Adaptive Critic Networks for Real-time Fault Diagnosis in Electric Power Production

用于电力生产实时故障诊断的自适应批评网络

• 批准号: 9113623
• 负责人: Benito Fernandez
• 金额: $ 19.84万
• 依托单位: University of Texas at Austin
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 1991
• 资助国家: 美国
• 起止时间: 1991-08-15 至 1995-01-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9113623&HistoricalAwards=false
• 关键词: Adaptive; Critic; Networks; Real; time

This project will develop a architecture for real fault diagnosis of electric power generating systems. It will focus on fossil fueled and nuclear power plants, characteristic of complex, large-scale systems amenable to analysis. The fault diagnosis architecture is a network of neural networks: At the signal processing level of the hierarchy, a recurrent multilayer perceptron network is used to recursively identify the nonlinear plant dynamics, whereas another neural network with similar structure is used for state and parameter estimation purposes. At the decision making level of the hierarchy, an adaptive critic network is used for detecting and isolating potential faults. The adaptive critic is composed of three neural networks, a faulty system model, a performance optimizer (critic), and a classifier (action) which selects and reports the time, location, magnitude, and nature of the faults. The resulting diagnosis procedure exhibits generic and therefore portable architecture. In the presence of real-world operating conditions characterized by noisy sensor and actuator measurements, as well as process noise and drifts, the proposed approach would be capable of detecting, isolating, and accommodating system faults which have not been encountered or anticipated during operation. It is anticipated that successful completion of the proposed project will significantly impact the electric power industry, and complex, large-scale nonlinear systems in general.

本项目将开发一个用于发电系统真实故障诊断的体系结构。它将侧重于化石燃料和核电站，其特点是复杂，大规模的系统易于分析。故障诊断体系结构是由神经网络组成的网络：在层次结构的信号处理层，使用递归多层感知器网络递归识别非线性植物动态；而另一种具有相似结构的神经网络用于状态和参数估计。在层次结构的决策层，采用自适应评价网络对潜在故障进行检测和隔离。自适应评论家由三个神经网络组成，一个故障系统模型，一个性能优化器（评论家）和一个分类器（行动），它选择并报告时间，地点，断层的大小和性质。由此产生的诊断程序显示出通用的、因此可移植的体系结构。在存在以传感器和执行器测量噪声以及过程噪声和漂移为特征的实际操作条件下，所提出的方法将能够检测，隔离和适应在运行过程中没有遇到或预料到的系统故障。预计该项目的成功完成将对电力工业和复杂的大型非线性系统产生重大影响。