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

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.

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Research and application of line switching based on distribution network topology analysis

10.1109/auteee52864.2021.9668697 ◽

2021 ◽

Author(s):

Pei Yang ◽

Menghan Xu ◽

Sen Pan ◽

Junfeng Qiao ◽

Kai Liu

Keyword(s):

Network Topology ◽

Distribution Network ◽

Topology Analysis ◽

Line Switching

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

2020 Asia Energy and Electrical Engineering Symposium (AEEES) ◽

10.1109/aeees48850.2020.9121462 ◽

2020 ◽

Author(s):

Bin Hua ◽

Yan Li ◽

Andi Liu ◽

Shaorong Wang ◽

Jing Xu ◽

...

Keyword(s):

Distribution Network ◽

Measurement Data ◽

Topology Analysis

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A New Hybrid Approach to Forecast Wind Power for Large Scale Wind Turbine Data Using Deep Learning with TensorFlow Framework and Principal Component Analysis

Energies ◽

10.3390/en12122229 ◽

2019 ◽

Vol 12 (12) ◽

pp. 2229 ◽

Cited By ~ 3

Author(s):

Mansoor Khan ◽

Tianqi Liu ◽

Farhan Ullah

Keyword(s):

Principal Component Analysis ◽

Renewable Energy ◽

Deep Learning ◽

Wind Power ◽

Learning Algorithm ◽

Hybrid Approach ◽

Principal Component ◽

Component Analysis ◽

Wind Data ◽

Deep Learning Algorithm

Wind power forecasting plays a vital role in renewable energy production. Accurately forecasting wind energy is a significant challenge due to the uncertain and complex behavior of wind signals. For this purpose, accurate prediction methods are required. This paper presents a new hybrid approach of principal component analysis (PCA) and deep learning to uncover the hidden patterns from wind data and to forecast accurate wind power. PCA is applied to wind data to extract the hidden features from wind data and to identify meaningful information. It is also used to remove high correlation among the values. Further, an optimized deep learning algorithm with a TensorFlow framework is used to accurately forecast wind power from significant features. Finally, the deep learning algorithm is fine-tuned with learning error rate, optimizer function, dropout layer, activation and loss function. The algorithm uses a neural network and intelligent algorithm to predict the wind signals. The proposed idea is applied to three different datasets (hourly, monthly, yearly) gathered from the National Renewable Energy Laboratory (NREL) transforming energy database. The forecasting results show that the proposed research can accurately predict wind power using a span ranging from hours to years. A comparison is made with popular state of the art algorithms and it is demonstrated that the proposed research yields better predictions results.

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Path Searching Based Fault Automated Recovery Scheme for Distribution Grid with DG

International Journal of Emerging Electric Power Systems ◽

10.1515/ijeeps-2016-0094 ◽

2016 ◽

Vol 17 (6) ◽

pp. 663-669

Author(s):

Lin Xia ◽

Wang Qun ◽

Xue Hui ◽

Zhu Simeng

Keyword(s):

Network Topology ◽

Distribution Network ◽

Automatic Generation ◽

Power Source ◽

Fault Isolation ◽

Good Effect ◽

Distribution Grid ◽

Line Load ◽

Recovery Scheme ◽

Searching Method

Abstract Applying the method of path searching based on distribution network topology in setting software has a good effect, and the path searching method containing DG power source is also applicable to the automatic generation and division of planned islands after the fault. This paper applies path searching algorithm in the automatic division of planned islands after faults: starting from the switch of fault isolation, ending in each power source, and according to the line load that the searching path traverses and the load integrated by important optimized searching path, forming optimized division scheme of planned islands that uses each DG as power source and is balanced to local important load. Finally, COBASE software and distribution network automation software applied are used to illustrate the effectiveness of the realization of such automatic restoration program.

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A Cascade-Based Emergency Model for Water Distribution Network

Mathematical Problems in Engineering ◽

10.1155/2015/827816 ◽

2015 ◽

Vol 2015 ◽

pp. 1-11 ◽

Cited By ~ 3

Author(s):

Qing Shuang ◽

Yongbo Yuan ◽

Mingyuan Zhang ◽

Yisheng Liu

Keyword(s):

Complex Network ◽

Network Topology ◽

Water Distribution ◽

Distribution Network ◽

Water Distribution Network ◽

Topology Analysis ◽

Physical Infrastructure ◽

Recovery Strategies ◽

Recovery Mechanisms ◽

Redistribution Function

Water distribution network is important in the critical physical infrastructure systems. The paper studies the emergency resource strategies on water distribution network with the approach of complex network and cascading failures. The model of cascade-based emergency for water distribution network is built. The cascade-based model considers the network topology analysis and hydraulic analysis to provide a more realistic result. A load redistribution function with emergency recovery mechanisms is established. From the aspects of uniform distribution, node betweenness, and node pressure, six recovery strategies are given to reflect the network topology and the failure information, respectively. The recovery strategies are evaluated with the complex network indicators to describe the failure scale and failure velocity. The proposed method is applied by an illustrative example. The results showed that the recovery strategy considering the node pressure can enhance the network robustness effectively. Besides, this strategy can reduce the failure nodes and generate the least failure nodes per time.

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