Advantages of a dedicated wind data logger

Although there are plenty of generic data loggers in the market, to measure the wind effectively you need to invest in a wind logger. Weather monitoring has special requirements that exceed the design parameters of most generic data loggers, and as a result they miss plenty of important information. The wind is characterised by changing without warning, and this applies for winds of any strength; a measurement tool must be fast enough to detect these changes to operate reliably as a wind logger.

Although wind information becomes data at the end of the day, not all data is made equal. This article will describe the advantages of using a dedicated solution like the GSM WIND monitor, pointing out some shortfalls of generic data loggers in wind monitoring - often missed. 

What does it take to measure the wind?
If you have ever taken some time to observe the wind, you surely noticed that it can change speed and direction without warning and very quickly. Therefore, any monitoring system deployed must have a fast enough sampling rate to be a source of reliable data. This is so important for feasibility analysis and safe operation in the wind industry, that the International Electrotechnical Commission developed standard IEC 61400 for wind turbines. With respect to wind measurement, the standard requires a minimum sampling rate of 0.5Hz or 1 Hz. 

But what is sampling rate? The number of measurements taken in a given time. When talking about Hz [Hertz], we are referring a measurements per second. 

The WINDLogger GSM monitor exceeds the IEC 61400 requirement with a sampling rate of 1 Hz (one data point per second). A sampling rate like this is suitable for detecting gusts - strong and dangerous winds that only last for a few seconds.

Compared with a generic data logger, our GSM WINDLogger also offers improved data quality. Many generic data loggers are just discrete counters, and all they provide is averaged wind data over time. Average data is of limited usefulness when dealing with the wind, due to how quickly it can change: the average wind speed value does not tell the difference between wind that changes drastically between extreme values and a constant wind. Basically you would be leaving the profile (like personality) of the wind out. 

To address this, GSM WINDLogger calculates two key metric in addition to average wind speed, for this a sample rate of 1Hz is needed:

  • Standard deviation: In statistics, standard deviation is a metric that indicates data dispersion. When applied to wind monitoring, a small standard deviation indicates that the wind does not change much, while a large standard deviation means it reaches extreme values above and below the calculated average.
  • Maximum wind speed: Gusts are strong and dangerous winds that only last for a few seconds, and they are invisible for a generic data logger that only provides average data. On the other hand, GSM WINDLogger also records the wind gust measured speed (3 second running average).

Assume the wind speed in two sites is 25 mph, in the range of 10-40 mph for the first site and 20-30 mph in the second site. A generic data logger will indicate 25 mph for both, but a dedicated system like the GSM WINDLogger will indicate a maximum value of 40 mph and a larger standard deviation for the first site. You now have two very different wind site profiles. 

Wind speed monitor

Another overlooked aspect of wind monitoring is that the system must withstand the weather conditions on site. WINDLogger has a 10-year long track record, and has been deployed successfully under all types of weather conditions, ranging from the Atacama desert to Antarctica. The organisations that have deployed WINDLogger for weather monitoring include NASA and prestigious academic institutions like the University of York.

Consider that weather monitoring equipment is often located in remote places, where access to electricity and Internet may be limited or inexistent. In addition, these services may be interrupted shortly with bad weather, even in urban applications. In these cases, the value of the data is much higher than the value of the data logger itself. 

Conclusion
Effective wind measurement is demanding, both in terms of data requirements and environmental conditions. A generic data logger is unlikely to yield the same results as a dedicated system like the GSM WINDLogger, since it may lack the data processing capabilities to provide a reliable snapshot of the wind.

Although the information from any data logger can be processed and analysed, GSM WINDLogger can perform these calculations in real time. Data processing becomes simultaneous with measurement, providing useful information on actual weather conditions. Although these calculations can be applied for data collected by a generic logger, the results are less valuable because they are for past weather events.



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