Multiarea Parallel Data-Driven Three-Phase Distribution System State Estimation Using Synchrophasor Measurements

Zargar, Behzad; Angioni, Andrea; Ponci, Ferdinanda; Monti, Antonello

doi:10.1109/tim.2020.2967512

Cited by 31 publications

(5 citation statements)

References 33 publications

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“…As an important branch of machine learning, neural networks have advantages in dealing with regression problems by mapping input values through multilayer nonlinear structures. Currently, many researchers have applied artificial neural networks to DSSE problem [25]- [26]. In [25], The effect of topology change is considered, but it is limited to the power on or off.…”

Section: A Related Workmentioning

confidence: 99%

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State Estimation for Reconfigurable Distribution Systems Using Graph Attention Network

Ren,

Chu,

2023

IEEE Access

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Section: A Related Workmentioning

confidence: 99%

“…C is a measurement data mask of the same length as D k . The value of the element in the measurement data mask is determined according to (26).…”

Section: State Estimation Model Structurementioning

confidence: 99%

State Estimation for Reconfigurable Distribution Systems Using Graph Attention Network

Ren,

Chu,

2023

IEEE Access

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“…The concept of DS-DSE technique (as it is expressed in part 3) is completely applicable in DD-DSE. In [96], 123-IEEE power network is divided into several areas, and an ANN method is performed in each area parallelly. Then, the results of state estimation in each area are more processed in the second step.…”

Section: Distribution State Estimation: Data-driven Approachesmentioning

confidence: 99%

A Review on State Estimation Techniques in Active Distribution Networks: Existing Practices and Their Challenges

et al. 2022

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This paper provides a comprehensive review of distribution system state estimation in terms of basic definition, different methods, and their application. In the last few years, the operation of distribution networks has been influenced by the installation of distributed generations. In order to control and manage an active distribution network’s performance, distribution system state estimation methods are introduced. A transmission system state estimation cannot be used directly in distribution networks since transmission and distribution networks are different due to topology configuration, the number of buses, line parameters, and the number of measurement instruments. So, the proper state estimation algorithms should be proposed according to the main distribution network features. Accuracy, computational efficiency, and practical implications should be considered in the designing of distribution state estimation techniques since technical issues and wrong decisions could emerge in the control center by inaccurate distribution state estimation results. In this study, conventional techniques are reviewed and compared with data-driven methods in order to highlight the pros and cons of different techniques. Furthermore, the integrated distribution state estimation methods are compared with the distributed approaches, and the different criteria, including the level of area overlapping execution time and computing architecture, are elaborated. Moreover, mathematical problem formulation and different measuring methods are discussed.

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“…In [18] a deep neural network framework is proposed to perform distribution system state estimation for different network configurations, considering real-time PMUs and non-Gaussian noise. In [19], an ANN-based multi-area state estimator that allows mitigating the impact of outliers in the case of non-Gaussian noise is presented.…”

Section: Introductionmentioning

confidence: 99%

Statistical Behavior of PMU Measurement Errors: An Experimental Characterization

Castello

Muscas

Pegoraro

2022

IEEE Open J. Instrum. Meas.

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Different power system applications based on synchrophasors measured in different nodes of the electric grid require information about the statistical distribution of the errors introduced by the Phasor Measurement Units (PMUs). The performance of these applications can be significantly affected by possible incorrect assumptions. The Gaussian distribution has been historically assumed in most of the approaches, but some more recent studies suggest the possibility of considering different distributions for a more accurate modeling of the actual situation. In this paper, proper statistical tools applied to the results achieved through a high performance experimental test system are proposed to assess the statistical distribution of PMU errors under controlled steady-state conditions, thus providing a basis for defining suitable models to be used in the specific applications.

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Multiarea Parallel Data-Driven Three-Phase Distribution System State Estimation Using Synchrophasor Measurements

Cited by 31 publications

References 33 publications

State Estimation for Reconfigurable Distribution Systems Using Graph Attention Network

State Estimation for Reconfigurable Distribution Systems Using Graph Attention Network

A Review on State Estimation Techniques in Active Distribution Networks: Existing Practices and Their Challenges

Statistical Behavior of PMU Measurement Errors: An Experimental Characterization

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