Online semidefinite programming for power system state estimation

Power system state estimation (PSSE) constitutes a crucial prerequisite for reliable operation of the power grid. A key challenge for accurate PSSE is the inherent nonlinearity of SCADA measurements in the system states. Recent proposals for static PSSE tackle this issue by exploiting hidden convexity structure and solving a semidefinite programming (SDP) relaxation. In this work, an online PSSE algorithm based on SDP relaxation is proposed, which enjoys a similar convexity advantage, while capitalizing on past measurements as well for improved performance. An online convex optimization technique is adopted to derive an efficient algorithm with strong performance guarantees. Numerical tests verify the efficacy of the proposed approach.