By John P. Desmond, AI Trends Editor
Among all its many activities, Google is forecasting the wind.
Google and its DeepMind AI subsidiary have combined weather data with power data from 700 megawatts of wind energy that Google sources in the Central US. Using machine learning, they have been able to better predict the wind, which pays off in the energy market.
“The way a lot of power markets work is you have to schedule your assets a day ahead,” stated Michael Terrell, the head of energy market strategy at Google, in a recent account in Forbes. “And you tend to get compensated higher when you do that than if you sell into the market real-time.”
This is an example of the application of AI to wind energy and the wind energy market, an effort being tried in many regions by a range of players.
“What we’ve been doing is working in partnership with the DeepMind team to use machine learning to take the weather data that’s available publicly, actually forecast what we think the wind production will be the next day, and bid that wind into the day-ahead markets,” Terrell stated during a recent seminar hosted virtually by the Precourt Institute for Energy of Stanford University.
The result has been a 20% increase in revenue for wind farms, Terrell stated. Google has been on a mission to radically reduce its carbon footprint. The company recently achieved a milestone by matching its annual energy use with its annual renewable-energy procurement, Terrell stated.
“Our hope is that this kind of machine learning approach can strengthen the business case for wind power and drive further adoption of carbon-free energy on electric grids worldwide,” stated Sam Witherspoon, a DeepMind program manager, in a blog post. He and software engineer Carl Elkin described how they boosted profits for Google’s wind farms in the Southwest Power Pool, an energy market that stretches across the plains from the Canadian border to north Texas.
European Commitment to Wind Energy Seen in SmartWind Project
European countries have made a big commitment to wind energy, with offshore wind farms being required to supply about 8.5% of all energy in the Netherlands and 40% of current electricity consumption by 2030, according to a recent account in Innovation Origins.
AI is expected to play a big role in this effort, helping to increase energy generation and reduce maintenance costs for wind farms. The related SmartWind project is being undertaken by a consortium of four companies and the Ruhr-University Bochum in Germany.
Prof. Constantinos Sourkounis, Institute for Power Systems Technology, Ruhr-University Bochum
“In SmartWind we can exploit the capabilities of artificial intelligence algorithms to optimize the management of wind farms,” stated Prof. Constantinos Sourkounis of the university’s Institute for Power Systems Technology, head of the German workgroup. The team aims to build an integrated cloud platform to reduce costs and optimize revenue, based on advanced and automated functions for data analysis, fault detection, diagnosis and operation and management recommendations.
The platform will collect data in real time from sensors and control systems, such as condition and maintenance management. Machine learning algorithms and other AI techniques form the backbone of early fault detection and diagnosis.
Turkish wind farm operator Zorlu Enerji, a SmartWind partner, will be able to put results of the research directly into practice. “The remarkable thing about this project is the close relationship between research and direct application. We are able to first test theoretical results in our laboratory, and then in a test wind farm run by our partner Zorlu Enerji,” stated Prof. Sourkounis.
Condition Monitoring Systems Help Manage Remote Wind Turbines
Machine condition monitoring systems (CMSs) are being applied to wind turbines to help ensure maximum availability and production.
Mike Hastings, Senior Application Engineer, Bruel & Kjaer Vibro
“This is what we call Big Data, which includes both machine vibration and process data under all kinds of operating conditions and with all kinds of wind turbine types and components,” stated Mike Hastings, a senior application engineer with Bruel & Kjaer Vibro (B&K Vibro) of Darmstadt, Germany, writing in Wind Systems Mag. Over the past 20 years, the company has installed more than 25,000 data acquisition systems worldwide, with up to 12,000 of them being remotely monitored. As a result, “B&K Vibro has accumulated a vast database of monitoring data that includes fault data on almost every imaginable potential failure mode,” Hastings wrote.
As the worldwide installed capacity of wind turbines increases and plays a bigger role in the energy market, so does the need to ensure maximum availability and production of these turbines. Machine condition monitoring is important in this respect and many of the new turbines delivered today already have a condition monitoring system installed as standard. For offshore wind turbines, all have such a system because of their remoteness for maintenance.
“Big data fits very well into data-driven artificial intelligence (AI) and machine learning (ML) development and implementation,” Hastings wrote. AI and ML could be implemented for the following condition-monitoring tasks: fault detection optimization, automatic fault identification and prognosis for failure.
For fault detection, descriptors are configured by specialists, and detection of those is done automatically by the SMA. The individual descriptors and their configuration for fault detection have been optimized to a high level of reliability by diagnostics specialists with many years of experience. “One of the inherent benefits of AI is its ability to sift through vast quantities of CMS data to find patterns,” he wrote. Hidden diagnostics can be found in historical data as well.
For fault detection before potential failures, the AI can present the results as a listing of several potential failure modes, each with a probability of certainty. “B&K Vibro has in development neural-network automatic fault diagnostic products in the past, and this remains an area of interest for future refinement,” Hastings wrote.