This paper proposes a sliding-mode control (SMC)-based scheme for the variable-speed direct-driven wind energy conversion systems (WECS) equipped with a permanent magnet synchronous generator connected to the grid. In this paper, diode rectifier, boost converter, neutral point clamped inverter, and L filter are used as the interface between the wind turbine and grid. This topology has abundant features such as simplicity for low- and medium-power wind turbine applications. It is also less costly than back-to-back two-level converters in medium-power applications. The SMC approach demonstrates great performance in complicated nonlinear systems control such as WECS. The proposed control strategy modifies reaching law (RL) of the sliding mode technique to reduce chattering issue and to improve total harmonic distortion property compared to conventional RL SMC. The effectiveness of the proposed control strategy is explored by simulation study on a 4-kW wind turbine, and then verified by experimental tests for a 2-kW setup.

Modern wind turbine controllers use wind speed information to improve power production and reduce loads on the turbine components. The turbine top wind speed measurement is unfortunately imprecise and not a good representative of the rotor effective wind speed. Consequently, many different model-based algorithms have been proposed that are able to estimate the wind speed using common turbine measurements. In this paper, we present a concise yet comprehensive analysis and comparison of these techniques, reviewing their advantages and drawbacks. We implement these techniques and compare the results on both aero-servo-elastic turbine simulations and real turbine field experiments in different wind scenarios.

A direct current (DC) microgrid has become a superior power system in recent years due to the development of DC loads and higher efficiency of DC systems. One of the challenging problems on DC microgrids operation is protection, and it is still a particular concern associated with the challenges of developing a proper protection scheme owing to its characteristics and lack of standards in DC protection. Due to the significant increasing interest on DC microgrid; this study investigates protection problems and schemes that need to be addressed in modern power systems involving DC microgrids. This study analyses and presents a comprehensive review of the most recent growth in the DC microgrids protection. Additionally, the fault characteristics of DC microgrids, the impact of constant power loads, the protection devices and several proposed methods to overcome the protection problems are discussed. The differences between the proposed protection methods for the DC microgrids are also discussed.

With the increasing size of wind farms, the impact of the wake effect on wind farm energy yields become more and more evident. The arrangement of locations of the wind turbines (WTs) will influence the capital investment and contribute to the wake losses, which incur the reduction of energy production. As a consequence, the optimized placement of the WTs may be done by considering the wake effect as well as the components cost within the wind farm. In this paper, a mathematical model which includes the variation of both wind direction and wake deficit is proposed. The problem is formulated by using levelized production cost (LPC) as the objective function. The optimization procedure is performed by a particle swarm optimization (PSO) algorithm with the purpose of maximizing the energy yields while minimizing the total investment. The simulation results indicate that the proposed method is effective to find the optimized layout, which minimizes the LPC. The optimization procedure is applicable for optimized placement of WTs within wind farms and extendible for different wind conditions and capacity of wind farms.

This paper presents a predictive home energy management system for a residential building with integration of a plug-in electric vehicle, a photovoltaic array and a heat pump. A stochastic model predictive control strategy is applied in the home energy management system in order to minimize the home electricity cost and reduce the plug-in electric vehicle battery degradation cost. Moreover, the model predictive control ensures that home load demand, plug-in electric vehicle battery charging requirements and household thermal comfort conditions are met. The model predictive control operates in real-time and thus minimizes the effects of gap between the forecasted and real data on the home energy management system performance by updating its control decisions and the forecast data as the stochastic parameters are realized in each time step. The obtained simulation results demonstrate that the proposed control strategy reaches 96% to 97% of ideal performance achieved by the off-line optimization counterpart with perfect data.

An optimal reactive power dispatch strategy is proposed to minimize the total electrical losses of a wind farm (WF), including not only losses in the transmission cables and wind turbine (WT) transformers, but also losses inside wind energy generation systems. The reactive power dispatch inside a WT uses optimal splitting strategy over the stator and the grid side converter (GSC), which aims to minimize the total loss of the wind energy generation system, including the generator, the converters, and the filters. Optimization problems are formulated based on established loss models and WT reactive power limits. A WF is carefully designed and used for case studies. Wake effect is considered when calculating the active power at each WT. The total losses of the WF are calculated by implementing the proposed strategy at different wind speeds and reactive power references. The simulation results show the effectiveness of the proposed strategy.