Research Work

Ongoing Research 

Development of Efficient Identification and Pitch Control Strategies for Horizontal Axis Wind Turbines Using Full-Scale Test Data 

Growing climate change coupled with an ever-increasing carbon footprint followed by the progressive depletion of fossil fuels has forced humans to look for alternative renewable energy sources (e.g., solar, wind, and wave) for power generation. Wind energy is a major renewable, pervasive, and clean energy source. The kinetic energy of wind turns a rotor attached to a slow-moving shaft of a turbine. To generate power, the spinning rotor turns the generator through a linking shaft and gearbox. Generally, wind turbines can be classified into two groups based on their spinning axis - vertical-axis wind turbines (VAWT) rotate around their vertical axis and horizontal-axis wind turbines (HAWT) rotate around their horizontal axis. The second category of turbines is the most prevalent type currently in use, which comes in various sizes. The power production of a HAWT mostly depends on two factors, i.e., wind speed and the rotor size. As a result, the blade length has continuously increased in the recent past to generate more power. For example, Haliade-X 12 MW, the World’s largest wind turbine to date, has a rotor diameter of 220 m, resulting in a more flexible tower and the blade that undergoes large deformation under the turbulent wind field leading to power fluctuation and catastrophic failure if they are not designed appropriately. 

Model identification and efficient control techniques are the two challenging problems in the wind turbine industry, intending to minimize power fluctuation and structural vibration for steady output and sustained operation. Thus, accurate system identification and efficient design of control systems form the main objectives of my research work. It may be noted that rotating blades in the gravitational and turbulent wind field lead to time-varying system matrices, which offer several difficulties while tuning the controllers in real time. Moreover, turbines operating beyond rated speed invariably demand different controllers for stable operation. All these challenges demand accurate system identification followed by optimal tuning of control parameters estimation from the actual measurements.

The primary focus of my work is the development of efficient identification and pitch control algorithm for HAWT, which is a collaborative effort between my supervisor Prof. Arunasis Chakraborty, IIT Guwahati, and Prof. Håkan Johansson, Chalmers University of Technology, Sweden. A full-scale 25 kW test turbine at Björkö, Sweden, managed by Prof. Johansson’s research group, provides measurements of various response quantities under different operating conditions. I am currently working on benchmarking the model of this turbine. This work on the identification of blade and tower frequencies has already been done along with validation of some simulated responses with actual measurements. Further, different existing and advanced pitch control techniques (e.g., LMS, SMC, and ML-based algorithms) for HAWT will be modeled and validated under different operating conditions.