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A method is proposed which avoids many limitations associated with traditional line loss estimation calculation. AI using neural network proposes the method of soft computing which gives accuracy and uniqueness both in linear and nonlinear operations. Line loss estimation incorporates variation of active and reactive load demand control treatments. In this paper, a 30-node system is tested where the system loss estimation is verified before and after training the data. The response was evaluated with change of total active and reactive power requirement by variations at different operating load conditions.

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ANN-Based Loss Estimation of Power Networks at Different Load Variations

  • 会议论文

  • 发表日期：

    2025年

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  • Apoorva Shegunashi,
  • Tamalika Chowdhury,
  • Tushar Birje

摘要

A method is proposed which avoids many limitations associated with traditional line loss estimation calculation. AI using neural network proposes the method of soft computing which gives accuracy and uniqueness both in linear and nonlinear operations. Line loss estimation incorporates variation of active and reactive load demand control treatments. In this paper, a 30-node system is tested where the system loss estimation is verified before and after training the data. The response was evaluated with change of total active and reactive power requirement by variations at different operating load conditions.