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Abstract <p>This article presents research results examining the feasibility of using artificial neural network (ANN) technologies to predict the performance of power plants with open-cathode hydrogen fuel cells (FCs) under changing environmental conditions. Results of experiments conducted in a climate chamber to study the effect of ambient temperature on FCs with a polymer proton-exchange membrane are presented and used to develop databases for training the ANN. A recurrent ANN architecture for predicting FC voltage and hydrogen consumption is proposed. Training results confirm the feasibility of using ANN technologies in developing adaptive control systems for FC-based power plants.</p>

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Application of Artificial Neural Network Technologies in Control Systems of Power Plants Based on Open-Cathode Hydrogen Fuel Cells

  • 期刊论文

  • 发表日期：

    2025年10月1日

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  • I. A. Lipuzhin,
  • A. V. Shalukho,
  • A. N. Sannikov

摘要

Abstract

This article presents research results examining the feasibility of using artificial neural network (ANN) technologies to predict the performance of power plants with open-cathode hydrogen fuel cells (FCs) under changing environmental conditions. Results of experiments conducted in a climate chamber to study the effect of ambient temperature on FCs with a polymer proton-exchange membrane are presented and used to develop databases for training the ANN. A recurrent ANN architecture for predicting FC voltage and hydrogen consumption is proposed. Training results confirm the feasibility of using ANN technologies in developing adaptive control systems for FC-based power plants.