Local Hybrid Linear State Estimation for Electric Power Systems Using Stream Processing

Abstract

The increasing penetration of renewable energy resources with highly fluctuating outputs has placed increasing concern on the accuracy and timeliness of electric power system state estimation (SE). Meanwhile, we note that only a fraction of system states fluctuate at the millisecond level and require to be updated. As such, refreshing only those states with significant variation would enhance the computational efficiency of SE and make fast-continuous update of states possible. However, this is difficult to achieve with conventional SE methods, which generally refresh states of the entire system every 4–5 s. In this context, we propose a local hybrid linear SE framework using stream processing, in which synchronized measurements received from phasor measurement units (PMUs), and trigger/timing-mode measurements received from remote terminal units (RTUs) are used to update the associated local states. Moreover, the measurement update process efficiency and timeliness are enhanced by proposing a trigger measurement-based fast dynamic partitioning algorithm for determining the areas of the system with states requiring recalculation. In particular, non-iterative hybrid linear formulations with both RTUs and PMUs are employed to solve the local SE problem. The timeliness, accuracy, and computational efficiency of the proposed method are demonstrated by extensive simulations based on IEEE 118-, 300-, and 2383- bus systems.

Keywords

• Fast dynamic partitioning
• local hybrid linear state estimation
• phasor measurement units
• stream processing
• timeliness
• trigger/timing-mode measurements