Algorithms simplify and predict blade maintenance

The associated cost of maintaining and repairing blades is an issue for wind-farm operators, and so far no remote inspection method has offered a viable solution. The best answer could lie in sophisticated algorithms using vibration measurements.

Since September 2011, an Energy Technology Development and Demonstration Program (EUDP) project has been underway to develop a practical way to detect, localize, and predict damage to wind-turbine blades. One reason this is important is because reducing the costs associated with wind energy is essential to staying competitive and attracting investors. The industry faces various challenges in this regard, especially as turbines and blades increase in size. Offshore wind farms face even more challenges which include new materials and designs, and transportation issues due to sheer size.

As the wind industry grows, so has our understanding of the stresses and strains turbines endure over prolonged exposure to nature. A wind turbine is made of thousands of components integrated into a finely balanced piece of engineering.

Any defect can result in a significant drop in performance leading to costly structural failures, safety issues and system downtime. Even though some parts of a wind turbine are monitored, such as the gearbox and main bearing, there is currently no viable means to check the integrity of the blades beyond expensive manual inspections, typically once a year. OEMs and major industry operators such as DONG Energy, EON and Vattenfall are researching this, but so far ideas to automate the process have been met with limited success. The EUDP initiated a project with Vattenfall, DTU Wind Energy and DTU Compute, Bladena, Total Wind and Brüel & Kjær to find a solution.

The EUDP partners and the skills and qualifications they provided include:

• Vattenfall, Bladena and Total Wind: Overall problem formulation, blade expertise, maintenance expertise
• Total Wind: Damage repair
• DTU Wind Energy: Mathematical modeling, and access to a test wind turbine and test facilities
• DTU Compute: Statistical models, decision-making algorithm, and changes in noise and environmental conditions
• Brüel & Kjær: Overall project design and management, measurement equipment, long-term monitoring, damage detection algorithms, and prototype design and implementation

Blade maintenance – a necessity, not a luxury
Even though they are intended to last for 20 years, damaged or faulty wind-turbine blades can reduce overall productivity and must be repaired. By carrying out a thorough yearly inspection, the operator reduces the risk of a catastrophic failure by addressing issues and taking action before damage becomes a serious problem resulting in costly repairs and lost revenue.

Blade damage is usually due to wear and tear from natural elements, or manufacturing defects and transportation mishaps. Most damage appears in the form of cracks and delamination. Over time, sand, ice, rain, sun and lightning strikes have serious adverse effects on a blade’s leading edge and structure. Worse still for offshore installations, salt crystals are a major cause of erosion and cause moisture diffusion within the blade structure. As a result, routine inspections and ongoing maintenance are not simply a luxury–they are a necessity.

Shortcomings of manual inspection
The wind industry is growing fast. Turbines and blades have increased in size and numbers, and as a result, the industry demands greater equipment reliability. Consequently, wind-farm operators and OEMs have been searching for a condition-monitoring system capable of detecting adverse conditions and predicting failures, to help minimize risks and prioritize repairs. Currently, there is no real-time health overview of blade fleets. For many operators, manual inspection continues as the method of choice to determine blade health. However, for many reasons this is not an effective solution. Manual inspection involves a hands-on, visual check of the rotor blades that must be conducted by qualified technicians hanging from ropes or using special platforms.

In both cases, the methods require well-trained specialists in both blade engineering and rope climbing to spot damage with the naked eye.

This means the inspection process is limited to the surface of the rotor blade or tapping the blade to get an idea of its structural integrity, both of which are widely open to human error. Furthermore, each blade has to be checked individually, which is time consuming, costly and dependent on weather conditions. Unfortunately, damage can always occur soon after an inspection.

What’s more, this maintenance can cause an exceptional amount of downtime. During inspections, the wind turbine has to be withdrawn from service and the process can only be performed under certain conditions, such as wind speeds of less than 10 m/s, by industrial climbers in groups of three, due to safety rules. These issues are magnified offshore where wind farms are considerably larger and the use of lifts and platforms is difficult due to the swell of the sea. The marine environment makes working conditions psychologically and physically stressful. Turbines can be up to 20 km from land, so the timeframe for working on location is limited, and most workers must be transported by boat or helicopter.

Taking up the automation challenge
As a result, a variety of automated methods and technologies, such as structural health monitoring (SHM), have been tried and tested in an attempt to find a better approach. SHM is a relatively new field and there are only a few projects running in other industries, such as monitoring bridges and buildings in seismic areas. A few SHM techniques have been adapted for wind turbines with limited success, including strain gauges, acoustics, lasers and thermography.

Working together, the teams behind the EUDP project developed a novel approach to the blade inspection issue. This led to a potentially effective SHM solution for wind-turbine blades that also has considerable potential beyond the wind industry. The approach is based on the premise that any structural change to the wind-turbine blade, such as damage, will cause a vibration pattern that deviates from normal. This results in unusual vibrations that can be detected and measured by sensors on the blade. Any deviation from the norm would suggest that damage has indeed occurred.

Initially, operational modal analysis appeared an excellent tool for measuring these changes. However, tests showed that the process was not sensitive enough to detect and identify where the damage had occurred, so an alternative method was developed with greater sensitivity. In this new method, mechanical energy is introduced into the blade by means of an electro-mechanical actuator and the resulting vibrations are measured using accelerometers placed along the blade. This technique proved so effective it allows detecting a crack or delamination as small as 20-cm long on a 34-m blade, even in the presence of environmental noise. Algorithms form the heart of the new method. To distinguish between actual damage and merely the effects of environmental noise, these algorithms are based on statistics collected from hundreds of careful measurements taken when the blade was in an undamaged state, during as many different weather and operating conditions as possible, a process known as model training.

Working toward an intelligent, accurate solution
This method points the way to a future solution that will let vibration data be gathered, pre-processed and transmitted wirelessly to the cloud, which allows for quick data transfer, storage and processing. Based on advanced algorithms, several levels of analysis take place in the cloud, and when damage is detected, a report is issued to the wind-farm operator via a Web-based interface that details which turbine blade is affected and to what extent.

To optimize inspections and maintenance schedules, blade-health will provide the owner or operator with the unique ability to get a daily updated overview of the health of its asset, and constantly assess which blades need maintenance and when. This results in the ability to optimize a scheduled-maintenance program that decreases the cost of energy because:

• In-time inspections and repair happen before a problem worsens.
• The ability to optimize schedules in both peak and off-peak seasons.
• Technicians arrive ready to inspect blades with identified problems.
• Crane and vessels are mobilized and ready for multiple inspections and sites.
• The company database receives a report, documentation and complete maintenance record, which improves the knowledge and understanding of the fleet health.
• A daily detailed assessment view provides insight and ability to react to changes in health before damage compounds and becomes catastrophic.

A step further
Typically, damages are categorized according to industry guidelines which determine the necessary follow-up. For example, in one category repairs must be immediate, while in another, repairs are ok within a three or six-month period. For the operator to categorize the detected damage and make a decision regarding follow up, he must know where on the blade the damage is located, and if it is progressing or stabilized. In effect, the new method constantly monitors the overall health of the blades and alerts the operator when a fault occurs, letting them prioritize repairs and schedule maintenance before it becomes a serious problem.

Currently, the algorithm provides a general damage location. The next stage of the project is to develop more advanced localization algorithms that can deduce exactly where the damage has occurred, even in its inner substructure. When done, this will help to give a more detailed overview of the blade’s health and let operators develop a repair and maintenance strategy in a cost-efficient way.

Healthy blades, happy operators
The outcome of the project represents a step forward for the industry and offers an invaluable new technique that will help operators maintain wind turbines at far lower costs and with improved reliability and efficiency. The solution fits all the criteria for an ideal SHM system, which is able to detect, locate and immediately alert the owner of damages in the blades. The system can be incorporated in new blades or retrofitted into existing ones.

At this stage the project has produced a working prototype that is currently being tested on a real turbine. Further testing and development is required with an OEM or owner to make the working prototype into a commercial application. WPE

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Filed Under: Blades, Featured, News

 

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