Examining the multi-timescales of European carbon market with grey relational analysis and empirical mode decomposition

2019 ◽  
Vol 517 ◽  
pp. 392-399 ◽  
Author(s):  
Bangzhu Zhu ◽  
Lili Yuan ◽  
Shunxin Ye
Keyword(s):  
Empirical Mode Decomposition ◽  
Grey Relational Analysis ◽  
Carbon Market ◽  
Relational Analysis ◽  
Mode Decomposition ◽  
Grey Relational

scholarly journals Prediction Model for Dissolved Gas Concentration in Transformer Oil Based on Modified Grey Wolf Optimizer and LSSVM with Grey Relational Analysis and Empirical Mode Decomposition

Energies ◽  
2020 ◽  
Vol 13 (2) ◽  
pp. 422 ◽  
Author(s):  
Bing Zeng ◽  
Jiang Guo ◽  
Fangqing Zhang ◽  
Wenqiang Zhu ◽  
Zhihuai Xiao ◽  
...  
Keyword(s):  
Prediction Model ◽  
Ambient Temperature ◽  
Grey Relational Analysis ◽  
Grey Wolf Optimizer ◽  
Grey Wolf ◽  
Dissolved Gas ◽  
Gas Concentration ◽  
Relational Analysis ◽  
Mode Decomposition ◽  
Grey Relational

Oil-immersed transformer is one of the most important components in the power system. The dissolved gas concentration prediction in oil is vital for early incipient fault detection of transformer. In this paper, a model for predicting the dissolved gas concentration in power transformer based on the modified grey wolf optimizer and least squares support vector machine (MGWO-LSSVM) with grey relational analysis (GRA) and empirical mode decomposition (EMD) is proposed, in which the influence of transformer load, oil temperature and ambient temperature on gas concentration is taken into consideration. Firstly, GRA is used to analyze the correlation between dissolved gas concentration and transformer load, oil temperature and ambient temperature, and the optimal feature set affecting gas concentration is extracted and selected as the input of the prediction model. Then, EMD is used to decompose the non-stationary series data of dissolved gas concentration into stationary subsequences with different scales. Finally, the MGWO-LSSVM is used to predict each subsequence, and the prediction values of all subsequences are combined to get the final result. DGA samples from two transformers are used to verify the proposed method, which shows high prediction accuracy, stronger generalization ability and robustness by comparing with LSSVM, particle swarm optimization (PSO)-LSSVM, GWO-LSSVM, MGWO-LSSVM, EMD-PSO-LSSVM, EMD-GWO-LSSVM, EMD-MGWO-LSSVM, GRA-EMD-PSO-LSSVM and GRA-EMD-GWO-LSSVM.

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