A novel distribution system state estimator based on robust cubature particle filter used for non‐gaussian noise and bad data scenarios

In engineering application, the nonlinearity effect of the environment noise is inconsistent with the successive starting state of MEMS gyroscope which will induce the random drifts. It manifests as the weak nonlinearity, non stability and slow time varying which cannot be compensated by the conventional method. In order to overcome the problems of the great random drift error model established based on the time series for MEMS gyroscope and the non Gaussian noise, the method of Iteration Unscented Kalman Particle Filter (IUKPF) is proposed in this paper. This method is based on the Particle Filter combing the Unscented Transformation (UT) with Iteration Kalman Filter (IKF), and it solved the instability of the precision for the conventional filtering methods and the degradation for the weight of the particle filter. The filtering result shows that the method of IUKPF can effectively restrain the random drift error under nonlinear and non Gaussian noise. The standard deviation for the output noise of MEMS gyroscope has decreased 81.9% by IUKPF which verifies the efficiency and superiority of this method.

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Bayesian Approach for Distribution System State Estimation With Non-Gaussian Uncertainty Models

IEEE Transactions on Instrumentation and Measurement ◽

10.1109/tim.2017.2728398 ◽

2017 ◽

Vol 66 (11) ◽

pp. 2957-2966 ◽

Cited By ~ 21

Author(s):

Paolo Attilio Pegoraro ◽

Andrea Angioni ◽

Marco Pau ◽

Antonello Monti ◽

Carlo Muscas ◽

...

Keyword(s):

State Estimation ◽

Bayesian Approach ◽

Distribution System ◽

System State ◽

Non Gaussian ◽

Uncertainty Models

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A distribution system state estimator accommodating large number of ampere measurements

International Journal of Electrical Power & Energy Systems ◽

10.1016/j.ijepes.2012.05.054 ◽

2012 ◽

Vol 43 (1) ◽

pp. 839-848 ◽

Cited By ~ 15

Author(s):

Wenchuan Wu ◽

Yuntao Ju ◽

Boming Zhang ◽

Hongbin Sun

Keyword(s):

Distribution System ◽

System State ◽

State Estimator

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Positioning Techniques in Indoor Environments Based on Stochastic Modeling of UWB Round Trip Time Measurements

10.36227/techrxiv.15016107 ◽

2021 ◽

Author(s):

Paolo Carbone

Keyword(s):

Particle Filter ◽

Gaussian Noise ◽

Additive White Gaussian Noise ◽

Least Square ◽

Indoor Environments ◽

Round Trip Time ◽

Round Trip ◽

Positioning Systems ◽

Trip Time ◽

Non Gaussian

<div><div><div><p>In this paper, a technique for modeling propagation of Ultra Wide Band (UWB) signals in indoor or outdoor environments is proposed, supporting the design of a positioning systems based on Round Trip Time (RTT) measurements and on a particle filter. By assuming that nonlinear pulses are transmitted in an Additive White Gaussian Noise Channel, and detected using a threshold based receiver, it is shown that RTT measurements may be affected by a non-Gaussian noise. RTT noise properties are analyzed, and the effects of non-Gaussian noise on the performance of a RTT based positioning system are investigated. To this aim, a classical Least Square, an extended Kalman Filter and a Particle Filter are compared when used to detect a slowly moving target in presence of the modeled noise. It is shown that, in a realistic indoor environment, the Particle Filter solution may be a competitive solution, at a price of increased computational complexity. Experimental verifications validate the presented approach.</p></div></div></div>

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