, University College of Engineering, Osmania University, Hyderabad, India.

Head, Department of Electrical Engineering, Anurag College of Engineering, Hyderabad, India.

*_**** PG Scholar, Department of Electrical Engineering, Anurag College of Engineering, Hyderabad, India.

Nagamani, C., Somanatham, R., Kumari, U. K., and Kumar, U. C. (2015). Permanent Magnet Synchronous Motor As Traction Motors For Diesel-Electric Traction in India. i-manager’s Journal on Electrical Engineering,9(2), 16-40 https://doi.org/10.26634/jee.9.2.3717

Abstract

The use of Insulated Gate Bi-polar Transistor as the switching device in rectifier and inverters for high power applications like electric traction as it is efficient, reliable, has improved power factor and also robust. Combined with the technology of manufacture of 3-φ Squirrel Cage Induction Motors, the modern diesel-electric locomotives are driven by dieselengine driven alternators. These modern diesel locomotives are called the Electro-motive Diesels. In this paper, a new drive system with Permanent Magnet Synchronous Motors (PMSM) as traction motors is being suggested by the authors as PMSM are robust, more efficient, work on near unity power factor, and have a higher torque/Volume ratio.

References

[1]. Product Brochure. Retrieved from www.dlw. indianrailways.gov.in.

[2]. L. Accardo, M. Fioretto, G. Giannini, and P. Marino (2006). “Techniques of PWM space vector modulation for the reduction of magnetic acoustic noise in Traction Motors”, International Symposium on Power Electronics, Electric Drives, Automation and Motion, SPEEDAM, pp 1084-1089.

[3]. Kondo Minoru, (2003). “Application of Permanent Magnet Synchronous Motor to Driving Railway Vehicles”, Railway Technology Avalanche, No. 1.

[4]. Thomas Finken, Marco Hombitzer, and Kay Hameyer, (2010). “Study and Comparison of several Permanent Magnet excited Rotor Types regarding their Applicability in Electric Vehicles”, Institute of Electrical Machines, Aachen, Germany.

[5]. Ramu Krishnan, (2009). Permanent Magnet Synchronous Motor and Brushless DC Motor Drives, CRC Press.

[6]. Toshiba, (2014). Products Brochure on adjustable Speed Drives, Toshiba, Japan.

[7]. Ashish Nigam and Subhana Siddiqui (2010). “Modelling and Simulation of Five-Phase Permanent Magnet Synchronous Motor”, VSRD Technical and Non- Technical Journal, Vol. 1, No. 2, pp.78-86.

[8]. K. Vinoth Kumar, Prawin Angel Micheal, Joseph P. John, S. Suresh Kumar (July 2010). “Simulation and Comparison of SPWM and SVPWM control for Three-Phase Inverter”, ARPN Journal of Engineering and Applied Sciences, Vol. 5, No. 7, pp. 61-74.

[9]. M. Franko, J. Kuchta, and J. Buday (2012). “Development and Performace Investigation of Permanent Magnet Synchronous Traction motor ”, International Symposium on Power Electronics, Electrical Drives, Automation and Motion. pp. 70-74.

[10]. Singh M.D., and Khanchandani K. B. Text Book of Power Electronics, Second Edition, Tata McGraw-Hill Publishing Company Limited, New Delhi.

[11]. Ned Mohan, Tore M. Undeland, and William P. Robbins. Power Electronics Converters, Application and Design, Third Edition John Wiley India.

[12]. Matthew P. Magill and Phillip T. Krein, (2012). “Examination of Design Strategies for Inverter-Driven Induction Machines”, Power and Energy Conference Illinois (PECI) 2012 IEEE, pp. 1-6.

[13]. B. Gao, G. N. Wu, J. Y. He, and K. G. Lei, (2007). “Investigation on Ageing Mechanism of Winding Insulation used in Inverter-Fed Traction Motors”, Annual Report on Electrical Insulation and Dielectric, pp. 107-111.

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Research Paper

Fault Classification of Induction Motor Bearing Using Statistical Features and Artificial Neural Network.

Raj Kumar Patel* , V. K. Giri**

* Research Scholar, Department of Electrical Engineering, M.M.M. University of Technology, Gorakhpur, U.P, India.

** Professor, Department of Electrical Engineering, M.M.M. University of Technology, Gorakhpur, U.P, India.

Patel, R. K., and Giri, V. K. (2015). Fault Classification of Induction Motor Bearing Using Statistical Features and Artificial Neural Network. i-manager’s Journal on Electrical Engineering,9(2), 41-48. https://doi.org/10.26634/jee.9.2.3718

Abstract

Bearings are one of the critical components in rotating machines and the majority of failure arises from the defective bearings. Bearing failure leads to failure of a machine and unpredicted productivity loss for production facilities. Hence, bearing fault detection and diagnosis is an integral part of the preventive maintenance procedures. In this paper, vibration signals for four conditions of a deep groove ball bearing Normal (N), fault on Inner Race (IR), fault on ball and fault on Outer Race (OR) were acquired from a customized bearing test rig, under no load and full load condition and each load condition with two fault size 0.007 inch and 0.021 inch has been taken. Statistical parameter from the time domain has been used as a feature of vibration signal for the classification purpose. Sensitivity analysis is performed to understand the significance of each input feature on the ANN (Artificial Neural Network) output. The ANN performance has been found to be comparatively higher to those feature which are highly sensitive

References

[1]. Ashwani Kumar Chandel and Raj Kumar Patel (2012). Induction Machine Bearing Fault Diagnosis With Discrete Wavelet Transform Using Vibration Signal. i-manager's Journal on Electrical Engineering, 5(4) Print ISSN 0973- 8835, E-ISSN 2230-7176, pp, 11-17.

[2]. A. Kazzaz, G. et.al, (2003). “Experimental investigations on induction machine condition monitoring and fault diagnosis using digital signal processing techniques,” Electr. Power Syst. Res., Vol. 65, No. 3, pp. 119–136.

[3]. D. K. M. R. B.Hulugappa, and Tajmul Pashab, (2012), “Condition Monitoring of Induction Motor Ball Bearing Using Monitoring Techniques,” International Journal of Scientific and Research Publications, Vol. 2, No. 11, pp. 1–8.

[4]. F. Jiang, W. Li, Z. Wang, and Z. Zhu, (2012). “Fault Severity Estimation of Rotating Machinery Based on Residual Signals,” Adv. Mech. Eng., Vol. 2012, No. pp. 1–8,

[5]. Z. Chen, N. Gao, W. Sun, Q. Chen, F. Yan, X. Zhang, M. Iftikhar, S. Liu, and Z. Ren, (2014). “A Signal Based Triangular Structuring Element for Mathematical Morphological Analysis and Its Application in Rolling Element Bearing Fault Diagnosis,” Shock Vib., pp. 1–16,

[6]. B. Zhang, G. Georgoulas, M. Orchard, A. Saxena, D. Brown, G. Vachtsevanos, and S. Liang, (2008). “Rolling Element Bearing Feature Extraction and Anomaly Detection Based on Vibration Monitoring,” Electr. Eng., pp. 1792–1797,

[7]. V. Muralidharan, V. Sugumaran, and V. Indira, (2014). “Fault diagnosis of monoblock centrifugal pump using SVM,” Eng. Sci. Technol. an Int. J., Vol. 17, No. 3, pp. 1–6.

[8]. J. Zhao, Y. Liu, J. Lou, and C. Hu, (2014). “Research of Mechanical Fault SVM Intelligent Recognition Based on EEMD Sample Entropy.,” Sensors & Transducers (1726- 5479), Vol. 179, No. 9, pp. 141–148,

[9]. B. Samanta, K. R. Al-Balushi, and S. A. Al-Araimi, (2005). “Artificial neural networks and genetic algorithm for bearing fault detection,” Soft Comput., Vol. 10, No. 3, pp. 264–271.

[10]. Y. Y. A and C. Junsheng, (2006). “A roller bearing fault diagnosis method based on EMD energy entropy and ANN,” J. Sound Vib., Vol. 294, pp. 269–277,

[11]. L. Majumder and C. Manohar, (2003). “A timedomain approach for damage detection in beam structures using vibration data with a moving oscillator as an excitation source,” J. Sound Vib., Vol. 268, pp. 699–716.

[12]. B. Jeon, J. Jung, B. Youn, Y. Kim, and Y. Bae, (2014), “Statistical Approach to Diagnostic Rules for Various Malfunctions of Journal Bearing System Using Fisher Discriminant Analysis,” European Conference of the Prognostics and Health Management Society 2014, pp. 1–9.

[13]. H. Khwaja, S. Gupta, and V. Kumar, (2010). “A Statistical Approach for Fault Diagnosis in Electrical Machines,” IETE J. Res., Vol. 56, No. 3, pp. 146–155.

[14]. S. Wu, W. Huang, F. Kong, Q. Wu, F. Zhou, and R. Zhang, (2010). “Extracting Power Transformer Vibration Features by a Time-Scale-Frequency Analysis Method,” J. Electromagn. Anal. Appl., Vol. 2, No. pp. 31–38,

[15]. P. K. Kankar, S. C. Sharma, and S. P. Harsha, (2011). “Fault diagnosis of ball bearings using continuous wavelet transform,” Appl. Soft Comput., Vol. 11, No. 2, pp. 2300–2312.

[16]. B. Sreejith, a. K. K. Verma, and A. Srividya, (2008). “Fault diagnosis of rolling element bearing using timedomain features and neural networks,” IEEE International Conference on ICIIS, No. 1, pp. 6–11.

[17]. H. M. Ertunc, H. Ocak, and C. Aliustaoglu, (2012). “ANN- and ANFIS-based multi-staged decision algorithm for the detection and diagnosis of bearing faults,” Neural Comput. Appl., Vol. 22, No. S1, pp. 435–446.

[18]. P. Jayaswal, S. N. Verma, and A. K. Wadhwani, (2010). “Application of ANN, Fuzzy Logic and Wavelet Transform in machine fault diagnosis using vibration signal analysis,” J. Qual. Maint. Eng., Vol. 16, No. 2, pp. 190–213.

[19]. Y. Lei, Z. He, Y. Zi, and Q. Hu, (2006). “Fault diagnosis of rotating machinery based on a new hybrid clustering algorithm,” Int. J. Adv. Manuf. Technol., Vol. 35, No. 9–10, pp. 968–977.

[20]. Case Western Reserve University, (2011). “Bearing D a t a c e n t e r ” [ o n l i n e ] , R e t r i e v e d f r o m : URL:http://www.eecs.cwru.edu/ laboratory/ bearing/ download. html.

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Review Paper

Parameter Estimation Of Permanent Magnet Synchronous Motor-A Review

P. Ramana* , 0, Surya Kalavathi M*, A. Swathi****

* Associate Professor, GMR Institute of Technology, Rajam, AP, India.

Professor and Principal, VIIT, Duvvada, Visakhapatnam, AP, India.

* Professor, JNTUCE, Hyderabad, AP, India.

**** Assistant Professor, Sarada Institute of Science & Technology & Management (SISTAM), Srikakulam, AP, India.

Ramana, P., Mary, K. A., Kalavathi, M. S., and Swathi, A. (2015). Parameter Estimation Of Permanent Magnet Synchronous Motor-A Review. i-manager’s Journal on Electrical Engineering,9(2), 49-59. https://doi.org/10.26634/jee.9.2.3719

Abstract

Permanent Magnet Synchronous Motor (PMSM) drives are being used increasingly in a wide range of applications, such as machine tools, robotics, aerospace generators, actuators and electric vehicles. It is extensively used in industrial applications due to it's advantageous features such as high efficiency, high torque to inertia ratio, low noise and robustness. In advanced motor control system, an accurate knowledge of motor parameters are essential in order to achieve better performance. Machine parameters are classified into electrical parameters such as resistances of windings and d, q axes inductances of both stator and rotor and mechanical parameters such as angular position, speed, moment of inertia and viscous friction coefficient. Various types of parameter estimation methods for PMSM are available in the literature such as back e.m.f. based method, signal injection based method, state observer based method, Model Reference Adaptive System (MRAS) based method etc. This paper presents a review of all these methods used to estimate the parameters of a Permanent Magnet Synchronous Motor.

References

[1]. Pragasen Pillay and Ramu Krishnan, (1988). “Modeling of Permanent Magnet Motor Drives,” IEEE Transactions on Industrial Electronics, Vol.55, No.4, pp.537-541.

[2]. Pragasen Pillay and Ramu Krishnan, (1989). “Modeling, simulation and analysis of Permanent Magnet Motor Drives, Part-I: The Permanent Magnet Synchronous Motor Drive,” IEEE Transactions on Industry Applications, Vol.25, No.2, pp.265-273.

[3]. A. B. Dehkordi and M. Gole, (2005) “Permanent Magnet Synchronous Machine Model for real time simulation,” International Conference on Power Systems Transients, pp.19-23.

[4]. Rolando P Burgos, Parag Kshirsagar, Alessandro Lidozzi, Fred Wang and Dushan Boroyevich, (2006), “Mathematical Model and Control Design for Sensorless Vector Control of Permanent Magnet Synchronous Machines,” 2006 IEEE Compel Workshop, Rensselaer Polytechnic Institute, New York, USA, pp.76-82.

[5]. Hasni Mourad, Touhami Omar, Ibtiouen Rachid, M. Fadel and Stephane Caux, (2010). “Modeling and Parameter Identification of Synchronous Machine by PWM Excitation Signals,” International Symposium on Power Electronics, Electrical Drives, Automation and Motion, pp. 442-447.

[6]. Yong Feng And Liuping Wang, (2010). “Terminal Sliding Mode Observer Based Parameter Estimation Method For Permanent Magnet Synchronous Motor th Control System,” 11 International Workshop on Variable Structure Systems, pp.184 – 189.

[7]. Y. Feng, J. Zheng, X. Yu and N. V. Truong, (2009). “Hybrid terminal sliding mode observer design method for a permanent-magnet synchronous motor control system,” IEEE Transaction on Industrial Electronics, Vol.56, No.9, pp.3424–3431.

[8]. Cunjian Xia, Xiaocui Wang, Shihua Li And Xisong Chen, (2011). “Improved Integral Sliding Mode Control Methods For Speed Control of PMSM System,” International Journal of Innovative Computing, Information and Control, Vol. 7, No. 4, pp. 1971-1982.

[9]. Lindita Dhamo and Aida Spahiu, (2013). “An Improved Performance of Sensor less PMSM Drive Control with Sliding Mode Observer in Low Speed Operation,” International Journal of Engineering Trends and Technology (IJETT), Vol. 4, No. 5.

[10]. Bao-Jun Wang and Jia-Jun Wang, (2011). “Sliding Mode Control of Surface-mount Permanent Magnet Synchronous Motor based on Error Model with Unknown Load”, Journal of Software, Vol.6, No.5, pp.819-825.

[11]. Babak Nahid-Mobarakeh, Farid Meibody-Tabar and Francois-Michel Sargos, (2004). “Mechanical Sensor Less Control of PMSM with Online Estimation of Stator Resistance,” IEEE Transactions on Industry Applications, Vol. 40, No. 2, pp.457-472.

[12]. Simon D. Wilson, Paul Stewart and Benjamin P. Taylor, (2005). “Methods of Resistance Estimation in Permanent Magnet Synchronous Motors for Real-Time Thermal Management,” IEEE Transactions on Industry Applications, Vol. 25, No. 3 pp.1-10.

[13]. E.S. Boje, J.E. Balda, Rg. Harley and R.C. Beck, (1990). “Time-Domain Identification of Synchronous Machine Parameters from Simple Standstill Tests”, IEEE Transactions on Energy Conversion, Vol. 5, No.1, pp.164- 176.

[14]. Mahmood Ghanbari, Iman Yousefi and Vahid Mossadegh, (2014). “Online Parameter Estimation of Permanent Magnet Synchronous Motor using Orthogonal Projection Algorithm,” Indian Journal of Scientific Research, Vol. 2, No. 1, pp. 32-36.

[15]. Shinji Ichikawa, Mutuwo Tomitat, Shinji Doki and Shigeru Okuma, (2004). “Sensor less Control for all Types of Synchronous Motors Using an on-line Parameter th Identification,” 30 Annual Conference of the IEEE Industrial Electronics Society, Busan, Korea, pp. 975-980.

[16]. Zedong Zheng , Yongdong LI , Maurice Fadel, (2007). “Sensor Less Control of PMSM based on Extended Kalman Filter”, EPE-2007, pp.P1-P8.

[17]. Dariusz Janiszewski, (2010). ”Extended Kalman Filter Based Speed Sensor Less PMSM Control With Load Reconstruction, Kalman Filter,” Vedran Kordic (Ed.), ISBN: 978-953-307-094-0, InTech, pp. 390.

[18]. S.Morimoto, M.Sanada and Y.Takeda, (2006). “Mechanical sensor less drives of IPMSM with online parameter identification,” IEEE Transactions Industry Applications, Vol. 42, No. 5, pp.1241-1248.

[19]. Li Liu, Wenxin Liu and David A. Cartes, (2008). “ Particle Swarm Optimization-Based Parameter Identification Applied Permanent Magnet Synchronous Motors,” Engineering Applications of Ar tificial Intelligence, Elsever, pp.1092-1100.

[20]. Li Liu, Wenxin Liu and David A. Cartes, (2008) ”Permanent Magnet Synchronous Motor Parameter Identification Using Particle Swarm Optimization”, International Journal of Computational Intelligence Research, Vol.4, No.2, pp. 211–218.

[21]. Konrad Urbanski, (2015). “Estimation of back emf for PMSM at Low Speed Range,” Science Journal, Vol. 40, No. 3, pp.564-569.

[22]. Z. Chen, M. Tomita, S. Doki, and S. Okuma, (2003). “An extended electromotive force model for sensor less control of interior permanent magnet synchronous motors,” IEEE Transactions Industrial Electronics, Vol. 50, No.2, pp. 288–295.

[23]. P. Brandstetter and T. Krecek, (2013). “Sensor Less Control of Permanent Magnet Synchronous Motor using Voltage Signal Injection,” Elektronika in Elektrotechnika, Vol. 19, No. 6, pp. 1392-1215.

[24]. Duro Basic, Francois, Malrait and Pierre Rouchon. (2010). “Initial Rotor Position Detection In PMSM Based on th Low Frequency Harmonic Current Injection,” 14 International Power Electronics and Motion Control Conference, pp. T4-1-T4-7.

[25]. P. Brandstetter, P. Rech and P. Simonik, (2010). “Sensor Less Control of Permanent Magnet Synchronous Motor using Luenberger Observer,” PIERS Proceedings, Cambridge, USA. pp. 5-8.

[26]. G. Zhu, A. Kaddouri, L. A. Dessaint and O. Akhrif, (2001). “A nonlinear state observer for the sensor less control of a permanent-magnet ac machine,” IEEE Transactions on Industrial Electronics, Vol.48, No.6, pp.1098–1108.

[27]. Fanglai Zhu, (2007). “The Design of Full- order and Reduced-order Adaptive Observers for Nonlinear Systems,” IEEE International Conference on Control and Automation, pp. 529-534.

[28]. Mutuwo Tomita, Masaru Hasegawa and Keiju Matsui, (2010). “A Design Method of Full-Order Extended Electromotive Force observer for Sensor-less Control of th IPMSM” 11 IEEE International Workshop on Advanced Motion Control, Nagaoka, Japan, pp. 206-209.

[29]. Suman Maiti, Chandan Chakraborty and Sabyasachi Sengupta, (2009). ”Simulation Studies on Model Reference Adaptive Controller based Speed Estimation Technique for the Vector Controlled Permanent Magnet Synchronous Motor Drive,” Simulation Modeling Practice and Theory 17, pp. 585–596.

[30]. Wei Gao, Zhirong Guo, (2013). “Speed Sensor Less Control of PMSM using Model Reference Adaptive System and RBFN,” Journal of Networks, Vol. 8, No. 1, pp. 213- 221.

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[32]. V. Divya Keerthi, J.S.V. Siva Kumar, (2013). ”Model Reference Adaptive Control Based Parameters Estimation of Permanent Magnet Synchronous Motor Drive,” International Journal of Application or Innovation in Engineering & Management (IJAIEM), Vol. 2, No. 8, pp. 267-276.

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

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Volume 9 Issue 2 October - December 2015

Performance Analysis of Model Reference Adaptive Control Using Lyapunov Approach for A Dynamical System.

Eshita Rastogi* , L. B. Prasad**

* Post Graduate, Department of Electrical Engineering, Madan Mohan Malaviya University of Technology, Gorakhpur, India ** Assistant Professor, Department of Electrical Engineering, Madan Mohan Malaviya University of Technology, Gorakhpur, India.

Rastogi, E., and Prasad, L. B. (2015). Performance Analysis of Model Reference Adaptive Control Using Lyapunov Approach for A Dynamical System. i-manager’s Journal on Electrical Engineering,9(2), 1-7. https://doi.org/10.26634/jee.9.2.3715

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Effect Of Resonance on the Performance Of Single phase Two Winding Self Excited Induction Generator.

M. Rizwan Khan* , M. Faisal Khan**

* Associate Professor, Department of Electrical Engineering, Aligarh Muslim University, Aligarh, India. ** Assistant Professor, Faculty of Engineering & Technology, University Polytechnic, Aligarh Muslim University, Aligarh, India.

Khan, M. R., and Khan, M. F. (2015). Effect of Resonance on the Performance of Single phase Two Winding Self Excited Induction Generator. i-manager’s Journal on Electrical Engineering,9(2), 8-15. https://doi.org/10.26634/jee.9.2.3716

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Permanent Magnet Synchronous Motor As Traction Motors For Diesel-Electric Traction in India.

C. Nagamani* , R. Somanatham**, U. Kusuma Kumari***, U. Chaitanya Kumar****

Nagamani, C., Somanatham, R., Kumari, U. K., and Kumar, U. C. (2015). Permanent Magnet Synchronous Motor As Traction Motors For Diesel-Electric Traction in India. i-manager’s Journal on Electrical Engineering,9(2), 16-40 https://doi.org/10.26634/jee.9.2.3717

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Fault Classification of Induction Motor Bearing Using Statistical Features and Artificial Neural Network.

Raj Kumar Patel* , V. K. Giri**

* Research Scholar, Department of Electrical Engineering, M.M.M. University of Technology, Gorakhpur, U.P, India. ** Professor, Department of Electrical Engineering, M.M.M. University of Technology, Gorakhpur, U.P, India.

Patel, R. K., and Giri, V. K. (2015). Fault Classification of Induction Motor Bearing Using Statistical Features and Artificial Neural Network. i-manager’s Journal on Electrical Engineering,9(2), 41-48. https://doi.org/10.26634/jee.9.2.3718

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Parameter Estimation Of Permanent Magnet Synchronous Motor-A Review

P. Ramana* , 0**, Surya Kalavathi M***, A. Swathi****

* Associate Professor, GMR Institute of Technology, Rajam, AP, India. ** Professor and Principal, VIIT, Duvvada, Visakhapatnam, AP, India. *** Professor, JNTUCE, Hyderabad, AP, India. **** Assistant Professor, Sarada Institute of Science & Technology & Management (SISTAM), Srikakulam, AP, India.

Ramana, P., Mary, K. A., Kalavathi, M. S., and Swathi, A. (2015). Parameter Estimation Of Permanent Magnet Synchronous Motor-A Review. i-manager’s Journal on Electrical Engineering,9(2), 49-59. https://doi.org/10.26634/jee.9.2.3719

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* Post Graduate, Department of Electrical Engineering, Madan Mohan Malaviya University of Technology, Gorakhpur, India

** Assistant Professor, Department of Electrical Engineering, Madan Mohan Malaviya University of Technology, Gorakhpur, India.

* Associate Professor, Department of Electrical Engineering, Aligarh Muslim University, Aligarh, India.

** Assistant Professor, Faculty of Engineering & Technology, University Polytechnic, Aligarh Muslim University, Aligarh, India.

C. Nagamani* , R. Somanatham, U. Kusuma Kumari*, U. Chaitanya Kumar****

* Research Scholar, Department of Electrical Engineering, M.M.M. University of Technology, Gorakhpur, U.P, India.

** Professor, Department of Electrical Engineering, M.M.M. University of Technology, Gorakhpur, U.P, India.

P. Ramana* , 0, Surya Kalavathi M*, A. Swathi****

* Associate Professor, GMR Institute of Technology, Rajam, AP, India.

Professor and Principal, VIIT, Duvvada, Visakhapatnam, AP, India.

* Professor, JNTUCE, Hyderabad, AP, India.

**** Assistant Professor, Sarada Institute of Science & Technology & Management (SISTAM), Srikakulam, AP, India.