Remedial Action Schemes (RAS) provide automatic control action with high impact on system performance. In order to improve the resilience of the RAS against any single node failure and to guarantee the secured and reliable operation of the power systems, distributed implementations of the RAS is researched upon. The input data to such a distributed RAS must be of high quality. In addition, RAS operation must be fast. Traditional centralized state estimation, which feeds data to RAS is slow and cannot meet the requirements of RAS. New approach needs to be developed to provide the fast and accurate data to RAS. In order to solve this problem, distributed state estimation is developed as an alternative strategy to feed data to the RAS. This paper discusses the implementation of Distributed Linear State Estimation (DLSE) in a decentralized platform called Resilient Information Architecture Platform for Smart Grid (RIAPS). The DLSE algorithm is fully implemented in RIAPS platform and validated on a real-time testbed consisting of Real Time Digital Simulator, Phasor Measurement Units and BeagleBones. The effectiveness of the proposed approach is validated through online simulations on IEEE 14-bus test system under various cyber failures.
V. V. G. Krishnan, S. Gopal, R. Liu, Z. Nie, A. Srivastava and D. Bakken, "Resilient Information Architecture Platform for Distributed Linear State Estimation," 2018 IEEE Power & Energy Society General Meeting (PESGM), Portland, OR, 2018, pp. 1-5.
KEYWORDS: distributed control; linear systems; phasor measurement; power system control; power system reliability; power system security; power system simulation; power system state estimation; smart power grids; Remedial Action Schemes; automatic control action; single node failure; power systems; Distributed Linear State Estimation; RIAPS platform; centralized state estimation; resilient information architecture platform; RAS; DLSE; smart grid; real time digital simulator; phasor measurement units; BeagleBones; IEEE 14-bus test system; cyber failures; State estimation; Phasor measurement units; Power system stability; Smart grids; Monitoring; Partitioning algorithms; Information architecture; Distributed algorithms; Decentralized Control; Smart Grid; State Estimation