A5074919238- Wind Turbine Control Systems
- Stability and Controllability of Differential Equations
- Advanced Control Systems Optimization
- Stability and Control of Uncertain Systems
- Energy Load and Power Forecasting
- Microgrid Control and Optimization
- Frequency Control in Power Systems
- Bayesian Methods and Mixture Models
- Wind Energy Research and Development
University of Pennsylvania
2024
Jiangsu University of Science and Technology
2022-2023
Linear Parameter Varying Dynamical Systems (LPV-DS) encode trajectories into an autonomous first-order DS that enables reactive responses to perturbations, while ensuring globally asymptotic stability at the target. However, current LPV-DS framework is established on Euclidean data only and has not been applicable broader robotic applications requiring pose control. In this paper we present extension framework, named Quaternion-DS, which efficiently learns a DS-based motion policy for...
This paper presents an advanced virtual inertia control strategy for wind farms to provide transient power support in the presence of frequency events, where a fuzzy active disturbance rejection controller is developed enable operation energy storage system (ESS) so as regulation grid. To effectively estimate under uncertain noises, rules are presented adaptively tune parameters extended state observer and realize power-sharing, improve anti-interference ability system. Finally, simulation...
Abstract This paper proposes a virtual inertia control strategy for wind farms based on auto disturbance rejection controller with artificial bee colony (ABC) algorithm. First, the system frequency dynamic response equation, an active is designed according to non‐linear feedback law and extended state observer improve anti‐interference capability of system. Second, in order solve problem difficulty tuning parameters (ADRC), ABC algorithm nectar collection behaviour was proposed iteratively...
The Linear Parameter Varying Dynamical System (LPV-DS) is an effective approach that learns stable, time-invariant motion policies using statistical modeling and semi-definite optimization to encode complex motions for reactive robot control. Despite its strengths, the LPV-DS learning faces challenges in achieving a high model accuracy without compromising computational efficiency. To address this, we introduce Directionality-Aware Mixture Model (DAMM), novel applies Riemannian metric on...
Abstract In order to improve the inertia support of wind power system in case load disturbance events, a control frequency response method based on Hammerstein-Neural Network (HNNFC) is proposed this paper. First, sliding mode (SMC) law designed according dynamic equation. Then, identified -Hammerstein-Neural (HNN). Moreover, ESO which used as compensator Hammerstein model fast and stable recovery performance system. Finally, simulation results show that can not only release active through...