摘要：【中文】本发明提供一种火力发电机组燃烧控制优化方法、装置及可读存储介质，方法包括：获取发电系统的真实特征数据集；根据所述真实特征数据集对预设的待训练火电燃烧模拟器进行训练，获得训练后的火电燃烧模拟器；通过所述真实特征数据集以及所述训练后的火电燃烧模拟器根据所述真实特征数据集生成的模拟特征数据集对预设的基于深度强化学习的框架中的策略网络、价值网络以及约束网络进行训练，获得训练后的策略网络；通过训练后的策略网络对发电系统进行优化。通过利用真实特征数据集进行训练，从而能够提高发电系统运行状态变化刻画可信度，进而能够提高优化效率。 【EN】The invention provides a combustion control optimization method, a combustion control optimization device and a readable storage medium for a thermal generator set, wherein the method comprises the following steps: acquiring a real characteristic data set of the power generation system; training a preset thermal power combustion simulator to be trained according to the real characteristic data set to obtain a trained thermal power combustion simulator; training a strategy network, a value network and a constraint network in a preset frame based on deep reinforcement learning through the real feature data set and a simulated feature data set generated by the trained thermal power combustion simulator according to the real feature data set to obtain a trained strategy network; and optimizing the power generation system through the trained strategy network. The real characteristic data set is used for training, so that the reliability of the operation state change depiction of the power generation system can be improved, and the optimization efficiency can be improved.

摘要：【中文】本发明公开了一种锅炉吹灰方法、装置和计算机可读存储介质，涉及火力发电技术领域。锅炉吹灰方法包括：获取表示当前锅炉运行状态的特征；将获取的特征输入到预先训练的预测模型中，得到预测的烟温，预测模型用于预测清洁工况的锅炉在当前锅炉运行状态下的烟温；将当前锅炉的实际烟温与预测的烟温之差作为积灰度；根据积灰度确定吹灰操作的执行策略。本发明根据锅炉运行状态的特征预测当前锅炉的积灰度，并确定相应的吹灰执行策略。从而，通过数据驱动的方式实现了吹灰控制，提高了积灰度预测的准确性，优化了吹灰效果。 【EN】The invention discloses a boiler soot blowing method, a boiler soot blowing device and a computer readable storage medium, and relates to the technical field of thermal power generation. The boiler soot blowing method comprises the following steps: acquiring characteristics representing the current boiler operation state; inputting the acquired characteristics into a pre-trained prediction model to obtain a predicted smoke temperature, wherein the prediction model is used for predicting the smoke temperature of the boiler under the clean working condition in the current boiler operation state; taking the difference between the actual smoke temperature of the current boiler and the predicted smoke temperature as the gray level; and determining an execution strategy of the soot blowing operation according to the gray scale. The method predicts the soot deposition of the current boiler according to the characteristics of the operating state of the boiler and determines a corresponding soot blowing execution strategy. Therefore, soot blowing control is realized in a data driving mode, the accuracy of soot deposition prediction is improved, and the soot blowing effect is optimized.