Secure Optimization Dispatch Framework with False Data Injection Attack in Hybrid-Energy Ship Power System Under the Constraints of Safety and Economic Efficiency

Hybrid-energy vessels offer significant advantages in reducing carbon emissions and air pollutants by integrating traditional internal combustion engines, electric motors, and new energy technologies. However, during operation, the high reliance of hybrid-energy ships on networks and communication s...

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Vydáno v:Electricity (Basel, Switzerland) Ročník 6; číslo 3; s. 38
Hlavní autoři: Luo, Xiaoyuan, Zhu, Weisong, Chang, Shaoping, Wang, Xinyu
Médium: Journal Article
Jazyk:angličtina
Vydáno: Basel MDPI AG 03.07.2025
Témata:
Accuracy
CNN-LSTM
Efficiency
Energy consumption
Energy resources
Energy storage
Global economy
hybrid-energy ship power system
improved multi-objective whale optimization algorithm
Methods
Neural networks
optimal power flow optimization
Optimization algorithms
Scheduling
Shipping industry
voyage optimization
Wind power
ISSN:2673-4826, 2673-4826
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Shrnutí:Hybrid-energy vessels offer significant advantages in reducing carbon emissions and air pollutants by integrating traditional internal combustion engines, electric motors, and new energy technologies. However, during operation, the high reliance of hybrid-energy ships on networks and communication systems poses serious data security risks. Meanwhile, the complexity of energy scheduling presents challenges in obtaining feasible solutions. To address these issues, this paper proposes an innovative two-stage security optimization scheduling framework aimed at simultaneously improving the security and economy of the system. Firstly, the framework employs a CNN-LSTM hybrid model (WOA-CNN-LSTM) optimized using the whale optimization algorithm to achieve real-time detection of false data injection attacks (FDIAs) and post-attack data recovery. By deeply mining the spatiotemporal characteristics of the measured data, the framework effectively identifies anomalies and repairs tampered data. Subsequently, based on the improved multi-objective whale optimization algorithm (IMOWOA), rapid optimization scheduling is conducted to ensure that the system can maintain an optimal operational state following an attack. Simulation results demonstrate that the proposed framework achieves a detection accuracy of 0.9864 and a recovery efficiency of 0.969 for anomaly data. Additionally, it reduces the ship’s operating cost, power loss, and carbon emissions by at least 1.96%, 5.67%, and 1.65%, respectively.
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ISSN:2673-4826
2673-4826
DOI:10.3390/electricity6030038