Predictive control of wind turbines by considering wind speed forecasting techniques

A wind turbine system is operated such that the points of wind rotor curve and electrical generator curve coincide. In order to obtain maximum power output of a wind turbine generator system, it is necessary to drive the wind turbine at an optimal rotor speed for a particular wind speed. A Maximum Power Point Tracking (MPPT) controller is used for this purpose. In fixed-pitch variable-speed wind turbines, wind-rotor parameters are fixed and the restoring torque of the generator needs to be adjusted to maintain optimum rotor speed at a particular wind speed for optimum power output. In turbulent wind environment, control of variable-speed fixed-pitch wind turbine systems to continuously operate at the maximum power points becomes difficult due to fluctuation of wind speeds. In this paper, wind speed forecasting techniques will be considered for predictive optimum control system of wind turbines

Maximum power point tracking for variable-speed fixed-pitch small wind turbines

Variable-speed, fixed-pitch wind turbines are required to optimize power output performance without the aerodynamic controls. A wind turbine generator system is operated such that the optimum points of wind rotor curve and electrical generator curve coincide. In order to obtain maximum power output of a wind turbine generator system, it is necessary to drive the wind turbine at an optimal rotor speed for a particular wind speed. In fixed-pitch variablespeed wind turbines, wind-rotor performance is fixed and the restoring torque of the generator needs to be adjusted to maintain optimum rotor speed at a particular wind speed for maximum aerodynamic power output. In turbulent wind environment, control of wind turbine systems to continuously operate at the maximum power points becomes difficult due to fluctuation of wind speeds. Therefore, special emphasis is given to operating at maximum aerodynamic power points of wind rotor. In this paper, the performance of a Fuzzy Logic Maximum Power Point Tracking (MPPT) controller is investigated for applications on variable-speed fixed-pitch small- scale wind turbines

Levilised Cost of Energy Analysis: a Comparison of Urban (Micro) Wind Turbines and Solar PV Systems

The relatively high capital cost associated with micro wind energy systems and the resulting long payback periods, makes for a challenging argument for these technologies. However, as the global population becomes increasingly concentrated in urban areas, the potential for accessing any available renewable energy resource, including wind and solar PV, could become a necessity. This infers that the economics associated with small/micro energy systems need to be better appreciated. This paper presents a levelised cost of energy (LCOE) analysis for rural/urban small/micro wind energy systems that is contextualised by a solar PV system comparison. Further insight is offered through a design of experiments (DOE) consideration that affords an understanding of how system parameters, such as primary energy (rural/urban wind resource and solar insolation), capital cost and loan/finance interest rate individually and collectively affect the respective technologies. The results suggest that from an economic justification perspective, urban installations are difficult to justify and solar PV systems, with the associated lowering system costs, are challenging the viability of small/micro rural wind energy systems

The cost of energy associated with micro wind generation: International case studies of rural and urban installations

National targets for increased renewable energy are common-place internationally and small/micro-generation may help achieve such goals. Energy yields from such technologies however, are very location and site specific. In rural environments, the average wind speed is relatively high and the homogeneous landscape promotes laminar air flow and stable (relatively) wind direction. In urban environments however, the wind resource has lower mean wind speeds and increased levels of atmospheric turbulence due to heterogeneous surface forms. This paper discusses the associated costs per unit of electricity generated by micro wind energy conversion systems from the perspective of both urban and rural locations, with three case studies that consider the potential and financial viability for such systems. The case studies ascertain the cost of energy associated with a standard HAWT (horizontal axis wind turbine), in terms of exemplar rural and urban locations. Sri Lanka, Ireland and the UK, are prioritised as countries that have progressive, conservative and ambitious goals respectively towards the integration of micro-generation. LCOE (Levelized cost of energy) analyses in this regard, offers a contextualised viability assessment that is applicable in decision making relating to economic incentive application or in the determination of suitable feed-in tariff rates

Wind Energy Harvesting and Conversion Systems: A Technical Review

Wind energy harvesting for electricity generation has a significant role in overcoming the challenges involved with climate change and the energy resource implications involved with population growth and political unrest. Indeed, there has been significant growth in wind energy capacity worldwide with turbine capacity growing significantly over the last two decades. This confidence is echoed in the wind power market and global wind energy statistics. However, wind energy capture and utilisation has always been challenging. Appreciation of the wind as a resource makes for difficulties in modelling and the sensitivities of how the wind resource maps to energy production results in an energy harvesting opportunity. An opportunity that is dependent on different system parameters, namely the wind as a resource, technology and system synergies in realizing an optimal wind energy harvest. This paper presents a thorough review of the state of the art concerning the realization of optimal wind energy harvesting and utilisation. The wind energy resource and, more specifically, the influence of wind speed and wind energy resource forecasting are considered in conjunction with technological considerations and how system optimization can realise more effective operational efficiencies. Moreover, non-technological issues affecting wind energy harvesting are also considered. These include standards and regulatory implications with higher levels of grid integration and higher system non-synchronous penetration (SNSP). The review concludes that hybrid forecasting techniques enable a more accurate and predictable resource appreciation and that a hybrid power system that employs a multi-objective optimization approach is most suitable in achieving an optimal configuration for maximum energy harvesting

Optimal control of wind turbine using neural networks

Variable-speed, fixed-pitch wind turbines are required to optimize power output performance without the aerodynamic controls. In steady-state, a wind turbine generator system is operated such that the optimum points of wind rotor curve and electrical generator curve coincide. In order to obtain maximum power output of a wind turbine generator system, it is necessary to drive the wind turbine at an optimal rotor speed for a particular wind speed. Therefore, accurate wind speed measurements are required for optimal operation of the wind turbine. In practice, it is difficult to accurately measure wind speed by an anemometer installed closed to the wind turbine, because the wind turbine experience different forces due to wake rotation. Therefore, it is useful to use a wind speed sensor less control strategy. In this study, a Nonlinear Autoregressive Moving Average (NARMA) neural network model is used to identify the combined performance of the wind rotor and generator. Wind speed sensorless optimum control strategy is introduced and comparison study is preformed with a controller that employs a wind speed sensor. According to the obtained results, proposed controller performs as good as to the controller that employed with wind sensor

Identification of control signals for optimal control of small-scale wind turbines

Perturbation & observation hill-climbing searching method is used for wind speed sensor-less optimal controlling of wind turbines. Aerodynamic power and system losses are required to identify the reference control signal. Wind turbine output power is interlaced with the rate of change of mechanically stored energy due to momentum of inertia of rotating parts. Therefore, reference control point is difficult to accurately evaluate from the electrical power output. Generally, in small scale wind turbines, only DC voltage and current are the accessible signals for optimal controlling. In this paper, adaptive digital filters are introduced to identify possible control signals from generator outputs time series data by eradicating the variation of mechanically stored energy and power losses in the system

Generic maximum power point tracking controller for small-scale wind turbines

The output power of a wind energy conversion system (WECS) is maximized if the wind rotor is driven at an optimal rotational speed for a particular wind speed. To achieve this, a Maximum Power Point Tracking (MPPT) controller is usually used. A successful implementation of the MPPT controller requires knowledge of the turbine dynamics and instantaneous measurements of the wind speed and rotor speed. To obtain the optimal operating point, rotor-generator characteristics should be known and these are different from one system to another. Therefore, there is a need for an efficient universal MPPT controller for WECS to operate without predetermined characteristics. MPPT control of WECSs becomes difficult due to fluctuation of wind speed and wind rotor inertia. This issue is analyzed in the paper, and an Adaptive Filter together with a Fuzzy Logic based MPPT controller suitable for small-scale WECSs is proposed. The proposed controller can be implemented without predetermined WECS characteristics

Adaptive linear prediction for optimal control of wind turbines

In order to obtain maximum power output of a Wind Energy Conversion System (WECS), the rotor speed needs to be optimised for a particular wind speed. However, due to inherent inertia, the rotor of a WECS cannot react instantaneously according to wind speed variations. As a consequence, the performance of the system and consequently the wind energy conversion capability of the rotor are negatively affected. This study considers the use of a time series Adaptive Linear Prediction (ALP) technique as a means to improve the performance and conversion efficiency of wind turbines. The ALP technique is introduced as a real time control reference to improve optimal control of wind turbines. In this study, a wind turbine emulator is developed to evaluate the performance of the predictive control strategy. In this regard, the ALP reference control method was applied as a means to control the torque/speed of the emulator. The results show that the employment of a predictive technique increases energy yield by almost 5%