01
Solar Photovoltaic (PV) Image Based Defect Detection
Solar Plant
Asset Management
AI-powered image analysis identifies defects in residential and commercial solar installations using drone imagery, thermal cameras, and electroluminescence photography. Machine learning algorithms detect issues such as cell cracking, hotspots, soiling, shading effects, and potential-induced degradation across large solar arrays. Computer vision models assess panel alignment, wiring integrity, and mounting system conditions to predict performance impacts and safety hazards. The system generates automated inspection reports with GPS-tagged defect locations and recommended remediation actions for maintenance crews.
02
Solar PV Connector Installation Quality Analysis
Solar Plant
Asset Management
Use AI-driven image recognition on X-ray images of PV solar connectors to quantify installation anomalies (contact length, cross-mating, under/over insertion, etc.) and provide actionable insights to manufacturers and installers for improving connector design and installation practices.
03
Power Generation Assets Automated Fault Diagnosis
Power Plant
Asset Management
Leverage data-driven fault signature libraries to enable rapid automated detection and diagnosis of equipment failures across all generation technologies, improving reliability and reducing downtime.
04
Power Plant Transformer Dissolved Gas Analysis (DGA) Condition Monitoring
Power Plant
Asset Management
Continuously analyze transformer oil gas composition along with temperature and pressure data to detect abnormal patterns and predict internal faults.
05
Power Plant Control Autotuning
Power Plant
Generation Operations
Apply AI‑driven autotuning algorithms to improve control robustness and performance of thermal and renewable power plants.
06
Nuclear Energy Deliverables Enhancement
Nuclear Plant
Asset Management
Leverage LLMs to identify scenarios and automate the creation of technical reports, risk analyses, and operational guidance for nuclear power generation, improving service quality.
07
Nuclear Plant Design Control AI Verification and Validation
Nuclear Plant
Asset Management
Develop recommendations and a design control methodology to safely integrate AI tools into nuclear power plant design processes.
08
Predictive Spare Part Reordering for Nuclear Facilities
Nuclear Plant
Asset Management
Apply AI-driven forecasting to predict spare part demand in nuclear plants, improving inventory management and reducing both stockouts and excess stock.
09
Nuclear Plant Troubleshooting Acceleration
Nuclear Plant
Asset Performance
Provides plant operators and engineers a query-based system to quickly retrieve relevant data, perform failure analyses, and receive tool recommendations, supported by a feedback loop for continuous improvement.
10
Nuclear Plant Data Retrieval Acceleration
Nuclear Plant
Data and Model Management
Enable users to quickly locate and extract relevant nuclear data from large databases via natural language queries.
11
Lithium-Ion Battery Cell Degradation Prediction
Energy Storage
Generation Operations
AI algorithms predict lithium-ion battery cell degradation using electrochemical models, operational data, and environmental conditions to forecast capacity fade, power loss, and remaining useful life. Machine learning models analyze charging patterns, temperature cycling, depth of discharge, and calendar aging effects to predict degradation trajectories for individual cells and battery packs. The system incorporates multiple degradation mechanisms including solid electrolyte interphase growth, lithium plating, and active material loss to provide comprehensive degradation assessments. Advanced analytics enable predictive maintenance strategies, warranty analysis, and optimal charging protocols to maximize battery life and performance while minimizing replacement costs.
