Abstract
With the global energy crisis and climate warming becoming more and more serious, people are striving for higher efficiency-lifetime-power density-specific costs of Photovoltaic (PV) Inverter systems, so as to promote the vigorous use of PV energy and resolve the energy and climate crisis. In order to achieve the above positive goals, obtain the best combination of efficiency-lifetime-power density-specific costs, this paper proposes a new customized optimization design method for power electronic converter systems, then applies it to 140kW photovoltaic inverter system. In particular, a new third generation non dominated sorting genetic algorithm based on Differential Evolution and opposition-based learning (DO-NSGAIII) is proposed. The IGD performance of DO-NSGAIII and other classical algorithms is compared on the test function DTLZ1 ~ 4. The results show the effectiveness of DO-NSGAIII. Finally, a specific optimization example is given.
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Acknowledgment
The authors would like to thank the General projects of National Natural Science Foundation of China (52077051) and the Hefei Comprehensive National Science Center Energy Research Institute (19KZS207) for funding.
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Wang, J., Xie, L., Peng, Q., Yang, R. (2022). Multi-objective Optimization Design of PV Inverter Based on DO-NSGAIII Algorithm. In: He, J., Li, Y., Yang, Q., Liang, X. (eds) The proceedings of the 16th Annual Conference of China Electrotechnical Society. Lecture Notes in Electrical Engineering, vol 891. Springer, Singapore. https://doi.org/10.1007/978-981-19-1532-1_116
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DOI: https://doi.org/10.1007/978-981-19-1532-1_116
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