The state of the art of wind energy conversion systems and technologies: A review

Wind power is the most reliable and developed renewable energy source over past decades. With the rapid penetration of the wind generators in the power system grid, it is very essential to utilize the maximum available power from the wind and to operate the wind turbine (WT) at its maximal energy conversion output. For this, the wind energy conversion system (WECS) has to track or operate at the maximum power point (MPP). A decent variety of publication report on various maximum power point tracking (MPPT) algorithms for a WECS. However, making a choice on an exact MPPT algorithm for a particular case require sufficient proficiency because each algorithm has its own merits and demerits. For this reason, an appropriate review of those algorithms is essential. However, only a few attempts have been made in this concern. In this paper, different available MPPT algorithms are described for extracting maximum power which are classified according to the power measurement i.e. direct or indirect power controller. Merits, demerits and comprehensive comparison of the different MPPT algorithms also highlighted in the terms of complexity, wind speed requirement, prior training, speed responses, etc. and also the ability to acquire the maximal energy output. This paper serves as a proper reference for future MPPT users in selecting appropriate MPPT algorithm for their requirement.

A review of conventional and advanced MPPT algorithms for wind energy systems

This paper proposes a wind speed estimation based sensorless maximum wind power tracking control for variable-speed wind turbine generators (WTGs). A specific design of the proposed control algorithm for a wind turbine equipped with a doubly fed induction generator (DFIG) is presented. The aerodynamic characteristics of the wind turbine are approximated by a Gaussian radial basis function network based nonlinear input-output mapping. Based on this nonlinear mapping, the wind speed is estimated from the measured generator electrical output power while taking into account the power losses in the WTG and the dynamics of the WTG shaft system. The estimated wind speed is then used to determine the optimal DFIG rotor speed command for maximum wind power extraction. The DFIG speed controller is suitably designed to effectively damp the low-frequency torsional oscillations. The resulting WTG system delivers maximum electrical power to the grid with high efficiency and high reliability without mechanical anemometers. The validity of the proposed control algorithm is verified by simulation studies on a 3.6MW WTG system. In addition, the effectiveness of the proposed wind speed estimation algorithm is demonstrated by experimental studies on a small emulational WTG system.

Wind Speed Estimation Based Sensorless Output Maximization Control for a Wind Turbine Driving a DFIG

A stand-alone hybrid power system is proposed in this paper. The system consists of solar power, wind power, diesel engine, and an intelligent power controller. MATLAB/Simulink was used to build the dynamic model and simulate the system. To achieve a fast and stable response for the real power control, the intelligent controller consists of a radial basis function network (RBFN) and an improved Elman neural network (ENN) for maximum power point tracking (MPPT). The pitch angle of wind turbine is controlled by the ENN, and the solar system uses RBFN, where the output signal is used to control the dc/dc boost converters to achieve the MPPT.

Neural-Network-Based MPPT Control of a Stand-Alone Hybrid Power Generation System

The microgrid concept is gaining popularity with the proliferation of distributed generation. Control techniques in the microgrid are an evolving research topic in the area of microgrids. A large volume of survey articles focuses on the control techniques of the microgrid; however, a systematic survey of the hierarchical control techniques based on different microgrid architectures is addressed very little. The hierarchy of control in microgrid comprises three layers, which are primary, secondary, and tertiary control layers. A review of the primary and secondary control strategies for the ac, dc, and hybrid ac–dc microgrid is addressed in this paper. Furthermore, it includes the highlights of the state-of-the-art control techniques and evolving trends in the microgrid research.

Control Techniques in AC, DC, and Hybrid AC–DC Microgrid: A Review

This work describes a small wind generation system where neural network principles are applied for wind speed estimation and robust maximum wind power extraction control against potential drift of wind turbine power coefficient curve. The new control system will deliver maximum electric power to a customer with lightweight, high efficiency, and high reliability without mechanical sensors. A turbine directly driven permanent magnet synchronous generator (PMSG) is considered for the proposed small wind generation system in this paper. The new control system has been developed, analyzed and verified by simulation studies. Performance has then been evaluated in detail. Finally, the proposed method is also applied to a 15 kW variable speed cage induction machine wind generation (CIWG) system and the experimental results are presented.

P. G. McLaren

Papers

Neural network based sensorless maximum wind energy capture with compensated power coefficient

Neural-Network-Based Sensorless Maximum Wind Energy Capture With Compensated