System for monitoring the smart grid management of electric transmission and distribution systems
This technology is a set of metrics that can be used for accurately monitoring the performance of capital, operations, and maintenance investments to infrastructure.
Unmet Need: Efficient distribution of energy resources in smart grid settings
Due to increased electricity demands and rapidly deteriorating infrastructure, the United States power grid faces enormous problems over the next several decades. Current efforts revolve around transitioning to a “smart grid” system with distributed, renewable sources of energy and connections between all components of the cyber-physical system to enhance control and optimize performance. However, these current approaches lack an empirical, unbiased method of determining the realized effectiveness of capital improvement projects. As such, there remains a need to evaluate the accuracy of such predictive models after the work has been performed, and if necessary, implement changes to these predictive models so that future predictions are more accurate.
The Technology: System for tracking the performance of capital, operations, and maintenance investments to an infrastructure
This technology includes a set of techniques for evaluating the predicted effectiveness of maintenance of and improvements to infrastructure. The accuracy of these predictions involves collecting data, representative of at least one pre-defined metric, from the infrastructure during first and second time periods corresponding to before and after a change has been implemented. A machine learning system can then receive compiled data representative of the different time periods and generate corresponding machine learning data. As such, this technology monitors the cause-and-effect implications of operational field actions and validates whether actual performance matches what is expected from efficient energy planning. This allows for the optimization of management and financial decisions that are made to various infrastructures based on their overall effectiveness.
Applications:
- Continuous monitoring of smart grid performance and financial investments
- Predicting points of failure in the power grid before they occur
- Identifying which components are most susceptible to failure
- Smart power grid integration in major cities
- Proactive maintenance in other cyber-physical systems, such as transportation, military or medical networks
Advantages:
- Allows identification of likely failure points in the electric grid before they occur
- Autocorrects with internal verification and user feedback, learning optimal performance over time
- Can be trained with historical system data and asset feature data
- Can be integrated into a smart grid system with distributed, real-time sensors and controls
- Saves money and decreases system downtime through proactive maintenance
- Cost-effective
Lead Inventor:
Patent Information:
Related Publications:
Tech Ventures Reference:
IR M10-068
Licensing Contact: Richard Nguyen