天然气集输异常工况处理的主动学习方法

Active Learning Method for Abnormal Operating Conditions of Natural Gas Gathering System

方宇 曹雪梅 李宾倩 闵帆 谯英

FANGYu CAOXuemei LIBinqian MINFan QIAOYing

西南石油大学计算机科学学院, 四川 成都 610500 西南石油大学电气信息学院, 四川 成都 610500

School of Computer Science, Southwest Petroleum University, Chengdu, Sichuan 610500, China School of Electrical Engineering, Southwest Petroleum University, Chengdu, Sichuan 610500, China

然气集输系统中出现的各种异常工况对安全生产构成威胁。提出一种针对异常工况的智能处理系统模型。该模型的异常工况类别预测模块采用了主动学习方法，既可实时、准确地判断异常类型，又可为系统向专家推荐合适的处理方案奠定基础。首先，利用SCADA系统实时监控数据并进行异常工况预警。其次，通过主动学习算法对预警异常工况进行分类，从而为构建异常工况推理机提供支撑，进而实现智能决策辅助。实验结果表明，该方法能节约专家成本，很好地识别异常工况类型，提出合理的解决方案。

Various abnormal operating conditions in natural gas gathering system pose a threat to safe production. This paper proposes an intelligent processing system model for abnormal operating conditions. The abnormal operating conditions classification prediction module of the model adopts the active learning method, which can classify the abnormal type in real time and accurately, and provide a basis for the system to recommend appropriate processing schemes to experts. Firstly, use the SCADA system to monitor data in real time and perform abnormal conditions warning. Secondly, we use the active learning algorithm to classify the early warning of abnormal operating conditions. The classification results provide support for constructing the abnormal working condition inference engine, and then implement intelligent decision-making assistance. The experimental results show that the proposed method can save the cost of experts, identify the types of abnormal operating conditions, and propose a reasonable solution.

10.11885/j.issn.1674-5086.2020.05.12.08