An Improved Power Control Approach for Wind Turbine Fatigue Balancing in an Offshore Wind Farm

Abstract

Increasing maintenance costs will hinder the expansion of the wind power industry in the coming decades. Training personnel, field maintenance, and frequent boat or helicopter visits to wind turbines (WTs) is becoming a large cost. One reason for this cost is the routine turbine inspection repair and other stochastic maintenance necessitated by increasingly unbalanced figure loads and unequal turbine fatigue distribution in large-scale offshore wind farms (OWFs). In order to solve the problems of unbalanced fatigue loads and unequal turbine fatigue distribution, thereby cutting the maintenance cost, this study analyzes the disadvantages of conventional turbine fatigue definitions. We propose an improved fatigue definition that simultaneously considers the mean wind speed, wind wake turbulence, and electric power generation. Further, based on timed automata theory, a power dispatch approach is proposed to balance the fatigue loads on turbines in a wind farm. A control topology is constructed to describe the logical states of the wind farm main controller (WFMC) in an offshore wind farm. With this novel power control approach, the WFMC can re-dispatch the reference power to the wind turbines according to their cumulative fatigue value and the real wind conditions around the individual turbines in every power dispatch time interval. A workflow is also designed for the control approach implementation. Finally, to validate this proposed approach, wind data from the Horns Rev offshore wind farm in Denmark are used for a numerical simulation. All the simulation results with 3D and 2D figures illustrate that this approach is feasible to balance the loads on an offshore wind farm. Some significant implications are that this novel approach can cut the maintenance cost and also prolong the service life of OWFs.

Self-reconfiguration simulations of turbines to reduce uneven farm degradation

“…Turbine use and failure rate are shown to be proportional to the wind speed experienced by a turbine [37]. This study utilised the concept of a Fatigue Coefficient (C fatigue ), adapted from previous studies [15,38]. Equation (3) shows C fatigue calculated using power produced at the site's average wind speed (P average ) instead of the rated turbine power (as in Refs.…”

“…Equation (3) shows C fatigue calculated using power produced at the site's average wind speed (P average ) instead of the rated turbine power (as in Refs. [15,38]); therefore, C fatigue = 1 will occur closer to the end of design life (T lifetime ) of 20 years (175200 h).…”

“…Turbulence Loading Coefficient (C Turb ) [38] experienced by the turbine represents an estimate of the lifetime loading on a turbine, as shown in Equation (10).…”

“…Three participants noted a control system approach (such as blade pitching) could produce a similar effect with lower risks and easier implementation. However, as noted previously, this has been investigated [15,38]. 5.…”

Machine Learning for Energy Systems

“…The work by R.Y. Zhao et al [10], "An Improved Power Control Approach for Wind Turbine Fatigue Balancing in an Offshore Wind Farm", proposed an improved power control approach to optimize the wind turbine (WT) fatigue distribution by balancing the turbulence loads to individual WTs. Then, a control topology was constructed to describe the logical states of the wind farm main controller (WFMC).…”