Recovery of energy losses using an online data-driven optimization technique
This paper investigates the energy loss due to rotor aging for a wind turbine and its recovery. Energy loss for a wind turbine is caused by formation of dust and bugs, and corrosion and erosion of the surface of the rotor by sand and rain. We present a real-time data-driven optimization algorithm that uses dither and demodulation signals to recover the energy loss by extracting online the sensitivity of the function of interest to optimize. We use a viscid–inviscid model to represent aerodynamic performance loss of the rotor due to aging. We employ time-domain aeroservoelastic simulation of the CART3 wind turbine of the National Renewable Energy Laboratory to provide a comprehensive assessment of aging and its recovery. We also investigate the impact of aging on structural loads and levelized cost of energy. Using annual energy production and WindPACT cost models of the National Renewable Energy Laboratory, the levelized cost of energy enables an overall assessment of aging and its recovery. To evaluate the impact of the energy loss recovery on structural loading, ultimate and fatigue loads for power production design load case are used. The results of this study show 6.9% reduction in the annual energy production due to aging for a class C wind condition based on IEC61400 standard. This energy loss increases the levelized cost of energy by 7.5%. Our online optimization algorithm can recover 1.7% of the energy losses, and it results in 2.0% reduction in the levelized cost of energy. Results of the ultimate loads indicate that an aged rotor reduces structural loading on most components except the main shaft where the bending moment shows an increase of 5.3%. Rotor aging also reduces the fatigue loads in most components except the tower bottom fore-aft moment with an increase of 5.5%. The statistical inference of the results shows that the proposed optimization algorithm is effective in recovering aging related energy losses of wind turbines with a confidence level of 84% based on the sampled data in this study. © 2020 Elsevier Ltd
Energy Conversion and Management
Ashuri, T., Li, Y., & Hosseini, S. E. (2020). Recovery of energy losses using an online data-driven optimization technique. Energy Conversion and Management. 225. 113339. doi: 10.1016/j.enconman.2020.113339.