Resources
Find case studies, use cases, white papers, eBooks, and more written by the staff at Avathon.
Use Case: Improving Grid Reliability & Resiliency Using Machine Learning
Optimize actionable insights that drive operational efficiency on the grid with an automated machine learning platform. In this use case, analyze physical operations in real time and identify areas of improvement, uncover equipment, utilization, and capacity issues, and more.
Case Study: Predict Rare Failures in Hydro Turbines
Utility companies that operate hydro turbines have a vested interest in performing regular maintenance to decrease asset downtime and prevent unexpected failures or catastrophic events. In this case study, learn how one leading hydropower utility company applied machine learning to better analyze problematic turbine behavior and predict uncommon failures.
Case Study: Identifying Yaw Misalignment
Learn how Avathon’s AI platform detects performance-impacting yaw misalignments with high accuracy—and faster than traditional methods. Discover why our machine learning-based solution is not only a much faster method, but a more accurate one.
Use Case: Predicting Pitch Bearing Failure with AI
In this use case, discover how Avathon minimizes costly turbine downtime by predicting pitch bearing failure remotely and accurately. Learn why traditional approaches for evaluating pitch-bearing wear and tear are inefficient, how AI optimizes repair scheduling, and more.
Use Case: Automated Power Curve Monitoring for Optimal Turbine Performance
Learn how Avathon helps you identify underperforming turbines more quickly and cost-effectively—so you can maximize profitability. Discover how Avathon’s platform automatically detects performance issues for large numbers of turbines at once, and see why continually monitoring turbine performance with machine learning models is more efficient and accurate.
Case Study: Accurately Accounting Energy Loss
Maximize the value of operation and management contracts with improved monitoring of energy losses. Learn how one company learned to identify sources of their energy loss and implemented a system that conducts maintenance steps with the highest ROI.
Case Study: Optimizing Production Via AI-powered Transparency
In this case study, learn how a global beverage manufacturer improved plant efficiency across multiple locations with quantified granular performance insights.
Use Case: Maximize Energy Production by Using AI to Detect Solar Tracker Misalignment
Solar tracking system misalignment leads to hidden energy losses. Current detection methods are imprecise or impractical. Avathon uses machine learning to analyze tracker actuator motor data and inverter DC inputs. Learn more about our AI-powered solution.
Use Case: Increase Solar Energy Production with AI-Powered Soiling Detection
Soiling—the natural accumulation of dust, pollen, dirt, bird droppings, and other debris—reduces current global solar power production by at least 3-4% annually, creating multi-billion dollar revenue losses. Learn how Avathon leverages AI to restore photovoltaic performance, extend asset lifespan, and much more.
Use Case: Maximize Solar Power Production Using AI to Detect DC Field Faults
On large solar sites with thousands of panels, fuses, and strings in a DC array, the task of finding and fixing DC faults becomes especially challenging and costly. An AI-powered solution not only pinpoints where failures have occurred through real-time monitoring and analysis, it also quantifies the resulting energy loss from each occurrence. Learn how.
Use Case: BESS Optimization with AI-Powered Asset Performance Management
Battery Energy Storage Systems (BESS) assets are critical enablers of reliable renewable energy, but optimizing their performance requires advanced monitoring and predictive analytics. Avathon Industrial AI for Renewables is an asset performance management (APM) solution that leverages artificial intelligence to detect anomalies and recommend maintenance actions for BESS owners and operators.
Case Study: Predicting ESP Failures with Variable Data Quality
Predict electric submersible pump (ESP) failures to decrease well downtime, reduce costly maintenance, and increase production. Learn how one major oil and gas company used normal behavior modeling (NBM) to identify unhealthy pump assets, identified 5 of 7 historical ESP failures with 13-35 days advance notice (potential savings of $1.35M in deferred production), and employed sensor data cluster analysis to classify potential ESP failures.