Topology Analysis of Distribution Network based on Multi-Source Measurement Data

2020 ◽  
Author(s):  
Bin Hua ◽  
Yan Li ◽  
Andi Liu ◽  
Shaorong Wang ◽  
Jing Xu ◽  
...  
Keyword(s):  
Distribution Network ◽  
Measurement Data ◽  
Topology Analysis

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

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Juhua Hong ◽  
Linyao Zhang ◽  
Yufei Yan ◽  
Zeqi Wang ◽  
Pengzhe Ren
Keyword(s):  
Renewable Energy ◽  
Deep Learning ◽  
Network Topology ◽  
Distribution Network ◽  
Measurement Data ◽  
Principal Component ◽  
Training Sample ◽  
Good Effect ◽  
Tree Model ◽  
Topology Analysis

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.

Research on hierarchical response recovery method of distribution network fault based on topology analysis

2021 ◽  
Vol 17 (3) ◽  
pp. 216
Author(s):  
Qiuyuan Zheng ◽  
Qiang Wu ◽  
Lianhang Fang ◽  
Wangcheng Zhu ◽  
Yu Liang ◽  
...  
Keyword(s):  
Distribution Network ◽  
Topology Analysis ◽  
Recovery Method ◽  
Response Recovery ◽  
Network Fault

scholarly journals Multi Objective for PMU Placement in Compressed Distribution Network Considering Cost and Accuracy of State Estimation

2019 ◽  
Vol 9 (7) ◽  
pp. 1515 ◽  
Author(s):  
Kong ◽  
Wang ◽  
Yuan ◽  
Yu
Keyword(s):  
State Estimation ◽  
Estimation Error ◽  
Distribution Network ◽  
Measurement Data ◽  
Optimal Solution ◽  
Pareto Set ◽  
Measurement Unit ◽  
Nsga Ii ◽  
Multi Objective ◽  
Pmu Placement

A phasor measurement unit (PMU) can provide phasor measurements to the distribution network to improve observability. Based on pre-configuration and existing measurements, a network compression method is proposed to reduce PMU candidate locations. Taking the minimum number of PMUs and the lowest state estimation error as the objective functions and taking full observability of distribution network as the constraint, a multi objective model of optimal PMU placement (OPP) is proposed. A hybrid state estimator based on supervisory control and data acquisition (SCADA) and PMU measurements is proposed. To reduce the number of PMUs required for full observability, SCADA measurement data are also considered into the constraint by update and equivalent. In addition, a non-dominated sorting genetic algorithm-II (NSGA-II) is applied to solve the model to get the Pareto set. Finally, the optimal solution is selected from the Pareto set by the technique for order preference by similarity to ideal solution (TOPSIS). The effectiveness of the proposed method is verified by IEEE standard bus systems.

scholarly journals An Association Rules-Based Method for Outliers Cleaning of Measurement Data in the Distribution Network

2021 ◽  
Vol 9 ◽  
Author(s):  
Hua Kuang ◽  
Risheng Qin ◽  
Mi He ◽  
Xin He ◽  
Ruimin Duan ◽  
...  
Keyword(s):  
Cost Function ◽  
Association Rules ◽  
Mahalanobis Distance ◽  
Spatial Clustering ◽  
Distribution Network ◽  
Measurement Data ◽  
Operation Management ◽  
Test Results ◽  
Cleaning Method ◽  
Outliers Detection

For any power system, the reliability of measurement data is essential in operation, management and also in planning. However, it is inevitable that the measurement data are prone to outliers, which may impact the results of data-based applications. In order to improve the data quality, the outliers cleaning method for measurement data in the distribution network is studied in this paper. The method is based on a set of association rules (AR) that are automatically generated form historical measurement data. First, the association rules are mining in conjunction with the density-based spatial clustering of application with noise (DBSCAN), k-means and Apriori technique to detect outliers. Then, for the outliers repairing process after outliers detection, the proposed method uses a distance-based model to calculate the repairing cost of outliers, which describes the similarity between outlier and normal data. Besides, the Mahalanobis distance is employed in the repairing cost function to reduce the errors, which could implement precise outliers cleaning of measurement data in the distribution network. The test results for the simulated datasets with artificial errors verify that the superiority of the proposed outliers cleaning method for outliers detection and repairing.

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