Challenge
Build an IoT energy management solution empowered with ML algorithms for real-time monitoring and predictive analysis of wind turbine performance. The main goal is to prevent system malfunctions that could cause power outages and costly repairs.
Build an IoT energy management solution empowered with ML algorithms for real-time monitoring and predictive analysis of wind turbine performance. The main goal is to prevent system malfunctions that could cause power outages and costly repairs.
Solution
The team developed an IoT & ML-driven energy management software solution that predicts energy production. An advanced platform provides real-time updates on the status of each wind turbine based on the information accumulated from meteorological sensors and turbines.
- Programmable logic controllers (PLC)
We utilized programmable logic controllers (PLCs) to gather data from sensors across the wind turbines. They monitor various operational metrics, like wind speed, turbine rotation speed, temperature, vibration, and torque, process the data to provide a precise overview of the wind turbine’s current performance, and identify faults and energy production efficiency. Additionally, the system detects deviations, like an unexpected temperature rise or increased vibration — to prevent damage, it triggers alarms or shuts down the turbine. Such timely maintenance and malfunction prevention ensures balanced energy production and extends equipment lifespan.
- Data visualization
Our project team chose Grafana dashboards to visualize data. We created customized, actionable charts for IoT energy management displaying data like daily power output, turbine locations, weather patterns, and predicting future trends. Thanks to these visualizations, operational managers have access to a real-time overview of turbine performance, while maintenance teams can quickly address turbine issues.
- Data lake
The client needed a robust data lake, as they operate wind turbines across various regions. Our developers created a central repository to collect and store data from all turbines, regardless of location, including structured, unstructured, and semi-structured data such as logs, sensor readings, and images. Data is collected from the PLCs and then stored and processed using AWS IoT Core and Lambda functions. Large datasets can be processed simultaneously, greatly supporting predictive maintenance and accelerating analysis and reporting.
- Error prediction
Leveraging data science and MLOps, we developed a predictive model that evaluates factors influencing turbine health, such as vibration, temperature levels, and performance metrics. This model continually learns from incoming data and enables the operational managers to detect warning signs of failures early. Upon identifying them, the energy management control system sends alerts to the maintenance teams so that they proactively address the issues before they cause breakdowns.
The team developed an IoT & ML-driven energy management software solution that predicts energy production. An advanced platform provides real-time updates on the status of each wind turbine based on the information accumulated from meteorological sensors and turbines.
- Programmable logic controllers (PLC)
We utilized programmable logic controllers (PLCs) to gather data from sensors across the wind turbines. They monitor various operational metrics, like wind speed, turbine rotation speed, temperature, vibration, and torque, process the data to provide a precise overview of the wind turbine’s current performance, and identify faults and energy production efficiency. Additionally, the system detects deviations, like an unexpected temperature rise or increased vibration — to prevent damage, it triggers alarms or shuts down the turbine. Such timely maintenance and malfunction prevention ensures balanced energy production and extends equipment lifespan.
- Data visualization
Our project team chose Grafana dashboards to visualize data. We created customized, actionable charts for IoT energy management displaying data like daily power output, turbine locations, weather patterns, and predicting future trends. Thanks to these visualizations, operational managers have access to a real-time overview of turbine performance, while maintenance teams can quickly address turbine issues.
- Data lake
The client needed a robust data lake, as they operate wind turbines across various regions. Our developers created a central repository to collect and store data from all turbines, regardless of location, including structured, unstructured, and semi-structured data such as logs, sensor readings, and images. Data is collected from the PLCs and then stored and processed using AWS IoT Core and Lambda functions. Large datasets can be processed simultaneously, greatly supporting predictive maintenance and accelerating analysis and reporting.
- Error prediction
Leveraging data science and MLOps, we developed a predictive model that evaluates factors influencing turbine health, such as vibration, temperature levels, and performance metrics. This model continually learns from incoming data and enables the operational managers to detect warning signs of failures early. Upon identifying them, the energy management control system sends alerts to the maintenance teams so that they proactively address the issues before they cause breakdowns.
Results
The team has developed a scalable IoT and ML-driven system designed to predict energy production using a network of programmable logic controllers. This advanced platform collects essential data from wind turbines, evaluates their performance, and delivers precise insights to support informed decision-making. With real-time monitoring, customer managers can track the condition of turbines and propose strategies to optimize energy output and minimize unnecessary costs. Leveraging ML algorithms, our innovative solution forecasts power generation based on weather data and historical analytics. It also identifies the optimal times to shut down wind farms for maintenance, a critical feature for turbines located in remote or challenging environments where repairs are costly and difficult.
The team has developed a scalable IoT and ML-driven system designed to predict energy production using a network of programmable logic controllers. This advanced platform collects essential data from wind turbines, evaluates their performance, and delivers precise insights to support informed decision-making. With real-time monitoring, customer managers can track the condition of turbines and propose strategies to optimize energy output and minimize unnecessary costs. Leveraging ML algorithms, our innovative solution forecasts power generation based on weather data and historical analytics. It also identifies the optimal times to shut down wind farms for maintenance, a critical feature for turbines located in remote or challenging environments where repairs are costly and difficult.