基于LightGBM-Transformer模型与NSGA-II的燃煤机组启停过程优化OA

[Objective]With the high penetration of renewable energy in power systems,coal-fired units are required to provide peak-shaving services with higher frequency and larger depth,making start-up load ramp-up and shutdown load ramp-down more time-critical and cost-sensitive.This study aims to improve the responsiveness and economic performance of non-steady-state operating phases during start-stop transitions under deep peak-shaving conditions,while ensuring compliance with operational safety constraints.[Methods]Multi-month DCS data from a 660 MW coal-fired unit sampled at 5 s were analyzed.Non-steady segments in start-up and shutdown were firstly identified by combining sliding-window range thresholds with steady-state criteria,yielding transient datasets aligned with the underlying operational transitions.Candidate variables were then screened using the maximal information coefficient to retain features most strongly associated with output power,supplemented with a process progress descriptor to represent phase evolution and improve discrimination across different load intervals.To accommodate cross-month operating variability,start-stop events were organized into source and target domains.Within each target-domain segment,a time-stratified split was adopted,with the first 10%used for validation and the remaining 90%reserved for testing,thereby preserving temporal causality and avoiding leakage from future samples.Given the distinct dynamics of start-up load ramp-up and shutdown load ramp-down,two surrogate predictors were developed(LightGBM for tabular feature interactions and a Transformer for temporal dependency representation),and their hyperparameters were tuned via Bayesian optimization.After model selection,source-domain data were combined with the target-domain validation subset for final training.The trained surrogate models were embedded in an NSGA-II multi-objective optimization framework.Under safety constraints and ramp-rate limits,the optimization jointly improved load-ramping capability and reduced fuel consumption,producing executable set-point trajectories and load-change strategies suitable for operational guidance.[Results]The phase-specific surrogate models achieved stronger predictive performance than common baselines such as XGBoost and random forests across operating conditions,and maintained robust generalization in cross-month evaluation.When applied to representative non-steady segments,NSGA-II produced feasible set-point trajectories that satisfied the imposed constraints throughout the transient.Relative to baseline operation,the optimized strategies reduced the average duration of non-steady start-stop segments by approximately 5.34%and lowered cumulative fuel consumption by approximately 3.53%,indicating concurrent gains in speed and economy.[Conclusion]A data-driven hybrid modeling and multi-objective evolutionary optimization approach is developed for coal-fired unit start-up and shutdown under deep peak-shaving conditions.By coupling phase-specific power surrogates with constraint-aware optimization,the method generates implementable set-point trajectories that jointly accelerate transient response and reduce fuel use within safe operating envelopes,providing practical support for flexible operation and online decision assistance.