Why Model-Based Lossy Compression is Great for Wind Turbine Analytics

Modern wind turbines are equipped with wired high-quality sensors that produce high-frequency sensor data in the form of time series as shown in Figure 1 a. From working with multiple different practitioners, we have learned that relatively few but very long high-quality time series are produced. The time series are either univariate, i.e., have one value per timestamp, or multivariate, i.e., have multiple values per timestamp. Further, they are either regular, i.e., have a fixed time interval between consecutive data points, or irregular. Despite these differences, the volume and velocity of the time series that are being produced are generally major challenges. For example, if the sensors are sampled at 100Hz, a single park of 100 wind turbines generates more than 11 PiB of data each year [1]. The sensor data is collected by weak edge devices and then transferred to powerful cloud servers over a relatively slow connection as shown in Figure 2. However, it is infeasible to transfer and store the raw time series due to their volume and velocity. Renewable energy system installations use low-end commodity PCs on the edge, e.g., 4 CPU cores, 4 GiB RAM, and an HDD [1]. In addition, the bandwidth between the edge and the cloud can be as low as 0.5-5 Mbit/s [1]. Thus, practitioners use simple aggregates, e.g., 10-minute averages, which remove valuable outliers and fluctuations as shown in Figure 1b. To remedy this, practitioners want to use lossy compression with a per-value error bound (E) to collect more high-frequency time series and thus improve their analytics.