A decision support system for predicting the Emergency Shutdown of the power plants by using association rule mining. case study in Maroon power plant-Behbahan

Sensors monitor the status of various parts of hydroelectric power station and control instruments issued instructions to operate the power plant.The experts based on the amount of numbers for which sensors and thermometers are fitted and shown, and also based on environmental conditions of the plant, and experience, make a decision for emergency power shutting down. In a hydroelectric power plant several factors such as: loaders, maintenance, signs warning sensors, physical damage to equipment or the height of the water behind the dam, may be stop the generation of the electricity.So appropriate activity or inactivity time detection of power plant according to the sensors is vital. Although the existing control systems to check the syntax of the favorable conditions but different ball fitted such as human error or equipment error may decide to continue with the emergency shutdown error or work together.In this article, using data mining techniques for a system that is designed to be fitted decision sweetheart meaningful relationships between data that sensors in all hydroelectric power plant can archive . These relationships can deduce laws such as fitted in a quick and accurate decision making experts are extremely helpful and damage to equipment in the wrong decisions or prevent late. data set is gotten from Maroon power plant-Behbahan from years 92 to 94. we extract 41 rules by association rule mining that experts have been recognized 4 of them are new knowledge.