Importance of AI-Driven Predictive Maintenance in Wind Energy Systems

This article describes the role of AI-driven predictive maintenance in wind energy systems. Wind energy systems are integral parts of sustainable operations across the renewable energy industry.  According to Forbes,  predictive maintenance is progressively used in the wind industry to improve efficiency and reduce operational expenses. This article explains how AI solutions are used for the early detection and diagnosis of faults in wind turbine systems.

What is AI?

Artificial Intelligence, or AI, is a machine’s ability to execute tasks such as learning, decision-making, and problem-solving, which is commonly associated with human intelligence. An artificial system includes computer software, hardware, a set of techniques including machine learning, and some other settings to solve problems calling for human-like perception.  It can perform tasks under unpredictable circumstances without any human involvement.

What are Wind Energy Systems?

Wind energy systems convert wind’s kinetic energy into electrical energy. The electrical energy is then used to power homes or commercial sectors.

The wind rotates the wind turbine blades connected to the rotor. Here, the wind’s kinetic energy is converted to mechanical energy. The rotational speed increases due to the gearbox attached to it. The increased rotational speed drives a generator and converts the mechanical energy into electrical energy. Thus, electricity is produced and stepped up in voltage through transformers and is supplied to the power grid.

There are 3 types of wind energy systems i.e., onshore, offshore, and hybrid wind energy systems. However, based on the rotational axis, the wind energy systems are of 2 types, i.e., horizontal axis wind energy conversion system (HAWECS) and vertical axis wind energy conversion system (VAWECS). The most commonly used types are horizontal-axis wind turbines.

What is Predictive Maintenance?

Predictive maintenance is a method that uses data analysis to assess equipment health continuously in real time. This method identifies equipment defects and predicts the potential future state of equipment to maximize equipment performance and its lifespan.

Predictive maintenance uses the data collected from various sensors, industrial control, and business software. It utilizes advanced analytical tools such as artificial intelligence (AI), machine learning, IoT, and integrated systems. The collected data is then processed to identify the equipment of the system that needs attention.

The systems that have a critical operational function and failure modes apply the predictive maintenance methods to monitor impending equipment failure.

AI-Driven Predictive Maintenance in Wind Energy Systems

A wind turbine system consists of various components, including blades, a gearbox, a generator, a controller, and bearings. It also consists of sensors and actuators that play key roles in monitoring wind turbine performance. Sensors are hardware devices used to measure physical parameters such as wind speed, rotor speed, generator speed, and blade pitch angle. At the same time, actuators are used to perform actions based on IoT requests.

 An AI-driven predictive maintenance method uses AI algorithms to analyze vibration data collected from the sensors attached to the wind energy system that signal mechanical problems. The data are real-time information about equipment, and also the historical data from the equipment’s meteorological database. The AI algorithms detect and analyze the faults, patterns, and anomalies in the vibration and predict malfunctions before they occur.

The AI-driven predictive maintenance method uses advanced technologies such as support vector machines (SVMs), Neural Networks (NNs), etc., to enhance maintenance prediction. The SVM technique analyzes the data pattern to identify the prospective failure, whereas the NN technique deals with the complex data relationships. It can predict possible system failures from machinery corrosion, such as seepages, misalignment, overheating, etc.

Integration of AI with wind energy systems also reduces operational costs and increases operational efficiency. The use of AI technology also avoids the requirement of manpower onsite to continuously monitor the system. One can monitor the operational status of the wind turbine remotely and assess its condition.

Case Studies on AI-Driven Predictive Maintenance in Wind Energy Systems

Siemens Gamesa:

This organization has developed a new technology called Visual-Based Asset Integrity, which incorporates imaging hardware and rolling AI to take multiple images of blades. The data are then analyzed for the early detection and diagnosis of faults. It also provides a prediction about when an issue may occur in the future.

It also implements digital twin technology and AI to predict gear failures. This leads to a 20% reduction in maintenance costs.

Reference: SG 7.0-170

GE Renewable Energy:

GE Renewable Energy utilizes AI-driven predictive maintenance techniques to monitor wind turbines and to predict mechanical failures. It also uses machine learning technology for blade and bearing fault detection.

Reference: GE Using AI/ML to Reduce Wind Turbine Logistics and Installation Costs

Vestas:

Vestas integrates sensor measurements with dynamic models of turbine components to assess various turbine states and detect potential issues before they occur. It also uses machine learning models to estimate the collected data from wind turbines and identify the root causes of faults. The main goal of AI-driven predictive maintenance here is to maximize energy production while minimizing operational costs.

Reference: Vestas’s onshore wind turbine

Envision:

Envision uses an advanced technology based on a cloud-based AI platform to detect irregularities in wind turbine systems.

Advantages

1. Increases the safety of the wind energy system
2. Avoids frequent maintenance work
3. Reduces maintenance costs
4. Optimization of maintenance schedules
5. Increases the overall life span of the wind turbine system
6. Lowers operational cost
7. Improves energy availability
8. Enhance equipment uptime
9. Reduction of energy waste
10. Enhance performance and boost reliability
11. Improves energy distribution and storage
12. Saves both time and money

Conclusion

AI-driven predictive maintenance methods are implemented in wind energy systems to ensure the smooth and efficient operation of various equipment within them. This method includes the collection of various data, and processing and analyzing the data using advanced technology such as AI, machine learning, etc., to identify the components that need to be checked.  

The integration of AI algorithms in wind energy systems enables more sustainable operations. These advanced predictive maintenance techniques identify the faults, predict failures, optimize operations, and schedule the repair and maintenance to avoid unwanted repairs and unanticipated interruptions caused by sudden malfunctions.

FAQS

1.  What is Predictive Maintenance?

Predictive maintenance is a method that uses data analysis to assess equipment health continuously in real time. This method identifies equipment defects and predicts the potential future state of equipment to maximize equipment performance and its lifespan.

2. What is AI-driven predictive maintenance in a wind energy system?

An AI-driven predictive maintenance method uses AI algorithms to analyze vibration data collected from the sensors attached to the wind energy system that signal mechanical problems.

Also Read:

1. How AI Is Used in the Renewable Energy Sector?
2. AI and Microgrids: Empowering Remote Communities with Smart Energy
3. AI Applications in Renewable Energy Trading and Market Optimization