Renewable energy is the future as more countries are looking to invest in eco-friendly alternatives to coal and gas power plants. Among the host of options, wind energy is high on the list. It is a sustainable, cost-effective, renewable and clean source of energy. However, wind energy production comes with its own set of drawbacks.

Firstly, wind energy is an unreliable energy source. Unlike solar energy, where we know when the sun will be around to power the solar panels, wind patterns are much more difficult to anticipate. This means energy producers have to involve traditional energy production methods like coal and gas plants to generate the required electricity. Also, the set-up and maintenance of wind turbines are expensive, especially for offshore rigs. These costs can go up to 20-25 percent of the total levelized cost of energy (LCOE).

Energy providers are now looking at AI technology to solve these drawbacks (among others) and make wind energy a more viable energy source.

Leveraging AI to Improve Wind Forecasting

Energy suppliers have to store the electricity in their power grids before supplying it to towns and homes. But since the storage capacity is limited, the demand and supply, which, in this case, is the wind, need to be perfectly balanced. But as was mentioned earlier, wind patterns are unpredictable, which makes the supply unpredictable. As a result, turbines may generate less because of low or no winds, or more than required because of stronger than expected winds. If they get this calculation wrong, the costs to the company are pretty high, almost up to 7% of the total profit.

With the help of artificial intelligence, energy suppliers can use previously recorded weather data to predict wind behaviors accurately. This information allows them to decide how much energy will be produced by the wind turbines and, in turn, stored in the energy grid.

The most popular name using AI to predict wind behavior and patterns is Google. Working with their AI subsidiary DeepMind, Google collects publicly available weather data and combines that with the power data of several states in Central USA to create an AI model that accurately predicts wind production for the next day. Using this information, Google has been able to place early bids on the market and boost the profits of their Southwest Power Pool wind farms by 20 percent.

Another company using artificial intelligence to tackle unpredictable weather conditions is Tekniska verken. The Swedish energy company has joined hands with Pelatrion, a cloud-based AI platform, to develop a deep learning model to improve weather forecasting. The model, Deep Weather, uses advanced machine learning algorithms to detect patterns in the weather using live and historical weather data. Since adopting Deep Weather to provide more accurate and localized weather predictions, the company has reduced errors in their weather forecasts by 9 percent. Also, the cost to the company in setting up the Deep Weather system is much less compared to traditional weather forecasting systems.

Predictive Turbine Maintenance with Actionable Insights

The vast amount of data collected by the sensors installed in wind turbines is compiled in a Supervisory Control and Data Acquisition (SCADA) system. However, SCADA systems do not assist in analyzing the data and it is left to the technicians to make sense of the unstructured mess. This is where artificial intelligence can help make the task easier.

AI can use the SCADA data to detect failures, determine severity, and schedule maintenance tasks. They can also help reduce maintenance costs by decreasing dependence on expensive sensors. Instead, companies can invest in developing AI models to monitor turbine performance like running speeds, vibrations, etc.

Turbit is an AI company catering specifically to the wind energy industry. The Turbit AI system uses historical SCADA data to analyze normal turbine performance at a granular level. They use multiple data types like wind speed, temperature, wind direction, and turbulence intensity, specific to the wind farm, to train their AI model. With the accumulated data, they can effectively predict turbine performance with 99 percent accuracy. These valuable insights allow energy companies to streamline their operations and avoid critical failure. Also, this predictive AI model enables energy companies to be more proactive in maintaining their assets.

B&K Vibro is another company that has revolutionized wind turbine condition monitoring. The company worked closely with various stakeholders to design and develop a holistic approach to turbine monitoring. Interfacing directly with the turbine sensors, their proprietary VibroSuite system provides market-leading fleet monitoring, diagnostics and data mining analysis capabilities. The system empowers technicians with all the information they need to make informed decisions. In addition, the AI-powered system uses historical data to identify possible critical failures, allowing technicians to plan maintenance activities and reduce downtime.

In Europe, countries have committed to making the switch to wind energy as the primary energy source, and artificial intelligence will play a major role in making this a reality. In their bid to achieve this goal, they have established the SmartWind project, undertaken by a consortium of four companies and the Ruhr-University Bochum in Germany. The team believes that AI and machine learning will be the backbone of wind turbine performance optimization. They aim to build an integrated cloud platform to improve fault detection and diagnosis. The system is based on advanced and automated functions for data analysis, fault detection, diagnosis, and operation and management recommendations.

The Future of AI in Wind Power Industry

The overall maintenance costs can reach up to 20-25 percent of the total levelized cost of energy (LCOE). However, thanks to artificial intelligence, energy companies have been able to reduce these costs and optimize maintenance timings. Also, artificial intelligence has empowered wind energy companies with the information they need to optimize their operations, reduce downtime, and increase energy production.

As more data is collected, energy companies can more accurately predict turbine performance, especially after the twenty-year mark. This will allow wind farm managers to make informed decisions to extend the useful lives of their turbines and maximize energy production capabilities.