Neural-Network-based State Estimation: the effect of Pseudo- measurements

Bragantini, Andrea; Baroli, Davide; Posada-Moreno, Andrés Felipe; Benigni, Andrea

doi:10.1109/isie45552.2021.9576442

Cited by 4 publications

(2 citation statements)

References 14 publications

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“…Pseudo-measurement generated from ANN was used for state estimation and resulted in higher accuracy. This approach was further investigated in [216]. It is concluded that the addition or removal of pseudo-measurement depends on the cases considered [217,218].…”

Section: Machine Learning/ Deep Learning Approaches In Dssementioning

confidence: 99%

Low and Medium Voltage Distribution Network Planning with Distributed Energy Resources: A Survey

Pham,

Shah,

Dao

et al. 2024

Preprint

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The penetration of distributed energy resources (DERs) such as photovoltaic systems, energy storage systems, and electric vehicles is increasing in the distribution system. The distinct characteristics of these resources, e.g., volatility and intermittency, introduce complexity in operation and planning of the distribution system. This paper first summarized the physical characteristics and morphological evaluation of the current and future distribution networks. Then, the impact of these changes on system operation and planning is outlined. Next, the tools, methods, and techniques for energy forecasting, optimal planning, and distribution system state estimation are reviewed and discussed, along with the challenges. As the main contributions, this research systematically organized the published works and assessed the relevant milestones regarding distribution system planning with DERs and emerging technologies. Finally, the key research directions in this domain are outlined.

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Section: Machine Learning/ Deep Learning Approaches In Dssementioning

confidence: 99%

Low and Medium Voltage Distribution Network Planning with Distributed Energy Resources: A Survey

Pham,

Shah,

Dao

et al. 2024

Preprint

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“…A single commercially available measurement device can fulfill this requirement, providing four timely synchronized input measurements at reasonable refreshing rates. Previous experiments [33] have shown that ANN-based state estimation can be effectively carried out in small low-voltage networks, such as a distribution feeder, without the use of user-generated pseudo measurements. If more explainable variability is needed between the input of the neural network, more input measurements could be added to the input layer from different locations of the power grid.…”

Section: Neural Network Modelsmentioning

confidence: 99%

Design and Evaluation of Low Voltage Neural Network-Based State Estimators in Scenarios With Minimal Measurement Infrastructure

Bragantini,

Sumper

2024

IEEE Access

Self Cite

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Learning-based state estimators can represent a cost-effective opportunity for distribution system operators to perform grid monitoring and control in low-voltage grids where the measuring infrastructure is minimal, if not absent. This study lays the foundation for designing and evaluating neural network-based state estimators for low-voltage radial distribution grids. A simulation-based methodology is proposed for generating synthetic training data-sets relying only on minimal grid data. Additionally, a novel framework for performance analysis of low voltage learning-based state estimators is considered, which relies on a bidimensional evaluation of the absolute error and the parallel observation of relative metrics. The applicability and potential of these estimators have been tested and validated through various low-voltage radial case studies, showing promising results especially for large distribution grids. Finally, a propagation error study has been conducted to observe how these estimators handle errors in input measurements.

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Data driven approach for power system state estimation with consideration of multi-level noise

Shaikh

Aman

Ahmed

2023

2023 International Conference on Emerging Power Technologies (ICEPT)

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Neural-Network-based State Estimation: the effect of Pseudo- measurements

Cited by 4 publications

References 14 publications

Low and Medium Voltage Distribution Network Planning with Distributed Energy Resources: A Survey

Low and Medium Voltage Distribution Network Planning with Distributed Energy Resources: A Survey

Design and Evaluation of Low Voltage Neural Network-Based State Estimators in Scenarios With Minimal Measurement Infrastructure

Data driven approach for power system state estimation with consideration of multi-level noise

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