Optimization of power generation from power sources using fault prediction based on intelligently tuned machine learning power management
AssigneeComap
InventorsBhuneshwar Prasad, Swardheep Babu, Nigel Watson
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Abstract
A power source fault prediction and control system includes a plurality of power sources, such as generator sets connected in parallel, a controller, and a data acquisition and analysis module. The data acquisition and analysis module is configured to receive sensor data from a sensor, analyze the sensor data, predict a future fault scenario at a first time, and optionally send an instruction to the controller to change an operational parameter of a respective power source, such as the generator set. The instruction is configured to delay the fault scenario to a second time after the first time.
Claims20
A system comprising:
- a plurality of power sources including a plurality of generator sets connected in parallel;
- a controller;
- and
- a data acquisition and analysis module configured to:
- receive sensor data from a sensor,
- analyze the sensor data, and
- predict a future fault scenario at a first time associated with at least one of the power sources.
A method comprising:
- measuring current operating values for at least one power source that is configured with first operating conditions;
- estimating a safe operation window for the at least one power source;
- comparing the safe operation window to a warning criteria;
- calculating second operating conditions for the at least one power source, wherein the second operating conditions are different than the first operating conditions;
- and
- reallocating a load for the at least one power source based on the second operating conditions for the at least one power source.
A method comprising:
- establishing limits for sensor data, wherein the sensor data is obtained by one or more sensors configured to monitor at least one power source;
- initializing a first cost function weight and a second cost function weight associated with the sensor data;
- determining convergence values for the first cost function weight and the second cost function weight using machine learning;
- outputting a trend line based on the sensor data based on a regression analysis of the sensor data and the convergence values for the first cost function weight and the second cost function weight;
- and
- dynamically adjusting at least one operating parameter of the at least one power source based on the fault scenario prediction, wherein the at least one operating parameter is related to the sensor data.
Dependencies are inferred from the claim text.