Date of Award
ANN, Cascade Failure, Interdependency
The main purpose of this thesis is to use Artificial Neural network as a tool to monitor system health and performance. In other word Using ANN can increase the system awareness and can be used as a tool to mitigate cascade failure in power grid due to loss of communication in a critical power node and as a result avoid catastrophic phenomena like electric blackout. In this thesis, a modified IEEE 30 bus system is used as a system under study. Modified IEE 30 bus system is IEEE 30 bus system in which 2 sets of its synchronous condensers changed to act as synchronous generator. Results of load flow analysis of modified IEEE 30 bus system is used to train ANN.
Electric power grid and communication system are interdependent. In other words, electric power grid relies on communication network to transmit control signal and from other side communication network relies on power grid to supply its required electricity for operation. Failure in a node in one of the interdependent networks may lead to failure of dependent nodes on the other network. If the failure is not very fast identified and the required actions did not taken place, the failure propagates in both networks and may lead to electric black out.
ANN is used to monitor the Vpu of some specific voltage buses in power grid and based on the difference between observed and desired value it can decide whether the communication has been failed due to the failure in communication network or the bus bar voltage is deviated due to outage of generator at that specific node.
Mhandi, Yassine, "Data Driven Approach for Increasing Power Grid Situational Awareness and Mitigating Cascaded Failures" (2017). CUNY Academic Works.