基于数字孪生的压气机水洗运维决策

Operation and Maintenance Decision of Compressor Water Wash Based on Digital Twin

为了提高燃气轮机的发电效率,针对压气机水洗运维经济成本过高的问题,对压气机进行基于数字孪生的运维决策研究,提出一种基于数字孪生的电厂燃气轮机健康管理框架,基于该框架对压气机运行数据进行处理,使用极端梯度提升树算法搭建水洗周期预测模型,选取数据集内部分参数作为模型输入量,气耗量为输出量,分析其变化规律及其与输入量之间的关系,对水洗周期及水洗恢复率进行计算和比较,得出合适的水洗周期,对压气机进行运维决策。模型预测结果表明:8次水洗气耗量预测的平均R 2_score达到0.98,预测结果准确;8次水洗中,第2、第3次水洗周期合适,第3次水洗恢复率最优,得出压气机水洗周期的指导小时数为1824 h;与电厂实际执行的平均水洗周期相比,每次水洗成本可以降低2190万元。

In order to improve the power generation efficiency of the gas turbine,and to solve the problem of the high cost of the operation and maintenance(O&M)of the compressor water washing system,a digital twin-based O&M decision study of the compressor was conducted.And a health management framework for gas turbine in power plants based on digital twin was proposed.Based on that the compressor operation data was processed,and the extreme gradient boosting algorithm was used to build a prediction model for the washing cycle,and some of the parameters within the dataset were selected as inputs to the model,with the gas consumption as the output quantity,analyzed the change rule and its relationship with the input quantity.the water washing cycle and water washing recovery rate were calculated and compared,and the appropriate water washing cycle for O&M decision of the compressor was derived.The model prediction results showed that:the average R 2_score of the eight water washing gas consumption prediction reached 0.98,and the prediction results were accurate.Among the eight times of water washing,the second and third water washing cycles were appropriate,and the third water washing recovery rate was optimal,resulting in the guiding hours of gas turbine compressor water washing cycle of 1824 hours.Compared with the average water washing cycle of the power plant in the actual implementation,the cost of water washing could be reduced by 21.9 million yuan per time.