In this work, we perform a comprehensive comparative study of two advanced control algorithms-the classical tracking model predictive control (MPC) and economic MPC (EMPC)-in the optimal operation of wind energy conversion systems (WECSs). A typical 5 MW wind turbine is considered in this work. The tracking MPC is designed to track steady-state optimal operating reference trajectories determined using a maximum power point tracking (MPPT) algorithm. In the design of the tracking MPC, the entire operating region of the wind turbine is divided into four subregions depending on the wind speed. The tracking MPC tracks different optimal reference trajectories determined by the MPPT algorithm in these subregions. In the designed EMPC, a uniform economic cost function is used for the entire operating region and the division of the operating region into subregions is not needed. Two common economic performance indices of WECSs are considered in the design of the economic cost function for EMPC. The relation between the two economic performance indices and the implications of the relation on EMPC performance are also investigated. Extensive simulations are performed to show the advantages and disadvantages of the two control algorithms under different conditions. It is found that when the near future wind speed can be predicted and used in control, EMPC can improve the energy utilization by about 2% and reduce the operating cost by about 30% compared to classical tracking MPC, especially when the wind speed varies such that the tracking MPC switches between operating subregions. It is also found that uncertainty in information (e.g., future wind speed, measurement noise in wind speed) may deteriorate the performance of EMPC.Energies 2018, 11, 3127 2 of 23 capture as much energy as possible from the wind; when the WECS is operated in the full load mode, the typical control objective is to regulate the blade pitch angle to maintain both the output power and the generator speed at their rated values to ensure the safety of the equipment [2]. The operation of a WECS may switch between these two operating modes frequently due to the variation of wind speed. This poses great challenges in the controller design for WECSs.In the literature, many control strategies have been proposed for the control of WECSs with the purpose of either maximizing wind energy capture or maintaining the system at rated power. When a WECS operates in partial load mode, the maximum power point tracking (MPPT) is one of the most effective approaches for extracting energy from wind. Existing research studies primarily focus on three MPPT algorithms, namely, tip speed ratio (TSR) control, hill-climb search (HCS) control, and power signal feedback control [3,4]. In these algorithms, the optimal steady-state reference trajectories are calculated. These reference trajectories are then sent to the feedback control layer. The main objective of the feedback control layer is to drive a WECS to track the optimal reference trajectories. In the feedback control lay...
Grid-connected and islanding operations of a microgrid are often influenced by system uncertainties, such as load parameter variations and unmodeled dynamics. This paper proposes a novel adaptive higher-order sliding mode (AHOSM) control strategy to enhance system robustness and handle an unknown uncertainty upper bounds problem. Firstly, microgrid models with uncertainties are established under islanding and grid-connected modes. Then, adaptive third-order sliding mode and adaptive second-order sliding mode control schemes are respectively designed for the two modes. Microgrid models' descriptions are divided into nominal part and uncertain part, and higher-order sliding mode (HOSM) control problems are transformed into finite time stability problems. Again, a scheduled law is proposed to increase or decrease sliding mode control gain adaptively. Real higher-order sliding modes are established, and finite time stability is proven based on the Lyapunov method. In order to achieve smooth mode transformation, an islanding mode detection algorithm is also adopted. The proposed control strategy accomplishes voltage control and current control of islanding mode and grid-connected mode. Control voltages are continuous, and uncertainty upper bounds are not required. Furthermore, adjustable control gain can further whittle control chattering. Simulation experiments verify the validity and robustness of the proposed control scheme.
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