The validation of data from sensors has be come an important issue in the operation and control of modern industrial plants. One ap proach is to use know ledge based techniques to detect inconsistencies in measured data.This article presents a probabilistic model for the detection of such inconsistencies. Based on probability propagation, this method is able to find the existence of a possible fault among the set of sensors. That is, if an er ror exists, many sensors present an apparent fault due to the propagation from the sen:. sor(s) with a real fault. So the fault detection mechanism can only tell if a sensor has a po tentwl fault, but it can not tell if the fault is real or apparent. So the central problem is to develop a theory, and then an algorithm, for distinguishing real and apparent faults, given that one or more sensors can fail at the same time. This article then, presents an approach based on two levels: (i) probabilistic reason ing, to detect a potential fault, and (ii) con straint management, to distinguish the real fault from the apparent. ones. The proposed approach is exemplified by applying it to a power plant model.

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Due to its ability to deal with non-determinism and partial observability, represent goals as an immediate reward function and find optimal solutions, planning under uncertainty using factored Markov Decision Processes (FMDPs) has increased its importance and usage in power plants and power systems. In this paper, three different applications using this approach are described: (i) optimal dam management in hydroelectric power plants, (ii) inspection and surveillance in electric substations, and (iii) optimization of steam generation in a combined cycle power plant. For each case, the technique has demonstrated to find optimal action policies in uncertain settings, present good response and compilation times, deal with stochastic variables and be a good alternative to traditional control systems. The main contributions of this work are as follows, a methodology to approximate a decision model using machine learning techniques, and examples of how to specify and solve problems in the electric power domain in terms of a FMDP.