# International Journal of Emerging Technologies in Computational and Applied Sciences(IJETCAS)

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2 inversion occurs: when the number of ions in state L2 exceeds the number of ions in the ground state, the probability of outcome 2 is higher than that of 1 and 3 [5]. Figure 1 Energy levels of EDFA. The SIMULINK model of the system is shown in Fig. 2. For our purpose we are using 980nm pump. Number of WDM signals used is two in this case. The out1 represents EDFA loss and out2 represents the Gain of EDFA. Here we are using cascaded stages of EDFAs. The Gain transients of EDFA is controlled with the help of a Fuzzy Logic Controller. Figure 2 Experimental setup for EDFA gain control using Fuzzy Logic Controller in SIMULINK III. Fuzzy Logic Controller The inherent characteristics of the channel add/drop, complexity and multi-variable conditions of the EDFA system limits the conventional control methods giving satisfactory solutions. Artificial intelligence based gain IETCAS ; 2012, IJETCAS All Rights Reserved Page 570

3 scheduling is an alternative technique that can be used in designing controllers for EDFA based systems. Fuzzy system transforms a human knowledge into mathematical formula. Fuzzy set theory and fuzzy logic establish the rules of a nonlinear mapping. Fuzzy control is based on a logical system called fuzzy logic which is much closer in spirit to human thinking and natural language than classical logical systems. Nowadays fuzzy logic is used in almost all sectors of industry and science. One of them is automatic generation control. The main goal of AGC is to protect the balance between production and consumption. Control algorithms based on fuzzy logic have been implemented in many processes[6][8]. The application of such control techniques has been motivated by the following reasons[7]: 1) Improved robustness over the conventional linear control algorithms; 2) Simplified control design for difficult system models; 3) simplified implementation The application of fuzzy logic control techniques appears to be most suitable one whenever a well-defined control objective cannot be specified, the system to be controlled is a complex one, or its exact mathematical model is not available. Recent research indicates that more emphasis has been placed on the combined usage of fuzzy systems and neural networks [6]-[10].The fuzzy logic controller designed can be of the form shown in Fig. 6[6]. Figure 3 Fuzzy Logic Controller The fuzzy logic controller is comprised of four main components [6]: the fuzzification, the inference engine, the rule base, and the defuzzification, as shown in Fig. 4. Figure 4 Components of Fuzzy Logic Controller [11] The fuzzifier transforms the numeric/crisp value into fuzzy sets; therefore this operation is called fuzzification. The main component of the fuzzy logic controller is the inference engine, which performs all logic manipulations in a fuzzy logic controller. The rule base consists of membership functions and control rules. Lastly, the results of the inference process is an output represented by a fuzzy set, however, the output of the fuzzy logic controller should be a numeric/crisp value. Therefore, fuzzy set is transformed into a numeric value by using the defuzzifier. This operation is called defuzzification. The control signal is given by[6][8] u(t) = -K p y + K i...(i) K p and K i are the proportional and the integral gains respectively and taken equal to one. IV. Simulations and Results We have proved the operation principle of the proposed system by using well-known commercially available numerical simulator, SIMULINK. Fig. 2 shows the experimental setupfor two channel WDM system. The major EDFA parameter values we have used in the experiment are shown in Table I. IETCAS ; 2012, IJETCAS All Rights Reserved Page 571

4 Table I Shows the Summary of EDFA parameters used for experiment Symbol Description Value Dimension N 2 Total erbium ions in excited state Variable Τ Spontaneous emission 10.5 ms L Length of erbium-doped fiber Variable (15 here) m R Radius of fiber core 1.2 x 10-6 m A Area of fiber core πr 2 m 2 Ρ Density of erbium ions 6.3 x m -3 α P Pump absorption coefficient 3.31 db/m β P Pump emission coefficient 0 db/m α S Signal absorption coefficient Varies with λ db/m β S Signal emission coefficient Varies with λ db/m λ p Pump wavelength 980 nm λ s Signal wavelength nm We have performed experiments initially without using Fuzzy Logic Controller and after that with using Fuzzy Logic Controller. Fig 5 shows the power fluctuations when channel add/drop occurs and also the noise when the power of laser is 18dBm. We have performed various kinds of experiments by not only changing the number of WDM channels to EDFA but also varying the number of add/drop channels. Here we are using cascaded EDFAs in the system. Fig. 6 shows the results when the Fuzzy Logic Controller is used in the system. The simulation results shows that the transients in gain and noise are removed completely. Figure 5 Power fluctuations of WDM channels due to channel add/drops without Fuzzy Logic Controller IETCAS ; 2012, IJETCAS All Rights Reserved Page 572

5 Figure 6 Removal of gain-transients and noise in EDFA using Fuzzy Logic Controller V. Conclusion In this paper we proposed a novel technique to minimize gain-transients in cascaded EDFAs using a Fuzzy Logic Controller in the feedback loop. VI. References [1] S. Shin, D. Kim, S. Kim, S. Lee, S. Song, A novel technique to minimize gain-transient time of WDM signals in EDFA, J. Opt. Soc. Korea 10(4), ( 2006). [2] J. L. Zyskind, et al., Fast power transients in optically amplified multiwavelength optical networks, Proc. OFC'96, (1996), Paper PD31. [3] S. Y. Park et al., "A gain-flattened two-stage EDFA for WDM optical networks with a fast link control channel," Optics Communications, pp , 1998 [4] E. Desurvire, Erbium-doped fiber amplifiers, John Wiley & Sons, New York, (1994) [5] P. C. Becker, N. A. Olsson, J. R. Simpson: Erbium-Doped Fiber Amplifiers Fundamentals and Technology, Academic Press, San Diego, 1999 [6] Yogendra Arya, Narender Kumar, Hitesh Dutt Mathur, Automatic Generation Control in Multi Area Interconnected Power System by using HVDC Links IJPEDS( International Journal of Power Electronics and Drive System) Vol.2,No.1,March 2012, pp.67~75, ISSN : [7] C.B.Bangal, A Ph.D Thesis Report on Automatic generation control of interconnected power systems using artificial neural network techniques Bharat University. Faculty of Engineering and Technology, Department of Electrical Engineering,May [8] Mukta, Balwinder Singh, Grid Stability of Interconnected System with Fuzzy-logic controller & HVDC in Deregulated Environment IJSCE( International Journal of Soft Computing and Engineering)Vol.2,Issue 6,January 2013,pp. 283~288,ISSN : [9] Dr. A. Taifour Ali, Dr. Eisa Bashier M Tayeb, Ms. Kawthar A. Adam, A Multi-Machine Power System Stabilizer Using Fuzzy Logic Controller International Journal Of Computational Engineering Research (ijceronline.com) Vol. 2 Issue. 6,pp28~33,ISSN ,October 2012 [10] Jenica Ileana Corcau, Eleonor Stoenescu, Fuzzy Logic Controller as a Frequency Stabilizer INTERNATIONAL JOURNAL OF CIRCUITS, SYSTEMS AND SIGNAL PROCESSING, Vol.1, Issue3, 2007, pp266~273 [11] Manuel A. Matos, The Fuzzy Power Flow Revisited,IEEE Transactions on Power Systems,Vol.23,No.1,February IETCAS ; 2012, IJETCAS All Rights Reserved Page

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