Cascaded ANN for Evaluation of Frequency and Air-gap Voltage of Self-Excited Induction Generator

Authors: Raja Singh Khela, R. K. Bansal, K. S. Sandhu, A. K. Goel

Abstract:

Self-Excited Induction Generator (SEIG) builds up voltage while it enters in its magnetic saturation region. Due to non-linear magnetic characteristics, the performance analysis of SEIG involves cumbersome mathematical computations. The dependence of air-gap voltage on saturated magnetizing reactance can only be established at rated frequency by conducting a laboratory test commonly known as synchronous run test. But, there is no laboratory method to determine saturated magnetizing reactance and air-gap voltage of SEIG at varying speed, terminal capacitance and other loading conditions. For overall analysis of SEIG, prior information of magnetizing reactance, generated frequency and air-gap voltage is essentially required. Thus, analytical methods are the only alternative to determine these variables. Non-existence of direct mathematical relationship of these variables for different terminal conditions has forced the researchers to evolve new computational techniques. Artificial Neural Networks (ANNs) are very useful for solution of such complex problems, as they do not require any a priori information about the system. In this paper, an attempt is made to use cascaded neural networks to first determine the generated frequency and magnetizing reactance with varying terminal conditions and then air-gap voltage of SEIG. The results obtained from the ANN model are used to evaluate the overall performance of SEIG and are found to be in good agreement with experimental results. Hence, it is concluded that analysis of SEIG can be carried out effectively using ANNs.

Keywords: Self-Excited Induction Generator, Artificial NeuralNetworks, Exciting Capacitance and Saturated magnetizingreactance.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1330397

References:

[1] E.D. Basset and F.M. Potter, "Capacitive excitation for induction generator," AIEE Trans. (Elect. Engg.) Vol. 54, pp 540-545, 1935.
[2] C.F. Wagner, "Self-excitation of induction motors," AIEE Trans. (Elect. Engg.), Vol, 58, pp 47-51, 1939.
[3] C.F. Wagner, "Self-Excitation of induction generator with series capacitors," Trans. AIEE, Vol. 60, pp 1241-1247, 1941.
[4] J.E. Barkle and R.W. Ferguson, "Induction generator theory and application," AIEE Trans. (Elect. Engg.), Vol. 73, pp 12-19, 1954.
[5] S.S. Murthy, O.P. Malik, A.K. Tandon, "Analysis of self excited induction generators," IEE Proc., Generation Transmission & Distribution, Vol. 129, No.6, pp 260-265, 1982.
[6] T. F. Chan, "Steady state analysis of self excited induction generators," IEEE Trans. on Energy Conversion, Vol. 9, No. 2, pp. 288-296, June- 1994.
[7] A. Al-Jabri, "Direct evaluation of output frequency and magnetizing reactance of three-phase isolated self-excited induction generators," IEEE Trans. on Energy Conversion, Vol. 5, No.2, pp 350-357, 1990.
[8] T. F. Chan, "Steady state analysis of self excited induction generators," IEEE Trans. on Energy Conversion, Vol. 9, No. 2, pp. 288-296, June- 1994.
[9] X .S. Chen, A.J. Flechsig, C.W. Pang, L.M. Jhuang, "Digital modeling of an induction generator," Proceedings of the IEE International Conference on Advances in Power System Control, Operation and Management, IEE Hong Kong Centre, 1991, Pp. 720-726.
[10] S. P. Singh, B. Singh, M.P. Jain, "A new technique for the analysis of self excited induction generator," Electric Machines and Power Systems, Vol. 23, No. 6, pp 647-656, 1995.
[11] K. S. Sandhu, and S. K. Jain, "Operational aspects of seig using a new model," Electric Machines and Power Systems, Vol. 27, 1999, pp 169- 180
[12] Siva Prakash Velpula and Biswarup Das, "Distribution system bus voltage estimation using ANN," Proc. of International Conf. on Computer Application in Electrical Engineering, Recent Advances, IITRoorkee, 2002.
[13] P. K. Chaturvedi, P.S. Satsangi, and P.K. Kalra, "Flexible neural network models for electric machines," Inst. of Engineers, Vol. 80, 1999.
[14] A. K. Goel, S. C. Saxena and Surekha Bhanot, "A genetic based neurofuzzy controller for thermal processes," Journal of Computer Sc. & Technology, vol 5, no. 1, pp. 37-43, April 2005.
[15] Narendra K.S. and K. Parthasarathy, (1990), "Identification and control of dynamical systems using neural networks," IEEE Transactions on Neural Networks, vol 1, pp. 4-27.
[16] Limanond, S. and Si, J., (1998), "Neural network based control design: An LMI approach," IEEE Transactions on Neural Networks, vol. 9, no. 6, pp. 1422-1429.
[17] A. K. Goel, S. C. Saxena and Surekha Bhanot, "Fast learning algorithm for training feedforward neural networks," International Journal of Systems Science, vol. 37, no. 10, 2006, pp. 709-722.