The transient characterization problem caused by high-power nonlinear load switching in a multi-electric aircraft power system is addressed in this work using a stability analysis method based on the combination of Relief and CNN-LSTMP. High correlation features are chosen by applying the Relief algorithm to determine the feature weights. In the meantime, to successfully address the sample imbalance problem, the cost factor is added to the CNN loss function. With the Projection layer, the LSTM is enhanced and optimized while resolving the large-scale computation issue and lowering computational complexity. The transient switching process was simulated in the article to produce time series data, which was then normalized and annotated for model training. A multi-electric aircraft power system simulation model was established using stability analysis, and it performed exceptionally well. Based on the experimental results, the proposed method meets the requirements of practical applications, achieves excellent transient analysis effect, and achieves 96.33% accuracy in transient stability analysis. This offers a workable solution for the application of deep learning to the field of stability analysis of airborne power systems.
BabakA. A novel full-electric aircraft propulsion based on the DEA compressor. P I Mech Eng G-J Aer2022; 6: 1196–1221.
2.
YanYQinHGongC, et al.Multi-electric aircraft and power electronics. J Nanjing Univ Aeronaut Astronaut2014; 46: 11–18.
3.
TaulMWangXDavariP, et al.An overview of assessment methods for synchronization stability of grid-connected converters under severe symmetrical grid faults. IEEE Trans Power Electron2019; 34: 9655–9670.
4.
JiFGaoLCuiX, et al.VSC instantaneous current control and Lyapunov stability analysis. Chinese Journal of Electrical Engineering2020; 40: 4142–4150.
5.
ZhangYZhangCCaiX. Large-signal grid-synchronization stability analysis of PLL-based VSCs using Lyapunov's direct method. IEEE Trans Power Syst2022; 37: 788–791.
6.
MartijnRPhuongNJohanM, et al.Direct-quadrature sequence models for energy-function based transient stability analysis of unbalanced inverter-based microgrids. IEEE Trans Smart Grid2021; 12: 3692–3704.
7.
SuFYangSWangH, et al.Research on fast termination algorithm for power system transient stabilization time domain simulation. Chinese Journal of Electrical Engineering2017; 37: 4372–4378.
8.
XueYZhangZZhangN, et al.Transient stability analysis and enhancement control strategies for interconnected AC systems with VSC-Based generations. Int J Elec Power2023; 149: 109017.
9.
LiuZDingZHuangX, et al.An online power system transient stability assessment method based on graph neural network and central moment discrepancy. Front Energy Res2023; 11: 1082534.
10.
PratyasaBNilanjanS. Real-time prediction and control of transient stability using transient energy function. IEEE Trans Power Syst2017; 32: 923–934.
11.
LiYLiuBHuJ. Fast assessment of power system transient stability based on the fusion of data-driven and time-domain simulation. Power Grid Technology2023; 47: 4386–4395.
12.
AntonioGWangJ. Large signal stability analysis of 'more electric' aircraft power systems with constant power loads. IEEE Trans Aero Electron Syst2012; 48: 477–489.
13.
TayoUNnamdiISaheedL. Neural network based approach for steady-state stability assessment of power systems. Sustainability2023; 15: 1667.
14.
ZhaoYChenYLiZ. A proposed algorithm based on long short-term memory network and gradient boosting for aeroengine thrust estimation on transition state. P I Mech Eng G-J Aer2021; 15: 2182–2192.
15.
MoulinLAlvesPEl-SharkawiM, et al.Support vector machines for transient stability analysis of large-scale power systems. IEEE Trans Power Syst2004; 19: 818–825.
16.
AnJYuJLiZ, et al.A data-driven method for transient stability margin prediction based on security region. J Mod Power Syst Cle2020; 8: 1060–1069.
17.
TayebMGezaJJoseR. A power system stability assessment framework using machine-learning. Elec Power Syst Res2023; 216: 108981.
18.
ShiZYaoWTangY, et al. Intelligent power system stability assessment and dominant instability mode identification using integrated active deep learning. IEEE T Neur Net Lear2023; 35: 1–15.
19.
LiBWuJ. Adaptive assessment of power system transient stability based on active transfer learning with deep Belief network. IEEE Trans Autom Sci Eng2023; 20: 1047–1058.
20.
ShenJ. EM-TSA: an ensemble machine learning-based transient stability assessment approach for operation of power systems. Math Biosci Eng2023; 20: 8226–8240.
21.
LiuPHanSRongN. Frequency stability prediction of renewable energy penetrated power systems using CoAtNet and SHAP values. Eng Appl Artif Intell2023; 8: 106403.
22.
MohamedAShimaaANawalA, et al.Efficient online detection system of power disturbances based on Deep-Learning approach. Alex Eng J2023; 70: 377–394.
23.
ZhaoHPengYShuaiZ, et al. Online transient stability prediction method of microgrid considering different distributed energy resources’ interaction under current saturation. Csee J Power Energy2023; 42: 1–13.
24.
WangTTangY. Transient stability preventive control based on graph convolution neural network and transfer deep reinforcement learning. Csee J Power Energy2023; 7: 1–13.
25.
KiraKRendellL. A practical approach to feature selection. Machine Learning Proceedings1992; 6: 249–256.
26.
LiuWGaoPYuW, et al.Quantum relief algorithm. Quant Inf Process2018; 17: 280.
27.
ZhouFJinLDongJ. A review of convolutional neural network research. Journal of Computing2017; 40: 1229–1251.
28.
KlausGRupeshKJanK, et al.LSTM: a search space odyssey. IEEE T Neur Net Lear2017; 28: 2222–2232.
29.
LuJZhangQYangZ, et al.Short-term load forecasting method based on CNN-LSTM hybrid neural network model. Power System Automation2019; 43: 131–137.
30.
WangZXuZHeJ, et al.Long short-term memory neural equalizer. IEEE Trans Signal and Power Integr2023; 2: 13–22.
31.
FranciscoRAthulaDUdayaD, et al.Support vector machine-based algorithm for post-fault transient stability status prediction using synchronized measurements. IEEE Trans Power Syst2011; 26: 1474–1483.
