USPTO Patent Granted for Proactive Fault Handling in Energy Assets

ChangeBridge: Patent Grants - Power Supply (H02J)

Published March 24th, 2026

Detected March 25th, 2026

Summary

The USPTO has granted a patent to Utopus Insights, Inc. for a system and method designed for proactive fault handling in electrical networks of energy assets. The invention utilizes historical and current sensor data to predict future faults in renewable energy assets.

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What changed

The United States Patent and Trademark Office (USPTO) has granted patent US12585265B2 to Utopus Insights, Inc. for a novel system and method focused on proactive fault handling within the electrical networks of energy assets. The patented technology involves analyzing historical sensor and log data to train hidden Markov models, which are then applied to current data to assess the likelihood of component faults and predict future failures in renewable energy assets. The system generates reports detailing these predictions.

This patent grant is primarily an intellectual property development and does not impose new regulatory obligations on energy companies. However, it signifies a technological advancement in predictive maintenance and grid reliability for renewable energy infrastructure. Companies in the energy sector, particularly those involved with renewable assets, may consider this technology for enhancing operational efficiency and preventing potential failures.

Source document (simplified)

← USPTO Patent Grants

System and method for proactive handling of multiple faults and failure modes in an electrical network of energy assets

Grant US12585265B2 Kind: B2 Mar 24, 2026

Assignee

Utopus Insights, Inc.

Inventors

Guruprasad Srinivasan, Younghun Kim

Abstract

An example method comprises receiving historical sensor data of a renewable energy asset for a first time period, identifying historical log data in one or more log sources, retrieving dates of the identified historical log data, retrieving sequences of historical sensor data using the dates, training hidden Markov models using the sequences of historical sensor data to identify probability of shifting states of one or more components of the renewable energy asset, receiving current sensor data of a second time period, identifying current log data in the one or more log sources, retrieving dates of the identified current log data, retrieving sequences of current sensor data using the dates, applying the hidden Markov models to the sequences of the current sensor data to assess likelihood of the one or more faults, creating a prediction of a future fault, and generating a report including the prediction of the future fault.