MAI OptiTek offers an AI-assisted Digital Twin platform for dynamic modeling and simulation of grid-connected inverters. Our solution uses machine learning to create physics-aware virtual models, enabling AI-powered stability analysis and optimal parameter tuning for smoother grid integration and enhanced system reliability.
Funding
Funding not disclosed
Founders
Product
Problem
Grid-connected inverters, essential for renewable energy integration, exhibit complex, nonlinear behavior due to their control systems. This inherent complexity, coupled with manufacturer-specific performance characteristics, introduces uncertainties that can lead to grid instability. Ensuring inverter stability during grid integration is a significant challenge for businesses managing modern electric grids.
Solution
MAI OptiTek provides an AI-assisted Digital Twin platform for dynamic modeling and simulation of grid-connected inverters. Our platform leverages advanced machine learning techniques, including GPT and State Space neural networks, to create physics-aware virtual models that accurately replicate inverter behavior. We offer AI-powered stability analysis and optimal parameter tuning using industry-standard simulation tools like PSSE and PSCAD. This enables businesses to accurately assess inverter performance, predict potential instabilities, and ensure compliance with grid operator requirements. Our solutions facilitate smoother grid integration, enhance system reliability, and optimize the efficiency of intelligent grids.
Target Audience
Our primary customers are power system consultants, grid operators, renewable energy developers, and inverter manufacturers who require robust modeling and stability analysis for grid integration projects.
Features
- AI-assisted Digital Twin platform for dynamic modeling and simulation of grid-connected inverters.
- Utilization of PSSE, PSCAD, and MATLAB for developing accurate dynamic models and simulation test results.
- AI-powered stability analysis employing deep learning architectures such as CNN, GANs, LSTM, GPTs, and SS networks.
- Automated parameter tuning for inverters across various operating points using AI-driven optimization.
- Generation of synthetic data from developed models for training AI agents and enhancing simulation accuracy.
- Support for grid-connection feasibility analysis (R0) and Dynamic Model Acceptance Tests (DMAT) and Generator Performance Standards (GPS) for R1 and R2 registration.
- Development of PSCAD EMT models and compatible DLL files for seamless PSSE DYR dynamic simulations.
- Provision of simulation-based reports for effective communication with grid operators and stakeholders.