In the renewable energy industry, a single component malfunction can significantly impact the entire network’s performance. Asset failures in solar and wind farms can bring down a whole network of infrastructure, affecting thousands of customers and decreasing network reliability, costing hundreds of thousands of dollars. Therefore, there is a strong imperative to predict and avoid malfunctions in such highly connected systems. Sophisticated reliability models using new Machine Leaning (ML) techniques are proving to be a game-changer for asset performance management. Data and artificial intelligence are being used to predict malfunctions at any future point in time and facilitate the shift to condition than time-based maintenance. Early detection and more precise information can indicate which components or equipment will need to be repaired or replaced and when. This is allowing asset managers to plan maintenance efficiently and avoid unplanned and expensive disruptions.

In this webinar, we outline how machine learning (ML), combined with industry expertise, can estimate the probability of failure for specific failure modes and components. The methodology illustrates a specific failure mode using a large wind farm case study, where a significant number of component failures occurred within a short space of time. The problem is solved using a two-step solution, firstly predicting the future probability distribution of a parameter given its current value, and secondly, determining the probability of the malfunction given the predicted parameter value. This allows a standardized and more accurate approach for prognosticating malfunctions of technical components, thus determining their remaining useful life (RUL).

Key takeaways from this webinar:
- Power system reliability
- Asset performance management (APM)
- Machine learning and POF prediction

Presenter: Dr. Naser Hashemnia, EIT Lecturer & Principal consultant at HitachiEnergy

Naser is a Principal Power Electrical Engineer (PhD) with over 12 years of experience in developing and analyzing the reliability of electrical networks and conducting system studies for various industries. His technical skills extend to conducting solution technical reviews, designing reliability models, and developing predictive analytics using machine learning. Naser has collaborated with numerous consultancies and academic institutions, performing power system studies and protection, as well as research and development.

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