Research on Construction Method of Power Grid Topology Integration Based on IEC 61970 CIM

Author(s):  
Jiran Zhu ◽  
Peng Liu ◽  
Guoqing Wang ◽  
Yiming Lu ◽  
Di Zhang ◽  
...  
2021 ◽  
pp. 694-704
Author(s):  
Yuecheng Liu ◽  
Yanjie Li ◽  
Qi Liu ◽  
Yunhong Xu ◽  
Shaohua Lv ◽  
...  

2014 ◽  
Vol 1006-1007 ◽  
pp. 1194-1198
Author(s):  
De Quan Yao ◽  
Yong Tao ◽  
Hao Ran Yu

On the basis of grid GIS platform, in the distribution network repair platform implementation is based on the integration of power grid topology model of distribution network fault analysis mechanism, a combination of customer information, grid electricity equipment operation information such as all kinds of information, the cause of the problem of fault repair service to provide intelligence, integration judge method, improve the accuracy of the fault forecasting speed, the fault information, improve the efficiency of fault repair, shorten fault repair time, reduce the loss of electricity, improve customer satisfaction.


Author(s):  
S. Neshvad ◽  
H. Margossian ◽  
J. Sachau
Keyword(s):  

Author(s):  
N. E. Gotman ◽  
G. P. Shumilova

THE PURPOSE. To consider the problem of detecting changes in a power grid topology that occurs as a result of the power line outage / turning on. Develop the algorithm for detecting changes in the status of transmission lines in real time by using voltage and current phasors captured by phasor measurement units (PMUs) are placed on buses. Carry out experimental research on IEEE 14-bus test system. METHODS. This paper proposes a method from the field of artificial intelligence such as machine learning in particular "Deep Learning" to solve the problem. Deep Learning arises as a computational learning technique in which high level abstractions are hierarchically modelled from raw data. One of the means to effectively extract the inherent hidden features in data are Convolutional Neural Networks (CNNs). RESULTS. The article describes the topic relevance, offers to apply the method for detecting status of lines using a CNN classifier. The combination of different CNN architectures and the number of time slices from the moment of line status change are used to detect the power grid topology. The effectiveness of the joint use of PMUs and CNN in solving this problem has been proven. CONCLUSION. A solution for the line status change detection in the transient states using a CNN classifier is proposed. A high accuracy of the line status detection was obtained despite the influence of noise on measurement data. A change in the network topology is detected at the very beginning of the transient state almost instantly. It will allow the operator several times during the first seconds to identify the line state in order to make sure that the decisions made are correct.


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