Artificial Neural Network Based Rotor Reactance Control Essay

Abstract Problem statement The Rotor reactance control by comprehension of external optical condenser in the rotor coil spell has been in freshly research for improving the performances of Wound Rotor consequence Motor (WRIM). The rotor capacitive reactance is adjusted such that for any desired load tortuosity the susceptibility of the WRIM is maximized. The rotor external capacitance can be controlled development dynamic capacitor in which the occupation 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 schmalzy Neural Network (ANN). The data for ANN training were obtained on a third 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 veritable and trained for determining the maximum efficiency of the labour wit h no foregoing knowledge of the machine parameters. The input variables to the ANN are stator current (Is), advance (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 full-blooded Error (MSE) and R2 parameters. The result of R2 value of the proposed ANN model is 0.99980. Conclusion The optimum duty ratio and corresponding optimal rotor capacitance for improving the performances of the repel are predicted for low, medium and full loads by using proposed ANN model. bring out 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 induction motor operating under varying load torques. IEEE Int. Conf. PACC11, 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. plectrum Of Input Data Type Of Artificial Neural Network To celebrate 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 chatter Phenomenon in the Control of Induction Motor. American Journal of Applied Sciences, 7 110-119. DOI 10.3844/ajassp.2010.110.119.