Comparison of complex-valued neural network and fuzzy clustering complex-valued neural network for load-flow analysis

  • Authors:

  • Murat Ceylan;Nurettin Çetinkaya;Rahime Ceylan;Yüksel Özbay

  • Affiliations:

  • Electrical & Electronics Dept., Selçuk University, Konya, Turkey;Electrical & Electronics Dept., Selçuk University, Konya, Turkey;Electrical & Electronics Dept., Selçuk University, Konya, Turkey;Electrical & Electronics Dept., Selçuk University, Konya, Turkey

  • Venue:
  • TAINN'05 Proceedings of the 14th Turkish conference on Artificial Intelligence and Neural Networks
  • Year:
  • 2005

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Abstract

Neural networks (NNs) have been widely used in the power industry for applications such as fault classification, protection, fault diagnosis, relaying schemes, load forecasting, power generation and optimal power flow etc. Most of NNs are built upon the environment of real numbers. However, it is well known that in computations related to electric power systems, such as load-flow analysis and fault level estimation etc., complex numbers are extensively involved. The reactive power drawn from a substation, the impedance, busbar voltages and currents are all expressed in complex numbers. Hence, NNs in the complex domain must be adopted for these applications. This paper proposes the complex-valued neural network (CVNN) and a new fuzzy clustering complex-valued neural network (FC-CVNN) to estimate busbar voltages in a load-flow problem. The aim of this paper is to present a comparative study of estimation busbar voltages in load-flow analysis using the conventional neural network (real-valued neural network, RVNN), the CVNN and the new FC-CVNN. The results suggest that a new proposed FC-CVNN and CVNN architecture can generalize better than ordinary RVNN and the FC-CVNN is also learn faster.