AI-Driven Predictive Maintenance Improves Wind Turbine Performance

Introduction

In an era where renewable energy sources are becoming increasingly vital, wind turbines play a pivotal role in the sustainable future of our planet. Ensuring the optimal performance and longevity of these towering structures presents a significant challenge. Artificial Intelligence (AI) offers innovative solutions that are reshaping the landscape of maintenance practices. As the wind energy industry explores the use of AI, companies like Suzlon are at the forefront of this revolution, strategically investing in AI technologies to proactively address maintenance challenges and keep their wind turbines operating efficiently and sustainably. Since its inception in 1995, Suzlon has rapidly expanded its footprint across the world and made a mark for itself through technological and product innovation. Suzlon is a pioneer in the field of wind energy and is currently present in 17 countries. 

Challenges Faced

Framing the problem and orchestrating the data

Suzlon was focused on predicting the time to failure for different subsystems within a wind turbine: the blades, motor, bearing and control/electric systems. For each of these, the sensor data required was different, and a minimum of year’s event data was required to train the ML model where an event is defined as an anomaly that potentially leads to turbine downtime.

Solution Implemented

Data extraction and preparation

In the first step, the different data sources including real-time sensor data and historical event records from SCADA systems, were orchestrated using RapidCanvas. The raw datasets were then cleaned and prepared for the machine learning process. This ensured a rich and relevant dataset for making accurate and timely predictions.

Automated modeling 

Using the RapidCanvas platform, automated modeling was carried out, leveraging regression machine learning algorithms to analyze the vast datasets. Through iterative model training and refinement, a high level of accuracy was achieved in predicting a turbine subsystem’s time to failure. 

Dashboards and alerts 

To empower Suzlon’s operators and maintenance teams, the insights garnered through the machine learning process were shared through user-friendly dashboards and real-time alerts. These dashboards provide instant insights into the health and the Remaining Useful Life (RUL)of each turbine. By using this RUL-based approach, the status of critical turbine components such as main bearing and gearbox is determined, while automated alerts are triggered when the system detects anomalies or upcoming maintenance needs. This allows operators to schedule maintenance efficiently, minimizing downtime and maximizing turbine productivity.

Results and benefits

Failures predicted in advance

Proactive maintenance and reducing downtime has helped the company avoid costly repairs and unplanned outages, potentially saving $1MM+ in lost revenue and maintenance expenses.

Total savings across all turbines

With the model implemented across over 700 turbines, the total savings for the company amounts to nearly $35M.

Data-driven insights into operations and maintenance

With the RapidCanvas solution, the Suzlon team could access comprehensive dashboards detailing the performance and maintenance requirements of the subsystems within their wind turbines. This enabled the teams to take the necessary steps to prevent an unplanned downtime and to manage their maintenance schedules based on real-time accurate data. 

Improved resource management

Implementing predictive maintenance for wind turbines optimized resource allocation, scheduling, and workloads, while also improving turbine parts and personnel management, resulting in enhanced operational efficiency and cost savings.

Extended useful life of wind turbines 

By identifying and addressing wind turbine issues before they escalate, Suzlon is able to effectively reduce wear and tear, ensuring that turbines continue to generate clean energy for years beyond their expected lifespan.