While renewable energy companies have developed advanced applications to improve diagnosing anomalies on wind turbines, for example, most are still taking a “trip first, analyse later” approach, writes Benjamin Decio, CEO of analytics firm NarrativeWave.
This operating method was acceptable when near real-time analytics or software applications were not available. Today, however, analytics can be applied seamlessly within the operational workflow of existing monitoring operations, resulting in several immediate benefits.
Automated real-time analytics for all renewables
First, for wind power, real-time analytics enable operators to avoid tripping turbines needlessly and reset tripped turbines faster, resulting in more power output due to higher availability. This application of automated analytics results in safer asset operations with consistent, automatic review of event data to drive decisions.
Second, complex analytics that are currently being processed in batch mode by diagnostic engineers can now happen automatically in near real time. This closes the gap between insights showing what is happening on the turbine and the ability to respond quickly, resulting in better wind farm performance.
Lastly, the use of real-time analytics makes monitoring operations of all renewable energy vastly more efficient, as diagnostic personnel and engineers are freed from routine tasks and can focus on increasing asset performance and up time.
While some companies have shied away from near real-time analytics in the past, it is now easier than ever to deploy this type of monitoring operation that has low risk and high impact on energy assets.
In order to create successful digital transformation using real-time analytics, companies must implement a solution that:
Incorporates their Subject Matter Experts’ (SMEs’) knowledge, enabling engineers to collaborate and build analytics;
Provides automation of key business processes (e.g. fault diagnostics) and builds a foundation for more complex analytics (e.g. machine learning).
Having software built on your Subject Matter Experts’ knowledge is the best starting point.
Your SMEs and engineers have been monitoring and maintaining critical assets for years. Their knowledge is the best available expertise on how your equipment should be operated, maintained, and evaluated. Incorporating their knowledge on how to best analyze data from critical equipment is the ideal starting point for the application of real time analytics.
Analytic platforms providing purely Machine Learning or Artificial Intelligence can lack insight into the data and recommended next steps, therefore many engineers do not trust the results. Without human interpretation, more complex analytics, such as Machine Learning, have a difficult time achieving the desired outcome.
Using software that allows engineers to create analytics by themselves helps with the adoption of analytics.
Adopting new analytics and data driven business models is about changing the way business has been done for many years. Having software where your SMEs can interact and engage, without needing assistance from a data scientist or a software developer allows users to impact business outcomes faster and drive higher adoption.
Implementing software that automates current processes creates both short and long-term significant value.
As the volume of data generated by assets continues to grow exponentially, it is becoming more important to automate data analysis and diagnostic operations. Furthermore, having a tool that can automate these processes in a highly accurate and trusted way is crucial to an organization’s ability to generate value from digital transformation initiatives.
Deploying software with a solid foundation of your SMEs knowledge is the best way to approach implementing an entire suite of analytics.
Software configured by your own SMEs creates the optimal foundation for an entire range of analytics. Once expert knowledge is embedded in an automated system, like NarrativeWave, adding a full range of analytics becomes financially impactful to operations. For example, knowledge of what determines a false alarm can lead to a business insight, describing what turned a false alarm into a valid alarm. In contrast, an approach that solely tries to use Machine Learning or AI techniques without these key understandings can struggle with accuracy and not delivering significant value to the business.