AI is the Future of Anomaly Detection: Here’s Why
Solar plants, just like anything else, need care and attention in order to maintain them and keep them functioning as they should. Solar panels are prone to deterioration for a number of reasons. Several types of anomalies can occur across the plant and each of them has a wide range of possible visual symptoms, either within the visible or the infrared spectra; external conditions like ambient temperature, solar irradiance, wind intensity and direction. They all heavily affect the appearances of the symptoms. Depending on their technical and economic priorities, customers may focus on detecting different anomalies and different severity scales.
That is why anomaly detection, even if based on widely trusted specifics like those enlisted in IEC TS 62446-3, Edition 1.0 2017-06, still requires deep knowledge, experience and flexibility, so that it ultimately remains a delicate time-consuming activity.
Having the privilege of being in the market since 2016, here at Wesii we have recognised that we have enough amount of data in order to educate an automatic anomaly detection through machine learning, which now allows us to maintain our usual accuracy standards with a minimum technical supervision.
Such an AI enhances our precision and repeatability, bypassing even the slightest subjectivity that may have been caused before due to manual elaboration. This fortunately leaves us the time and the resources to perform further analysis. Additionally it has become easier satisfying the different needs of the customer because we can quickly adapt the detection process by changing specific parameters quantitatively.
A risk of machine learning is that it can lead to altered results if not correctly trained: fortunately for us, the inspections that we have collected throughout the years of activity were key to calibrate and validate the procedure across all the possible external conditions and anomaly types, and every new acquisition contributes in making it smarter and better.
AI is certainly the future of our anomaly detection… will it be yours?