Methods of linear and nonlinear state estimation of distribution network

The paper proposes a new approach to the problem of state estimation of a low voltage distribution network by the measurements coming from smart meters. The problem of nonlinear state estimation based on the measurements of nodal powers and voltages is solved by the method of simple iteration which minimizes the quadratic function of the residues with and without the consideration of the constraint on the zero currents in the transit nodes. The same algorithms are proposed to use for linear state estimation based on the measurements of nodal currents and voltages. The effectiveness of the proposed methods for linear and nonlinear state estimation is illustrated on the 33 nodes three-phase four-wire low-voltage network.

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Placement of measurements for electrical distribution network state estimation

E3S Web of Conferences ◽

10.1051/e3sconf/201839013001 ◽

2018 ◽

Vol 39 ◽

pp. 03001

Author(s):

Irina Golub ◽

Yana Kuzkina

Keyword(s):

State Estimation ◽

Distribution Network ◽

Estimation Procedure ◽

State Variables ◽

Smart Meters ◽

Minimum Number ◽

Network Operation ◽

Measurement Units ◽

Linear State ◽

Electrical Distribution Network

The paper is concerned with the problem of placement of the minimum number of smart meters to ensure either the observability of all state variables in distribution network or the observability of voltage magnitudes. Voltage control is important in the distribution network with distributed generation sources which adoption can lead to unpredictable overvoltage exceeding admissible values. The algorithm for smart meters including measurements of voltage magnitudes and active and reactive current injections is similar to the algorithm of choosing the minimum number of phasor measurement units to ensure topological observability. Optimal control of the active distribution network operation requires monitoring to be based on a classical linear state estimation procedure. The results of the research demonstrate the effectiveness of the proposed approaches and are illustrated by example of a test distribution network.

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Smart meters as a key component of modern measuring infrastructure providing observability and state estimation of low-voltage distribution networks

E3S Web of Conferences ◽

10.1051/e3sconf/20186902012 ◽

2018 ◽

Vol 69 ◽

pp. 02012

Author(s):

Yana Kuzkina ◽

Irina Golub

Keyword(s):

State Estimation ◽

Low Voltage ◽

Distribution Network ◽

Distribution Networks ◽

The State ◽

Voltage Distribution ◽

Smart Meters ◽

The Real ◽

Real Distribution

The paper presents a solution to the problem of organization of a system for collecting and transmitting information about measurements from smart meters necessary for the state estimation of a low-voltage distribution network. The problems of providing the sufficiency of measurements for the observability of the network and the influence of errors in the information about load connection to phases on the quality of the observability are considered. The results of allocation of smart meters and the state estimation of the real distribution network are given.

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Pendulum state estimation using nonlinear state estimators

International Journal of Engineering & Technology ◽

10.14419/ijet.v7i2.7.10244 ◽

2018 ◽

Vol 7 (2.7) ◽

pp. 9

Author(s):

M Naga Anusha ◽

Y Swara ◽

S Koteswara Rao ◽

V Gopi Tilak

Keyword(s):

Kalman Filter ◽

Performance Analysis ◽

State Estimation ◽

Research Work ◽

Nonlinear State Estimation ◽

State Estimators ◽

Estimation Problems ◽

Linear State ◽

Mean Square Errors ◽

Nonlinear State

The convergence over Non-linear state estimation is not satisfied by Kalman filter. For nonlinear state estimation problems, the present research work on the performance analysis of linearized kalman filter and extended kalman filter for a simple nonlinear state estimation problem. The simple pendulum is the best example for simple nonlinear state dynamics. The performance analysis based on the root mean square errors of the estimates also specified through Monte-Carlo simulation.

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Linear and Nonlinear State Estimation in the Czochralski Process*

IFAC Proceedings Volumes ◽

10.3182/20131218-3-in-2045.00043 ◽

2013 ◽

Vol 46 (32) ◽

pp. 523-528 ◽

Cited By ~ 2

Author(s):

Parsa Rahmanpour ◽

John Atle Bones ◽

Morten Hovd ◽

Jan Tommy Gravdahl

Keyword(s):

State Estimation ◽

Nonlinear State Estimation ◽

Linear And Nonlinear ◽

Czochralski Process ◽

Nonlinear State

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Three-Phase State Estimation of a Low-Voltage Distribution Network with Kalman Filter

Energies ◽

10.3390/en14217421 ◽

2021 ◽

Vol 14 (21) ◽

pp. 7421

Author(s):

Fabio Napolitano ◽

Juan Diego Rios Penaloza ◽

Fabio Tossani ◽

Alberto Borghetti ◽

Carlo Alberto Nucci

Keyword(s):

Kalman Filter ◽

State Estimation ◽

Phase State ◽

Low Voltage ◽

Distribution Network ◽

Estimation Algorithm ◽

The State ◽

Voltage Distribution ◽

Smart Meters ◽

Three Phase

The state estimation of distribution networks has long been considered a challenging task for the reduced availability of real-time measures with respect to the transmission network case. This issue is expected to be improved by the deployment of modern smart meters that can be polled at relatively short time intervals. On the other hand, the management of the information coming from many heterogeneous meters still poses major issues. If low-voltage distribution systems are of interest, a three-phase formulation should be employed for the state estimation due to the typical load imbalance. Moreover, smart meter data may not be perfectly synchronized. This paper presents the implementation of a three-phase state estimation algorithm of a real portion of a low-voltage distribution network with distributed generation equipped with smart meters. The paper compares the typical state estimation algorithm that implements the weighted least squares method with an algorithm based on an iterated Kalman filter. The influence of nonsynchronicity of measurements and of delays in communication and processing is analyzed for both approaches.

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A Strong Tracking Cubature Kalman Filter for Nonlinear Estimation

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.313-314.1115 ◽

2013 ◽

Vol 313-314 ◽

pp. 1115-1119

Author(s):

Yong Qi Wang ◽

Feng Yang ◽

Yan Liang ◽

Quan Pan

Keyword(s):

Kalman Filter ◽

State Estimation ◽

Scaling Factor ◽

Estimation Accuracy ◽

Nonlinear State Estimation ◽

Cubature Kalman Filter ◽

Novel Method ◽

Simulation Results ◽

Tracking Filter ◽

Nonlinear State

In this paper, a novel method based on cubature Kalman filter (CKF) and strong tracking filter (STF) has been proposed for nonlinear state estimation problem. The proposed method is named as strong tracking cubature Kalman filter (STCKF). In the STCKF, a scaling factor derived from STF is added and it can be tuned online to adjust the filtering gain accordingly. Simulation results indicate STCKF outperforms over EKF and CKF in state estimation accuracy.

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