Future Tech Lab (FTL) is launching IoT and digital transformation services for the renewable energy market. Our hardware engineer Debayan Paul continues his first post with more detailed discussion about Horizontal Axis Wind Turbine (HAWT). You have already seen one deployed in country-sides when you take an intercity train or a flight. In this blog, you will learn the about different parts of HAWT and how IoT, Big Data and Machine Learning are changing the wind energy market.
Wind has served mankind as a primary source of power for over 3000 years now. Before the inception of steam engines, wind power was primarily utilized for sailing ships. Wind power was probably used in Persia (present-day Iran) about 500–900 A.D. The wind wheel of Hero of Alexandria marks one of the first recorded instances of wind powering a machine in history. However, the first known practical wind power plants were built in Sistan, an Eastern province of Iran, from the 7th century. In the next few centuries, with the advent of wind mills, wind power was being converted to mechanical power through wind mills for grinding grains and pumping water. Wind mills have also been known to drive water through pipes for irrigation. With the development of the steam engine, the dependence on wind energy dropped drastically which also resulted in lower interest in research into the field of wind power. In the late nineteenth century, electricity had become the currency of energy, and thermal and hydroelectric power plants became the favored sources of electricity. But not every country had the luxury of fossil fuel or water resources. Denmark, being one of those, invested in the development of wind turbines to provide for its electricity demand. The 1890s saw Denmark lead the path in the development of wind turbines. Wind energy is fast becoming a preferable alternative to conventional sources of electric power. Owing to the perennial availability of the wind, and the considerable range of power control, wind turbines are now coming up in almost all parts of the world. In the early days of development, wind turbines were designed to rotate at a constant speed through pitch control or stall control. The modern wind turbines implement pitch control in order to tap maximum energy at wind speeds lower than rated wind speed.
Horizontal Axis Wind Turbine
A wind turbine is a device that converts the wind's kinetic energy into electrical power. Wind turbines can rotate about either a horizontal or a vertical axis, the former being both older and more common. We will discuss about the Horizontal Axis Wind Turbine (HAWT) in the upcoming discussion.
The main rotor shaft and electrical generator are generally at the top of a tower for a horizontal axis wind turbine (HAWT). A horizontal axis wind turbine has a design which demands that it should be pointed to the wind to capture maximum power. This process is called yawing. The turbine shaft is generally coupled to the shaft of the generator through a gearbox which turns the slow rotation of the blades into a quicker rotation that is more suitable to drive an electrical generator.
Different parts of Horizontal Axis Wind Turbine
Anemometer: - Measures the wind speed and transmits wind speed data to the controller.
Brake: - Stops the rotor mechanically, electrically, or hydraulically, in emergencies.
Controller: - Starts up the machine at wind speeds of about 8 to 16 miles per hour (mph) and shuts off the machine at about 55 mph. Turbines do not operate at wind speeds above about 55 mph because they may be damaged by the high winds.
Gear box: - Connects the low-speed shaft to the high-speed shaft and increases the rotational speeds from about 30-60 rotations per minute (rpm), to about 1,000-1,800 rpm; this is the rotational speed required by most generators to produce electricity. The gear box is a costly (and heavy) part of the wind turbine and engineers are exploring "direct-drive" generators that operate at lower rotational speeds and don't need gear boxes.
Generator: - Produces 60-cycle AC electricity; it is usually an off-the-shelf induction generator.
High-speed shaft: - Drives the generator at about 900-1500 rpm.
Low-speed shaft: - Turns the low-speed shaft at about 30-60 rpm.
Nacelle: - Sits atop the tower and contains the gear box, low- and high-speed shafts, generator, controller, and brake. Some nacelles are large enough for a helicopter to land on.
Pitch: - Turns (or pitches) blades out of the wind to control the rotor speed, and to keep the rotor from turning in winds that are too high or too low to produce electricity.
Blades & Rotor: -
(a) The lifting style wind turbine blade. These are the most efficiently designed, especially for capturing energy of strong, fast winds. Some European companies manufacture a single blade turbine.
(b) The drag style wind turbine blade, most popularly used for water mills, as seen in the Old Dutch windmills. The blades are flattened plates which catch the wind. These are poorly designed for capturing the energy of heightened winds.
(c) The rotor is designed aerodynamically to capture the maximum surface area of wind in order to spin the most ergonomically. The blades are lightweight, durable and corrosion-resistant material. The best materials are composites of fiberglass and reinforced plastic.
Tower: - Made from tubular steel (shown here), concrete, or steel lattice. Supports the structure of the turbine. Because wind speed increases with height, taller towers enable turbines to capture more energy and generate more electricity.
Wind vane: - Measures wind direction and communicates with the yaw drive to orient the turbine properly with respect to the wind.
Yaw drive: - Orients upwind turbines to keep them facing the wind when the direction changes. Downwind turbines don't require a yaw drive because the wind manually blows the rotor away from it.
Yaw motor: - Powers the yaw drive.
Figure 1: Different parts of a Horizontal Axis Wind Turbine (HAWT)
How the Wind energy sector is changing
Alternative energy technologies are quickly becoming globally accepted and reliable source of electrical power. With a growing installed capacity of renewable energy plants comes a growing number of remote monitoring solutions to track the performance of these plants. Enormous amounts of data are being generated by these renewable energy plants and it is becoming ever important to create valuable insights from this data. Big data analytics performed on the data collected from these plants, enables owners and O&M crews to operate the renewable plants at the plants maximum potential. Among all the types of big data analytics that could be performed on the plant data, predictive analytics holds the most promising of providing insights by leveraging performance data to create correlations and outcomes. Predictive analytics when used deftly on renewable energy power plants can provide accurate energy production forecasts. One study estimates that a good predictive model can increase the power generating capacity of a wind farm by about 10%, which practically revitalizes the entire business. It is also important to note that Predictive Analytics doesn’t only improve operational efficiencies but also improves the lifespan of the valuable renewable energy technology assets. The current growth of renewable energy technologies could be amplified if there is enough data to prove that they are credible investment options. Numerous renewable energy power projects still lack appropriate funding because of the lack of historic data that raises suspicions on the long-term viability of the projects. Predictive analytics can address this problem by accurately forecasting energy generation based on historic performance, weather and other parameters. These quantifiable results associated with revenues generated from the future performance can improve the bankability of renewable energy projects. People working in the renewable energy field are increasingly turning their attention to the "Industrial Internet" to maximize the efficiency of existing equipment. Such as Offshore wind energy power plants are remotely located - and so it's not only investment costs that need to be considered, it's also about the operating costs. Based on this data gathered, they can see how wind and wave conditions are affecting the base of the turbine. Even not all the wind sites are the same. Some locations can wear out the turbines quicker than others because of harsher power surges, greater wake effects or sporadic surges in power. Using big data, the company can recreate the conditions experienced by the turbine - creating a replay of certain events to learn how the turbine responds to them.
Machine Learning helps make complex systems more efficient. Regardless of whether the systems in question are steel mills or gas turbines, they can learn from collected data, detect regular patterns, and optimize their own operations. Siemens engineers have been studying machine learning for the past 25 years. They have used machine learning to optimize industrial facilities such as steel mills and gas turbines. Machine learning can also be used to reliably forecast the prices of energy and raw materials or to predict energy demand in entire regions. Sensors in and on such systems routinely record data regarding the direction and speed of the wind, temperatures, electric currents and voltages, as well as vibrations produced by major components such as the generator and the rotor blades. Based on past measurement data, software calculates the optimal settings for various weather scenarios that involve a variety of factors such as sunshine duration, hazy conditions, and thunderstorms. The data is transmitted to the wind turbines’ control units, which take it into account from then on as they adjust the functions. If familiar wind conditions arise, the control units immediately use the optimal settings that were ascertained as a result of machine learning.
Figure 2: Big Data platform for a wind energy firm.
- http://www.turbinesinfo.com/horizontal-axis-wind-turbines-hawt/ (Figure 1)
- http://www.dw.com/en/big-data-is-about-to-transform-renewable-energy/a-36189374 (Figure 2)