为提升台风等灾害性气候下电网运行的风险预警预控能力,将气象环境数据、地理环境、输变电设备状态与电网实时状态结合,通过机器学习技术提取电网历史故障特征,将数字化输电通道的气象风险概率量化研判,实现台风气象环境下电网高风险故障的超前量化预警,并根据当时的电网状态实时生成当前和未来态N-m故障处置预案,显著增强故障预想的及时性与针对性.该方法克服了气象风险模型需要不断根据实际情况修正的困难,考虑了高风险概率设备故障后电网中的各类安全自动装置的响应情况和需要采取的控制措施.实际应用表明, 该方法可为电网调度运行风险预控提供重要决策支持.

In order to improve the power grid operation risk early warning and pre-control capabilities in typhoon and other disastrous climates, the historical fault features of power grid are extracted by utilizing the machine learning technology based on the meteorological environment data, geographic environment, and the status of power transmission and transformation equipment. The meteorological risk probability of digital transmission channel is quantified to realize the advanced quantitative warning of high-risk faults in the power grid under typhoon weather environment. The proposed method can genarate the current and future N-m fault handling plans based on the current power grid status and significantly enhance the timeliness and pertinence of fault prediction. This method overcomes the difficulty that the meteorological risk model needs to be continuously revised according to the actual situation, and takes into account the response of various safety automatic devices in the power grid after a high-risk equipment failure and the control measures that need to be taken. The practical application shows that this method can provide significant decision supports for risk pre-control in the power grid dispatching operation.