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With the increasing proportion of new energy generation, the frequency and voltage regulation capabilities of thermal power units have been further tested. This requires both the fast regulation of thermal power units and the stability of key operating parameters of the units. Among them, the temperature of superheated steam plays an important role in the safety and economy of unit operation. The traditional cascade PID control method is difficult to meet the control requirements due to its inability to fundamentally solve the hysteresis of the steam temperature system. This article proposes a superheater steam temperature prediction model based on LSTM recurrent neural network, which can accurately predict the changes in superheater steam temperature, in order to control the opening of the desuperheating water valve in advance for precise temperature control.

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Optimization of Steam Temperature Control for Superheater in 300 MW Power Plant

  • 会议论文

  • 发表日期：

    2025年

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  • Yuzhu Gong,
  • Weihu Li,
  • Chuanfang Liu,
  • Tao Zhang,
  • Quan Zhou

摘要

With the increasing proportion of new energy generation, the frequency and voltage regulation capabilities of thermal power units have been further tested. This requires both the fast regulation of thermal power units and the stability of key operating parameters of the units. Among them, the temperature of superheated steam plays an important role in the safety and economy of unit operation. The traditional cascade PID control method is difficult to meet the control requirements due to its inability to fundamentally solve the hysteresis of the steam temperature system. This article proposes a superheater steam temperature prediction model based on LSTM recurrent neural network, which can accurately predict the changes in superheater steam temperature, in order to control the opening of the desuperheating water valve in advance for precise temperature control.