Abstract: Problem statement: The Rotor reactance control by inclusion of external capacitance in the rotor circuit has been in recent research for improving the performances of Wound Rotor Induction Motor (WRIM). The rotor capacitive reactance is adjusted such that for any desired load torque the efficiency of the WRIM is maximized. The rotor external capacitance can be controlled using dynamic capacitor in which the duty ratio is varied for emulating the capacitance value. This study presents a novel technique for tracking maximum efficiency point in the entire operating range of WRIM using Artificial Neural Network (ANN). The data for ANN training were obtained on a three phase WRIM with dynamic capacitor control and rotor short circuit at different speed and load torque values. Approach: A novel nueral network model based on back-propagation algorithm has been developed and trained for determining the maximum efficiency of the motor with no prior knowledge of the machine parameters. The input variables to the ANN are stator current (Is), Speed (N) and Torque(Tm) and the output variable is duty ratio (D). Results: The target is set with a goal of 0.00001. The accuracy of the ANN model is measured using Mean Square Error (MSE) and R2 parameters. The result of R2 value of the proposed ANN model is 0.99980. Conclusion: The optimal duty ratio and corresponding optimal rotor capacitance for improving the performances of the motor are predicted for low, medium and full loads by using proposed ANN model. Key words: Artificial Neural Network (ANN), Wound Rotor Induction Motor (WRIM), Torque(Tm), Digital Signal Processor (DSP), rotor reactance control, corresponding optimal rotor INTRODUCTION It is known from the literatu... ...11. Neural network based new energy conservation scheme for three phase induction motor operating under varying load torques. IEEE Int. Conf. PACCâ€™11, pp: 1-6. R. A. Jayabarathi and N. Devarajan, 2007. ANN Based DSPIC Controller for Reactive Power Compensation. American Journal of Applied Sciences, 4: 508-515. DOI: 10.3844/ajassp.2007.508.515. T. Benslimane, B. Chetate and R. Beguenane, 2006. Choice Of Input Data Type Of Artificial Neural Network To Detect Faults In Alternative Current Systems. American Journal of Applied Sciences, 3: 1979-1983. DOI: 10.3844/ajassp.2006.1979.1983. M. M. Krishan, L. Barazane and A. Khwaldeh, 2010. Using an Adaptative Fuzzy-Logic System to Optimize the Performances and the Reduction of Chattering Phenomenon in the Control of Induction Motor. American Journal of Applied Sciences, 7: 110-119. DOI: 10.3844/ajassp.2010.110.119.
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