PaToPaEM: A Data-Driven Parameter and Topology Joint Estimation Framework for Time-Varying System in Distribution Grids

Yu, Jiafan; Weng, Yang; Rajagopal, Ram

doi:10.1109/tpwrs.2018.2888619

Cited by 94 publications

(46 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The computer needs to identify each primitive by the coordinate property. It identifies not only the function type of each primitive but also the connection relationship between the primitives [31].…”

Section: Autonomic Identification Methods For the Hmivsmmentioning

confidence: 99%

Graphical Topology Autonomic Identification Method for Human-Machine Interface Modelling Based on Quadratic Space Mapping

Wang

et al. 2020

IEEE Access

View full text Add to dashboard Cite

To realize a computerized autonomous recognition of human-computer interface graphics and autonomously interface an interface graphic model with a simulation analysis program of a systems operation, a human-computer interface topology identification algorithm based on quadratic space mapping (QSM) is proposed, which includes three mapping spaces and two mapping methods. The three mapping spaces are defined as the interface modelling graphics space, the primitive information space and the abstract topology space. The first mapping method uses hierarchical topology mapping based on coordinate properties, and the second uses abstract topology mapping based on breadth first search. The QSM algorithm converts a visual graphical physical model on a human-machine interface into a computerrecognizable abstract model. Then, a simulation analysis of the system is performed automatically by a computer using the converted abstract model. Finally, taking the distribution network system as an example, the proposed topology recognition algorithm realizes the conversion of the interface modelling graph to the abstract model of the distribution network. Furthermore, the simulation of the power flow algorithm is performed by interfacing perfectly with the abstract model, and the feasibility and practicability of the algorithm are verified.

show abstract

Section: Autonomic Identification Methods For the Hmivsmmentioning

confidence: 99%

Graphical Topology Autonomic Identification Method for Human-Machine Interface Modelling Based on Quadratic Space Mapping

Wang

et al. 2020

IEEE Access

View full text Add to dashboard Cite

show abstract

“…Equation (17) indicates that the connection relationship between nodes is 0-1 variable, 0 means break, and 1 means connection. Equation 18…”

Section: B the Constraintmentioning

confidence: 99%

“…In reference [14], firstly, the bus of substation is partitioned, and then the topology of each area is identified. PMU has been widely concerned because of its high precision [15], [16].Based on a large number of historical voltage data provided by high-precision PMU, references [17]- [21] mine the correlation between distribution network topology and voltage, and realize the topology identification of distribution network. All of these methods require the distribution network to be equipped with high-cost PMU, and provide a large number of historical data such as voltage amplitude and phase angle, which are difficult to obtain in the actual distribution network.…”

Section: Introductionmentioning

confidence: 99%

Distribution Network Topology Identification Considering Nonsynchronous Multi-Prosumer Data Measurement

Zhang¹,

Zhang²,

Li³

et al. 2022

IEEE Access

View full text Add to dashboard Cite

Accurate topology is the basis of fine management and safe operation of distribution network. With the development of distributed generation, more and more users participate in the distribution network, which makes the flow direction of distribution network more complex and brings some difficulties to the topology identification of distribution network. The existing distribution network topology identification methods lack of multi period measurement data, and the utilization rate of multiuser data is low, which leads to the low accuracy of distribution network topology identification. To solve this problem, a distribution network topology identification method based on multiuser fusion data is proposed. Firstly, the existing distribution network measurement system is analyzed, and the data and characteristics that can be collected in the actual project are obtained. Then, according to the characteristics of different data sampling frequency and accuracy, multiuser data fusion is carried out, and a PMU based multiuser data time scale alignment method and a pseudo measurement generation method based on linear extrapolation are proposed. Finally, an optimization model of distribution network topology identification based on multiuser and multi period fusion data is proposed. By minimizing the error between the measured value and the estimated value in several time periods, the model realizes the identification of distribution network topology. Simulation results show that the method is correct and effective. INDEX TERMS Distribution network,nonsynchronous measurement, multi-prosumer data, topology identification, prosumer gourp.

show abstract

“…e noise in measurements will adversely affect the accuracy of distribution network topology identification [24]. In addition, the differences of ZIP model proportions also have varying degrees of impact on topology identification accuracy, as the proportions of the ZIP model will affect the voltage distribution.…”

Section: Robust Analysis Of Topology Identification Accuracymentioning

confidence: 99%

Deep-Learning-Assisted Topology Identification and Sensor Placement for Active Distribution Network

Hong

Zhang

Yan

et al. 2021

Mathematical Problems in Engineering

View full text Add to dashboard Cite

In response to the demand for identification of distribution network topology with a high percentage of renewable energy penetration, a distribution network topology analysis method based on decision trees and deep learning methods is proposed. First, the decision tree model is constructed to analyze the importance of each node’s characteristics to the observability of the distribution network topology. Next, we arrange the node feature importance from large to small and select the node measurement data with high importance as the training sample set. Then, the principal component analysis (PCA)-deep belief network (DBN) model is used to analyze the changes in the observability of the distribution network topology, and the nodes are selected as the optimal location for the measurement device when the distribution network is completely observable. Finally, the IEEE-33 bus system with a high proportion of renewable energy is used to verify that the method proposed has a good effect in the identification of the distribution network topology.

show abstract

PaToPaEM: A Data-Driven Parameter and Topology Joint Estimation Framework for Time-Varying System in Distribution Grids

Cited by 94 publications

References 22 publications

Graphical Topology Autonomic Identification Method for Human-Machine Interface Modelling Based on Quadratic Space Mapping

Graphical Topology Autonomic Identification Method for Human-Machine Interface Modelling Based on Quadratic Space Mapping

Distribution Network Topology Identification Considering Nonsynchronous Multi-Prosumer Data Measurement

Deep-Learning-Assisted Topology Identification and Sensor Placement for Active Distribution Network

Contact Info

Product

Resources

About