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Thermoelectric plants are one of the main forms of energy generation in the world, being the second main source of generation in Brazil. However, with rising fuel costs and greater concern for the environment, controlling the efficiency levels of these plants has become critical. This work presents a system to identify failures and evaluate the efficiency of internal combustion engines used in thermal power plants. To assess efficiency, the developed system monitors subsystem losses (such as cooling, lubrication, turbocharger, etc.). In addition, sensors for cylinder pressure and instantaneous speed were installed and comprise an online monitoring system for the pressure condition of each cylinder of the engines. All this is combined into a supervisory system that presents the Sankey diagram of the engine as its main information online and remotely. To validate the system, experiments were carried out in a controlled configuration (where faults were purposely inserted) and in a Brazilian thermal power plant. The results show that by using in-cylinder pressure and the WOIS database, it was possible to detect the presence of a fault as well as pinpoint its location. [ABSTRACT FROM AUTHOR]

This paper presents the practical aspects of development of a reduced scale laboratory and a set of monitoring tools for Internal Combustion Engines used in Thermal Power Plants. The reduced scale laboratory is based on the necessity of researchers to test new sensors and monitoring strategies that, otherwise, are seldom allowed to be installed in real plants without certification. In addition, the reduced scale laboratory allows the flexibility to insert failures on purpose, in order to evaluate the performance of new sensors/strategies in a safe and controlled environment. The paper also presents the development of a set of reduced cost sensors for monitoring in-cylinder pressure, crank angle, and the position of inlet and exhaust valves (without using ultrasound sensors, which may produce noisy readings on engines operating on gas-diesel fuel mode). [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The culture of excellence in the area of maintenance combined with the needs of production cost reduction and increased productivity makes the reliability of electric power generation process becomes increasingly important, making room for the emergence of new maintenance techniques, especially predictive techniques that use continuous monitoring equipment systems. Predictive maintenance is the technique that establishes the planned corrective maintenance based on the changing monitored parameters. These monitored parameters may come from continuous condition monitoring system, which are more effective. Within this context, the most ancient and widespread technique is the vibration analysis. However, a relatively new technique and, until so little used, is the electrical signature analysis of generators. This paper presents the development of a predictive system for diagnosing internal combustion engines faults as well excitation and coupling through the electrical signature analysis of voltage and current signals of the generator. The main goal of this system is to obtain the signature of the electrical signals to identify the magnitude and frequency of each individual component allowing the identification of different faults in the generator group. The paper presents some measurements acquired directly through the installation of the developed system in SUAPE II thermal power plant. [ABSTRACT FROM AUTHOR]