Optimization of initial main steam pressure under ultra-low loads of a steam turbine based on machine learning

To enhance the grid capacity for accommodating renewable energy, large coal-fired power units frequently undergo load regulations and even operate at ultra-low loads, inevitably reducing the units' operating efficiency. To improve the thermal economy of turbines under ultra-low loads, it is essential to optimize the initial main steam pressure. Based on the operating data of a power generation unit, a heat rate prediction model is established by using the support vector regression (SVR) algorithm, and the improved sand cat swarm optimization (ISCSO) algorithm is proposed to optimize the hyperparameters of the SVR model. Subsequently, the ISCSO algorithm is employed to search for an optimal solution within the feasible pressure range under low and ultra-low loads, yielding an optimized sliding pressure curve for the steam turbine. Finally, this approach is validated by using a case study. The results indicate that the optimized prediction model demonstrates strong generalization capabilities and accurately predicts the heat rate under low and ultra-low loads. The heat rate after optimization decreases compared to that before optimization, which highlights that the proposed optimization scheme effectively raises the thermal economy of steam turbines operating at ultra-low loads.