| 引用本文: | 吴忠强,戚松岐,尚梦瑶,申丹丹.基于优化极限学习机的直流微电网并网等效建模[J].电力自动化设备,2020,40(6): |
| WU Zhongqiang,QI Songqi,SHANG Mengyao,SHEN Dandan.Grid-connected equivalent modeling of DC microgrid based on optimized extreme learning machine[J].Electric Power Automation Equipment,2020,40(6): |
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| 摘要: |
| 针对直流微电网的并网建模问题，提出了一种基于优化极限学习机的直流微电网并网等效建模方法。以直流微电网并网接入点的电压和功率数据分别作为极限学习机的输入和输出，构建基于极限学习机的直流微电网并网等效模型。由于极限学习机在初始化过程中，输入权值和隐层阈值随机设定且不再改变，会导致极限学习机建模缺乏自适应性，影响建模精度。利用鲨鱼气味优化算法对极限学习机的输入权值和隐层阈值进行优化，进一步提高建模精度。鲨鱼气味优化算法通过模拟鲨鱼捕猎过程进行寻优，通过气味粒子浓度引导鲨鱼位置的更新，是一种效率极高的优化算法。通过与微电网的实际仿真模型对比，验证了建模方法的合理性和准确性，说明所提方法具有较好的实际应用价值。 |
| 关键词: 直流微电网 极限学习机 等效建模 鲨鱼气味优化算法 优化 |
| DOI：10.16081/j.epae.202005030 |
| 分类号:TM732;TP273.4 |
| 基金项目:河北省自然科学基金资助项目(F2016203006) |
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| Grid-connected equivalent modeling of DC microgrid based on optimized extreme learning machine |
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WU Zhongqiang, QI Songqi, SHANG Mengyao, SHEN Dandan
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School of Electrical Engineering, Yanshan University, Qinhuangdao 066004, China
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| Abstract: |
| Aiming at the grid-connected modeling problem of DC microgrid, a grid-connected equivalent mode-ling method of DC microgrid based on optimized extreme learning machine is proposed. The voltage and power data of the grid-connected point of DC microgrid are taken as the input and output of the extreme learning machine respectively, the grid-connected equivalent model of DC microgrid based on extreme learning machine is constructed. In the initialization process of extreme learning machine, input weight and hidden layer node bias are randomly set without any change later, which leads to the lack of adaptability in modeling and affects the modeling accuracy. The shark smell optimization algorithm is used to optimize the input weight and hidden layer node bias of the extreme learning machine, so as to improve the mode-ling accuracy. Shark smell optimization algorithm is a highly efficient optimization algorithm, which can simulates the hunting process of sharks for optimization, and the concentration of smell particles guides the updating of shark position. By comparing with the actual simulation model of microgrid, the rationality and accuracy of the modeling method are verified, indicating that the model has good practical application value. |
| Key words: DC microgrid extreme learning machine equivalent modeling shark smell optimization algorithm optimization |
