Adaptive Regulated Sparsity Promoting Approach for Data-Driven Modeling and Control of Grid-Connected Solar Photovoltaic Generation

IF 8.6 1区 工程技术 Q1 ENERGY & FUELS
Zhongtian Zhang;Javad Khazaei;Rick S. Blum
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Abstract

This paper introduces a new statistical learning technique based on sparsity promotion for data-driven modeling and control of solar photovoltaic (PV) systems. Compared with conventional sparse regression techniques that might introduce computational complexities when the number of candidate functions increases, an innovative algorithm, named adaptive regulated sparse regression (ARSR) is proposed. The ARSR adaptively regulates the hyperparameter weights of candidate functions to best represent the dynamics of PV systems. This method allows for the application of different sparsity-promoting hyperparameters for each state variable, whereas the conventional approach uses the same hyperparameter for all state variables, which may result in not excluding all the unrelated terms from the dynamics. Consequently, the proposed method can identify more complex dynamics with greater accuracy. Utilizing this algorithm, open-loop and closed-loop models of single-stage and two-stage PV systems are obtained from measurements and are utilized for control design purposes. Moreover, it is demonstrated that the proposed data-driven approach can be successfully employed for fault analysis studies, which distinguishes its capabilities from other data-driven techniques. Finally, the proposed approach is validated through real-time simulations.
并网太阳能光伏发电数据驱动建模与控制的自适应调节稀疏度提升方法
介绍了一种新的基于稀疏度提升的统计学习技术,用于太阳能光伏系统的数据驱动建模和控制。针对传统稀疏回归算法在候选函数数量增加时可能带来计算复杂度的问题,提出了一种自适应调节稀疏回归算法(ARSR)。ARSR自适应调节候选函数的超参数权重,以最好地代表光伏系统的动态。这种方法允许对每个状态变量应用不同的促进稀疏性的超参数,而传统方法对所有状态变量使用相同的超参数,这可能导致不排除动力学中所有不相关的项。因此,该方法能够以更高的精度识别更复杂的动态。利用该算法,从测量中得到单级和两级光伏系统的开环和闭环模型,并用于控制设计目的。此外,数据驱动方法可以成功地用于故障分析研究,这是其与其他数据驱动技术的区别。最后,通过实时仿真验证了该方法的有效性。
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来源期刊
IEEE Transactions on Sustainable Energy
IEEE Transactions on Sustainable Energy ENERGY & FUELS-ENGINEERING, ELECTRICAL & ELECTRONIC
CiteScore
21.40
自引率
5.70%
发文量
215
审稿时长
5 months
期刊介绍: The IEEE Transactions on Sustainable Energy serves as a pivotal platform for sharing groundbreaking research findings on sustainable energy systems, with a focus on their seamless integration into power transmission and/or distribution grids. The journal showcases original research spanning the design, implementation, grid-integration, and control of sustainable energy technologies and systems. Additionally, the Transactions warmly welcomes manuscripts addressing the design, implementation, and evaluation of power systems influenced by sustainable energy systems and devices.
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