SYSTEM AND METHOD FOR VOLTAGE DRIFT MONITORING
A method for monitoring voltage drift includes measuring a voltage across a diode of a power device, providing the measured voltage as an input to a controller, the controller being configured to run a transformer-based model, and forecasting a range of expected future values of the voltage across the diode of the power device with the transformer-based model. The transformer-based model may include a temporal fusion transformer with a temporal convolutional neural network and an adversarial compensation model with a backpropagation algorithm.
1 . A method for monitoring voltage drift, the method comprising:
measuring a voltage across a diode of a power device;
providing the measured voltage as an input to a controller, the controller being configured to run a transformer-based model, wherein the transformer-based model comprises a temporal fusion transformer with a temporal convolutional neural network, the transformer-based model further comprising an adversarial compensation model with a backpropagation algorithm; and
forecasting a range of expected future values of the voltage across the diode of the power device with the transformer-based model.
2 . The method of claim 1 , wherein the adversarial compensation model is configured to compensate for noise with Jacobian regularization.
3 . The method of claim 1 , further comprising training the transformer-based model with a power cycling test.
4 . The method of claim 1 , further comprising forecasting when the power device will reach an end of its operational lifetime based on the range of expected future values of the voltage across the diode of the power device.
5 . The method of claim 1 , wherein the temporal fusion transformer comprises a multi-head attention block.
6 . The method of claim 1 , wherein the backpropagation algorithm computes a gradient of a loss function, the loss function being a Mean Square Error (MSE) loss function.
7 . A method for monitoring health of a power device, the method comprising:
training an artificial intelligence model with voltage inputs, the voltage inputs being produced by power-cycling a first power device;
loading the trained artificial intelligence model into firmware of a microcontroller; and
using the trained artificial intelligence model to forecast when a second power device will reach an end of its operational lifetime, the second power device being coupled with the microcontroller.
8 . The method of claim 7 , wherein the voltage inputs are drain-source voltages across a body diode of the first power device.
9 . The method of claim 7 , wherein the first power device and the second power device comprise silicon carbide MOSFETs.
10 . The method of claim 7 , wherein the trained artificial intelligence model forecasts when the second power device reaches the end of its operational lifetime at least two weeks before the end of its operational lifetime is reached.
11 . The method of claim 7 , wherein the artificial intelligence model comprises a temporal fusion transformer.
12 . The method of claim 11 , wherein the temporal fusion transformer comprises a temporal convolutional neural network.
13 . The method of claim 7 , wherein training the artificial intelligence model comprises an adversarial compensation model using Jacobian regularization.
14 . The method of claim 7 , wherein the second power device is coupled to an electric traction drive.
15 . The method of claim 7 , wherein the first power device and the second power device are different power devices.
16 . A system for monitoring voltage drift, the system comprising:
a power device;
a non-transitory memory comprising a program; and
a microprocessor coupled to the non-transitory memory and the power device, the microprocessor being configured to execute the program, the program comprising a transformer-based model, wherein the transformer-based model comprises a temporal fusion transformer with a temporal convolutional neural network, the transformer-based model further comprising an adversarial compensation model with a backpropagation algorithm, and based on the transformer-based model monitoring the voltage drift of the power device and predicting future voltage drift of the power device.
17 . The system of claim 16 , wherein the non-transitory memory is firmware.
18 . The system of claim 16 , wherein the power device is a SiC power device.
19 . The system of claim 16 , wherein the power device is coupled to a cooling system of a traction drive.
20 . The system of claim 16 , wherein the program further comprises instructions to produce an alert message in response to determining that degradation of the performance of the power device is predicted to occur within twenty weeks of additional use.