A tractable ellipsoidal approximation for voltage regulation problems
This addresses voltage regulation for power system operators, but appears incremental as it builds on existing SVM methods for a specific domain.
The paper tackles voltage regulation in power systems by approximating uncertainty regions with ellipsoids, using an SVM-like learning model and a sampling algorithm, and demonstrates it on IEEE test feeders with unspecified performance metrics.
We present a machine learning approach to the solution of chance constrained optimizations in the context of voltage regulation problems in power system operation. The novelty of our approach resides in approximating the feasible region of uncertainty with an ellipsoid. We formulate this problem using a learning model similar to Support Vector Machines (SVM) and propose a sampling algorithm that efficiently trains the model. We demonstrate our approach on a voltage regulation problem using standard IEEE distribution test feeders.