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Keywords

renewable energy; data‑physical fusion; machine learning; data‑driven; over‑limit short‑circuit current

Abstract

The problem of over‑limit short‑circuit current in power grid containing renewable energy is becoming increasingly serious. Because of its faster and larger state changes, offline over‑limit short‑circuit current analysis may not be able to cover all the over‑limit scenarios. Therefore, online analysis is quite necessary. Considering that the mainstream physical model calculation method can hardly meet the online calculation speed demand, faster calculation is of great significance. Therefore, this paper proposes a strategy of combining data‑driven and model‑driven method for over‑limit short‑circuit current evaluation for power grid with high penetration of renewable energy. Firstly, based on the analysis of the main factors affecting the short‑circuit current, in order to improve the calculation speed, the original dimension is reduced to consider only the influence of load. Then, the optimal power flow and random simulation methods are combined to generate a large set of samples, and the data‑driven model is obtained through machine learning algorithm training. On this basis, the error analysis and threshold setting of the model are carried out by using false positive rate and false negative rate. Then, the data‑driven model is used to screen over‑limit short‑circuit current scenarios; Finally, the theoretical physical model proposed in the latest research is used to verify the short‑circuit current scenario after preliminary screening with high accuracy. It is verified on the IEEE 39 bus model with photovoltaic power supply. The simulation results show that this strategy can effectively improve the verification speed without omitting the over‑limit short‑circuit current scenarios.

DOI

10.19781/j.issn.1673-9140.2023.04.003

First Page

24

Last Page

34

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