Placement of measurements for electrical distribution network state estimation

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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Methods of linear and nonlinear state estimation of distribution network

E3S Web of Conferences ◽

10.1051/e3sconf/20185803010 ◽

2018 ◽

Vol 58 ◽

pp. 03010

Author(s):

Irina Golub ◽

Evgeny Boloev

Keyword(s):

State Estimation ◽

Low Voltage ◽

Distribution Network ◽

Quadratic Function ◽

Smart Meters ◽

Nonlinear State Estimation ◽

Estimation Of Distribution ◽

Linear And Nonlinear ◽

Linear State ◽

Nonlinear State

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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A Harmonic Source Location Method Based on PMU Measurements in Distribution Network

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.775.409 ◽

2015 ◽

Vol 775 ◽

pp. 409-414

Author(s):

Bing Jun Li ◽

Su Quan Zhou ◽

Xiao Xiang Lun

Keyword(s):

Power System ◽

State Estimation ◽

Distribution Network ◽

Measured Data ◽

Optimal Measurement ◽

Location Method ◽

Measurement Placement ◽

Minimum Number ◽

Harmonic Source ◽

The Cost

It is of great importance to identify the location of the harmonic sources for the harmonic governance in the power system. Applied with optimal measurement placement (OMP) and harmonic state estimation (HSE), this paper presents a novel process based on PMU measurements to locate the harmonic sources in the distribution network. Considering the cost and the observability, the OMP can provide a scheme of the measurement placement with the minimum number of PMU measurements. In order to simplify the HSE equation, the measured data are converted to the form of voltage by the method proposed in this paper.By solving the HSE equation, the location and magnitude of the harmonic source are evaluated. The methodology is applied to the IEEE 33-bus system, and the obtained results are properly analyzed.

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State Estimation in Electric Power Systems Leveraging Graph Neural Networks

10.36227/techrxiv.18131207.v1 ◽

2022 ◽

Author(s):

Ognjen Kundacina ◽

Mirsad Cosovic ◽

Dejan Vukobratovic

Keyword(s):

Power Systems ◽

Power System ◽

State Estimation ◽

Electric Power Systems ◽

State Variables ◽

Evaluation Phase ◽

Measurement Units ◽

Synthetic Datasets ◽

Graph Neural Networks ◽

Test Scenarios

The goal of the state estimation (SE) algorithm is to estimate complex bus voltages as state variables based on the available set of measurements in the power system. Because phasor measurement units (PMUs) are increasingly being used in transmission power systems, there is a need for a fast SE solver that can take advantage of PMU high sampling rates. This paper proposes training a graph neural network (GNN) to learn the estimates given the PMU voltage and current measurements as inputs, with the intent of obtaining fast and accurate predictions during the evaluation phase. GNN is trained using synthetic datasets, created by randomly sampling sets of measurements in the power system and labelling them with a solution obtained using a linear SE with PMUs solver. The presented results display the accuracy of GNN predictions in various test scenarios and tackle the sensitivity of the predictions to the missing input data.

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