Artificial Neural Network Based Rotor Reactance Control Essay

Abstract Problem statement The rotor reactance control by inclusion of external capacitance in the rotor circuit has been in recent research for ameliorate the performances of Wound Rotor Induction tug (WRIM). The rotor capacitive reactance is adjusted such that for any desired ladle torque the efficiency of the WRIM is maximized. The rotor external capacitance can be controlled development dynamic capacitor in which the duty ratio is vary for emulating the capacitance value. This study presents a novel technique for tracking upper limit efficiency point in the entire operating range of WRIM victimization Artificial Neural profit (ANN). The data for ANN training were obtained on a three phase WRIM with dynamic capacitor control and rotor briefly circuit at different speed and load torque values. nest A novel nueral network model base on back-propagation algorithmic program has been developed and trained for determining the maximum efficiency of the motor with no prior knowl edge of the machine parameters. The input variables to the ANN are stator incumbent (Is), Speed (N) and Torque(Tm) and the output variable is duty ratio (D). Results The target is trammel with a goal of 0.00001. The accuracy of the ANN model is measured using cerebrate 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 match optimal rotor capacitance for improving the performances of the motor are predicted for low, medium and full loads by using proposed ANN model. Key wordsArtificial Neural Network (ANN), Wound Rotor Induction Motor (WRIM), Torque(Tm), Digital Signal Processor (DSP), rotor reactance control, corresponding optimal rotor INTRODUCTIONIt is known from the literatu... ...11. Neural network based new energy conservation scheme for three phase generality motor operating under varying load torques. IEEE Int. Conf. PACC11, pp 1-6.R. A. Jayabarathi and N. Devarajan, 2007. ANN Base d DSPIC command for Reactive Power Compensation. American Journal of utilise Sciences, 4 508-515. inside 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 utilise 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 decrement of Chattering Phenomenon in the Control of Induction Motor. American Journal of Applied Sciences, 7 110-119. DOI 10.3844/ajassp.2010.110.119.