Publication: Unsupervised neural network for forecasting alarms in hydroelectric power plant
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1997
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Springer
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Abstract
Power plant management relies on monitoring many signals that represent the technical parameters of the real plant. The use of neural networks (NN) is a novel approach that can help to produce decisions when integrated in a more general system. In this paper we introduce a NN module using an ART-MAP to discriminate different situations from the plant in order to prevent future malfunctions. A special process to generate of a complete training set has been designed. This process is developed in order to deal with the absence of data in abnormal plant situations. This module belongs to a more general system for predictive maintenance that has been implemented and incorporated in an hydroelectric plant.
This project has been supported by CDTI (belonging to Spanish Industry Ministery) and EEC (European Economic Comunity), reference number: PASO PC067.
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Proceeding of: International Work-Conference on Artificial and Natural Neural Networks, IWANN'97 Lanzarote, Canary Islands, Spain, June 4–6, 1997
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Biological and Artificial Computation: from Neuroscience to Technology. Berlin: Springer, 1997. P. 1298-1306 (Lecture Notes in Computer Science; 1240)