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Machine Learning for Energy Generation

3 use cases for electric power plants

Markus Schmitt

If you are managing wind parks or any other power plant, then you are well aware:  Maintenance, human errors, downtime and planning inefficiencies can cost millions per year.

Where simpler statistics has made little progress, machine learning is proving to be a very effective tool to:

  • Predict malfunctions sooner and more accurately
  • Detect human errors, before they become a big problem
  • Optimize power plant schedules, to increase your profitability

Predict Turbine Malfunction 

Wind is a great renewable energy source, but wind turbine maintenance is notoriously expensive. It accounts for up to 25% of the cost per kWh. And fixing problems after they occur can be even more expensive.

Machine learning can help you get ahead of this problem, reducing maintenance costs by catching problems before the turbine malfunctions. This is particularly important when wind farms are located in hard-to-access places, such as the middle of the ocean, which makes repair costs even higher.

Real-time data gathered with Supervisory Control and Data Acquisition (SCADA) can help identify possible malfunctions in the system far enough in advance to prevent failure. 

For example, we can use data from sensors found within the turbines – such as oil, grease, and vibration sensors – to train machine learning models to identify precursors to failure, such as low levels of lubricant. This method can predict failures up to 60 days in advance.

Reduce the Potential for Human Error

Humans cause 25% of power plant failures

Each year, human error accounts for as much as 25% of power plant failures. Along with the loss of up to 30 million megawatt-hours of energy generation annually, this causes service interruptions for customers, or worse – just think of Chernobyl and Three Mile Island. It also means unnecessary costs associated with fixing the error and getting the system back online. 

To combat this, we can use machine learning to support decisions made by control room operators.

Machine learning provides constant systems monitoring that helps you detect anomalies. We also automatically suggest an action plan to prevent the situation from getting worse. It can even deal with a problem before human intervention becomes necessary.

This reduces the risk of human error due to distraction, lack of knowledge, or reaction speed – sometimes control room operators simply can’t move fast enough to stop the problem. 

Increase Power Plant Profitability with Optimized Scheduling and Pricing

Illustration of electricity market purchase decisions

The volatile nature of energy prices means that running a generation plant can be more or less profitable depending on something as simple as the time of day. 

But because the utilities market is so fast-paced, it can be hard to manually track all the data required to make these decisions. 

We can use machine learning to help. By feeding historical data on prices and usage into a machine learning algorithm, you can predict the best times to run your plant – and make more money. Machine learning can find times when usage is high but prices for the raw materials used to produce energy are low. These extremely accurate predictions creates an optimized generation schedule that maximizes profitability.

If you are looking into any of these use cases and need some advice, reach out to us – we've helped companies like E.ON, Danske Commodities, Rheingas and other utilities optimize profitability with custom machine learning software.

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