Enhanced Dynamic State Estimation of Regional New Energy Power System Under Different Abnormal Scenarios
Article
Article Title | Enhanced Dynamic State Estimation of Regional New Energy Power System Under Different Abnormal Scenarios |
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ERA Journal ID | 4481 |
Article Category | Article |
Authors | Li, Shuaibing, Jiang, Ziwei, Cui, Yi, Kang, Yongqiang, Li, Xingming, Li, Hongwei and Dong, Haiying |
Journal Title | International Journal of Numerical Modelling: Electronic Networks, Devices and Fields |
Journal Citation | 37 (2) |
Article Number | e3216 |
Number of Pages | 19 |
Year | 2024 |
Publisher | John Wiley & Sons |
Place of Publication | United Kingdom |
ISSN | 0894-3370 |
1099-1204 | |
Digital Object Identifier (DOI) | https://doi.org/10.1002/jnm.3216 |
Web Address (URL) | https://onlinelibrary.wiley.com/doi/10.1002/jnm.3216 |
Abstract | The high proportion of renewable energy sources in the power grid increases the failure probability of the system, which becomes a new challenge for the safe and stable operation of the regional power grid. To ensure stable control of the power network with substantial renewable energy integration, this article proposes a new method that combines the long short-term memory (LSTM) neural networks and adaptive cubature Kalman filter (ACKF) to improve the prediction accuracy of mutation data inherited from the renewable generation. Four abnormal scenarios, including low voltage ride-through (LVRT), high voltage ride-through (HVRT), continuous fault ride-through and bad data injection of the regional power grid are investigated through extensive case studies. The proposed method is implemented on the IEEE 30-node system for performance verification. The simulation results demonstrate that the proposed method has considerably higher robustness than the traditional Kalman filter algorithm and can effectively improve the overall state estimation accuracy of the renewable energy power system under different scenarios. |
Keywords | adaptive cubature Kalman filter; long-term and short-term memory network; new energypower system; state estimation |
Contains Sensitive Content | Does not contain sensitive content |
ANZSRC Field of Research 2020 | 400803. Electrical energy generation (incl. renewables, excl. photovoltaics) |
Public Notes | Files associated with this item cannot be displayed due to copyright restrictions. |
Byline Affiliations | Lanzhou Jiaotong University, China |
School of Engineering |
https://research.usq.edu.au/item/z4y32/enhanced-dynamic-state-estimation-of-regional-new-energy-power-system-under-different-abnormal-scenarios
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