Ma Suliang, Jia Bowen, Wu Jianwen, Yuan Yang, Jiang Yuan, Li Weixin
School of Automation Science and Electrical Engineering, Beihang University, Beijing 100191, China.
School of Automation Science and Electrical Engineering, Beihang University, Beijing 100191, China.
ISA Trans. 2021 Jul;113:210-221. doi: 10.1016/j.isatra.2020.05.011. Epub 2020 May 22.
The condition of a high-voltage circuit breaker (HVCB) may have a major effect on a power system. In the practical application of artificial intelligence, many advanced technologies have been applied to the assessment of the state of health of a HVCB or the identification of a fault. To date, most related research related to the improvement of a feature extraction process or a classification method intended to attain a higher level of precision have been based on a single sensor. However, any method that relies on data from a single sensor cannot exceed a given level of precision. Most studies have neglected to consider whether the information provided by a single vibration signal is sufficient and effective. Therefore, this study proposes a multi-vibration Information joint diagnosis method to improve the diagnosis of HVCB faults. The procedure has two key steps: 1) the basic probability assigns an acquisition using a classification and regression tree (CART); and 2) a combination rule design based on the Gini index in the CART. By comparing the results of eight typical classifiers and three traditional fusion methods in a case of HVCB system, the validity and superiority of the proposed method has been verified.
高压断路器（HVCB）的状态可能会对电力系统产生重大影响。在人工智能的实际应用中，许多先进技术已被应用于高压断路器健康状态评估或故障识别。迄今为止，大多数旨在提高特征提取过程或分类方法精度的相关研究都是基于单个传感器进行的。然而，任何依赖单个传感器数据的方法都无法超越给定的精度水平。大多数研究都忽略了考虑单个振动信号提供的信息是否充分和有效。因此，本研究提出一种多振动信息联合诊断方法，以改进高压断路器故障诊断。该过程有两个关键步骤：1）使用分类回归树（CART）进行基本概率分配获取；2）基于CART中的基尼指数进行组合规则设计。通过在高压断路器系统案例中比较八种典型分类器和三种传统融合方法的结果，验证了所提方法的有效性和优越性。
