基于多模型鲁棒输入训练神经网络协同的燃气-蒸汽联合循环机组传感器故障诊断方法

为提高燃气-蒸汽联合循环机组传感器测量值的准确性及可靠性，提出了一种基于多模型鲁棒输入训练神经网络（RITNN）的燃气-蒸汽联合循环机组传感器故障诊断方法。该方法建立若干燃气-蒸汽联合循环重要参数的数据重构模型，并对各模型进行优先级划分，以串并联方式设定模型间关系，通过可靠参数的逐级生成和传递，有效抑制了多传感器显著故障产生的残差污染，提高了故障诊断的准确性及可靠性，进而给出了传感器故障诊断流程，建立了完整的传感器故障诊断系统。以某200 MW级燃气-蒸汽联合循环机组为研究对象，对多传感器故障进行诊断，并与RITNN单一模型方法和输入训练神经网络（ITNN）单一模型方法进行对比，结果表明，提出的多模型RITNN故障诊断方法诊断精度更高，可保证燃气-蒸汽联合循环机组稳定运行。

Multi-sensor Fault Detection for Natural Gas Combined Cycle Power Plants Based on Multiple Robust Input Training Neural Network Models

In order to enhance the accuracy and reliability of multiple sensor measurements in natural gas combined cycle (NGCC) power plants, a multi-sensor fault detection method based on multi-model robust input training neural network is proposed in this paper, in which multiple robust input training neural network (RITNN) models are built and prioritized for the purpose of sensor faults reconstruction and monitoring. The relationship between the models are set in terms of serial or parallel connections. The influence of numerous failure data with significant errors can be effectively inhibited by virtue of reliable sensor data calculated from cooperative multi-model, such that the accuracy and reliability of fault detection is greatly improved. In addition, the process for sensor fault detection is presented to establish a complete fault dectection system. The proposed method was evaluated in a 200 MW NGCC power plant, where the multi-sensor fault detection was conducted and the results were compared with those from single RITNN model and single input training neural network (ITNN) model detection.The proposed method demonstrated higher accuracy in multiple sensor failure cases than the single RITNN model or the single ITNN model.