Distributed Systematic Grid-Connected Inverter Using IGBT Junction Temperature Predictive Control Method: An Optimization Approach
| Autor: | Guoyi Li, Zhengping Wang, Bo-Ying Liu, Wai Peng Wong, Ming-Lang Tseng |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
improved chicken swarm optimization algorithm
insulated gate bipolar transistor module in symmetry Physics and Astronomy (miscellaneous) distributed systematic grid-connected inverter practice Computer science lcsh:Mathematics 020209 energy General Mathematics 020208 electrical & electronic engineering Swarm behaviour Particle swarm optimization 02 engineering and technology Insulated-gate bipolar transistor lcsh:QA1-939 Power (physics) Model predictive control Reliability (semiconductor) Chemistry (miscellaneous) Control theory 0202 electrical engineering electronic engineering information engineering Computer Science (miscellaneous) Inverter support vector machine Junction temperature |
| Zdroj: | Symmetry Volume 12 Issue 5 Symmetry, Vol 12, Iss 825, p 825 (2020) |
| ISSN: | 2073-8994 |
| DOI: | 10.3390/sym12050825 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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