Open AccessKey Parameter Identification and Optimization of Photovoltaic Power Plants Based on Genetic Algorithm
Author(s) -
Zhimei Zhang,
Shaowei Huang,
Ying Chen,
Wei Wei
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1449/1/012044
Subject(s) - photovoltaic system , key (lock) , identification (biology) , genetic algorithm , computer science , stability (learning theory) , transient (computer programming) , power (physics) , electric power system , algorithm , mathematical optimization , engineering , mathematics , machine learning , electrical engineering , botany , physics , computer security , quantum mechanics , biology , operating system
As the penetration rate of the photovoltaic power continues to grow, its impact on the stability of the power system becomes more considerable ever than before. However, due to the relatively low accuracy of the parameters, the traditional electromagnetic transient simulation used to assess the impact is biased. Therefore, it is of great importance to perform key parameter identification and optimization on a solar power plant containing many photovoltaic panels, which can avoid the problem of combination explosion. In this paper, a scheme of key parameter identification is proposed. Then, an optimization method based on genetic algorithm is also established to improve the accuracy. Simulation tests validate the effectiveness of the proposed method.