基于神经网络的故障预警平台设计与开发

Design and Development of Fault Early Warning Platform Based on Neural Network

在电站实际运行过程中,火电机组设备众多、结构复杂,而热工参数也存在着多维、耦合性强的特性,他们是设备运行状态的直接反应,一个参数发生的任何异变都会对多个参数、多设备乃至整个热工过程产生严重的影响,给发电过程带来严重的后果,因此有必要对热工设备的运行状态进行实时监测和预警,故障检测、故障预警、故障诊断已经成为热工过程最为重视的问题。基于热工过程海量数据流,利用自联想神经网络建模方法,开发了预警系统。试验数据表明,该系统为电厂运行人员提供了灵活的建模方法和准确的预警,能为运行人员提供可靠指导。

In the actual operation of the power station,there are many equipment and complex structures in thermal power plants.Thermal parameters are a direct response to the operating status of the equipment.Any change in the parameters have a serious impact on the thermal process and bring serious consequences to power production and users.Therefore,it is necessary to carry out the operating status of the thermal equipment.Real-time monitoring and early warning,fault detection,early warning,and fault diagnosis have become the most important issues in the thermal process.This paper develops an early warning system based on the massive data flow of the thermal process and using the auto-associative neural network modeling method.Test data show that the system provides flexible modeling methods and accurate early warning for power plant operators,and can provide reliable guidance for operators.