Wind resource database and visualisation
Wind power is becoming one of the most important power sources in the power grid. At present, China’s accumulated wind power capacity is 188 GW, and the total installed capacity has leapt to first in the world. While the penetration rate of wind power is increasing, it generates a huge amount of data for recording the operational status of wind turbines, and so it needs to be studied using big data technology
The key technologies of power big data include the following five parts: data acquisition, data storage, data pre-processing, data analysis, and data visualization. This wind energy production data often comes from multiple heterogeneous sources and different types of sensors. The data set may include recorded weather data containing temperature and humidity readings, recordings of precipitation, levels and wavelengths of incident solar radiation. The data includes recordings of wind and gust speeds along with their dominant directions. The wind speed is usually defined as the average air velocity over a chosen time frame, whereas the gust speed is defined by the highest recorded speed in this timeframe. Additionally, barometric pressure levels from different locations may be recorded to estimate and analyze the development of winds. As the weather actively influences the power output of a wind park, understanding its influences and trends are important to network and power plant operators.
Visualization is the computer-aided technique of creating images or animations in order to communicate a message to a viewer. Visualization uses the remarkable perceptual abilities of the human’s visual system and the brain’s visual cortex. The visual cortex is the part of the brain responsible for processing any visual information. Humans can scan, recognize, and recall images in a fraction of a second. The brain can detect changes or patterns in size, colour, shape, movement, or texture. Visualization is valuable in many different application domains by providing a valuable assistance for data analysis and decision-making tasks. Depending on the source and purpose of the data, which is to be visualized, the research field is traditionally subdivided into the two areas Scientific Visualization and Information Visualization, which are both discussed below.
Scientific visualization is the research field of generating a graphical representation of physical phenomena, which aims to assist scientific investigations. The goal is to discover things that might not be apparent in numerical form. Scientific Visualization involves scientific data with an inherent physical component. Common visualization techniques include direct volume rendering, ray tracing or projection, two- or three-dimensional flow visualization and many more. Applications are found in every area where large amounts of data with a physical component are created and need to be processed.