Machine learning offers constant systems monitoring that helps detect anomalies in power generation.
FREMONT, CA: Wind is a significant renewable energy source, but wind turbine maintenance is expensive. It accounts for up to 25 percent of the cost per kWh. And fixing issues after they occur can be expensive. Machine learning can assist firms to get ahead of this problem, mitigating maintenance costs by catching issues before the turbine malfunctions. This is especially important when wind farms are located in hard-to-access places, like the middle of the ocean, which makes maintenance costs even higher. Real-time data collected with Supervisory Control and Data Acquisition (SCADA) can identify possible malfunctions in the system far enough to prevent failure.
Each year, human error accounts for as much as 25 percent of power plant failures. Along with the loss of up to 30 million megawatt-hours of energy production annually, this causes service interruptions for customers. It also means unnecessary expenses associated with fixing the error and getting the system back online. To combat this, firms can use machine learning to help decisions made by control room operators. Machine learning offers constant systems monitoring that helps detect anomalies. Firms also automatically suggest an action plan to limit the situation from getting worse. It can even manage an issue before human intervention becomes vital. This mitigates the risk of human error due to distraction, lack of knowledge, or reaction speed – sometimes manage room operators simply can’t move fast enough to stop the issue.
The volatile nature of power prices means that running a generation plant can be profitable depending on something as simple as the time of day. Because the utility market is so fast-paced, it can be hard to track all the data required to make these decisions manually. Machine learning can come to help. By feeding data on prices and usage into a machine learning algorithm, firms can predict the best times to run the plant – and make money. Machine learning can find times when usage is high, but costs for the raw materials utilized to produce power are low. These accurate predictions create an optimized generation schedule that increases profitability.