Investigation and assessment of the benefits for power systems from wind farm control

As wind turbines are increasingly situated in large arrays offshore, connected to power grids by a single long cable, it is necessary to consider the operation of the whole wind farm as a single plant rather than as a series of individual units. To achieve this, the development of advanced wind farm modelling software is required to test and evaluate new control strategies for wind farm operation. This thesis considers the use of Strathfarm, The University of Strathclyde’s in-house wind farm modelling software, presenting novel wind farm control algorithms which significantly reduce the fatigue of wind turbine towers and wind turbine blades. The thesis also further develops Strathfarm in two key areas, presenting improvements to the modelled wakes and also details the development of a novel power system model. The power system model can be used to show the efficacy of previously developed dispatch algorithms for wind farms to support power grids.As wind turbines are increasingly situated in large arrays offshore, connected to power grids by a single long cable, it is necessary to consider the operation of the whole wind farm as a single plant rather than as a series of individual units. To achieve this, the development of advanced wind farm modelling software is required to test and evaluate new control strategies for wind farm operation. This thesis considers the use of Strathfarm, The University of Strathclyde’s in-house wind farm modelling software, presenting novel wind farm control algorithms which significantly reduce the fatigue of wind turbine towers and wind turbine blades. The thesis also further develops Strathfarm in two key areas, presenting improvements to the modelled wakes and also details the development of a novel power system model. The power system model can be used to show the efficacy of previously developed dispatch algorithms for wind farms to support power grids

Model predictive control strategy in waked wind farms for optimal fatigue loads

With the rapid growth of wind power penetration, wind farms (WFs) are required to implement frequency regulation that active power control to track a given power reference. Due to the wake interaction of the wind turbines (WTs), there is more than one solution to distributing power reference among the operating WTs, which can be exploited as an optimization problem for the second goal, such as fatigue load alleviation. In this paper, a closed-loop model predictive controller is developed that minimizes the wind farm tracking errors, the dynamical fatigue load, and and the load equalization. The controller is evaluated in a mediumfidelity model. A 64 WTs simulation case study is used to demonstrate the control performance for different penalty factor settings. The results indicated the WF can alleviate dynamical fatigue load and have no significant impact on power tracking. However, the uneven load distribution in the wind turbine system poses challenges for maintenance. By adding a trade-off between the load equalization and dynamical fatigue load, the load differences between WTs are significantly reduced, while the dynamical fatigue load slightly increases when selecting a proper penalty factor.Comment: Accepted by Electric Power Systems Researc

Data-driven model-based approaches to condition monitoring and improving power output of wind turbines

The development of the wind farm has grown dramatically in worldwide over the past 20 years. In order to satisfy the reliability requirement of the power grid, the wind farm should generate sufficient active power to make the frequency stable. Consequently, many methods have been proposed to achieve optimizing wind farm active power dispatch strategy. In previous research, it assumed that each wind turbine has the same health condition in the wind farm, hence the power dispatch for healthy and sub-healthy wind turbines are treated equally. It will accelerate the sub-healthy wind turbines damage, which may leads to decrease generating efficiency and increases operating cost of the wind farm. Thus, a novel wind farm active power dispatch strategy considering the health condition of wind turbines and wind turbine health condition estimation method are the proposed. A modelbased CM approach for wind turbines based on the extreme learning machine (ELM) algorithm and analytic hierarchy process (AHP) are used to estimate health condition of the wind turbine. Essentially, the aim of the proposed method is to make the healthy wind turbines generate power as much as possible and reduce fatigue loads on the sub-healthy wind turbines. Compared with previous methods, the proposed methods is able to dramatically reduce the fatigue loads on subhealthy wind turbines under the condition of satisfying network operator active power demand and maximize the operation efficiency of those healthy turbines. Subsequently, shunt active power filters (SAPFs) are used to improve power quality of the grid by mitigating harmonics injected from nonlinear loads, which is further to increase the reliability of the wind turbine system

Wind farm power optimization and fault ride-through under inter-turn short-circuit fault

Inter-Turn Short Circuit (ITSC) fault in stator winding is a common fault in Doubly-Fed Induction Generator (DFIG)-based Wind Turbines (WTs). Improper measures in the ITSC fault affect the safety of the faulty WT and the power output of the Wind Farm (WF). This paper combines derating WTs and the power optimization of the WF to diminish the fault effect. At the turbine level, switching the derating strategy and the ITSC Fault Ride-Through (FRT) strategy is adopted to ensure that WTs safely operate under fault. At the farm level, the Particle Swarm Optimization (PSO)-based active power dispatch strategy is used to address proper power references in all of the WTs. The simulation results demonstrate the effectiveness of the proposed method. Switching the derating strategy can increase the power limit of the faulty WT, and the ITSC FRT strategy can ensure that the WT operates without excessive faulty current. The PSO-based power optimization can improve the power of the WF to compensate for the power loss caused by the faulty WT. With the proposed method, the competitiveness and the operational capacity of offshore WFs can be upgraded