iosr-JEEE   Volume-11 ~ Issue-1 ~ Jan-Feb 2016

Abstract:Automatic generation control or AGC system is substantial control process that operates perpetually to balance the generation and load in power system at minimum cost. If any kind of mismatch occurs between generation and demand causes the variation in the system frequency from its nominal value. This high frequency deviation may cause system breakdown. In order to maintain the stability, a very fast, reliable and accurate controller is needed to maintain the system frequency within the range. This paper presents the particle swarm optimization (PSO) technique to tune the Fuzzy based PID controller gains for the automatic generation control (AGC) of the interconnected two area power system. Each control area includes the dynamics response of the power systems. The results reported in this paper present the potency of the particle swarm optimizer in the tuning of the Fuzzy based PID controller parameter for two area power system. The improvement in the dynamic response of the power system is verified. The output response of the proposed work is compared with PSO-PID controller.

Keywords: Automatic Generation Control, Particle Swarm Optimization, Fuzzy PID controller

[1]. https://en.wikipedia.org/wiki/Wireless_power
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[5]. http://ijcee.org/papers/480-N015.pdf

Abstract:A fault tree analysis can be simply described as an analytical technique, whereby an undesired state of the system is specified (usually a state that is critical from a safety standpoint), and the system is then analyzed in the context of its environment and operation to find all credible ways in which the undesired event can occur. The fault tree itself is a graphic model of the various parallel and sequential combinations of faults that will result in the occurrence of the predefined undesired event. The faults can be events that are associated with component hardware failures, human errors, or any other pertinent events which can lead to the undesired event. A fault tree thus depicts the logical interrelationship of basic events that lead to the undesired event-which is the top event of the fault tree. Here I try to construct the Fault Tree of different rotating electrical machines also try to reduce this Fault Tree using Boolean algebra. Keywords: Faults in Faults in rotating electrical machines; Fault tree construction; Reduction of fault tree using Boolean algebra.

[1] Fault Tree Handbook, Date Published: January 1981, W.E. Vesely, U.S. Nuclear Regulatory Commission, F.F. Goldberg, U.S. Nuclear Regulatory Commission, N.H. Roberts, University of Washington, D.F. Haasl, Institute of System Sciences, Inc

[2] G.R. Burdick, N.H. Marshall and J.R. Wilson, "COMCAN-A Computer Program for Common Cause Analysis", Aero jet Nuclear Company, ANCR-1314, May 1976

[3] C.L. Cate and J.B. Fussell, "BACKFIRE-A Computer Code for Common Cause Failure Analysis", University of Tennessee, Knoxville, Tennessee, May 1977 [4] R.C. Erdmann, F.L. Leverenz, and H. Kirch, "WAMCUT, A Computer Code for Fault Tree Evaluation", Science Applications, Inc., EPRI NP-803, June 1978

[5] J.B. Fussell and W.E. Vesely, "A New Methodology for Obtaining Cut Sets for Fault Trees", Trans, ANS, Vol. 15, p.262, 1972

Abstract:Switched Reluctance Motor (SRM) has become a competitive selection for many applications of electric machine drive systems recently due to its relative simple construction and its robustness. . This paper describes the design of new converter, consisting of "half-bridge" IGBT modules and SCRs, for closed loop control of switched reluctance motor drives are proposed. The proposed converter topology is a variation of the conventional asymmetric bridge converter for switched reluctance motor drives. However, utilization of switch modules is enhanced considerably. The requirements of converters for switched reluctance motor drives and the operation of the proposed new converter are analysed and discussed. In this paper a new converter topology for speed control of a switched reluctance motor (SRM) is proposed. The topology is verified through MATLAB simulation.
Keywords: Switched reluctance motor drives, Converter topology.

[1] Slobodan Vukosavic, Victor R.Stefanovic, "SRM inverter topologies: A comparative evaluation," IEEE Transactions on industry
applications, Vol.27, No.6, pp.1034-1047, 1991.
[2] R. Krishnan and P. Materu, "Design of a Single-Switch-Per-Phase Converter for Switched Reluctance Motor Drives", IEEE
Transactions on Industrial Electronics, vol. 37, no.6, pp469-476, 1990.
[3] R.M. Davis and R.J. Blake, "Inverter Drive for Switched Reluctance Motor Circuits and Component Ratings", IEE Proc., pp. 126-
136, 1981.
[4] W. A. Harris, "Switched Reluctance Motor Control Circuit with Energy Recovery Capability", U.S. Patent No. 5,075,610, Dec.
24, 1991.
[5] C. Pollock and B.W. Williams, "Power Converter Circuits for Switched Reluctance with the Minimum Number of Switches",
IEE Proc., vol. 137, Pt. B, no. 6, pp. 373-384, 1990.
[6] M. Ehsani, J.T. Bass, T.J.E. Miller, and R. L. Steigerwald, "Development of a Unipolar Converter for Variable Reluctance
Motor Drives", IEEE Transactions on Industry Applications, vol. 23, no. 3, pp. 545-553, 1987.

Abstract:Infusion Pump is a precision electromechanical device used to administer a required amount of highly potent medicines to the patient over a period of time. The Linear Switched Reluctance Motor based design of this pump improves the resolution and the power utilization of the motor without increasing the cost or compromising any other aspect of the device.

[1] R Krishnan, Switched Reluctance Motor Drives: Modeling, Simulation, Analysis, Design, and Applications, 1st ed. Boca Raton,
FL: CRC, 2001.
[2] Byeong-Seok, Han-Kyung Bae, Praveen Vijayraghavan, and R. Krishnan, "Design of a linear switched reluctance machine", IEEE
Transactions on Industrial Applications, Vol. 33, No. 3, November/December 2000, pp.1571-1580.
[3] Bae, Han-Kyung, Byeong-Seok Lee, Praveen Vijayraghavan, and R. Krishnan, "Linear switched reluctance motor: converter and
control", in Conf. Rec. of the 1999 IEEE IAS Ann. Mtg., Oct. 1999, Phoenix, AZ, pp. 547–554.
[4] GeBaoming, Aníbal T. de Almeida, and Fernando J. T. E. Ferreira, "Design of Transverse Flux Linear Switched Reluctance
Motor", IEEE Transactions on Magnetics, Vol. 45, No. 1, Jan 2009, pp.113-119.
[5] B. S. Lee, "Linear switched reluctance machine drives with electromagnetic levitation and guidance systems." Ph.D. thesis, The
Bradley Department of Electrical and Computer Engineering, Virginia Tech., Blacksburg, VA, November 2000.
[6] Seok-Myeong Jang, Ji-Hoon Park, Dae-Joon You, Han-Wook Cho, Ho-Kyung Sung "Design of High Speed Linear Switched
Reluctance Motor", Proceeding of International Conference on Electrical Machines and Systems 2007, Oct. 8~11, Seoul, Korea, pp.
1368-1671.

Abstract:This paper going to design the two important stages of aircraft – takeoff and landing. In physical form it is to difficult the study of takeoff and landing stages of aircraft. In this paper we will include a new way to analysis the takeoff and landing of aircraft by Simulink which is the branch of Matlab. On matlab simulator we can see the takeoff and landing position of aircraft and analysis the graphs on different parameters. The take-off and landing of an aircraft is often the most critical and accident prone portion. This paper describes the design of an aircraft take-off and landing algorithm implemented on an existing low-cost flight control system. This paper
also describes the takeoff and landing algorithm development and gives validation results from matlab in the loop simulation

[1] FM(AG08), Robust Flight Control Design Challenge Problem Formulation and Manual: the Research Civil Aircraft Model (RCAM),
Technical Report GARTEUR/TP-088-3, GARTEUR, 1997.
[2] L. J. N. Jones and R. Akmeliawati and C. P. Tan, "Roll and Yaw Stabilisation using Nonlinear Energy Method", in Proceedings of the
9th International Conference on Control, Automation, Robotics and Vision, 2006, pp. 803-806.
[3] L. J. N. Jones and R. Akmeliawati and C. P. Tan, "Aircraft Automatic Maneouvering System Using Energy-based Control Technique",
in Proceedings of the 17th World Congress of The International Federation of Automatic Control, 2008, pp. 1200-1205.
[4] R Kristiansen and P. J. Nicklasson and J. T. Gravdahl, Spacecraft coordination control in 6DOF: Integrator backstepping vs
passivitybased control, Automatica, vol. 44, 2008, pp. 2896-2901.

Abstract: Power system structure is undergoing through restructuring process since a decade. Everyday a bulk amount of power is generated, transmitted and distributed via transmission network. The active power or the real power generated from the generator needs the reactive power for supporting its own transmission. This reactive power generation has some minimum limit which if generator fails to produce it has to take support from system operator for smooth real power flow in worth of paying money. While the generator produces excess reactive power it can expect remuneration from the system operator.

[1] L. L. Lai. "Power system restructuring and deregulation", Fourteenth reprint, John Wiely and Sons Limited, April, 2002.
[2] H. Yang, H. Chao and C. Kun-wei, "Optimal power flow in deregulated electricity markets"
[3] V.L. Paucar and M.J. Rider, "Reactive power pricing in deregulated electrical market using a methodology based on the theory of marginal costs", IEEE Proc, 2001.
[4] Saini and A. K. Saxena, "Optimal power flow based congestion management methods for competitive electricity markets", IJCEE, vol. 2(1), pp. 1793-81633, 2010.
[5] Y. Wang and W. Xu, "Assessing the reactive power support requirements for generators in a competitive electricity market", IEEE Proceedings of Power System Management and Control, 2002.
[6] Y. Wang and W. Xu, "An investigation on reactive power support service needs for power producers", IEEE trans Power Systems, vol. 19(1), pp. 586–593, 2004.
[7] H. Wu, C. W. Yu, N. Xu and X. J. Lin, "An OPF based approach for assessing the minimal reactive power support for generators in deregulated power systems", Electric Power & Energy Systems, vol. 30, pp. 23-30, 2008.

Abstract:Over the past few decades, general-purpose processor designs have evolved by leaps and bounds with the ability to automatically find and exploit increasing amount of parallelism in sequential programs[1]. In this continuation, the first was narrow pipelined single-issue processors.One huge bottleneck that a particular single core processor faces is the inability of its ALUs to perform more than only a subset of its full instruction set at higher frequencies [2]. Its complex instruction set can only be fully executed during normal (lower) clock speeds.All these limiting factors lead to the advent of multi-core system.

[1] Stephen W. Keckler, KunleOlukotun, H. Peter Hofste, Multi-core processors and systems.
[2] Sung-Mo Kang and Yusuf Leblebici, CMOS Digital Integrated Circuits, 3rd
edition.http://www.ece.eng.wayne.edu/~czxu/ece7660_f05/network.pdf
[4] John P. Hayes, "Computer Architecture and Organization", 4th Edition, 2003.
[5] Morris Mano, "Computer System Architecture", 3rd Edition, 1992.
[6] Young, S.D.; Yalamanchili, S. "Adaptive routing in generalized hypercube architectures", Paralleland Distributed Processing, on
page(s): 564-571, Dec 1991.
[7] Marium, T.; Anam,,S.M.T.; Fahaduzzaman, A. S.M.; Ahmed, J.U. "Universal Functional Block Design for All Purpose Network
Realization in Multi-core System", IOSR Journal of Electrical and Electronics Engineering (IOSR-JEEE), e-ISSN: 2278-1676, p-
ISSN: 2320-3331, Volume 9, Issue 2 Ver. V (Mar – Apr. 2014), PP 07-11, www.iosrjournals.org

Abstract:Forecasting of electrical load is very crucial to the effective and efficient operation of any power system. This is achieved by obtaining the most accurate forecast which help in minimizing the risk in decision making and reducesthe costs of operating the power plant. Therefore, the comparative study of time series and artificial neural network methods for short term load forecasting is carried out in this paper using real time load data of Covenant University,withthe moving average, exponential smoothing (time series method) and
the Artificial Neural Network (ANN) models. The work was done for the day-to-day operation of the soon-to-becompleted
power station of the university. For each of the methods, models were developed for the load forecast. The Artificial Neural Network proved to be the best forecast method when the results are compared in terms of error measurementswith amean absolutedeviation(MAD) having 0.225, mean squared error (MSE) having 0.095 and the mean absolute percent error(MAPE)having 8.25.

Keywords:Artificial neural network, Error measurement, Exponential smoothing, Load forecasting, Moving average.

[1]. L.Ghods and M. Kalantar, Different Methods of Long-Term Electric Load Demand Forecasting; A Comprehensive Review.
[2]. Raman Kamboj and Mr. Ram Avtar, Electric Load Forecasting Using Different Techniques In BPN,International Journal of
Advanced Engineering Technology.
[3]. Isaac A. Samuel, Felly-NjokuChihurumanya F., Adewale Adeyinka A. and Ayokunle A. Awelewa, Medium-Term Load
Forecasting Of Covenant University Using the Regression Analysis Methods, Journal of Energy Technologies and Policy, Vol.4,
No.4, 2014.
[4]. M. A. Farahat and M. Talaat, A New Approach for Short-Term Load Forecasting Using Curve Fitting Prediction Optimized by
Genetic Algorithms.
[5]. SamsherKadir Sheikh and M.G. Unde, Short-Term Load Forecasting using ANN Technique,International Journal of Engineering
Sciences & Emerging Technologies, Feb 2012. Volume 1, Issue 2, pp: 97-107.
[6]. Che-Chiang Hsu and Chia-Yon Chen, Regional load forecasting in Taiwan––applications of artificial neural networks.

Abstract:This paper deals with the development of an electronic power factor and phase angle meter using pic Microcontroller. The design is based on generating two signal values, the first is proportional to the peak value (Im) of the line current i(t), the second is proportional to the instantaneous value of the line current at the instant of v(t)=Vm , i.e. Imcosø, where Im is the peak value of the line current and ø is the phase angle between line voltage and line current. These two components are inserted into PIC16F877A by means of analog circuit. The power factor is calculated by division of these two components and the phase angle is then calculated as Cos-1ø.The calculated values will provide a realization and displayable form on LCD screen.

Keywords: Microcontroller, Power factor, Phase angle Measurements.

[1]. Al – Ani. M. S. M. , and Abdul – Karim. M.A.H. , 1982, A digital power factor meter design based on voltage – to – frequency
conversion. INT. Journal. Electronics. 52, 463 – 470.
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Transactions on Circuits and Systems, Issue 7, Volume 9, July
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method. IEEE Transactions on Magnetics, Vol. 46, No. 8, August
[5]. Mr. Anant Kumar Tiwari, Mrs. Durga Sharma, Mr.Vijay Kumar Sharma, 2014, automatic power factor correction using capacitive
bank, Journal Of Engineering Research and Applications, Vol. 4, Issue 2( Version 1), February, pp.393-395
[6]. Md. Shohel Rana, Md. Naim Miah & Habibur Rahman,2013, automatic power factor improvement by using microcontroller, Global
Journal of Researches in Engineering Electrical and Electronics Engineering, Volume 13 Issue 6 Version 1.0 Year 2013
[7]. Microchip 2001, PIC 16F87X Data Sheet, USA.

Abstract:The polarized signals impinging on adaptive antenna arrays have significant impact on their performance. In this paper an investigation of the effect of polarized signals (desired and interference) on the performance of uniformly spaced steered beam adaptive array antennas is conducted, and a comparison between adaptive arrays with single dipole and cross-dipole elements is presented to show the effect of polarization on both of them. It is shown that the cross-dipole array antennas have better performance than the single dipole antenna array antennas if the polarization of the desired signal is unknown, while the single dipole arrays give better performance if the polarization of the desired signal is known.

[1]. S. P. Applebaum, "Adaptive Arrays", IEEE Trans. Antennas propagation, Vol. Ap-24. pp. 585-598, Sept. 1976.
[2]. Compton , R., Jr. "On the performance of a polarization sensitive adaptive array"; Antennas and Propagation, IEEE Trans. on,
Volume 29, Issue 5, Sep 1981 pp:718 – 725.
[3]. G. A. Deschamps, "Geometrical representation of thepolarization of a plane electromagnetic wave, Proc. IRE,vol. 39, pp.540-544,
May 1951.
[4]. Amin Al-Ka'bi, John Homer and MarekBialkowski,"The Effect of Polarization on the Performance of Steered Beam Adaptive Array
Antennas," Proceedings of the 5th Workshop on the Internet, Telecommunications and Signal Processing (WITSP'06), Hobart,
Australia, Dec. 2006.