Asset protection, monitoring, or control device and method, and electric power system
An asset protection, monitoring, or control device is operative to execute a decision-making logic to process inputs and generate a decision-making logic output that comprises one or more time series, process the decision-making logic output using a machine learning model, and cause an action to be performed responsive to a machine learning model output.
1 . An asset protection, monitoring, or control device, the device comprising:
an interface to receive inputs related to an asset; and
at least one integrated circuit operative to
execute a decision-making logic to process the inputs and generate a decision-making logic output that comprises one or more time series, wherein the decision-making logic processes the inputs to determine whether or not a fault is present, and wherein the one or more time series in the decision-making logic output indicate a presence or absence of a fault, as determined by the decision-making logic, at each time interval in the one or more time series,
process the decision-making logic output using a machine learning (ML) model to generate a ML model output that indicates whether or not an action is to be taken, and
cause the action to be performed or not performed responsive to the ML model output.
2 . The device of claim 1 , wherein the ML model is operative to receive the decision-making logic output as a ML model input.
3 . The device of claim 1 , wherein the device is an asset protection device, the action is a protective action, and the asset protection device further comprises an output interface to output a control signal to effect the protective action.
4 . The device of claim 1 , wherein the one or more time series toggle between two, three, or more distinct discrete values.
5 . The device of claim 1 , wherein the ML model has a recurrent neural network (RNN) layer.
6 . The device of claim 1 , wherein the ML model has a plurality of RNN layers.
7 . The device of claim 6 , wherein the ML model is operative to process at least one additional decision-making logic output provided by at least one additional decision-making logic.
8 . The device of claim 1 , wherein the ML model comprises a long short-term memory (LSTM) cell, a gated recurrent unit (GRU) cell, or at least one other gated cell.
9 . The device of claim 1 , wherein the ML model has an output dense layer.
10 . The device of claim 1 , wherein the ML model is operative to perform non-linear low-pass filtering of the decision-making logic output.
11 . The device of claim 1 , wherein the inputs comprise voltage and current measurements at an end of a transmission line of a power transmission system or of a distribution line of a power distribution system.
12 . The device of claim 1 , wherein the action comprises one or more of:
a corrective action;
a mitigating action;
a protective action, including a circuit breaker trip; or
causing information to be output via a human machine interface (HMI).
13 . The device of claim 1 , wherein the asset monitoring or control device is a power system asset, including a distance protection relay.
14 . An electric power system, comprising:
an asset; and
a device comprising
an interface to receive inputs related to the asset, and
at least one integrated circuit operative to
execute a decision-making logic to process the inputs and generate a decision-making logic output that comprises one or more time series, wherein the decision-making logic processes the inputs to determine whether or not a fault is present, and wherein the one or more time series in the decision-making logic output indicate a presence or absence of a fault, as determined by the decision-making logic, at each time interval in the one or more time series,
process the decision-making logic output using a machine learning (ML) model to generate a ML model output that indicates whether or not an action is to be taken, and
cause the action to be performed or not performed responsive to the ML model output.
15 . The electric power system of claim 14 , wherein the asset is a power transmission or distribution line, and the device is a protection relay operative to cause a circuit breaker trip responsive to the ML model output.
16 . A method of protecting, monitoring, or controlling an asset, the method comprising:
executing a decision-making logic to process inputs and generate a decision-making logic output that comprises one or more time series, wherein the decision-making logic processes the inputs to determine whether or not a fault is present, and wherein the one or more time series in the decision-making logic output indicate a presence or absence of a fault, as determined by the decision-making logic, at each time interval in the one or more time series;
processing the decision-making logic output using a machine learning (ML) model to generate a ML model output that indicates whether or not an action is to be taken; and
causing the action to be performed responsive to the ML model output.
17 . The method of claim 16 , wherein the ML model is operative to receive the decision-making logic output as an ML model input.
18 . The method of claim 16 , wherein the method is a method of protecting an asset, the action is a protective action, and the method further comprises outputting, via an output interface of an asset protection device, a control signal to effect the protective action.
19 . The method of claim 16 , wherein the one or more time series toggle between two, three, or more distinct discrete values.
20 . The method of claim 16 , wherein the ML model comprises one or more recurrent neural network (RNN) layers, and the ML model performs non-linear low-pass filtering of the decision-making logic output.