Envision's IoT Analytics Success Story: 15% Renewable Energy Output Increase With Predictive Maintenance Cost Savings

About Envision
New York based Envision Energy is one of the world’s ten largest wind turbine companies. Along with focusing on producing wind turbines, the company also has a software business which manages up to 13 gigawatts of renewable energy assets—both wind and solar—globally.

The Challenge
The energy business today goes beyond the engineering expertise your organization has. A key driver of competitive advantage in the business is the ability to monitor and maintain high performance of energy assets. In case of Envision, each of it's 20,000 wind turbines are built with over 150 advanced sensors that continually assess acceleration, temperature and vibration of the turbines. With these more than 3 million sensors, the company is managing massive amounts of real-time data - more than 20 terabytes of data at a time - to help continuously monitor this vast wind turbine network. Furthermore, the data volume is growing at over 50 percent annually as Envision continues to collect more data, more frequently from each of their wind turbines. The fact that the wind farms which house their turbines are geographically dispersed only adds the complexity of the whole exercise.

The Approach
Envision's team identified that the key to managing all this real-time data and making it available to the business boiled down to analyzing turbine sensor data with greater granularity. To better address this challenge, they have moved from analyzing turbine data every 10 minutes to every minute, and then on to every few seconds.The approach was to take this raw data and turn it into trends. Post that the trends were to be used for predictions about what will happen based on what we’ve seen in the past, to minimize downtime and take advantage of performance increases. By immediately analyzing real-time sensor data from their wind turbines, Envision is able to quickly identify actionable insights with significant business benefits.

Encashing Advanced Analytics Technology capabilities for building the solution 
Envision needed an analytics solution which enabled them to handle these multiple terabytes of data with sub-second response time, along with the capability to run distributed queries/edge analytics closer to the source of data. The company also needed to be able to continuously import and store large amounts of real-time sensor data with the ability run fast and flexible queries locally, in their central data center, or in the cloud. Envision employed ParStream's Analytics Platform, which handles massive volumes and high velocity of IoT data, to manage data streaming from its highly distributed network.

The Business Benefits of the solution
  • Performance Optimization: Envision uses sensor data to make smart decisions about altering the angle and speed of the turbine blades in order to optimize performance at any given time, based on changing environmental conditions. Through the use of real-time sensor data, Envision could boost a customer's total energy output by up to 15% from their wind farms
  • Predictive maintenance: Envision's sensor technology helps the company perform checks for any irregularities in operational performance for its 20,000 wind turbines, enabling technicians to predict potential failures before they happen. Real-time data is matched against historical data to determine which parts need adjustments or replacements, significantly reducing downtime.

Observations and Takeaways
While Envision's choice of analytics technology solution has certainly been the differentiator when the solution was implemented, what is most notable is the clarity of vision and and strategy and specificity of requirements laid by Envision while evaluating a technology alternative. Clear approach and objectives in terms of 'how many sensors?', 'where to place sensors?', 'what data to collect and how often?' play a critical role in getting the right technology partnership.