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دسته بندی:
داده های بزرگ - big data
سال انتشار:
2018
عنوان انگلیسی مقاله:
A Novel Association Rule Mining Method of Big Data for Power Transformers State Parameters Based on Probabilistic Graph Model
ترجمه فارسی عنوان مقاله:
یک روش کاوش قانون انجمنی معادلات داده های بزرگ برای پارامترهای ترانسفورماتور قدرت براساس مدل نمودار احتمالاتی
منبع:
IEEE - IEEE TRANSACTIONS ON SMART GRID, VOL: 9, NO: 2, MARCH 2018
نویسنده:
Gehao Sheng, Huijuan Hou, Xiuchen Jiang, and Yufeng Chen
چکیده انگلیسی:
The correlative change analysis of state parameters
can provide powerful technical supports for safe, reliable, and
high-efficient operation of the power transformers. However, the
analysis methods are primarily based on a single or a few state
parameters, and hence the potential failures can hardly be found
and predicted. In this paper, a data-driven method of association
rule mining for transformer state parameters has been proposed
by combining the Apriori algorithm and probabilistic graphical
model. In this method the disadvantage that whenever the frequent items are searched the whole data items have to be scanned
cyclically has been overcame. This method is used in mining association rules of the numerical solutions of differential equations.
The result indicates that association rules among the numerical
solutions can be accurately mined. Finally, practical measured
data of five 500 kV transformers is analyzed by the proposed
method. The association rules of various state parameters have
been excavated, and then the mined association rules are used in
modifying the prediction results of single state parameters. The
results indicate that the application of the mined association rules
improves the accuracy of prediction. Therefore, the effectiveness
and feasibility of the proposed method in association rule mining
has been proved
Index Terms: Power transformers, state parameters, association rules, big data, data-driven method, Apriori algorithm, probabilistic graph, state prediction
قیمت: رایگان
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