International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 –6545(Print), ISSN 0976 – 6553(Online) V...
International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 –6545(Print), ISSN 0976 – 6553(Online) V...
International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 –6545(Print), ISSN 0976 – 6553(Online) V...
International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 –6545(Print), ISSN 0976 – 6553(Online) V...
International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 –6545(Print), ISSN 0976 – 6553(Online) V...
International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 –6545(Print), ISSN 0976 – 6553(Online) V...
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Performance evaluation of ann based plasma position controllers for aditya tokamak

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Performance evaluation of ann based plasma position controllers for aditya tokamak

  1. 1. International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 –6545(Print), ISSN 0976 – 6553(Online) Volume 4, Issue 2, March – April (2013), © IAEME324PERFORMANCE EVALUATION OF ANN BASED PLASMA POSITIONCONTROLLERS FOR ADITYA TOKAMAKJ. Femila Roseline1, Jigneshkumar J.Patel2, J.Govindarajan3, N.M.Nandhitha4,B.Sheela Rani51Asst.Professor, Dept. of Electrical and Electronics Engg., Sathyabama University,Jeppiaar Nagar, Old Mahabalipuram Road, Chennai 600 1192Engineer-SD, Electronics Group, Institute of Plasma Research.3Associate Professor-II,Institute of Plasma Research,4Professor & Head, Dept. of Electronics and Communication Engg., Jeppiaar Nagar,Old Mahabalipuram Road, Chennai 600 119,5Vice Chancellor, Prof. Electronics & Instrumentation,Sathyabama University, Chennai 600 119ABSTRACTIn Aditya tokamaks, electrical energy is generated through plasma confinement in thetorroidal chamber. The amount of energy generated is directly related to the confinement ofthe plasma within the chamber. Also if the plasma hits the limiters or the walls it leads toplasma disruption. Extensive research has been done to develop controllers for confining theplasma within the chamber. However these techniques had inherent limitations as they areeither linear models or fuzzy based controllers. The Fuzzy based controllers are stronglydependent on the membership functions. Hence in this paper Artificial Neural Network basedclassifiers are developed to overcome the limitations of the existing system. GRNN, RBNbased networks were developed and the performance is evaluated with that of the alreadydeveloped BPN based controller. It is found that BPN based controllers provide higher SignalTo noise ratio than the other controllers.Keywords : Tokamaks, Plasma Position, plasma Confinement, radial position, plasmacurrent, BPN, voltage, RBN, GRNN;INTERNATIONAL JOURNAL OF ELECTRICAL ENGINEERING& TECHNOLOGY (IJEET)ISSN 0976 – 6545(Print)ISSN 0976 – 6553(Online)Volume 4, Issue 2, March – April (2013), pp. 324-329© IAEME: www.iaeme.com/ijeet.aspJournal Impact Factor (2013): 5.5028 (Calculated by GISI)www.jifactor.comIJEET© I A E M E
  2. 2. International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 –6545(Print), ISSN 0976 – 6553(Online) Volume 4, Issue 2, March – April (2013), © IAEME325I. INTRODUCTIONIn Aditya Tokamaks, hot plasma is contained by a magnetic field which keeps it awayfrom the machine walls. The combination of two sets of magnetic coils known as toroidal andpoloidal field coils creates a field in both vertical and horizontal directions, acting as amagnetic toroidal chamber. The performance of the machine is dependent on the density ofthe plasma, position of the plasma within the chamber and the time duration for which theplasma is stabilized. From the literature, it is found that confinement of plasma within thechamber yields better results in Aditya Tokamak. Plasma position control is basically a non-linear process. However the initial controllers were linear PID controllers. Performance hasreduced as the constants can not be fixed accurately. Fuzzy based controller is the first non-linear controller used for plasma position controller. However the performance of the systemis strongly dependent on the membership functions, defuzzification rules and the knowledgebase. Also certain assumptions made in Fuzzy based controllers are unrealistic in nature.Hence it is necessary to develop an intelligent non-linear based controller that adapts to thereal time conditions and provides the results. General Recurrent Neural Network (GRNN)and Radial Basis Function Network (RBFN) have been developed for controlling the plasmacurrent in Adithya Tokamak. The network accepts radial position and current as inputs andpredicts the stabilization voltage. The inputs and output variables for training and testing areobtained from Aditya RZIP model.The paper is organized as follows: Section II provides the related work. Section III gives anoverview of the neural networks chosen for developing plasma position controllers. Theproposed methodology is explained in section IV. Section V is about results and discussionand Section VI concludes the work.II. RELATED WORKD. Wroblewski et al (1997) trained a neural network which combines signals from alarge number of plasma diagnostics and estimated the high- beta disruption boundary in theDIII-D tokamak. The proposed neural network maps the disruption boundary throughout mostof the discharge. It can predict the high- beta disruption boundary on a time-scale of the orderof 100 ms (much longer than the precursor growth time), which makes this approach ideallysuitable for real time application in a disruption avoidance scheme [1]. J.V. Hernandez et al(1996) described the use of neural network algorithms for predicting minor and majordisruptions in tokamaks by analyzing disruption data from the TEXT tokamak with twonetwork architectures. Fluctuating magnetic signal was extrapolated based on L past values ofthe magnetic fluctuation signal measured by a single Mirnov coil [2]. A. Vannucci et al(1999) used a neural network is trained with one disruptive plasma discharge and is validatedusing soft X ray signals as input. After training they used the same set of weights to find outthe disruptions in two other plasma discharges and they observed that neural network is able topredict the disruptions more than 3ms in advance when compared to the previously usedMirnov coil [3]. Barbara Cannas et al proposed dynamic neural networks to predict theplasma disruptions in a nuclear fusion device. Dynamic neural networks act as filters, whichpredict one step ahead the value of diagnostic signals acquired during a plasma pulse [4]. A.Sengupta et al (2002) developed two modified neural network techniques which are used forindentifying equilibrium plasma parameters of the Superconducting Steady State Tokamak Ifrom external magnetic measurements. They used a multi network system which is connectedin parallel. By using this double neural network the accuracy of the recovered result is better
  3. 3. International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 –6545(Print), ISSN 0976 – 6553(Online) Volume 4, Issue 2, March – April (2013), © IAEME326than the conventional method. They fed the reduced and transformed input set rather than theentire set, into the neural network input and called that as the principal component transformation-based neural network [5]. A.B. Trunov (2004) developed several neural network approximatorswhich were computed on the basis of training data and analyzed their performance. It was foundthat neural networks have better generalization properties than their linear counterparts, and cantherefore produce reasonably good prediction even with severely reduced input datasets [6].III. OVERVIEW OF RBFN AND GRNNRadial Basis Function Neural network (RBFN) consists of three layers: an input layer, ahidden (kernel) layer, and an output layer. The nodes within each layer are fully connected to theprevious layer. The input variables are each assigned to the nodes in the input layer and they passdirectly to the hidden layer without weights. The transfer functions of the hidden nodes are RBF.An RBF is symmetrical about a given mean or center point in a multidimensional space. AGeneralized Recursion Neural Network (GRNN) is a variation of the radial basis neuralnetworks, which is based on kernel regression networks. A GRNN does not require an iterativetraining procedure as back propagation networks. It approximates any arbitrary function betweeninput and output vectors, drawing the function estimate directly from the training data. Inaddition, it is consistent that as the training set size becomes large, the estimation errorapproaches zero, with only mild restrictions on the function.IV. RBFN AND GRNN BASED PLASMA POSITION CONTROLLERSRBFN is chosen with two neurons in the input layer and one neuron in the output layer.As the architecture of GRNN can not be modified the general four layered GRNN was chosen forplasma position control. The exemplars are generated from Aditya RZIP model. Different sets ofexemplars are used for training and testing the neural network. A set of exemplars used fortraining is shown in Table 1. The input parameters are the radial position and plasma current andthe output parameter is the plasma stabilization voltage. In order to prevent overflow the valuesare normalized.Table I.Exemplars Used For Trainiga The Neural NetworkInput Parameters Output VoltagePlasma current(Ip) in ARadial position(Rp)Desired output voltage (Va )in Volts60.0186 0.7498 0.009560.1467 0.7483 0.075960.4243 0.7443 0.237661.0378 0.7303 0.736061.5420 0.7003 1.669360.8311 0.6932 2.322260.2984 0.6891 3.345360.0457 0.6872 5.522760.0155 0.6873 4.575260.0188 0.6875 2.4668
  4. 4. International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 –6545(Print), ISSN 0976 – 6553(Online) Volume 4, Issue 2, March – April (2013), © IAEME327V. RESULTS AND DISCUSSIONThe developed ANN is trained with 75 values. With the adapted weight, the ANN istested using another set of 70 values. The relationship between the actual and desired output forthe corresponding input parameters is shown in Table 1. From the last two columns of Table 1the actual and desired values are nearly same. The relationship between actual and desiredvalues for different ANN is shown in figure 1. The performance of the ANN bases plasmaposition controllers are tabulated in Table 2. Performance metrics are shown in Table 3. Thecomparison of Signal to Noise Ratio for GRNN, RBN and BPN is shown in Figure 2.Figer 1.Relation between desired and actual outputs for different ANNTABLE II :PERFORMANCE OF ANN BASED PLASMA POSITION CONTROLLERSInput Parameters Output ParametersPlasma current(Ip) in ARadialposition(Rp)Desired outputvoltage (Va) inVoltsActual output voltage (Va)GRNN RBN BPN0.9751 0.99999 0.0007 0.0010 0.0101 0.000750.9773 0.9977 0.0113 0.0113 -0.0086 0.01120.9918 0.9737 0.1092 0.0944 0.0995 0.44680.9752 0.9167 0.2279 0.4791 0.5007 -0.16270.9752 0.9168 0.4188 0.4695 0.4966 0.42090.9752 0.9187 0.4561 0.4558 0.4667 0.45620.9752 0.9194 0.4673 0.4672 0.4682 0.46730.9752 0.9207 0.4892 0.4892 0.4810 0.48930.9752 0.9213 0.5001 0.4999 0.4898 0.50030.9752 0.9220 0.5110 0.5092 0.4982 0.51110.9752 0.9226 0.5218 0.5151 0.5053 0.5053
  5. 5. International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 –6545(Print), ISSN 0976 – 6553(Online) Volume 4, Issue 2, March – April (2013), © IAEME328TABLE III.PERFOMANCE METRICSParameters GRNN RBN BPNRoot MeanSquare Error0.0896 0.1002 0.0863StandardDeviation0.0827 0.0854 0.0816Signal to NoiseRatio4.8633 4.4095 5.2915Figer 2.SNR comparison for GRNN, RBN and BPNVI. CONCLUSION AND FUTURE WORKGRNN and RBFN based plasma position controllers were developed successfully.Exemplars were generated using Aditya RZIP model. The performance of these networks iscompared with that of BPN. Though GRNN and RBFN are best suited for predicting theplasma stabilization voltage from incomplete set of exemplars, BPN based approach providesbetter results in terms of Signal to Noise ratio and root mean square. As the exemplars data isgenerated from Aditya RZIP model, the data is linear in nature. Hence it is necessary to testand train the neural network with the plasma discharge shots obtained from Aditya Tokamak.Also the feasibility of Neuro Fuzzy controller for plasma position control should also beexploited.REFERENCES[1] D. Wroblewski, G.L. Jahns and J.A. Leuer, ‘Tokamak disruption alarm based on a neuralnetwork model of the high- beta limit ’, Nuclear Fusion, Vol. 37, Number 6, Issue 6 (June1997)[2] J.V. Hernandez, A. Vannucci, T. Tajima, Z. Lin, W. Horton and S.C. Mc Cool, ‘Neuralnetwork prediction of some classes of tokamak disruptions ’, Nuclear Fusion, Vol. 36,Number 8, Issue 8 (August 1996).5.29154.86334.40950123456GRNN RBN BPNSNR
  6. 6. International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 –6545(Print), ISSN 0976 – 6553(Online) Volume 4, Issue 2, March – April (2013), © IAEME329[3] A. Vannucci*, K.A. Oliveira*and T. Tajima, ‘Forecast of TEXT plasma disruptions usingsoft X rays as input signal in a neural network’, Nuclear Fusion, Vol. 39, Number 2,Issue2 (February 1999)[4] Barbara Cannas, Alessandra Fanni and Augusto Montisci ‘Dynamic Neural Networks forPrediction of Disruptions in Tokamaks’[5] A. Sengupta and P. Ranjan, ‘Modified neural networks for rapid recovery of tokamakplasma parameters for real time control’, Review of Scientific Instruments , Volume 73,Issue 7, American Institute of physics, 2002.[6] A. B. Trunov ‘Design of the Real Time neural network control system for the DII-Dplasma fusion reactor’, Progress in Electromagnetic research symposiun, Pisa, Italy,March 28-31, 2004.[7] Shaikh Abdul Hannan,R.R.ManzaR.J.Ramteke, ‘Generalised Neural Network and RadialBasis function for Heart Disease Diagnosis’, International Journal of ComputerApplications, Volume 7, Number.13, pp 0975-8887, october 2010.[8] Chaitrali S. Dangare and Dr. Sulabha S. Apte, “A Data Mining Approach for Predictionof Heart Disease using Neural Networks”, International journal of Computer Engineering& Technology (IJCET), Volume 3, Issue 3, 2012, pp. 30 - 40, ISSN Print: 0976 – 6367,ISSN Online: 0976 – 6375.[9] Dr.Muhanned Alfarras, “Early Detection of Adult Valve Disease–Mitral Stenosis usingThe Elman Artificial Neural Network”, International Journal of Computer Engineering &Technology (IJCET), Volume 3, Issue 3, 2012, pp. 255 - 264, ISSN Print: 0976 – 6367,ISSN Online: 0976 – 6375.

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