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Condition monitoring in wind turbines (Video)


Systems installer Dirk-Jan Wienen explains three aspects of MCSA that make it particularly suitable for monitoring equipment health in offshore wind turbines.

To learn more about how an MCSA driven monitorización de la condición solution can improve the reliability of your wind farm, book a demo here.

Reproductor de vídeo YouTube

Video transcript

If you’re here watching this video, then you’re probably in the process of evaluating different techniques to monitor equipment health. There’s a lot of good material out there on análisis de vibraciones, acoustic analysis, análisis del aceite and more, but there isn’t so much available on MCSA. It’s a relatively new technique. So today I’d like to tell you more about it. In particular, three reasons why it’s a good choice for offshore wind farms.

First, MCSA (motor current signature analysis) is extremely easy to install.

As the name suggests, MCSA uses current and voltage data to evaluate equipment health.

That means sensors attach to the three-phase power lines in the motor control cabinet, not on the equipment to be monitored.

And that means you can do most of the installation work onshore. This is exactly what we did in our most recent installation.

You attach the system’s components to DIN rails bolted onto a magnetic mounting plate and set up all the wiring between them, including fused terminal blocks for the voltage, onshore.

Then, when you travel to the offshore site, all you have to do is snap the mounting plate onto its magnet in the MCC, connect the fused terminal blocks to circuit breakers, click the three current clamps around the phase wires, connect the power supply to the power source, and you’re all set.

What’s more, if you’ve got identical network configurations onshore and offshore, you can even set up the network connection onshore—saving even more time on the turbine.

Second, MCSA can automatically account for varying load and speed. There are a lot of industries where the motors operate for long stretches under known, constant loads and speeds, but wind energy is not one of them.

The wind is constantly in flux, and those variations directly affect the vibration you’ll measure across the whole drive train in a wind turbine, from the main bearing at the hub all the way up through the generator.

If your condition monitoring system is going to be useful at all in the drive train, you have to know whether a change in your data reflects a developing malfunction, or just a change in the weather.

That means your condition monitoring system needs to know the load and speed that accompany every piece of data that it measures. For most techniques, that requires a separate system of tachometers or rotary encoders and some fancy calculations to synchronize the two measurement streams.

But MCSA is different, it has load and speed built into the box and directly measures the frequency supplied to the motor—which means you automatically have the speed for every data point you collect.

And because MCSA also directly measures the voltage, you automatically know the instantaneous load at every data point, too.

So MCSA systems can automatically, without any extra work, compare every new measurement with the right set of data: the model of healthy behavior for that specific combination of speed and load.

That brings me to the third point: electrical faults. Like other high-quality técnicas de monitorización del estado, MCSA can detect mechanical degradation very early, often months in advance. But it’s unique in being able to detect electrical degradation at an equally early stage. That’s because MCSA has electrical information: it can directly see the changes in current and/or voltage that indicate the start of an electrical problem.

Take a degrading stator winding. Long before the problem starts to have a mechanical effect that can be measured, there will be electrical changes, such as an imbalance between the three phases of the power supply. MCSA will detect this imbalance right from the start, and that means you’ll have weeks or even months to plan maintenance before the problem turns severe. That’s obviously a huge win for wind farms, where the ability to cluster maintenance in optimally planned windows is key in raising reliability and lowering O&M cost.

So there you have it: three reasons why MCSA is particularly well suited to monitor the health of wind turbine components. I hope this has been a helpful addition to your research on condition monitoring systems, and I wish you the best of luck in putting together the right mantenimiento predictivo strategy for your wind farm.

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