基于多智能体和人工神经网络的水电厂预知维护系统的研究

随着电力市场的发展，水电厂维护的自动化是提高水电厂整体经济效益的必然要求。在ICMMS框架下，提出了一种水电厂预知维护系统的多智能体模型。该模型由数据采集层、数据处理层、诊断与预诊断层以及维护决策层组成，从信号采集到维护决策的所有维护活动都集成到这个模型中。利用这个模型，建立了一个水电厂预知维护系统的原型系统，并应用ANN技术成功地解决了原型系统动态特性的在线监测、识别和故障诊断。

PREDICTIVE MAINTENANCE FOR HYDROPOWER PLANT BASED ON MAS AND ANN

As the development of the electrical power market, the maintenance automation has become an intrinsic need to increase the overall economic efficiency of hydropower plants. A Multi-Agent System (MAS) based model for the predictive maintenance system of hydropower plant within the framework of Intelligent Control-Maintenance-Management System (ICMMS) is proposed. All maintenance activities, from data collection through the recommendation of specific maintenance actions, are integrated into the system. In this model, the predictive maintenance system composed of four layers: Signal Collection, Data Processing, Diagnosis and Prognosis, and Maintenance Decision-Making. Using this model, a prototype of predictive maintenance for hydropower plant is established. Artificial Neural-Network (ANN) is successfully applied to monitor, identify and diagnosis the dynamic performance of the prototype system online.