Dynamic Security Assessment For Power System Using Attribute Selection Technique

The evaluation of the dynamic security of the electrical power system after the occurrence of disturbances in the network is one of the most important tools that the control center uses to maintain the system in a safe operating mode, as well as prevent cases of system out of control and cases of complete shutdown. With the annual increase in the size of the electrical system and its distribution over a very wide geographical area, this led to a new challenge to assess dynamic security assessment (DSA), which is dealing with a huge and varied amount of data that requires processing in a very short time. To address these challenges, this study presented a new technique of artificial intelligence, which is the attribute selection technique, to reduce the size of this data and thus improve the accuracy and speed of results. This method relied on the combination of decision tree algorithms and a technique (Attribute selection) in the data obtained from the test system (IEEE-30Bus). The results of this method showed a significant reduction in the number of data used, which amounted to (45.55%) of the total data, Which led to an improvement in the classification accuracy, as the classification accuracy reached (97.27%). This reduction is very important when dealing in the online operating environment, as it saves the time necessary to reach the most accurate evaluation decision and thus issue gives a greater opportunity to take the appropriate decision in the event of disturbances and keep the electrical system in a secure state