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Equivalent Modeling of Microgrids Based on Optimized Broad Learning System

Author

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  • Lin Wang

    (School of Automation, Hangzhou Dianzi University, Hangzhou 310018, China
    School of Electrical Engineering, Yancheng Institute of Technology, Yancheng 224051, China)

  • Anke Xue

    (School of Automation, Hangzhou Dianzi University, Hangzhou 310018, China)

Abstract

The DC microgrid is an important structure of microgrids. Aiming at the problem of the grid-connected DC microgrid modeling, a grid-connected DC microgrid equivalent modeling method based on the optimized Broad Learning System (BLS) is proposed. Taking the electrical parameter data of the grid-connected DC microgrid access point as the training data set of BLS, the equivalent model of the grid-connected equivalent model is constructed. In order to further improve the accuracy and generalization performance of the model, the shark smell optimization (SSO) algorithm is used to optimize the input weights and thresholds of the BLS. Furthermore, the shark smell optimization-Broad Learning System (SSO-BLS) algorithm is proposed. SSO-BLS is compared with RBF, BLS, BFO-ELM, and other algorithms. The results show that the grid-connected DC microgrid model based on SSO-BLS has good accuracy and generalization characteristics.

Suggested Citation

  • Lin Wang & Anke Xue, 2021. "Equivalent Modeling of Microgrids Based on Optimized Broad Learning System," Energies, MDPI, vol. 14(23), pages 1-11, November.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:23:p:7911-:d:687649
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    References listed on IDEAS

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    2. Giaouris, Damian & Papadopoulos, Athanasios I. & Patsios, Charalampos & Walker, Sara & Ziogou, Chrysovalantou & Taylor, Phil & Voutetakis, Spyros & Papadopoulou, Simira & Seferlis, Panos, 2018. "A systems approach for management of microgrids considering multiple energy carriers, stochastic loads, forecasting and demand side response," Applied Energy, Elsevier, vol. 226(C), pages 546-559.
    3. Massimiliano Luna & Antonino Sferlazza & Angelo Accetta & Maria Carmela Di Piazza & Giuseppe La Tona & Marcello Pucci, 2021. "Modeling and Performance Assessment of the Split-Pi Used as a Storage Converter in All the Possible DC Microgrid Scenarios. Part II: Simulation and Experimental Results," Energies, MDPI, vol. 14(18), pages 1-22, September.
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    Cited by:

    1. Grzegorz Maślak & Przemysław Orłowski, 2022. "Microgrid Operation Optimization Using Hybrid System Modeling and Switched Model Predictive Control," Energies, MDPI, vol. 15(3), pages 1-21, January.

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