A nonlinear support vector machine model with hard penalty function based on glowworm swarm optimization for forecasting daily global solar radiation

2016 ◽  
Vol 126 ◽  
pp. 991-1002 ◽  
Author(s):  
He Jiang ◽  
Yao Dong
Keyword(s):  
Support Vector Machine ◽  
Solar Radiation ◽  
Penalty Function ◽  
Support Vector Machine Model ◽  
Global Solar Radiation ◽  
Support Vector ◽  
Machine Model ◽  
Swarm Optimization ◽  
Glowworm Swarm Optimization ◽  
Nonlinear Support

scholarly journals Distributed Systematic Grid-Connected Inverter Using IGBT Junction Temperature Predictive Control Method: An Optimization Approach

Symmetry ◽  
2020 ◽  
Vol 12 (5) ◽  
pp. 825
Author(s):  
Zhengping Wang ◽  
Guoyi Li ◽  
Ming-Lang Tseng ◽  
Wai-Peng Wong ◽  
Boying Liu
Keyword(s):  
Support Vector Machine ◽  
Support Vector Machine Model ◽  
Learning Ability ◽  
Junction Temperature ◽  
Support Vector ◽  
Machine Model ◽  
Swarm Optimization ◽  
Proposed Model ◽  
Grid Connected Inverter ◽  
Chicken Swarm Optimization

Distributed systematic grid-connected inverter practice needs to improve insulated gate bipolar transistor (IGBT) stability to ensure the safe operation. This study is to ensure the safety and reliability operation of the IGBT module in symmetry to meet the reliable and stable distributed systematic grid-connected inverter practice and the junction temperature is a parameter to assess its operating state. It is difficult to accurately acquire the IGBT junction temperature to be solved by a single method of combining the test and the modeling. The saturation voltage drop or collector current and module junction temperature data under different power cycles are measured by the power cycle test and the single pulse test. The improved chicken swarm optimization increases the chickens diversity and self-learning ability. The prediction model of the improved chicken swarm optimization-support vector machine is proposed to forecast the module junction temperature. The result showed to compare with the particle swarm optimization-support vector machine model and chicken swarm optimization-support vector machine model and showed the coincidence degree between the proposed model prediction value and the true value is higher. The mean absolute error ratio indicates the proposed model has a smaller error and a better prediction performance. The proposed model has a positive impact on improving the distributed systematic grid-connected inverter industrial development and promotes the new energy usage.

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