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In thermal power plants, the internal combustion engines are constantly subjected to stresses, requiring a continuous monitoring system in order to check their operating conditions. However, most of the time, these monitoring systems only indicate if the monitored parameters are in nonconformity close to the occurrence of a catastrophic failure—they do not allow a predictive analysis of the operating conditions of the machine. In this paper, a statistical model, based on the statistical control process and Nelson Rules, is proposed to analyze the operational conditions of the machine based on the supervisory system data. The statistical model is validated through comparisons with entries of the plant logbook. It is demonstrated that the results obtained with the proposed statistical model match perfectly with the entries of the logbook, showing our model to be a promising tool for making decisions concerning maintenance in the plant.

The importance of power in society is indisputable. Virtually all economic activities depend on electricity. The electric power systems are complex, and move studies in different areas are motivated to make them more efficient and solve their operational problems. The smart grids emerged from this approach and aimed to improve the current systems and integrate electric power using alternative and renewable sources. Restoration techniques of these networks are being developed to reduce the impacts caused by the usual power supply interruptions due to failures in the distribution networks. This paper presents the development and evaluation of the performance of a multi-objective version of the Bacterial Foraging Optimization Algorithm for finding the minor handling switches that maximize the number of buses served, keeping the configuration radial system and within the limits of current in the conductors and bus voltage. An electrical system model was created, and routines were implemented for the network verification, which was used as a function of the Multi-Objective Bacterial Foraging Optimization Hybrid Algorithm. The proposed method has been applied in two distribution systems with 70 buses and 201 buses, respectively, and the algorithm’s effectiveness to solve the restoration problem is discussed.

Smart alarm and event processing is a tool to aid decision-making for the operators during the electric system monitoring in real time. The main function of this processing is elimination of secondary and unnecessary information while important data are highlighted to the operator during any failure or malfunction monitored in the electrical system. This processing is composed of four main parts: a rule-editor, an event-classifier, a rule-extractor and a rule-assembling. The two main parts composes a program named FASE, which runs in the control centers. The two last parts composes the program named EASE which produces rules for FASE. Currently the EDP-ESCELSA Power Distribution Company uses for monitoring of your electrical system the platform, named SCATEX, developed by EDP Group in partnership with the EFACEC Group. The SCATEX is a SCADA system, integrating supervision and control of electrical systems. This paper presents de the development of the four main parts of the program, with some practical examples from the ESCELSA database.

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