Neural Networks-Based Inverter Control: Modeling and Adaptive Optimization for Smart Distribution Networks

Journal Article · 11 October 2023 · IEEE Transactions on Sustainable Energy

DOI: https://doi.org/10.1109/tste.2023.3324219 · OSTI ID:2333852

Qiu, Wei  ^[1]; Yadav, Ajay Pratap  ^[2]; You, Shutang  ^[1]; Dong, Jin  ^[2]; Kuruganti, Teja  ^[2]; Liu, Yilu  ^[3]; Yin, He  ^[1]

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1. Univ. of Tennessee, Knoxville, TN (United States)
2. Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
3. Univ. of Tennessee, Knoxville, TN (United States); Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)

The optimal voltage control of inverter-based resources, especially under the high penetration of solar photovoltaics, is critical to the stability of the distribution power system. However, the computational complexity as well as the coordinated operation performance of the voltage control optimization in the distribution power system limits the real-time applications. To mitigate this issue, a model-free based adaptive optimal control scheme for the smart inverter is proposed to maximize the active power generation, minimize the power loss, and maintain the bus voltages in smart distribution networks. An inverter-based optimization model for coordinated operation is first established, considering the uncertainties of renewable power generation. Subsequently, by collecting the data and control strategies, the neural networks (NNs) based algorithm is proposed to efficiently predict the best possible control strategy. The main objective of this scheme is to accurately predict candidate optimal solutions with near-negligible feasibility and optimization gaps, with the advantage of avoiding complicated iteration-based numerical algorithms. Thereafter, the co-simulation among OpenDSS, MATLAB, and Python is set up to fully take advantage of the three individual software. Experiments are conducted based on different control parameter characteristics and structures of NNs. Finally, the results reveal that an average mean squared error of 0.013 and 1 ms response time are achieved, which is lower than some state-of-the-art methods.

• Research Organization: Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
• Sponsoring Organization: USDOE Office of Energy Efficiency and Renewable Energy (EERE), Renewable Power Office. Solar Energy Technologies Office
• Grant/Contract Number: AC05-00OR22725
• OSTI ID: 2333852
• Alternate ID(s): OSTI ID: 2447269
• Journal Information: IEEE Transactions on Sustainable Energy, Journal Name: IEEE Transactions on Sustainable Energy Journal Issue: 2 Vol. 15; ISSN 1949-3029
• Publisher: IEEECopyright Statement
• Country of Publication: United States
• Language: English