It’s not difficult to imagine a world where artificial intelligence (AI) -enabled systems operate our world’s power plants, solar and otherwise. Although the time when AI runs generation facilities may be a bit farther down the road, the increasing application of machine learning, automated data analysis and the Industrial Internet of Things (IIoT) has begun to make a profound difference in several business sectors—including energy generation. Where in the past we seemed to be drowning in a sea of data without meaningful insights, these tools have started to create information-rich environments, allowing us to make better decisions and putting us on the path toward greater efficiency, control, uptime and profitability.
There is a plethora of opportunities provided by big data, machine learning and IIoT technology to drive new levels of performance, efficiency and cost savings. Digital asset management and operations are very much part of this narrative. It’s time to redefine asset management and operations by creating value through data connectivity and analytics, focusing on how data can help solar and storage asset owners, maintenance teams and operators in optimizing their plant performance, reducing their downtimes and maximizing their financial returns.
Predictive analytics and automated root cause analysis (RCA) are two of the most powerful machine learning techniques that can enhance digital asset management. As the amount of data grows and becomes more diverse, tools such as regression modeling can be used to perform predictive analytics to analyze the relationship between variables in the field and to find patterns in these large data sets, with the goal of predicting future events. Automated RCA uses techniques such as supervised learning algorithms to identify why a particular component or system has experienced an issue in the field.
The value of predictive analytics in digital asset management lies in the ability to predict impending issues with ample look-ahead time. This is critical because it provides huge cost savings not only by reducing any potential production downtime but also by enabling an efficient process for proactive corrective maintenance. At NEXTracker, with all the digitized asset information at our fingertips, we can scrape and analyze data from more than 400,000 sensor-embedded, Internet-connected tracker systems worldwide.
We can detect and manage issues remotely and react even before our customers report it. By gathering historical sensor data such as tracker angles, motor currents, weather-related data and other inputs, we can capture information about specific field issues, including component- and site-level concerns. The ability to run such predictive analytics in detecting and identifying field issues before they occur strikes a balance that optimizes maintenance and drives down costs.
When it comes to root cause analysis for equipment issues, many manufacturers have traditionally relied on onsite expert knowledge. Thanks to significant advances in data analytics and machine learning, root cause analysis can be performed using automated methods. The value of automated RCA translates into spending less time figuring out the problem and more time spent on actually fixing and—importantly—predicting it and dealing with it proactively.
Automated RCA benefits from the ability to analyze incoming sensor-data from field equipment and performing a time-series analysis of this data. Trends and data signatures associated with regular healthy operating conditions as well as anomalous conditions can be identified. A repository of such data signatures can be systematically built up for different anomalies that can then be used for automated RCA whenever a field issue is encountered. Eventually, work orders will be created automatically by the software to resolve any such issues.
As an example of application of these aforementioned machine learning techniques and advanced data analytics, let’s consider the scenario where there are field issues from installation variances within these tracker systems. While many EPC firms do stellar work, the quality and consistency of as-built conditions does vary significantly from site to site and around the world. Using the data garnered from aerial inspection on such factors as tracker pier height as well as tracker angles and motor currents drawn to move the trackers, contractors and asset owners can address the impacts of variabilities during and after the construction process—and minimize them in the future.
The ultimate goal will be to fully automate solar and storage asset management and operations based on accurate forecasting and planning, where a digital twin of a healthy functioning asset will be used as a blueprint to compare with real-time performance and to automatically detect anomalies and flag unhealthy trends—and reduce downtime to a bare minimum. One day in the not-too-distant future, these ever-smarter, data-connected systems will incorporate neural networks and deep learning capabilities—the building blocks of true AI.