Owing to the rotational sampling of turbulence, wind shear and tower shadow effects grid connected variable speed wind turbines could lead to the power fluctuations which may produce flicker during continuous operation. A model of an megawatt (MW)-level variable speed wind turbine with a doubly fed induction generator is presented to investigate the flicker mitigation. Taking advantage of the large inertia of the wind turbine rotor, a generator torque control (GTC) strategy is proposed, so that the power oscillation is stored as the kinetic energy of the wind turbine rotor, thus the flicker emission could be reduced. The GTC scheme is proposed and designed according to the generator rotational speed. The simulations are performed on the national renewable energy laboratory 1.5 MW upwind reference wind turbine model. Simulation results show that damping the generator active power by GTC is an effective means for flicker mitigation of variable speed wind turbines during continuous operation.

This study presents analysis, design and implementation of a microcontroller based electronic load controller (ELC) for efficient voltage regulation of a three-phase self-excited induction generator (SEIG) feeding single-phase loads in remote locations. The proposed ELC has an uncontrolled rectifier, a filtering capacitor, an insulated gate bipolar transistor switch and a series dump load. The pulse-width modulation (PWM) pulses with appropriate duty cycle are generated using the dsPIC30F6010 microcontroller. The duty ratio is determined based on the closed-loop control scheme which decides the amount of power diverted to the dump load. The proposed SEIG–ELC system demonstrates an effective power switching between the main load and the dump load thereby providing an efficient voltage regulation at the machine terminals. The controller is modelled in Matlab/Simulink and the simulated results are validated by experimental results.

This study investigates a novel control methodology for the doubly-fed induction generators (DFIGs) in the real situation of wind energy generation when the machine is connected to a power system. The machine speed, DC voltage, active and reactive grid powers have been controlled using a reduced-order representation of the DFIG coupled to the grid. This framework allows several methods for the synthesis of the regulator. A state-feedback controller is designed based on the linear quadratic method. This regulator is tested in comparison with the standard vector control approach and the more recent flux magnitude angle control via simulations performed with Simulink and SimPowerSystem Toolbox of MATLAB.

In the case of operating in a weak grid system, when wind power becomes a significant portion of the power system or even the sole energy source, the wind power generators and converters are expected to help maintain the grid voltage. The grid-side converter needs to work as a voltage source to help regulate the terminal (grid) voltage amplitude and frequency by adjusting the reactive power and active power flow, respectively. For a direct-drive permanent magnet synchronous generator with a full power converter, the active power must be provided by the captured wind power. The active power flow between the source (captured wind power) and the grid (load) must be balanced by actively controlling the generator speed and wind turbine pitch angle. In the study, the coordinated control of generator speed and blade pitch angle is proposed together with a dc-link voltage controller. A model of the grid-side converter operating as a voltage source has been built and the strategy regarding voltage and frequency regulation is presented. Simulation is carried out with different wind and load profile. The results show the wind energy can help support the weak grid and power the local grid in stand-alone mode as well.

The historical time series data or Monte Carlo simulation approaches that are often used to represent wind power in transmission planning models will lead to large-scale optimisation problems. The complexity of such problems will be further compounded if advanced techniques for wind variability and wind forecast uncertainty management are also endogenously included, corresponding to a merging of the traditionally separate ‘real-time operations’ and ‘long-term planning’ analysis timeframes in power system analysis. A stochastic mixed-integer scheduling model is applied here to investigate the likely transmission planning model formulation impacts of advanced wind forecast techniques, and to determine whether any additional optimal transmission planning model precisions offered justify the associated very-large-scale computational burden. Results indicate that power-flow modelling is only significantly influenced in a small subset of the network branches associated with major interconnections and flexible/inflexible conventional generation locations. Model sensitivity analysis also suggests that even at high wind penetrations, such power-flow modelling differences may be overshadowed by the impact of general uncertainty in fuel price volatility and demand profile that is systemic to long-term planning problems. Such trade-offs have significant practical relevance to the many researchers currently investigating formulations of this class of optimisation problem.

Owing to the rapid growth in the use of wind power, there is a need to carry out an evaluation of the frequency and voltage control of this technology. This study focuses on the voltage control of the evacuation network which connects different wind farms to the transmission network bus. The main contribution of this study is to present a novel real time algorithm which can be used as an alternative to classical techniques such as optimal power flow or artificial intelligence to determine the amount of reactive power that each wind farm should supply in order to minimise the power losses of a whole evacuation network. The optimal voltage control proposed in this study uses data mining techniques (regression rules to estimate the optimum reactive power of the wind farms and classification trees to estimate the optimum transformer taps). The methodology proposed in this study is illustrated with a study of two actual evacuation networks in the Spanish power system. The first one is representative of long feeders whereas the second one is representative of short feeders. The variability in the results of the methodology seems to be dependent on the features of the grids.

Increasing deployment of large wind turbines (WT) offshore and in remote areas requires reliable condition monitoring (CM) techniques to guarantee the high availability of these WTs and economic return. To meet this need, much effort has been expended to improve the capability of analysing the WT CM signals. However, a fully satisfactory technique has not been achieved today. One of the major reasons is that the developed techniques still cannot provide accurate interpretation of the WT CM signals, which are usually non-linear and non-stationary in nature because of the constantly varying loads and non-linear operations of the turbines. To deal with this issue, a new data-driven signal processing technique is developed in this study based on the concepts of intrinsic time-scale decomposition (ITD) and energy operator separation algorithm (EOSA). The advantages of the proposed technique over the traditional data-driven techniques have been demonstrated and validated experimentally. It has been shown that in comparison with the Hilbert–Huang transform the combination of ITD and EOSA provided more accurate and explicit presentations of the instantaneous information of the signals tested. Thus, it provides a much improved offline tool for accurately interpreting WT CM signals.

Unlike traditional distribution generation (DG) units, the production of photovoltaic DG (PVDG) units is non-dispatchable and largely driven by the rates of solar irradiance fall on its PV array. In this work, a suitable procedure for optimal sizing and location of single PVDGs on radial distribution feeders has been developed. It goes along with the current trend of interfacing renewable energy generators with the grid due to global warming concern. The procedure applies single PVDG unit at points on the feeder while allowing reverse power flow (RPF) within the feeder line sections. The optimisation objective is to minimise the accumulated line power loss over the day (line energy loss) along the feeder, while keeping the voltage profile along the feeder within permissible limits. A method has been applied to rate the line energy loss considering one time interval, namely feasible optimisation interval. The procedure has been applied to an actual 11 kV feeder in Abu Dhabi city. The application showed obvious benefits in terms of line loss reduction and improvement of the voltage profile. The procedures also resulted in alternative feasible solutions in case the optimal solution cannot be applied for any reason – like inconvenience/limitation of land or investment.

The integration of renewable energy technology is making the power distribution system more flexible, but also introducing challenges for traditional technology. With the nature of intermittent and less inertial, renewable energy-based generations need effective control methods to cooperate with other devices, such as storage, loads and the utility grid. The widely used power frequency (P–f) droop control is based on the precondition of inductive line impedance, but the low-voltage system is mainly resistive, and also the different load character needs to be considered. This study presents an adaptive droop control method based on online evaluation of power decouple matrix for inverter connected distributed generations in distribution system. Traditional decoupled power control is simply based on line impedance parameter, but the load characteristics also cause the power coupling, and alter the equivalent system impedance characteristic. In this study, the decoupled matrix is evaluated by the ratio of the variations of active power and reactive power under a small perturbation on the voltage magnitude. As analysed, the proposed decoupled control method can take the effects both the transmission line and the load characteristics into account, and it will be more practical, and flexible in various network and load conditions. The performance has been validated by simulation and experimental results.

This study presents an adaptive perturb and observe (P&O)-fuzzy control maximum power point tracking (MPPT) for photovoltaic (PV) boost dc–dc converter. P&O is known as a very simple MPPT algorithm and used widely. Fuzzy logic is also simple to be developed and provides fast response. The proposed technique combines both of their advantages. It should improve MPPT performance especially with existing of noise. For evaluation and comparison analysis, conventional P&O and fuzzy logic control algorithms have been developed too. All the algorithms were simulated in MATLAB-Simulink, respectively, together with PV module of Kyocera KD210GH-2PU connected to PV boost dc–dc converter. For hardware implementation, the proposed adaptive P&O-fuzzy control MPPT was programmed in TMS320F28335 digital signal processing board. The other two conventional MPPT methods were also programmed for comparison purpose. Performance assessment covers overshoot, time response, maximum power ratio, oscillation and stability as described further in this study. From the results and analysis, the adaptive P&O-fuzzy control MPPT shows the best performance with fast time response, less overshoot and more stable operation. It has high maximum power ratio as compared to the other two conventional MPPT algorithms especially with existing of noise in the system at low irradiance.

Wind turbines are an important source of renewable energy. Although the amount of wind turbine installations has known a considerable increase in recent years, technological improvements are still needed to increase their efficiency. An important subject is the presence of vibrations. For instance, ripples can be present in the torque and shaft speed, which can be caused by turbulence of the air flow, resonance or mechanical problems. Furthermore, tower shadow and wind shear are able to cause significant torque oscillations. In literature, a mathematical model of the torque oscillations has been presented for three-bladed horizontal-axis upwind turbines. However, it remains unclear what the impact is of these torque oscillations on the shaft speed. When ripples are present in the shaft speed, they affect the back-electromotive force and electrical power of the generator and could propagate further in the system. Therefore this study investigates whether this effect is large enough to have a considerable impact on the system. The turbine inertia and size are both relevant parameters in this research. However, it will be shown by mathematical proof that the relative amount of shaft speed ripples caused by tower shadow and wind shear is independent of the turbine size.

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.