基于多元状态估计与自适应闽值的电站辅机故障预警

Early Fault Warning for Auxiliary Equipment of Power Plants Based on Multivariate State Estimation and Adaptive Threshold

为了有效识别辅机运行过程中的故障,将多元状态估计技术应用于辅机的故障预警中。根据机组功率高低将辅机的历史状态矩阵分为3类,通过等距抽样选取典型状态分别建立子模型。对于输入的观测向量,模型给出相应的估计向量,两者的偏差可用相似度函数表示,并基于区间估计的思想设计了相似度的自适应阈值方法。最后利用某350 MW热电机组的中速磨煤机堵煤故障前的数据进行仿真。结果表明:模型在磨煤机跳闸前264 s做出预警,具有较高的故障检测效率;与传统的固定阈值方法相比,采用自适应阈值方法可有效降低误报率。

To effectively identify the faults occurring in the operation of auxiliary equipment, the multivariate state estimation technique was applied for the fault warning of related auxiliaries. The historical state matrix of these auxiliaries was divided into three categories according to the power level of the unit, while typical states were selected to establish submodels based on isometric sampling. For input observation vectors, corresponding estimation vectors were given, and the deviation between them was reflected by a similarity function. Based on interval estimation, the adaptive threshold of similarity degree was designed. Numerical simulations were finally conducted with the data obtained before coal clogging in the medium speed mill of a 350 MW thermoelectric unit. Results show that the model is able to send an early warning 264 s before coal mill tripping, indicating that the method has a high efficiency in fault detection, which helps to reduce the false alarm rate, compared with traditional fixed threshold methods.