Lee Jun, Kim Kiyoung, Sohn Hoon
Department of Civil and Environmental Engineering, Korea Advanced Institute of Science and Technology, Yuseong-gu, Daejeon 34141, Republic of Korea.
Sensors (Basel). 2023 Jul 12;23(14):6336. doi: 10.3390/s23146336.
Pumped-storage hydroelectricity (PSH) is a facility that stores energy in the form of the gravitational potential energy of water by pumping water from a lower to a higher elevation reservoir in a hydroelectric power plant. The operation of PSH can be divided into two states: the turbine state, during which electric energy is generated, and the pump state, during which this generated electric energy is stored as potential energy. Additionally, the condition monitoring of PSH is generally challenging because the hydropower turbine, which is one of the primary components of PSH, is immersed in water and continuously rotates. This study presents a method that automatically detects new abnormal conditions in target structures without the intervention of experts. The proposed method automatically updates and optimizes existing abnormal condition classification models to accommodate new abnormal conditions. The performance of the proposed method was evaluated with sensor data obtained from on-site PSH. The test results show that the proposed method detects new abnormal PSH conditions with an 85.89% accuracy using fewer than three datapoints and classifies each condition with a 99.73% accuracy on average.
抽水蓄能水电（PSH）是一种通过在水力发电厂中将水从较低海拔水库泵送到较高海拔水库，以水的重力势能形式存储能量的设施。PSH的运行可分为两种状态：发电的涡轮机状态和将产生的电能存储为势能的泵状态。此外，PSH的状态监测通常具有挑战性，因为作为PSH主要部件之一的水轮机浸没在水中且持续旋转。本研究提出了一种无需专家干预即可自动检测目标结构中新异常状况的方法。该方法能自动更新和优化现有的异常状况分类模型，以适应新的异常状况。使用从现场PSH获得的传感器数据对该方法的性能进行了评估。测试结果表明，该方法使用少于三个数据点就能以85.89%的准确率检测PSH的新异常状况，并且平均以99.73%的准确率对每种状况进行分类。
