Research on Fault Diagnosis of Photovoltaic Array Based on Random Forest Algorithm

Author(s):  
Liu Yun ◽  
Yan Bofeng ◽  
Qian Dan ◽  
Liu Fengshuo
2014 ◽  
Vol 519-520 ◽  
pp. 98-101
Author(s):  
De Wen Wang ◽  
Zhi Wei Sun

Dissolved gas analysis (DGA) in oil is an important method for transformer fault diagnosis. This paper use random forest parallelization algorithm to analysis the dissolved gases in transformer oil. This method can achieve a fast parallel fault diagnosis for power equipment. Experimental results of the diagnosis of parallelization of random forest algorithm with DGA samples show that this algorithm not only can improve the accuracy of fault diagnosis, and more appropriate for dealing with huge amounts of data, but also can meet the smart grid requirements for fast fault diagnosis for power transformer. And this result also verifies the feasibility and effectiveness of the algorithm.


Author(s):  
A.E. Semenov

The method of pedestrian navigation in the cities illustrated by the example of Saint-Petersburg was investigated. The factors influencing people when they choose a route for their walk were determined. Based on acquired factors corresponding data was collected and used to develop model determining attractiveness of a street in the city using Random Forest algorithm. The results obtained shows that routes provided by the method are 14% more attractive and just 6% longer compared with the shortest ones.


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