Shemeng Wu
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Shemeng Wu has a total of 4 co-authors. Here are the top co-authors by number of shared publications:
- Kai-Hung Lu
School of Electronic and Electrical Engineering, Minnan University of Science and Technology, Quanzhou 362700, China
2 shared publications - Chih-Ming Hong
National Kaohsiung University of Science and Technology, Taiwan
1 shared publication - Zhaozhi Wang
School of Electronic and Electrical Engineering, Minnan University of Science and Technology, Quanzhou 362700, China
1 shared publication - Zhigang Han
School of Electronic and Electrical Engineering, Minnan University of Science and Technology, Quanzhou 362700, China
1 shared publication
Latest Publications
- Open Access
Improvement of Stability in an Oscillating Water Column Wave Energy Using an Adaptive Intelligent Controller
Presently, among the global ocean energy technologies, the most conventional one is the wave energy power generation device based on the oscillating water column (OWC) wave energy converter. Given the fluctuation and randomness of waves and the complexity of the current power grid, the dynamic response of grid connections must be considered. Furthermore, considering the characteristics of the wave energy converter, this paper proposed an adaptive intelligent controller (AIC) for the permanent magnet synchronous generator (PMSG) in an OWC. The proposed controller includes a grey predictor, a recurrent wavelet-based Elman neural network (RWENN), and an adaptive critical network (ACN) to improve the stability of OWC power generation. This scheme can increase the maximum power output and improve dynamic performance when a transient occurs under the operating conditions of random wave changes. The proposed AIC for the PMSG based on OWC has a faster response speed, a smaller overshoot, and better stability than the traditional PI controller. This further verifies the availability of the proposed control strategy.
Open AccessImprovement of Stability in an Oscillating Water Column Wave Energy Using an Adaptive Intelligent Controller
Presently, among the global ocean energy technologies, the most conventional one is the wave energy power generation device based on the oscillating water column (OWC) wave energy converter. Given the fluctuation and randomness of waves and the complexity of the current power grid, the dynamic response of grid connections must be considered. Furthermore, considering the characteristics of the wave energy converter, this paper proposed an adaptive intelligent controller (AIC) for the permanent magnet synchronous generator (PMSG) in an OWC. The proposed controller includes a grey predictor, a recurrent wavelet-based Elman neural network (RWENN), and an adaptive critical network (ACN) to improve the stability of OWC power generation. This scheme can increase the maximum power output and improve dynamic performance when a transient occurs under the operating conditions of random wave changes. The proposed AIC for the PMSG based on OWC has a faster response speed, a smaller overshoot, and better stability than the traditional PI controller. This further verifies the availability of the proposed control strategy.
- Open Access
New Intelligent Control Strategy Hybrid Grey–RCMAC Algorithm for Ocean Wave Power Generation Systems
In this article, the characteristics of the wave energy converter are considered and a novel dynamic controller (NDC) for a permanent magnet synchronous generator (PMSG) is proposed for Wells turbine applications. The proposed NDC includes a recursive cerebellum model articulation controller (RCMAC) with a grey predictor and innovative particle swarm optimization (IPSO). IPSO is developed to adjust the learning speed and improve learning capability. Based on the supervised learning method, online adjustment law of RCMAC parameters is derived to ensure the system’s stability. The NDC scheme is designed to maintain a supply–demand balance between intermittent power generation and grid power supply. The proposed NDC exhibits an improved power regulation and dynamic performance of the wave energy system under various operation conditions. Furthermore, better results are obtained when the RCMAC is used with the grey predictive model method.
Open AccessNew Intelligent Control Strategy Hybrid Grey–RCMAC Algorithm for Ocean Wave Power Generation Systems
In this article, the characteristics of the wave energy converter are considered and a novel dynamic controller (NDC) for a permanent magnet synchronous generator (PMSG) is proposed for Wells turbine applications. The proposed NDC includes a recursive cerebellum model articulation controller (RCMAC) with a grey predictor and innovative particle swarm optimization (IPSO). IPSO is developed to adjust the learning speed and improve learning capability. Based on the supervised learning method, online adjustment law of RCMAC parameters is derived to ensure the system’s stability. The NDC scheme is designed to maintain a supply–demand balance between intermittent power generation and grid power supply. The proposed NDC exhibits an improved power regulation and dynamic performance of the wave energy system under various operation conditions. Furthermore, better results are obtained when the RCMAC is used with the grey predictive model method.