Adaptive sliding mode-based feedback linearization control for floating offshore wind turbine in region II DOI
Hao Chen,

Junjie Niu,

Youming Cai

и другие.

International Journal of Green Energy, Год журнала: 2024, Номер 22(2), С. 467 - 486

Опубликована: Ноя. 29, 2024

Wind turbine systems are highly nonlinear and time-variable. Under external interference, the normal stable operation of system is seriously affected. In addition, inertia wind causes a serious lag in speed tracking. These effects even more severe for floating offshore turbines (FOWT). To solve these problems, this paper proposes new optimal torque control form, further improves optimizes it. Firstly, feedback linearization used to eliminate part time-varying parameters, form obtained. Then, adaptive sliding mode optimize controller enhance robustness system. Finally, mode-based linearized (ASMFLOTC) was ASMFLOTC applied FOWT verify effectiveness its maximum power point tracking (MPPT) control. The results show that can better track reference speed, effectively reduce relative error, improve utilization rate energy. And from results, platform motion proposed not significantly different other controllers. does exacerbate while increasing output power. This shows feasibility.

Язык: Английский

Design of advanced intrusion detection in cybersecurity using ensemble of deep learning models with an improved beluga whale optimization algorithm DOI
Fatimah Alhayan,

Nuha Alruwais,

Mohammad Alamgeer

и другие.

Alexandria Engineering Journal, Год журнала: 2025, Номер 121, С. 90 - 102

Опубликована: Фев. 26, 2025

Язык: Английский

Процитировано

0
Federated learning with Blockchain on Denial-of-Service attacks detection and classification of edge IIoT networks using Deep Transfer Learning model DOI
Monir Abdullah, Hanan Abdullah Mengash, Mohammed Maray

и другие.

Computers & Electrical Engineering, Год журнала: 2025, Номер 124, С. 110319 - 110319

Опубликована: Апрель 18, 2025

Язык: Английский

Процитировано

0
Weighted Self-Paced Learning with Belief Functions DOI
Shixing Zhang, Deqiang Han, Jean Dezert

и другие.

Expert Systems with Applications, Год журнала: 2024, Номер 255, С. 124535 - 124535

Опубликована: Дек. 1, 2024

Язык: Английский

Процитировано

2
Explainable artificial intelligence in web phishing classification on secure IoT with cloud-based cyber-physical systems DOI Creative Commons
Sultan Alotaibi, Hend Khalid Alkahtani, Mohammed Aljebreen

и другие.

Alexandria Engineering Journal, Год журнала: 2024, Номер 110, С. 490 - 505

Опубликована: Окт. 15, 2024

Язык: Английский

Процитировано

1
Adaptive sliding mode-based feedback linearization control for floating offshore wind turbine in region II DOI
Hao Chen,

Junjie Niu,

Youming Cai

и другие.

International Journal of Green Energy, Год журнала: 2024, Номер 22(2), С. 467 - 486

Опубликована: Ноя. 29, 2024

Wind turbine systems are highly nonlinear and time-variable. Under external interference, the normal stable operation of system is seriously affected. In addition, inertia wind causes a serious lag in speed tracking. These effects even more severe for floating offshore turbines (FOWT). To solve these problems, this paper proposes new optimal torque control form, further improves optimizes it. Firstly, feedback linearization used to eliminate part time-varying parameters, form obtained. Then, adaptive sliding mode optimize controller enhance robustness system. Finally, mode-based linearized (ASMFLOTC) was ASMFLOTC applied FOWT verify effectiveness its maximum power point tracking (MPPT) control. The results show that can better track reference speed, effectively reduce relative error, improve utilization rate energy. And from results, platform motion proposed not significantly different other controllers. does exacerbate while increasing output power. This shows feasibility.

Язык: Английский

Процитировано

0