Statistics – Machine Learning
Scientific paper
2012-04-04
Statistics
Machine Learning
Scientific paper
Deregulation of energy markets, penetration of renewables, advanced metering capabilities, and the urge for situational awareness, all call for system-wide power system state estimation (PSSE). Implementing a centralized estimator though is practically infeasible due to the complexity scale of an interconnection, the communication bottleneck in real-time monitoring, regional disclosure policies, and reliability issues. In this context, distributed PSSE methods are treated here under a unified and systematic framework. A novel algorithm is developed based on the alternating direction method of multipliers. It leverages existing PSSE solvers, respects privacy policies, exhibits low communication load, and its convergence to the centralized estimates is guaranteed even in the absence of local observability. Beyond the conventional least-squares based PSSE, the decentralized framework accommodates a robust state estimator. By exploiting interesting links to the compressive sampling advances, the latter jointly estimates the state and identifies corrupted measurements. The novel algorithms are numerically evaluated on IEEE 14- and 118-bus benchmarks. Simulations show that the attainable accuracy can be reached within a few inter-area exchanges, while largest residual tests are outperformed.
Giannakis Georgios B.
Kekatos Vassilis
No associations
LandOfFree
Distributed Robust Power System State Estimation does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.
If you have personal experience with Distributed Robust Power System State Estimation, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Distributed Robust Power System State Estimation will most certainly appreciate the feedback.
Profile ID: LFWR-SCP-O-32558