Data Driven Approach for Increasing Power Grid Situational Awareness and Mitigating Cascaded Failures

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

Impact of Clustering Microgrids on Their Stability and Resilience during Blackouts

In this paper, the impact of clustering multiple microgrids during blackouts, on their stability and supply availability, will be investigated. Microgrids have the capability of satisfying their emergency loads during blackouts. However, distributed energy resources (DERs)-dominated microgrids are affected by the uncertainty of their input energy supply, e.g. impact of solar irradiance on photovoltaic (PV) output. Moreover, an individual islanded microgrid is prone to instability issues due to large sudden load/generation changes. In order to increase the supply security, and enhance system stability, we propose to use the existing distribution grid infrastructure, if applicable, during blackouts to form microgrid clusters. The paper discusses the required control hierarchy required to manage the microgrid clusters, and communicate with the Distribution Network Operator (DNO). A case study based on the 13-bus standard distribution feeder, and two microgrid models, is presented. Results show that microgrids clustering helps improve their performance and that the microgrid generator inertia has a direct impact on the stability of the microgrid cluster

Design and implementation of CCNY DC microgrid testbed

This paper presents the design, control, energy management, and implementation of the City College of New York (CCNY) direct current (DC) microgrid laboratory testbed. This facility was custom designed and implemented by researchers at CCNY with minimal off-the-shelf components to enable significant flexibility and reconfiguration capability. The microgrid consists of renewable energy resources, energy storage system and controllable loads, and can operate in either a grid-connected or an islanded mode. The design steps, requirements, and results of the developed testbed were discussed. Moreover, several operational scenarios were tested. The experimental results verify the applicability and flexibility of the developed microgrid testbed