Diagnostics and prognostics utilising dynamic Bayesian networks applied to a wind turbine gearbox

The UK has the largest installed capacity of offshore wind and this is set to increase significantly in future years. The difficulty in conducting maintenance offshore leads to increased operation and maintenance costs compared to onshore but with better condition monitoring and preventative maintenance strategies these costs could be reduced. In this paper an on-line condition monitoring system is created that is capable of diagnosing machine component conditions based on an array of sensor readings. It then informs the operator of actions required. This simplifies the role of the operator and the actions required can be optimised within the program to minimise costs. The program has been applied to a gearbox oil testbed to demonstrate its operational suitability. In addition a method for determining the most cost effective maintenance strategy is examined. This method uses a Dynamic Bayesian Network to simulate the degradation of wind turbine components, effectively acting as a prognostics tool, and calculates the cost of various preventative maintenance strategies compared to purely corrective maintenance actions. These methods are shown to reduce the cost of operating wind turbines in the offshore environment

Supplementing wind turbine pitch control with a trailing edge flap smart rotor

Placement of additional control devices along the span of the wind turbine blades is being considered for multi-MW wind turbines to actively alter the local aerodynamic characteristics of the blades. This smart rotor approach can reduce loads on the rotor due to wind field non-uniformity, but also, as presented in this paper, can supplement the pitch control system. Rotor speed and tower vibration damping are actively controlled using pitch. By supplementing the speed control using smart rotor control, pitch actuator travel is reduced by 15 pitch rates by 23 and pitch accelerations by 42 This is achieved through filtering the pitch demand such that high frequency signals are dealt with by the smart rotor devices while the low frequency signal is dealt with by pitching the blades. It is also shown that this may be achieved while also using the smart rotor control for load reduction, though with reduced effectiveness. This shows that smart rotor control can be used to trade pitch actuator requirements as well as load reductions with the cost of installing and maintaining the distributed devices

Validating wind field models for power system impact studies

To integrate wind power into the electrical power system studies need to be made of its impact. Wind plants are stochastic in nature and this makes them differ from traditional plants. One method to model the stochastic nature of wind plants is to create a wind field model and simulate wind data that can then be used to determine power outputs. There are a large number of models available, however often these models are not adequately validated. This paper proposes a number of criteria that all wind field models should be validated against and the methods that can be used to do this

Fault ride-through for a smart rotor DQ-axis controlled wind turbine with a jammed trailing edge flap

A Smart Rotor wind turbine is able to reduce fatigue loads by deploying active aerodynamic devices along the span of the blades, which can lead to a reduced cost of energy. However, a major drawback is the complexity and potential for unreliability of the system. Faults can cause catastrophic damage and without compensation would require shutdown of the turbine, resulting in lost revenue. This is the first study to look at a fault ride-through solution to avoid shutdown of the turbine and lost revenue during a fault, while keeping additional damage to a minimum. A worst case scenario of a jammed flap with no direct knowledge of its occurrence is considered, while operating a DQ-axis Smart Rotor wind turbine. A method for detecting the fault using 1P cyclic loadings is presented, as well as two fault ride-through options: setting the remaining active flap angles to zero and setting the remaining flap angles to that of the jammed flap if known. These are analysed using IEC standard load cases. It is found that rapid detection of faults is vital for Smart Rotor controllers to avoid highly damaging cyclic loads caused by rotor imbalance, but that fault ride-through is fairly simple to implement and this allows the load benefits of the Smart Rotor to be accessible even with long fault periods

Comparison of individual pitch and smart rotor control strategies for load reduction

Load reduction is increasingly seen as an essential part of controller and wind turbine design. On large multi-MW wind turbines that experience high levels of wind shear and turbulence across the rotor, individual pitch control and smart rotor control are being considered. While individual pitch control involves adjusting the pitch of each blade individually to reduce the cyclic loadings on the rotor, smart rotor control involves activating control devices distributed along the blades to alter the local aerodynamics of the blades. Here we investigate the effectiveness of using a DQ-axis control and a distributed (independent) control for both individual pitch and trailing edge flap smart rotor control. While load reductions are similar amongst the four strategies across a wide range of variables, including blade root bending moments, yaw bearing and shaft, the pitch actuator requirements vary. The smart rotor pitch actuator has reduced travel, rates, accelerations and power requirements than that of the individual pitch controlled wind turbines. This benefit alone however would be hard to justify the added design complexities of using a smart rotor, which can be seen as an alternative to upgrading the pitch actuator and bearing. In addition, it is found that the independent control strategy is apt at roles that the collective pitch usually targets, such as tower motion and speed control, and it is perhaps here, in supplementing other systems, that the future of the smart rotor lies