Contact Us

Case Study

Case Study

Renewable.AI uses advanced AI and deep learning tools to improve wind and solar farm uptime, augmenting financial returns for asset owners.

Renewable.AI uses advanced AI and deep learning tools to improve wind and solar farm uptime, augmenting financial returns for asset owners.

As one of the largest energy producers in Europe, maintaining the performance and reliability of wind turbines is crucial for maximizing energy production and minimizing unplanned downtime. However, the company had been facing significant challenges, including critical components not reaching their designed lifespan, severe events during high wind seasons, inaccurate measures for asset health, and low throughput. In this blog, we shall explore how Industry.AI addressed these challenges by detecting component failures well in advance, enabling proactive maintenance and optimization of wind turbine performance. By leveraging Industry.AI, the energy producer enhanced asset management, improved operational efficiency, and achieved higher energy generation.

As one of the largest energy producers in Europe, maintaining the performance and reliability of wind turbines is crucial for maximizing energy production and minimizing unplanned downtime. However, the company had been facing significant challenges, including critical components not reaching their designed lifespan, severe events during high wind seasons, inaccurate measures for asset health, and low throughput. In this blog, we shall explore how Industry.AI addressed these challenges by detecting component failures well in advance, enabling proactive maintenance and optimization of wind turbine performance. By leveraging Industry.AI, the energy producer enhanced asset management, improved operational efficiency, and achieved higher energy generation.

Challenges
Challenges

The energy producer faced the following challenges in wind turbine operations:

The energy producer faced the following challenges in wind turbine operations:

Shortened Component Lifespan: Critical components in the wind turbines were failing before reaching their designed lifespan which led to unplanned downtime and increased maintenance costs.
Shortened Component Lifespan: Critical components in the wind turbines were failing before reaching their designed lifespan which led to unplanned downtime and increased maintenance costs.
Severe Events During High Wind Seasons: High wind seasons poses a significant risk to wind turbine operations. The company needed minute control over spare inventory and manpower contracts to address such events promptly.
Severe Events During High Wind Seasons: High wind seasons poses a significant risk to wind turbine operations. The company needed minute control over spare inventory and manpower contracts to address such events promptly.
Inaccurate Asset Health Assessment: The existing measures for identifying the health of assets were inadequate, resulting in delays in detecting potential failures and inefficient maintenance practices.
Inaccurate Asset Health Assessment: The existing measures for identifying the health of assets were inadequate, resulting in delays in detecting potential failures and inefficient maintenance practices.
Low Throughput and Performance: The turbines were not achieving the expected throughput, leading to suboptimal energy generation and reduced operational efficiency.
Low Throughput and Performance: The turbines were not achieving the expected throughput, leading to suboptimal energy generation and reduced operational efficiency.
Industry.AI Approach
Industry.AI Approach

To address the challenges and enhance wind turbine performance, the energy producer adopted the following approach using Industry.AI:

To address the challenges and enhance wind turbine performance, the energy producer adopted the following approach using Industry.AI:

Data Integration and Analysis

Integrated data from various sensors installed in the wind turbines, capturing parameters such as temperature, vibration, rotational speed, and power output.
Analyzed real-time and historical data using Industry.AI to identify patterns, anomalies, and deviations from normal operating conditions.

Data Integration and Analysis

Integrated data from various sensors installed in the wind turbines, capturing parameters such as temperature, vibration, rotational speed, and power output.
Analyzed real-time and historical data using Industry.AI to identify patterns, anomalies, and deviations from normal operating conditions.

Machine Learning-based Anomaly Detection

Utilized Industry.AI's machine learning algorithms to detect early signs of component failures and performance degradation.
Established baseline models for normal turbine behavior and identified deviations that indicated potential issues, enabling proactive maintenance actions.

Machine Learning-based Anomaly Detection

Utilized Industry.AI's machine learning algorithms to detect early signs of component failures and performance degradation.
Established baseline models for normal turbine behavior and identified deviations that indicated potential issues, enabling proactive maintenance actions.

Predictive Maintenance:

Used the insights from condition monitoring and anomaly detection to predict component failures and schedule maintenance activities proactively.
Implemented predictive maintenance strategies based on data-driven indicators, reducing unplanned downtime and optimizing maintenance schedules.

Predictive Maintenance:

Used the insights from condition monitoring and anomaly detection to predict component failures and schedule maintenance activities proactively.
Implemented predictive maintenance strategies based on data-driven indicators, reducing unplanned downtime and optimizing maintenance schedules.

Inventory and Resource Optimization

Leveraged Industry.AI to analyze historical data and weather patterns to optimize spare part inventory levels, ensuring sufficient availability during high wind seasons.
Optimized manpower contracts by aligning staffing levels with anticipated maintenance and repair needs, minimizing costs while maintaining operational readiness.

Inventory and Resource Optimization

Leveraged Industry.AI to analyze historical data and weather patterns to optimize spare part inventory levels, ensuring sufficient availability during high wind seasons.
Optimized manpower contracts by aligning staffing levels with anticipated maintenance and repair needs, minimizing costs while maintaining operational readiness.

Benefits
Benefits

Implementing Industry.AI in wind turbine operations brings several benefits:

Implementing Industry.AI in wind turbine operations brings several benefits:

Early Detection of Component Failures:
By leveraging machine learning-based anomaly detection, the energy producer identified component failures well in advance, enabling proactive maintenance and reducing unplanned downtime.
Improved Asset Management:
Accurate measures of asset health and performance allowed for timely maintenance interventions, extending the lifespan of critical components and optimizing maintenance practices.
Enhanced Operational Efficiency:
Through predictive maintenance and optimized resource allocation, the energy producer can improve operational efficiency, maximize energy generation, and minimize downtime.
Cost Savings:
Proactive maintenance and optimized inventory and resource management led to cost savings by reducing unplanned repairs, minimizing spare part inventory costs, and optimizing manpower contracts.
Early Detection of Component Failures: By leveraging machine learning-based anomaly detection, the energy producer identified component failures well in advance, enabling proactive maintenance and reducing unplanned downtime.
Improved Asset Management: Accurate measures of asset health and performance allowed for timely maintenance interventions, extending the lifespan of critical components and optimizing maintenance practices.
Enhanced Operational Efficiency: Through predictive maintenance and optimized resource allocation, the energy producer can improve operational efficiency, maximize energy generation, and minimize downtime.
Cost Savings: Proactive maintenance and optimized inventory and resource management led to cost savings by reducing unplanned repairs, minimizing spare part inventory costs, and optimizing manpower contracts.
Results
Results
Improved Accuracy:
Predictions of component failure up to 2 months in advance
Increased Production:
Increased generation for a 2.5 MW wind turbine.
Reduced Downtime:
5 days of turbine shutdown time reduced through equipment procurement ahead of failure.
Improved Accuracy:
Predictions of component failure up to 2 months in advance.
Increased Production:
Increased generation for a 2.5 MW wind turbine.
Reduced Downtime:
5 days of turbine shutdown time reduced through equipment procurement ahead of failure.
Conclusion: Deploying Industry.AI’s solutions on wind turbine operations empowers energy producers to detect component failures in advance, improve asset management, enhance operational efficiency, and increase energy generation. By leveraging real-time data analysis, machine learning-based anomaly detection, and predictive maintenance strategies, the energy producer can reduce unplanned downtime, optimize resource utilization, and achieve cost savings. Ultimately, this results in improved performance, increased reliability, and a more sustainable energy production process.
Conclusion: Deploying Industry.AI’s solutions on wind turbine operations empowers energy producers to detect component failures in advance, improve asset management, enhance operational efficiency, and increase energy generation. By leveraging real-time data analysis, machine learning-based anomaly detection, and predictive maintenance strategies, the energy producer can reduce unplanned downtime, optimize resource utilization, and achieve cost savings. Ultimately, this results in improved performance, increased reliability, and a more sustainable energy production process.

Get In Touch

Contact Us

Get In Touch

Contact Us
Scroll to Top