ipower: Physics-Informed AI for Power Electronics

Key Features

• Physics-Informed AI

  Combines domain knowledge and physical models with machine learning to support accuracy and robustness.

• Design Optimization Tools

  Tools designed to assist in the design and development of power electronic circuits.

• Maintenance Diagnostics

  Supports diagnostics to help manage the maintenance of power electronics systems.

• Open-Source Data Platform

  Provides a platform with degradation and physics-informed ML datasets for research and development.

• Digital Twin Support

  Uses physics-informed machine learning for parameter estimation to support the creation of digital twins for power converters.

• Control Algorithms

  Provides AI-based tools for the control and operation of power converters.

Use Cases

• Power Electronic Circuit Design

  Using physics-informed tools to support the design phase of power electronics.

• Power Converter Control

  Implementing AI-driven control algorithms to manage power converters.

• Predictive Maintenance

  Applying diagnostics and degradation datasets to identify potential failures.

• Digital Twin Development

  Using the open-source data platform and PIML methods to create digital replicas of power converters.

FAQ

What is ipower and who is it for?

ipower is a set of physics-informed AI tools designed for engineering companies and electronics manufacturers to help with the design, control, and maintenance of power electronics.

Does ipower provide any open-source resources?

Yes, it provides an open-source data platform that includes degradation datasets and physics-informed ML datasets for digital twin applications.

How does physics-informed AI differ from standard AI in this tool?

Unlike typical data-driven approaches, ipower's tools couple deep neural networks with dynamic models of converters to address challenges related to training data and robustness.