We present a dynamic frequency and amplitude estimation algorithm for unbalanced three-phase power systems with harmonics. To develop the algorithm, we first construct a nonlinear dynamic model of the complex representation of the three-phase voltage with harmonic content and then apply an unscented Kalman filter to estimate magnitudes and frequencies of the fundamental component and its harmonics. Performance evaluation of the algorithm via Monte Carlo simulation shows the accurate tracking and fast convergence time when the frequency and amplitude of the grid voltage waveform change continuously or abruptly. Specifically, we benchmark the proposed algorithm with two types of input amplitudes and two types of frequency changes.

This study presents a model predictive control (MPC) scheme for a wave energy converter (WEC); in particular, for a buoy‐type point absorber. The WEC is a two‐body system which is taut‐moored to the sea floor with three cables. Much research has been done recently to achieve optimal operation of WECs. The goal is to maximise the power conversion without violating system limits. In practice, there are physical constraints on position, velocity and the power take‐off (PTO) force. MPC is a promising and beneficial approach to achieve this goal. It poses a control formulation including constraints in a natural way. Furthermore, MPC can exploit predictions for the sea motion a standard MPC approach always needs a reference trajectory. For one‐body point absorber, an optimal velocity trajectory can be calculated. However, an optimal trajectory is not easily available for the two‐body case. The proposed formulation in the presented work does not require an optimal trajectory. For this reason it is possible to apply this MPC scheme to a two‐body model as well. This work demonstrates that the proposed control algorithm optimises the power extraction without violating the system constraints. Finally, the performance of MPC is compared to linear passive load control through simulation.

Ocean wave energy is a renewable energy which remains too costly for large-scale electricity generation. The oscillating water column (OWC) wave energy converter (WEC) is a promising device-type with a rectifying air turbine and generator which convert alternating airflow induced by the water motion into kinetic energy then into electric energy. Applying control at each stage of energy conversion could increase the electric energy output of the device. As researchers overcome the modeling challenges of OWC, such as the nonlinearities due to air compressibility and power take-off (PTO) dynamics, we can integrate specific control algorithms to test their ability to improve the efficiency of the OWC. Herein, we present a state-space model of an array of OWC WECs restricted to heave motion with nonlinear PTO dynamics. We apply second-order sliding mode control (SMC) which commands a smooth torque signal to a direct-drive generator to maintain a reference turbine angular velocity. Because the algorithm can yield high turbine torques, we investigate a simple feed-forward relation for the control of a valve to limit the turbine airflow and discard mechanical power. We find that implementing the SMC algorithm and valve control can improve electric energy conversion most effectively in less energetic sea states.

Many recent works on wave energy conversion use a form of model predictive control (MPC) to maximise energy harvest. This active control strategy is generally used because it permits the inclusion of constraints that represent the physical limits of wave energy conversion devices in the optimisation algorithm. In this study, the authors present a centralised MPC design for an array configuration of wave energy converters. Its performance is evaluated via computer simulation and is compared against that of decentralised MPC and linear damping controller for several values of damping in both regular and irregular waves.

The frequency of the grid voltage is a time‐varying parameter caused by mismatches between power generation and power consumption. In fact, the fundamental frequency decreases when large loads are connected to the system or when a large generation source goes offline. The opposite holds true for an increase in the fundamental frequency, e.g. when generation exceeds consumption. Hence, in order to protect a power system against loss of synchronism, under‐frequency relaying, and power system stabilisation, accurate frequency estimation is necessary. This study proposes an adaptive algorithm based on a master–slave unscented Kalman filter (UKF) configuration to estimate both the voltage frequency and the measurement noise. Specifically, the master UKF uses the strong tracking filter condition to improve tracking accuracy, speed of convergence, and to desensitise the filter from initial conditions. The slave UKF uses the master UKF innovation to estimate the measurement noise covariance. The proposed approach addresses the tracking weaknesses of other frequency estimation algorithms when the frequency of the grid voltage waveform changes abruptly. Algorithm performance is measured through computer simulation.

The oscillating water column (OWC) wave energy converter (WEC) together with a self-rectifying air turbine and generator which convert alternating airflow induced by the water motion into kinetic energy and then into electric energy is a promising device for the advancement of marine renewable energy. As researchers overcome the modeling challenges of OWCs, such as the nonlinearities due to air compressibility and power take-off (PTO) dynamics, we can integrate control algorithms to improve the efficiency of the OWC. Herein, we present a nonlinear model predictive controller (NMPC) that maximizes the generated electro-mechanical power while maintaining the efficiency of a self-rectifying turbine attached to an OWC WEC. To achieve this goal, a good trade-off between maximizing generated power and turbine efficiency is found by adjusting the importance of both objectives in the computation of the control signal. Furthermore, we apply the proposed NMPC to an array of three WECs that takes into account the hydrodynamic interactions between the devices in the computation of optimal generator torque control signal.