RT Journal Article
T1 Hydroelectric power plant management relying on neural networks and expert system integration
A1 Molina López, José Manuel
A1 Isasi, Pedro
A1 Berlanga de Jesús, Antonio
A1 Sanchis de Miguel, María Araceli
AB The use of Neural Networks (NN) is a novel approach that can help in taking decisions when integrated in a more general system, in particular with expert systems. In this paper, an architecture for the management of hydroelectric power plants is introduced. This relies on monitoring a large number of signals, representing the technical parameters of the real plant. The general architecture is composed of an Expert System and two NN modules: Acoustic Prediction (NNAP) and Predictive Maintenance (NNPM). The NNAP is based on Kohonen Learning Vector Quantization (LVQ) Networks in order to distinguish the sounds emitted by electricity-generating machine groups. The NNPM uses an ART-MAP to identify different situations from the plant state variables, in order to prevent future malfunctions. In addition, a special process to generate a complete training set has been designed for the ART-MAP module. This process has been developed to deal with the absence of data about abnormal plant situations, and is based on neural nets trained with the backpropagation algorithm.
PB Elsevier
SN 0952-1976
YR 2000
FD 2000-06
LK https://hdl.handle.net/10016/3939
UL https://hdl.handle.net/10016/3939
LA eng
DS e-Archivo
RD 3 may. 2024