Bayesian linear state estimation using smart meters and PMUs measurements in distribution grids

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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State Estimation for Medium and Low Voltage Distribution Grids Based on Near Real-time Grid Measurements and Delayed Smart Meters Data

2020 22nd European Conference on Power Electronics and Applications (EPE'20 ECCE Europe) ◽

10.23919/epe20ecceeurope43536.2020.9215829 ◽

2020 ◽

Author(s):

Mohammad Rayati ◽

Thomas Pidancier ◽

Mauro Carpita ◽

Mokhtar Bozorg

Keyword(s):

State Estimation ◽

Real Time ◽

Low Voltage ◽

Voltage Distribution ◽

Smart Meters ◽

Distribution Grids ◽

Time Grid

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Multi-Area Distribution System State Estimation Using Decentralized Physics-Aware Neural Networks

Energies ◽

10.3390/en14113025 ◽

2021 ◽

Vol 14 (11) ◽

pp. 3025

Author(s):

Minh-Quan Tran ◽

Ahmed S. Zamzam ◽

Phuong H. Nguyen ◽

Guus Pemen

Keyword(s):

State Estimation ◽

Distribution System ◽

Phase Distribution ◽

State Of The Art ◽

Weighted Least Squares ◽

Computational Cost ◽

Estimation Accuracy ◽

System State ◽

Smart Meters ◽

Distribution Grids

The development of active distribution grids requires more accurate and lower computational cost state estimation. In this paper, the authors investigate a decentralized learning-based distribution system state estimation (DSSE) approach for large distribution grids. The proposed approach decomposes the feeder-level DSSE into subarea-level estimation problems that can be solved independently. The proposed method is decentralized pruned physics-aware neural network (D-P2N2). The physical grid topology is used to parsimoniously design the connections between different hidden layers of the D-P2N2. Monte Carlo simulations based on one-year of load consumption data collected from smart meters for a three-phase distribution system power flow are developed to generate the measurement and voltage state data. The IEEE 123-node system is selected as the test network to benchmark the proposed algorithm against the classic weighted least squares and state-of-the-art learning-based DSSE approaches. Numerical results show that the D-P2N2 outperforms the state-of-the-art methods in terms of estimation accuracy and computational efficiency.

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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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Practical experiences on PMU-based linear state estimation in distribution grids

2016 IEEE Power and Energy Society General Meeting (PESGM) ◽

10.1109/pesgm.2016.7741683 ◽

2016 ◽

Cited By ~ 3

Author(s):

M. Richter ◽

M. Wolter ◽

A. Naumann ◽

P. Komarnicki

Keyword(s):

State Estimation ◽

Practical Experiences ◽

Linear State ◽

Distribution Grids

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A Two-Step State Estimation Algorithm for Hybrid AC-DC Distribution Grids

Energies ◽

10.3390/en14071967 ◽

2021 ◽

Vol 14 (7) ◽

pp. 1967

Author(s):

Gaurav Kumar Roy ◽

Marco Pau ◽

Ferdinanda Ponci ◽

Antonello Monti

Keyword(s):

State Estimation ◽

Distribution System ◽

Renewable Energy Sources ◽

Estimation Algorithm ◽

The State ◽

Second Step ◽

Distribution Grid ◽

Dc Distribution ◽

Attractive Option ◽

Distribution Grids

Direct Current (DC) grids are considered an attractive option for integrating high shares of renewable energy sources in the electrical distribution grid. Hence, in the future, Alternating Current (AC) and DC systems could be interconnected to form hybrid AC-DC distribution grids. This paper presents a two-step state estimation formulation for the monitoring of hybrid AC-DC grids. In the first step, state estimation is executed independently for the AC and DC areas of the distribution system. The second step refines the estimation results by exchanging boundary quantities at the AC-DC converters. To this purpose, the modulation index and phase angle control of the AC-DC converters are integrated into the second step of the proposed state estimation formulation. This allows providing additional inputs to the state estimation algorithm, which eventually leads to improve the accuracy of the state estimation results. Simulations on a sample AC-DC distribution grid are performed to highlight the benefits resulting from the integration of these converter control parameters for the estimation of both the AC and DC grid quantities.

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