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i-manager's Journal on Instrumentation and Control Engineering (JIC)

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Neural Network-Based Monitoring of Critical Parameters in Drinking Water for Enhanced Quality Assurance
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Volume 4 Issue 4 August - October 2016

Research Paper

Approximation Signal: An Efficient Approach to Diagnose Induction Motor Faults in Inverter Fed Induction Motor Drives

Khadim Moin Siddiqui* , Kuldeep Sahay, V. K. Giri*

* Teaching-cum Research Scholar, Department of Electrical Engineering, Institute of Engineering & Technology, Lucknow, India.

Professor, Department of Electrical Engineering, Institute of Engineering & Technology, Lucknow, India.

* Director, Government Rajkiya Engineering College, Sonbhadra, Uttar Pradesh, India.

Siddiqui, K.M., Sahay, K., and Giri, V.K. (2016). Approximation Signal: An Efficient Approach to Diagnose Induction Motor Faults in Inverter Fed Induction Motor Drives. i-manager’s Journal on Instrumentation and Control Engineering, 4(4), 1-15. https://doi.org/10.26634/jic.4.4.8228

Abstract

Nowadays, the inverter driven induction motor drives are being popular in the industries for variable speed applications and replacing existing techniques such as DC motors and thyristor bridges. In the present paper, a proposed dynamic simulation model is used to diagnose squirrel cage induction motor faults by wavelet transform's low frequency band approximation signal on the transient condition. The obtained results clearly demonstrate that the developed diagnostic technique may reliably separate healthy and faulty conditions of the motor in the transient conditions. Therefore, averted the motor faults before reaching in the disastrous condition and may save large revenues and unexpected shutdown for industries.

References

[1]. Khadim Moin Siddiqui, Kuldeep Sahay and V.K. Giri, (2015). “Detection of Bearing Fault in Inverter fed Induction th Motor by Transformative Techniques”. Proc. on 12 IEEE India Annual Int. Conf. on Electronics, Energy, Environment, Communication, Computer, Control, (E3-C3), Jamia Millia Islamia, New Delhi, India, pp. 1-6.

[2]. Khadim Moin Siddiqui, Kuldeep Sahay and V.K. Giri, (2015). “Modelling and Detection of Bearing Fault in SPWM Inverter Fed Squirrel Cage Induction Motor Drives”. Proc. on IEEE Int. Conf. on Circuits, Power and Computing Technologies (ICCPCT-2015), Noorul Islam Centre for Higher Education, Kumaracoil, Tamil Nadu, India, pp. 1-9.

[3]. Khadim Moin Siddiqui, Kuldeep Sahay and V.K. Giri, (2016). “Early, Diagnosis of Stator Inter-turn Fault in Inverter Driven Induction Motor by Wavelet Transform”. IEEE First International Conference on Power Electronics, Intelligent Control and Energy Systems (ICPEICES 2016), Delhi Technological University, New Delhi, India, pp. 1-6.

[4]. Khadim Moin Siddiqui, Kuldeep Sahay and V.K. Giri, (2016). “Air-Gap Eccentricity Fault Detection in the Transient Condition for Inverter Driven Induction Motor Drives”. Proc. on IEEE Int. Conf. on Circuits, Power and Computing Technologies (ICCPCT-2016), Noorul Islam Centre for Higher Education, Kumaracoil, Tamil Nadu, India, pp. 1-6.

[5]. Khadim Moin Siddiqui, Kuldeep Sahay and V.K. Giri, (2016). “Early, Diagnosis of Bearing Fault in the Inverter Driven Induction Motor by Wavelet Transform”. Proc. on IEEE Int. Conf. on Circuits, Power and Computing Technologies (ICCPCT-2016), Noorul Islam Centre for Higher Education, Kumaracoil, Tamil Nadu, India, pp. 1-8.

[6]. Khadim Moin Siddiqui, Kuldeep Sahay and V.K. Giri, (2016). “Diagnosis of Airgap Eccentricity Fault in the Inverter Driven Induction Motor Drives by Transformative Techniques”. Elsevier Journal of Perspectives in Science, Vol. 8, pp. 127-131.

[7]. Khadim Moin Siddiqui, Kuldeep Sahay and V.K. Giri, (2016). “Early, Diagnosis of Airgap Eccentricity Fault in the Inverter Driven Induction Motor Drives by Wavelet Transform”. Journal of Electrical Engineering, Vol. 16, No. 2, pp. 90-99.

[8]. Khadim Moin Siddiqui, Kuldeep Sahay and V.K. Giri, (2015). “Rotor Broken Bar Fault Detection in Induction Motor using Transformative Techniques”. Journal of Electrical Engineering, Vol. 15, No. 1, pp. 135-141.

[9]. Khadim Moin Siddiqui, Kuldeep Sahay and V.K. Giri, (2016). “Transient Model and Detection of Stator Inter-Turn Fault in Inverter Driven Induction Motor Drives by Time Domain Technique”. i-manager’s Journal on Electrical Engineering, Vol. 9 ,No. 3, Jan-Mar 2016, Print ISSN 0973- 8835, E-ISSN 2230-7176, pp. 20-31.

[10]. Khadim Moin Siddiqui, Kuldeep Sahay and V.K. Giri, (2015). “Modelling and Detection of Bearing Fault in PWM Inverter Fed Induction Motor Drives”. i-manager's Journal on Electrical Engineering, Vol. 8, No. 1. Jul-Sep 2014, Print ISSN 0973-8835, E-ISSN 2230-7176, pp. 11-24.

[11]. Khadim Moin Siddiqui, Kuldeep Sahay and V.K. Giri, (2014). “Simulation and Transient Analysis of PWM Inverter Fed Squirrel Cage Induction Motor Drives”. i-manager's Journal on Electrical Engineering, Vol. 7, No. 3, Jan-Mar 2014, Print ISSN 0973-8835, E-ISSN 2230-7176, pp. 9-19.

[12]. Khadim Moin Siddiqui, Kuldeep Sahay and V.K. Giri, (2014). “Health Monitoring and Fault Diagnosis in Induction Motor-A Review”. International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering, Vol. 3, No. 1, pp. 6549-6565.

[13]. M.E.H. Benbouzid and G.B. Kliman, (2003). “What Stator Current Processing-Based Technique to use for Induction Motor Faults Diagnosis?”. IEEE Trans. Energy Conversion, Vol. 18, No. 2, pp. 238-244.

[14]. R. Puche-Panadero, M. Pineda-Sanchez, M. Riera- Guasp, J. Roger-Folch, E. Hurtado-Perez and J. Perez- Cruz, (2009). “Improved Resolution of the MCSA Method Via Hilbert Transform, Enabling the Diagnosis of Rotor Asymmetries at Very Low Slip”. IEEE Trans. Energy Conversion, Vol. 24, No. 1, pp. 52-59.

[15]. S.B. Lee, K. Younsi, and G.B. Kliman, (2005). “An On- Line Technique for Monitoring the Insulation Condition of AC Machine Stator Windings”. IEEE Trans. Energy Conversion, Vol. 20, No. 4, pp. 737-745.

[16]. M.A.S.K. Khan, S.T. Radwan and M.A. Rahman, (2007). “Real-Time Implementation of Wavelet Packet Transform-Based Diagnosis and Protection of Three-Phase Induction Motors”. IEEE Trans. Energy Conversion, Vol. 22, No. 3, pp. 647-655.

[17]. M. Dlamini, P.S. Barendse and A. Khan, (2013). “Autonomous Detection of Inter-turn Stator Faults in Induction Motors”. IEEE International Conference on Industrial Technology, pp. 1700-1705.

[18]. L. Frosini, E. Bassi and L. Girometta, (2012). “Detection th of Stator Short Circuits in Inverter-Fed Induction Motors”. 38 IEEE International Conference on Industrial Electronics Society (IECON-2012), pp. 5102-5107.

[19]. O. Duque-Perez, D. Morinigo-Sotelo and M. Perez- Alonso, (2011). “Diagnosis of Induction Motors fed by Supplies with High Harmonic Content using Motor Current Signature Analysis”. IEEE International Conference on Power Engineering, Energy and Electrical Drives (POWERENG-2011), pp.1-6.

[20] I. Daubechies, (1990). “The Wavelet Transform, Time- Frequency Localization and Signal Analysis”. IEEE Transactions on Information Theory, Vol. 36, pp. 961-1005.

[21]. R.R. Schoen and T.G. Habetler, (1997). “Evaluation and Implementation of a System to Eliminate Arbitrary Load Effects in Current-Based Monitoring of Induction Machines”. IEEE Trans. Ind. Appl., Vol. 33, No. 6, pp.1571-1577.

[22]. A.H. Bonnett and G.C. Soukup, (1992). “Cause and Analysis of Stator and Rotor Failures in Three- Phase Squirrel- Cage Induction Motors”. IEEE Transactions on Industry Applications, Vol. 28, No. 4, pp. 921-937.

[23]. Khadim Moin Siddiqui, Kuldeep Sahay and V.K. Giri, (2015). “Diagnosis of Air-gap Eccentrictiy Fault for Inverter Driven Induction Motor Drives in the Transient Condition”. i-manager's Journal on Instrumentation & Control Engineering, Vol. 3, No. 1, Nov-Jan 2015, Print ISSN 2321- 113X, E-ISSN 2321-1148, pp. 30-41.

[24]. Khadim Moin Siddiqui, Kuldeep Sahay and V.K. Giri, (2016). “Diagnosis of Rotor Broken Bar Fault in Inverter Driven Induction Motor Drive by Signal Processing Techniques”. i-manager's Journal on Instrumentation & Control Engineering, Vol. 4, No. 3, May-Jul 2016, Print ISSN 2321- 113X, E-ISSN 2321-1148, pp. 34-45.

[25]. Khadim Moin Siddiqui, Kuldeep Sahay and V.K. Giri, (2015). “Bearing Fault Detection in PWM Inverter Fed Induction Motor Drives by Transformative Techniques”. i-manager's Journal on Electrical Engineering, Vol. 8, No. 4. Apr-Jun 2015, Print ISSN 0973-8835, E-ISSN 2230-7176, pp. 7-18.

[26]. Khadim Moin Siddiqui and V.K. Giri, (2012). “Broken Rotor Bar Fault Detection in Induction Motors using Wavelet Transform”. Int. Conf. Proc., IEEE, Computing, Electronics and Electrical Technologies [ICCEET], Noorul Islam Centre for Higher Education, Kumaracoil, Tamil Nadu India, pp. 1- 6.

[27]. IEEE Motor Reliability Working Group, (1985). “Report Large Motor Reliability Survey in Industrial and Commercial Installations”. IEEE Trans. Ind. Applicat., Vol. 31, No. 6, pp. 853-872.

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Research Paper

Frequency Control of a Hybrid Power System with Wind Energy in Presence of Classical Particle Swarm Optimization tuned PI-Controller

Ch. Durga Prasad* , G.Pavan Kumar**

*-** Assistant Professor, Department of Electrical and Electronics Engineering, SRKREC, Bhimavaram, Andhra Pradesh, India.

Prasad, Ch,D., and Kumar, G.P. (2016). Frequency Control of a Hybrid Power System with Wind Energy in Presence of Classical Particle Swarm Optimization tuned PI-Controller. i-manager’s Journal on Instrumentation and Control Engineering, 4(4), 16-22. https://doi.org/10.26634/jic.4.4.8229

Abstract

In this paper, an Inter Connected Hybrid Power System with Wind Energy in presence of Classical Controllers Tuned by Particle Swarm Optimization is studied. Integration of renewable resources into existing plants will affect the system frequency and hence design of a suitable controller is needed to maintain frequency within limits. In this paper, PSO technique is used to design conventional controllers such as I-controller, and PI- controller. For classical thermal and diesel units, the PSO method will tune the controller parameters in the secondary loop, whereas in case of wind turbine model; the pitch angle is controlled by the same Particle Swarm Optimization algorithm. Results are carried out in Matlab/Simulink software.

References

[1]. Hadi Saadat, (2002). Power System Analysis. Tata McGraw- Hill Edition.

[2]. N. Ibraheem, P. Kumar, D.P. Kothari, (2005). “Recent Philosophies of Automatic Generation Control Strategies in Power Systems”. IEEE Trans. Power Syst., Vol. 20, pp. 346–357.

[3]. D.J. Lee and L. Wang, (2008). “Small-Signal Stability Analysis of an Autonomous Hybrid Renewable Energy Power Generation/Energy Storage System Part I: Time- domain Simulations”. IEEE Trans. Energy Convers., Vol. 23, No. 1, pp. 311–320.

[4]. Cuk Supriyadi Ali Nandar, (2013). “Robust PI Control of Smart Controllable Load for Frequency Stabilization of Micro Grid Power System”. Renewable Energy, Elsevier, Vol. 56, pp. 16-23.

[5]. T.S. Bhatti, A.A.F. Al-Ademi and N.K. Bansal, (1997). “Load Frequency Control of Isolated Wind-Diesel-Micro Hydro Hybrid Power Systems (WDMHPS)”. Energy, Vol. 22, No. 5, pp. 461-470.

[6]. S.K. Pandey, S.R. Mohanty, N. Kishor and J.P.S. Catalao, (2014). “Frequency Regulation in Hybrid Power Systems using Particle Swarm Optimization and Linear Matrix Inequalities based Robust Controller Design”. Electrical Power and Energy Systems, Vol. 63, pp. 887–900.

[7]. R. Durga Kalyan, G. Pavan Kumar and Ch. Durga Prasad, (2014). “Effect of Load Variations on AGC of an Interconnected Hydro-Thermal Power System in Presence of Conventional PI and FL Controllers”. In 2014 IEEE International Conference on Advanced Communication, Control and Computing Technologies (ICACCCT-2014).

[8]. Aditya, (2003). “Design of Load Frequency Controller using Genetic Algorithm for Two Areas Interconnected Power System”. Int J Electric Power Compon. Syst., Vol. 31, pp. 81–94.

[9]. F. Daneshfar, and H. Bevrani, (2010). “Load–Frequency Control: GA-based Multi Agent Reinforcement Learning”. Generat, Transm. Distrib., IET, Vol. 4, No. 1, pp. 13–26.

[10]. Bhatt P, Roy R, and Ghoshal S.P. (2010). “GA/Particle Swarm Intelligence based Optimization of Two Specific Varieties of Controller Devices Applied to Two-Area Multi- Units Automatic Generation Control”. Int. J. Electric Power Energy Syst, Vol. 32, pp. 299–310.

[11]. Sidhartha P, Banaja M, Hota P.K. (2013). “Hybrid BFOA–PSO Algorithm for Automatic Generation Control of Linear and Nonlinear Interconnected Power Systems”. Appl Soft Comput., Vol. 13, pp. 4718–4730.

[12]. Umesh K.R, Rabindra K.S, and Sidhartha P. (2013). “Design and Analysis of Differential Evolution Algorithm based Automatic Generation Control for Interconnected Power System”. Ain Shams Eng J., Vol. 4, pp. 409–421.

[13]. Banaja M, Sidhartha P, and Hota P.K. (2014). “Differential Evolution Algorithm based Automatic Generation Control for Interconnected Power Systems with Nonlinearity”. Alexandria Eng J., Vol. 53, pp. 537–552.

[14]. Rabindra K.S, Sidhartha P, and Umesh K.R. (2013). “DE Optimized Parallel 2- DOF PID Controller for Load Frequency Control of Power System with Governor Dead-band Nonlinearity”. Electr Power Energy Syst, Vol. 49, pp. 19–33.

[15]. Mohanty B, Panda S, and Hota P. (2014). “Controller Parameters Tuning of Differential Evolution Algorithm and its Application to Load Frequency Control of Multi-source Power System”. Int J Electr Power Energy Syst., Vol. 54, pp. 77–85.

[16]. E.S. Ali, and S.M. Abd-Elazim, (2011). “Bacteria Foraging Optimization Algorithm based Load Frequency Controller for Interconnected Power System”. Int. J. Elec. Power Energ. Syst., Vol. 33, pp. 633-638.

[17]. Dash P, Saikia L.C, and Sinha N. (2015). “Comparison of Performances of Several FACTS Devices using Cuckoo Search Algorithm Optimized 2DOF Controllers in Multi-area AGC”. Int J Electr Power Energy Syst., Vol. 65, pp. 316–324.

[18]. Bhatt Praghnesh, Roy Ranjit, and Ghosal S.P. (2011). Comparative Performance Evaluation of SMES-SMES, TCPSSMES & SSSC-SMES Controllers in AGC for a Two-Area Hydrohydro System”. Int J Electric Power Energy Syst, Vol. 33, pp. 1585–1597.

[19]. Ngamroo I, and Kongprawechnon W. (2003). “A Robust Controller Design of SSSC for Stabilization of Frequency Oscillation in the Interconnected Power System”. Electric Power Syst Res., Vol. 67, pp. 161–176.

[20]. C.F. Juang and C.F. Lu, (2006). “Load-Frequency Control by Hybrid Evolutionary Fuzzy PI Controller”. IEEE Proc. Gener. Trans. Distrib., Vol. 153, No. 2, pp. 196–204.

[21]. J. Talaq and F. Al-Basri, (1999). “Adaptive Fuzzy Gain Scheduling for Load Frequency Control”. IEEE Trans. Power Syst., Vol. 14, No. 1, pp. 145–150.

[22]. P. Subbaraj and K. Manickavasagam, (2008). “Automatic Generation Control of Multi-Area Power System using Fuzzy Logic Controller”. Eur. Trans. Electric. Power, Vol. 18, No. 3, pp. 266–280.

[23]. Kuntia S.R, and Panda S. (2012). “Simulation Study for Automatic Generation Control of a Multi Area Power System by ANFIS Approach”. Appl Soft Comput, Vol. 12, pp. 333–341.

[24]. L.C. Saikia, S. Mishra, N. Sinha, and J. Nanda, (2011). “Automatic Generation Control of a Multi Area Hydrothermal System using Reinforced Learning Neural Network Controller”. Int. J. Elec. Power Energ. Syst., Vol.33, No.4, pp.1101-1108.

[25]. R. Farhangi, M. Boroushaki, S. Hosseini, (2011). “Load–Frequency Control of Interconnected Power System using Emotional Learning-based Intelligent Controller”. Int. J. Electr. Power Energy Syst., Vol.36, No.1, pp.76-83.

[26]. H. Bevrani, and P.R. Daneshmand, (2012). “Fuzzy Logic-based Load–Frequency Control Concerning High Penetration of Wind Turbines”. IEEE Syst. J. Vol.6, No.1, pp.13–19.

[27]. Bethala Prasad, Ch. Durga Prasad and G. Pavan Kumar, (2015). “Effect of Load Parameters Variation on AGC of Two Area Thermal Power System in the Presence of Integral and PSO-PID Controllers”. i-manager's Journal on Instrumentation and Control Engineering, Vol.3, No.2, pp.16-23.

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Research Paper

Dynamic Analysis of Vector Controlled Induction Motor Using Artitifcial Intelligent Controller

Prashant Menghal* , Brajpal Singh**

* Associate Professor, Cadets Training Wing (CTW), Military College of Electronics and Mechanical Engineering, Secunderabad, India.

** Assistant Professor, Cadets Training Wing (CTW), Military College of Electronics and Mechanical Engineering, Secunderabad, India.

Menghal, P.M., and Singh, B. (2016). Dynamic Analysis of Vector Controlled Induction Motor Using Artitifcial Intelligent Controller. i-manager’s Journal on Instrumentation and Control Engineering, 4(4), 23-30. https://doi.org/10.26634/jic.4.4.8230

Abstract

In the industrial sector, especially in the field of electric drives & control, induction motors play a vital role. Without proper controlling of the speed, it is virtually impossible to achieve the desired task for a specific application. Vector control has been preferred over scalar controlled induction motor due to its superior dynamic performance. As the PI controller has bounded operating limits and poor transient response, a search for an alternative controller arises. Due to advancement in Artificial Intelligence Technique such Fuzzy Logic (FL), Artificial Neural Network, ANFIS has gained momentum as a controller for nonlinear systems. In this paper, artificial intelligent controller such as FLC and ANN have been designed in a vector controlled induction motor drive. The dynamic modeling and simulation of induction motor drive has been done in MATLAB/SIMULINK. The performance evaluation of vector controlled drive has been presented for no load, positive and negative load as well zero speed conditions. The simulation results are presented with the PI controller and Artificial Intelligent controller such as Fuzzy logic and Neural Network. It proves that ANN based vector controlled induction motor drive have better performance as compared to PI and FLC controllers.

References

[1]. 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”. Int.J. Elect. Engg. Educ., Vol. 36, pp. 163–172.

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

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

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

[5]. P.M. Menghal and A. Jaya Laxmi, (2016). “Modelling, simulation and analysis of induction motor using artificial intelligent controller”. International Journal of Modelling and Simulation, pp. 1-16.

[6]. J.W. Finch, D.J. Atkinson and P.P. Acarnleg, (1991). “Scalar To Vector: General Principles of Modern Induction Motor Control”. Fourth International Conference on Power Electronics and Variable-Speed Drives, pp. 364-369.

[7]. Arunima Dey, Bharti Dwivedi, Bhim Singh, and Dinesh Chandra, (2009). “Vector Controlled Induction Motor Drive using Genetic Algorithms Tuned PI Speed Controller”. Journal of Electrical Power Quality and Utilization, Vol. 15, No. 1, pp. 3-8.

[8]. P. Rama Mohan, T. Brahmananda Reddy, and M. Vijaya Kumar, (2014). “A High - Performance PWM Algorithm for Vector Controlled Induction Motor Drive for Reduced Current Ripple”. Int. Conf. on Recent Advances and Innovations in Engineering (ICRAIE), pp. 1-7.

[9]. Maurício Beltrão de Rossiter Corrêa, Cursino Brandão Jacobina, Edison Roberto Cabral da Silva, and Antonio Marcus Nogueira Lima, (2004). “Vector Control Strategies for Single-Phase Induction Motor Drive Systems”. IEEE Trans. on Industrial Electronics, Vol. 51, No. 5, pp. 1073-1080.

[10]. C. Versèle, O. Deblecker and J. Lobry, (2008). “Implementation of a Vector Control Scheme using dSPACE Material for Teaching Induction Motor Drive and th Parameters Identification”. 18 International Conference on Electrical Machines, pp. 1-6.

[11]. Joao O.P. Pinto, and Bimal K. Bose, (2000). “A Neural- Network-Based Space-Vector PWM Controller for Voltagefed Inverter Induction Motor Drive”. IEEE Trans. on Industry Applications, Vol. 36, No. 6, pp. 1628-1636.

[12]. Dong Hwa Kim, (2007). “GA–PSO based Vector Control of Indirect Three Phase Induction Motor”. Journal of Applied Soft Computing, Vol. 7, pp. 601–611.

[13]. Lulian Birou, Virgil Maier, Sorin Pavel, Calin Rusu, (2010). “Indirect Vector Control of an Induction Motor with Fuzzy-Logic based Speed Controller”. Journal of Advances in Electrical and Computer Engineering, Vol. 10, No. 1, pp. 116-120.

[14]. M.N. Uddin, T.S. Radwan, and M.A. Rahman, (2002). “Performances of fuzzy-logic-based indirect vector control for induction motor drive”. IEEE Trans. on Industry Applications, Vol. 38, No. 5, pp. 1219-1225.

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Research Paper

Designing an optimal controller for Cart Triple Link Inverted Pendulum

Kalyani Sharma* , Vikas Sahu**

* M.E. Scholar, Department of Electronics and Telecommunication Engineering, Shri Shankaracharya Technical Campus, Bhilai, C.G, India.

** Assistant Professor, Department of Electronics and Telecommunication Engineering, Shri Shankaracharya Technical Campus, Bhilai, C.G,

Sharma, K., and Sahu, V. (2016). Designing an optimal controller for Cart Triple Link Inverted Pendulum. i-manager’s Journal on Instrumentation and Control Engineering, 4(4), 1-36. https://doi.org/10.26634/jic.4.4.8231

Abstract

In this paper the main aim is to stabilize the Triple Link Inverted Pendulum (TLIP) system on a cart. The mathematical model and state space dynamics of the TLIP system is discussed. The TLIP system is inherently unstable, so a Linear Quadratic l Regulator Design (with a degree of stability) is presented. The stability, controllability, and observability are investigated. The choice of weighting matrices in LQR is also discussed. Then an Observer-based controller is designed for the TLIP system. The simulation is done with MATLAB environment and a comparative study of the time domain characteristics are shown.

References

[1]. Haiquan Wang, Huaqiang Zhou, Dongyun Wang, and Shengjun Wen, (2013). “Optimization of LQR Controller for Inverted Pendulum System with Artificial Bee Colony Algorithm”. Proceedings of the 2013 International Conference on Advanced Mechatronic Systems, pp.158- 162 Luoyang, China.

[2]. Aribowo A.G. Nazaruddin Y.Y. Joelianto E., and H.Y. Sutarto, (2007). “Stabilization of Rotary Double Inverted Pendulum using Robust Gain Scheduling Control”. SICE Annual Conference 2007, pp. 504-511, Kagawa University, Japan, IEEE.

[3]. Lijuan Zhang, and Yaqing Tu, (2006). “Research of Car Inverted Pendulum Model Based on Lagrange Equation”. Proceedings of the 6^th World Congress on Intelligent Control and Automation, IEEE, pp. 820-824.

[4]. Feizhou Zhang, Weiji Chen, and Chengzhi Shen, (1999). “The Mechanics Research of Triple inverted Pendulum under the control of Anthropopathic Control”. Beijing University Aeronautics & Astronautics Transaction, Vol. 25, No. 4, pp. 151-155.

[5]. Wang Luhao, and Sheng Zhanshi, (2010). “LQR-Fuzzy Control for Double Inverted Pendulum”. International Conference on Digital Manufacturing & Automation, China, pp. 900-903.

[6]. Mukul K. Gupta, Kamal Bansal and Arun Kumar Singh, (2014). “Stabilization of triple link inverted pendulum system based on LQR control technique”. IEEE International Conference on Recent Advances and Innovations in Engineering, pp. 09-11.

[7]. Sucheta Sehgal and Sheela Tiwari, (2012). “LQR Control for Stabilizing Triple Link Inverted Pendulum System”. 2^nd International Conference on Power, Control and Embedded System, pp. 121-125.

[8]. Sandeep Kumar Yadav, Sachin Sharma and Narinder Singh, (2012). “Optimal control of double inverted pendulum using LQR controller”. IJARCS, Vol. 2, No. 2, pp 189-192.

[9]. Ling Wang ,Haoqi Ni, Weifeng Zhou, Panos M. Pardalos, Jiating Fang and Minrui Fei, (2014). “MBPOA based LQR controller and its application to the doubleparallel inverted pendulum system”. Engineering Applications of Artificial Intelligence, Vol. 36, pp. 262-268.

[10]. Mudita Juneja and Sheela Tiwari, (2014). “Reduced order modeling of triple link inverted pendulum using particle swarm optimization algorithm”. International Journal of Advancements in Electronics and Electrical Engineering (IJAEEE), Vol. 3, No. 3.

[11]. C.J Zhang, C.Y. Bai, Y.Y. Ding and Q. Zhang, (2012). “LQR Optimal Control of Triple Inverted Pendulum based on Fuzzy quotient space theory ”. IEEE International Conference on Granular Computing, pp. 633-638.

[12]. K. Furut, T. Ochiai, and N. Ono, (1984). “Attitude Control of a triple invertedpendulum”. International Journal of Control, Vo1. 39, No. 6, pp. 1351-1365.

[13]. Olfa Boubaker, (2012). “The inverted Pendulum: A fundamental Benchmark in Control Theory and Robotics”. International Conference on Education and E-Learning Innovations, pp. 1-6.

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Research Paper

Detection and Control of Saturation Level in the Magnetic Core of a Welding Transformer by ANN Detector and PI Controller

S. Rama Subbanna* , Suryakalavarthi, M**

* Research Scholar, Jawaharlal Nehru Technological University, Hyderabad, India.

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

Subbanna, S. R., and Suryakalavathi, M. (2016). Detection And Control Of Saturation Level In The Magnetic Core of a Welding Transformer by ANN Detector and PI Controller. i-manager’s Journal on Instrumentation and Control Engineering, 4(4), 37-44. https://doi.org/10.26634/jic.4.4.8232

Abstract

In the paper, an Artificial Intelligence is proposed for detecting and controlling the magnetization level in magnetic core of a Welding Transformer, which is a part of a Middle-Frequency Direct Current (MFDC) Resistance Spot Welding System (RSWS). It consists of an input rectifier, which produces a DC bus voltage, an inverter, a welding transformer, and a fullwave rectifier that is mounted on the output of a transformer. During normal RSWS operation, welding transformer's magnetic core can become saturated due to the unbalanced resistances of both transformer secondary windings and different characteristics of output rectifier diodes, which causes current spikes and over-current protection switch-off of the entire system. In order to prevent saturation of the transformer magnetic core, the RSWS control must detect that the magnetic core is approaching the saturated region and maintains the saturation within optimal limits. The aim of this paper is to present a reliable method for detection and control of magnetic core saturation. Here, an Artificial Neural Network (ANN) is proposed for detecting the current spikes in the primary current and PI controller is used to maintain the saturation within optimal limits. This is achieved by a combined closed-loop control of the welding current and iron core saturation level.

References

[1]. K. Deželak, J. Pihler, G. Štumberger, B. Klopcic and 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.

[2]. 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.

[3]. 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.

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Volume 4 Issue 4 August - October 2016

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Detection and Control of Saturation Level in the Magnetic Core of a Welding Transformer by ANN Detector and PI Controller

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Khadim Moin Siddiqui* , Kuldeep Sahay, V. K. Giri*

* Associate Professor, Cadets Training Wing (CTW), Military College of Electronics and Mechanical Engineering, Secunderabad, India.

** Assistant Professor, Cadets Training Wing (CTW), Military College of Electronics and Mechanical Engineering, Secunderabad, India.

* M.E. Scholar, Department of Electronics and Telecommunication Engineering, Shri Shankaracharya Technical Campus, Bhilai, C.G, India.

** Assistant Professor, Department of Electronics and Telecommunication Engineering, Shri Shankaracharya Technical Campus, Bhilai, C.G,

* Research Scholar, Jawaharlal Nehru Technological University, Hyderabad, India.

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