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Mitigating furnace pressure fluctuations under rapid load ramping using a wavelet-LSTM-PPO based intelligent control framework
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DOI：10.3389/fenrg.2025.1658163.png)
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Abstract
En 中文
Rapid load ramping in coal-fired power plants with high renewable energy integration often induces severe furnace pressure fluctuations; threatening combustion stability and operational safety. To address this challenge; we propose a predictive and adaptive control framework that integrates wavelet transform; long short-term memory (LSTM) neural networks; and proximal policy optimization (PPO) reinforcement learning. Wavelet-based multi-resolution decomposition is employed to extract key features from pressure signals; while an LSTM model forecasts short-term pressure dynamics. Based on predictive feedback; a PPO agent learns an optimal control strategy to regulate secondary air and fuel inputs in real time. Validation on a 600 MW supercritical boiler unit demonstrates a 42.2% reduction in the standard deviation of furnace pressure fluctuations; improved stability under variable load conditions; and smoother actuator response compared with conventional control schemes. These results highlight the potential of combining deep learning and reinforcement learning techniques to enhance combustion stability and support secure; flexible operation of coal-fired power plants under high renewable energy penetration.
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