可视化增强的火电机组控制策略的交互式探索分析OA

The optimization of control strategies in thermal power units is crucial for improving operational efficiency.Traditional approaches primarily rely on expert experience and model-based simulations to generate control strategies through empirical evaluation or mechanistic modeling.However,the complexity of decision spaces,coupling between measurement points,and differences in decision-maker's preferences pose significant challenges for strategy exploration.To address these challenges,a visual analytics method is proposed for the progressive exploration and optimization of control strategies in thermal power plants.The method supports the identification of key parameters,analysis of causal chains among parameters,and mining of regulation rule sets to generate multiple optimized decision schemes.Then,a visual analytics system named StrategyLens is developed to assisting domain experts to interactively explore and optimize control strategies from large-scale historical operational data.A case study with real-world thermal power plant operational data was conducted,and domain experts were invited for hands-on evaluation.The results demonstrate that StrategyLens can effectively derive control strategies that enhance thermal power units performance.Expert feedback further confirms the system's usability and its potential for broader application.