| Adaptive adjustment method for control parameters of grid-forming inverter based on DBN-ELM |
| 投稿时间：2023-03-11 修订日期：2023-07-31 |
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| English Abstract: |
| In the “double-high” power system, the grid impedance exhibits a wide range of time-varying characteristics. The grid-forming control parameters of grid-forming inverter lack the adaptive adjustment ability, resulting in the risk of instability. To solve this problem, an adaptive adjustment method for control parameters of grid-forming inverters based on deep belief network and extreme learning machine is proposed. A closed-loop pole mapping model is established and the mapping relationship between control parameters and key poles is trained by using the deep architecture. Then, the corresponding key poles are predicted by the trained closed-loop pole mapping model, and the control parameters of the grid-forming inverter are identified when the key poles are closest to the reference poles. By adjusting the control parameters adaptively, the system can keep track the reference poles when the grid impedance changes, and realize the adaptive stability control. It is shown by both theoretical analysis and simulative results that the proposed method can realize the adaptive adjustment of control parameters, and effectively improve the adaptability of the grid-forming inverter to the changes of the grid impedance. |
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| English and key words:grid-forming inverter adaptive adjustment deep belief network-extreme learning machine complex vector modeling grid impedance |
| 基金项目:国网辽宁省电力有限公司管理科技项目(2022YF-36) |
| DOI：10.16081/j.epae.202308008 |
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