Search

Oscillating-water-column wave energy converters (OWC-WECs) are gaining attention for their high energy potential and environmental friendliness. However, their irregular input energy characteristics pose challenges to achieving stable power generation, particularly due to high peak power compared to average power. This study focuses on stable rating control to enable continuous power generation in the presence of irregular wave energy. It is difficult to precisely configure the existing rated power controllers due to physical time delays; this impacts system stability and utilization. To address this, we propose a rated power controller that compensates for system time delays using a deep learning algorithm. By predicting the valve control angle in advance and analyzing the input data for angle estimation, we successfully compensate for the physical time delay. The performance of the proposed rated power controller, incorporating the deep learning algorithm, is evaluated by analyzing the algorithm’s error rate. The results demonstrate that the proposed method improves power generation under various wave conditions by compensating for the unavoidable time delay of OWC-WECs, leading to a significant increase in annual power generation. In conclusion, the proposed method achieves approximately 31% higher annual power generation compared to the time delay controller.

A power take-off (PTO) system for an oscillating water column (OWC) wave energy converter comprises a turbine-generator-power converter. In this study, only the topologies of the power converter that affect the load control algorithm are compared. A power converter for renewable energy is composed of a diode-dc/dc converter and a pulse-width modulation (PWM) converter operating at small and large capacities, respectively. However, selecting a power converter according to the capacity based on the characteristics of the wave energy converter, in which the input energy is highly fluctuating, can significantly reduce the power performance. Thus, to verify load control characteristics according to the topology of the power converter, the turbine-generator-power converter was incorporated in the modeling, and the power performance based on the power converter topology under various wave conditions was analyzed. Further, torque control to obtain the maximum power among load control algorithms was applied under irregular wave conditions, and the power performance and PTO system characteristics according to the torque coefficient were analyzed. The results of this study suggested an increase in the torque coefficient of the maximum power control for the operational stability of the OWC-WEC, and it was confirmed that the RPM characteristics of the PTO system were reduced.

In order to apply the model predictive control to the power converter, a study on the calculation amount reduction algorithm was performed in various topologies such as 2-level, 3-level, multi-level converter. Similarly, in order to apply the model predictive control to the 3-level 4-leg converter, it is necessary to study the calculation amount reduction algorithm. In this paper, instead of considering 81 candidate voltage vectors for every sampling period as in the conventional model predictive control, the optimal switching state is selected considering only 7 candidate voltage vectors located near the reference voltage vector. The sector, prism, and tetrahedron are selected sequentially by using the position of the reference voltage vector, and the preselected 7 candidate voltage vectors are the vectors constituting the tetrahedron. The proposed method represents an improved model predictive control which reduces the amount of computation and does not affect performance. This method constructs the 3-level 4-leg NPC inverter simulation and experimental setup to compare the performance with the conventional method.

In this paper, a predictive direct power control (DPC) method based on a Kalman filter is presented for three-phase pulse-width modulation (PWM) rectifiers to improve the performance of rectifiers with source voltages that are distorted with harmonic components. This method can eliminate the most significant harmonic components of the source voltage using a Kalman filter algorithm. In the process of predicting the future real and reactive power to select an optimal voltage vector in the predictive DPC, the proposed method utilizes source voltages filtered by a Kalman filter, which can mitigate the adverse effects of distorted source voltages on control performance. As a result, the quality of the source currents synthesized using the PWM rectifier is improved, and the total harmonic distortion (THD) values are reduced, even under distorted source voltages.

Model Predictive Control (MPC) method offers advantages of easy implementation and fast dynamics. However Model predictive control method has variable switching frequency, which makes design of the output filter difficult for power converter. Despite the problems of MPC algorithm, there are little methods to fix the switching frequency using MPC algorithm in power converter. Therefore, this paper proposes constant switching frequency with MPC algorithm, which method simply selects the optimal voltages and divides the optimal voltages in one-sampling period. The performance is proved comparing to the conventional method and carrier based method in experimental results with average switching frequency for single-phase VSI.

Model predictive control (MPC) method with finite control set needs calculations according to the total number of voltage vectors made by multi-level converters. This operating principle of the MPC tends dramatically to increase the computational load and number of calculations when the level of cascaded H-bridge (CHB) inverters is increased. A new MPC algorithm is proposed in this paper for decreasing the number of calculation in multi-level CHB inverters, in which only three voltage vectors are evaluated to determine optimal vectors regardless of the level of CHB inverters. As a result, the proposed MPC method needs fewer calculations and decreases the computational load compared to the conventional MPC method for CHB inverters, without affecting the performance and dynamic response of 5-level CHB inverter.