A Joint Distribution System State Estimation Framework via Deep Actor-Critic Learning Method
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Engineering↗
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Results for “State estimation”
Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
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Spoofing Resilient State Estimation for the Power Grid Using an Extended Kalman Filter
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Engineering↗
Nonlinear sparse Bayesian learning using EnKF based state estimator.
Abstract not provided.
Robinson, Brandon↗
Distribution System State Estimation Sensitivity to Errors in Phase Connections.
Abstract not provided.
Trevizan, Rodrigo Daniel↗
Detecting False Data Injection Attacks to Battery State Estimation Using Cumulative Sum Algorithm.
Abstract not provided.
O'brien, Victoria↗
Wake State Estimation of Downwind Turbines using Recurrent Neural Networks.
Abstract not provided.
Herges, Thomas↗
Detection of False Data Injection Attacks in Power System State Estimation Using Sensor Encoding.
Abstract not provided.
Trevizan, Rodrigo↗
Adaptive Battery State Estimation Considering Input Noise Compensation
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Trevizan, Rodrigo Daniel↗
Microgrid State Estimation Consensus Using Collaborative Autonomy
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Engineering - Power transmission and distribution↗
From κ to χ: evaluating hygroscopicity-based mixing state estimates with a particle-resolved model
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Liu, Yicen↗
Coupled orbit–attitude dynamics and relative state estimation of spacecraft near small Solar System bodies
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Gaurav Misra↗
Space shuttle state estimation during approach and landing
Space shuttle position and velocity estimates for approach, using inertial subsystem and scanning beam microwave ILS receiver
Keene, D. W.↗
A versatile Kalman technique for aircraft or missile state estimation and error analysis using radar tracking data.
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Aldrich, G. T.↗
Microwave scatter and sea state estimation - Two-scale ocean wave models
The efficient estimation of ocean wave-structure parameters with a remote, narrow-beam, pulsed, microwave radar at intermediate incidence angles is discussed. The sea surface is described as the sum of random small- and large-scale structures (waves), the latter the sum of a sinusoid and a random field. It is shown that the small-scale structure is responsible for scattering and that the scatter depends parametrically on the large-scale structure. For the estimation problem it is assumed, reasonably, that the received signal is normally distributed: a relatively simple processor which will efficiently estimate parameters of the sinusoidal structure is illustrated and its performance discussed. A brief comparison is made with a normal incidence system with which the variance of the large-scale random roughness can be estimated.
Harger, R. O.↗
A separated bias identification and state estimation algorithm for nonlinear systems
A computational algorithm for the identification of biases in discrete-time, nonlinear, stochastic systems is derived by extending the separate bias estimation results for linear systems to the extended Kalman filter formulation. The merits of the approach are illustrated by identifying instrument biases using a terminal configured vehicle simulation.
Caglayan, A. K.↗