A set membership filtering approach to low-complexity state estimation from PMU measurements

The widespread use of phasor measurement units (PMUs) in power-grids can greatly enhance state-estimation (SE) by making use of accurate, GPS time-stamped synchronous phasor measurements. Unlike conventional SCADA measurements which are reported every 4 seconds, synchro-phasor measurements are typically available as frequently as 30-60 measurements per second. While the availability of more measurements can provide accurate state estimates in real-time, the sheer amount of data can overwhelm the computational capabilities of most data processing systems. One potential approach to reducing the computational complexity of SE under high measurement rates is set-membership filtering (SMF). SMF algorithms are related to the normalized least mean squares (NLMS) algorithm. However the SMF algorithms not only exhibit better convergence and tracking properties, but also require parameter-updates only for a fraction of observations. This make SMF approach ideal for SE in PMU-equipped power grids. Even though SMF algorithms have been widely considered for many statistical signal processing applications including SE in communication networks, this rich class of algorithms has received very little attention in the context of SE in power grids.

Faculty Supervisor:

Pradeepa Yahampath


Rashmi Boragolla


RTDS Technologies Inc.


Engineering - computer / electrical






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