State Estimation for Distribution Networks with Asynchronous Sensors Using Stochastic Descent: Preprint
This paper investigates the problem of state estimation for distribution networks with asynchronous sensors comprising of a mix of smart meters and phasor measurement units (PMUs) with multiple sampling and reporting rates. We consider two independent scenarios of state estimation and tracking, with either voltages or currents as states. With these two sets, we investigate estimation under (a) full data, assuming all measurements are available and (b) limited data, where an online algorithmic approach is adopted to estimate the possibly time-varying states by processing measurements as and when available. The proposed algorithm, inspired by the classical Stochastic Gradient Descent (SGD) approach updates the states based on the previous estimate and the newly available measurements. Finally, we demonstrate the estimation and tracking efficacy through numerical simulations on the IEEE-37 test network, while also highlighting how estimation with currents as states leads to faster convergence.
- Research Organization:
- National Renewable Energy Lab. (NREL), Golden, CO (United States)
- Sponsoring Organization:
- USDOE National Renewable Energy Laboratory (NREL), Laboratory Directed Research and Development (LDRD) Program
- DOE Contract Number:
- AC36-08GO28308
- OSTI ID:
- 1878551
- Report Number(s):
- NREL/CP-5D00-82329; MainId:83102; UUID:5d9f85d1-aaa2-43c2-b282-354550085555; MainAdminID:64054
- Resource Relation:
- Conference: Presented at the 2022 IEEE Power & Energy Society General Meeting, 17-21 July 2022, Denver, Colorado
- Country of Publication:
- United States
- Language:
- English
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