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子神经网络
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    After analysis of the current techniques of computerized monitoring and control systems of hydropower plants in China, a new computerized monitoring and control system based on elegant incorporation of expert system with neural network is put forward. Furthermore, multi neural network and data fusion techniques are also introduced into the system.
    在分析国内水电厂状态监测技术现状后 ,提出了一种基于神经网络和专家系统相结合的状态监测系统 ,并引入了子神经网络和数据融合技术。
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    The system adopts the sub-network to diagnose preliminarily from different sides of the equipment, and then gets combination of the sub-network decisions.
    该系统采用子神经网络从不同侧面对设备进行初步诊断 ,然后将各子神经网络进行决策融合。
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    After fault feature data were classified and processed,several shunt-wound BP networks were used to carry on local HGS fault diagnosis and acquire independent evidences each other. Then D-S evidence theory fusion algorithms were used to fuse evidences. Accurate HGS fault diagnosis was fulfilled finally.
    根据故障特征量将故障进行分类处理,采用多个并联的BP子神经网络进行水轮发电机组故障的局部诊断,获得彼此独立的证据,再运用D-S证据理论融合算法对各证据进行融合,最终实现对水轮发电机组故障的准确诊断。
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A multi ANN adaptive controller for turbogenerators, addressed in this paper, is composed of two sub ANNs for predicting and control respectively. In the study it gives an on line realtime self learning and self adjusted algorithm based on errors back propagation. As a result of a simulation, it shows that this controller has a good performance for turbogenerators with nonlinear, time varying and multivariable characteristics.

一种水轮发电机组分层神经网络自适应预测控制器,采用多层前向神经网络,由两个分别用于预测和控制的子神经网络组成.提出一种误差反向传播在线实时自变学习率算法.仿真结果表明,该控制器对具有非线性、时变、多变量特性的水轮发电机组,具有良好性能.

After analysis of the current techniques of computerized monitoring and control systems of hydropower plants in China, a new computerized monitoring and control system based on elegant incorporation of expert system with neural network is put forward. Furthermore, multi neural network and data fusion techniques are also introduced into the system. In this paper, the system framework and its corresponding function modules are introduced in detail.

在分析国内水电厂状态监测技术现状后 ,提出了一种基于神经网络和专家系统相结合的状态监测系统 ,并引入了子神经网络和数据融合技术。重点介绍了该系统的框架和相应的功能模块。

The method for the turbine fast valving control strategies with the single forward neural network is presented. The neural network is divided into two sub-networks. The first neural sub-network is to determine power system transient stability and to output the valve minimal opening and the time during which the valve remains closed. The second neural sub-network is to output the final opening of the valve under instability. Applying the neural network controller to a disturbed three machine system, the result...

The method for the turbine fast valving control strategies with the single forward neural network is presented. The neural network is divided into two sub-networks. The first neural sub-network is to determine power system transient stability and to output the valve minimal opening and the time during which the valve remains closed. The second neural sub-network is to output the final opening of the valve under instability. Applying the neural network controller to a disturbed three machine system, the result is satisfactory.

提出一种应用单隐层前向神经网络决定电力系统暂态稳定快速汽门控制规律的方法。将实现控制规律的神经网络控制器分为两个子神经网络:子神经网络1,用来确定系统的暂态稳定情况,同时给出相应汽门最小开度和汽门持续关闭的时间;子神经网络2,用于确定不稳定情况下汽门的最终开度。该神经网络控制器用于受扰动后的三机系统中,其仿真结果令人满意。

 
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