Motor control apparatus having protection operation unit, and machine learning apparatus and method thereof
First Claim
1. A machine learning apparatus for learning (i) a power failure detection level, which is a value of a power supply voltage defined as a criterion for determination as to whether power failure has occurred on an AC power supply side of a motor control apparatus, and (ii) a power failure detection time, which is a time for which the power supply voltage on the AC power supply side of the motor control apparatus has been lower than the power failure detection level, in the motor control apparatus which converts AC power supplied from the AC power supply side into DC power, outputs the DC power to a DC link, further converts the DC power into AC power for driving a motor, and supplies the AC power to the motor,the machine learning apparatus comprising:
- a DC voltage measuring unit which measures a DC link voltage across two terminals of a DC link capacitor in the DC link;
an AC voltage measuring unit which measures a value of the power supply voltage on the AC power supply side;
an energy amount computation unit which computes an amount of energy stored in the DC link capacitor;
a state observation unit which observes a state variable includingdata associated with the measured value of the power supply voltage on the AC power supply side,data associated with the computed amount of energy stored in the DC link capacitor,data indicating whether a protective operation for the motor control apparatus is successful, anddata associated with motor output; and
a learning unit which learns the power failure detection level and the power failure detection time, by repeating update of a function for changing the power failure detection level and the power failure detection time, based on the state variable.
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Accused Products
Abstract
A machine learning apparatus learns conditions associated with power failure on the side of an AC power supply in a motor control apparatus which converts AC power into DC power, outputs the DC power to a DC link, further converts the DC power into AC power for driving a motor, and supplies the AC power to the motor, includes a state observation unit which observes a state variable including at least one of data associated with the value of a power supply voltage on the AC power supply side, data associated with the amount of energy stored in a DC link capacitor provided in the DC link, and data indicating whether a protective operation for the motor control apparatus is successful, and a learning unit which learns conditions associated with power failure on the AC power supply side in accordance with a training data set defined by the state variable.
43 Citations
8 Claims
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1. A machine learning apparatus for learning (i) a power failure detection level, which is a value of a power supply voltage defined as a criterion for determination as to whether power failure has occurred on an AC power supply side of a motor control apparatus, and (ii) a power failure detection time, which is a time for which the power supply voltage on the AC power supply side of the motor control apparatus has been lower than the power failure detection level, in the motor control apparatus which converts AC power supplied from the AC power supply side into DC power, outputs the DC power to a DC link, further converts the DC power into AC power for driving a motor, and supplies the AC power to the motor,
the machine learning apparatus comprising: -
a DC voltage measuring unit which measures a DC link voltage across two terminals of a DC link capacitor in the DC link; an AC voltage measuring unit which measures a value of the power supply voltage on the AC power supply side; an energy amount computation unit which computes an amount of energy stored in the DC link capacitor; a state observation unit which observes a state variable including data associated with the measured value of the power supply voltage on the AC power supply side, data associated with the computed amount of energy stored in the DC link capacitor, data indicating whether a protective operation for the motor control apparatus is successful, and data associated with motor output; and a learning unit which learns the power failure detection level and the power failure detection time, by repeating update of a function for changing the power failure detection level and the power failure detection time, based on the state variable. - View Dependent Claims (2, 3, 4)
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5. A machine learning apparatus for learning (i) a power failure detection level, which is a value of a power supply voltage defined as a criterion for determination as to whether power failure has occurred on an AC power supply side of a motor control apparatus, and (ii) a power failure detection time, which is a time for which the power supply voltage on the AC power supply side of the motor control apparatus has been lower than the power failure detection level, in the motor control apparatus which converts AC power supplied from the AC power supply side into DC power, outputs the DC power to a DC link, further converts the DC power into AC power for driving a motor, and supplies the AC power to the motor,
the machine learning apparatus comprising: -
a state observation unit which observes a state variable including data associated with a value of the power supply voltage on the AC power supply side, data associated with an amount of energy stored in a DC link capacitor provided in the DC link, data indicating whether a protective operation for the motor control apparatus is successful, and data associated with motor output; and a learning unit which learns the power failure detection level and the power failure detection time, by repeating update of a function for changing the power failure detection level and the power failure detection time, based on the state variable, wherein the learning unit comprises; a reward computation unit which computes a reward for the result of changing the power failure detection level and the power failure detection time, based on the state variable; and a function update unit which updates, based on the reward, the function, and the learning unit learns the power failure detection level and the power failure detection time which achieve the greatest reward, by repeating update of the function by the function update unit. - View Dependent Claims (6, 7)
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8. A machine learning method for learning (i) a power failure detection level, which is a value of a power supply voltage defined as a criterion for determination as to whether power failure has occurred on an AC power supply side of a motor control apparatus, and (ii) a power failure detection time, which is a time for which the power supply voltage on the AC power supply side of the motor control apparatus has been lower than the power failure detection level, in the motor control apparatus which converts AC power supplied from the AC power supply side into DC power, outputs the DC power to a DC link, further converts the DC power into AC power for driving a motor, and supplies the AC power to the motor,
the method comprising: -
a state observation step of observing a state variable including data associated with a value of the power supply voltage on the AC power supply side, data associated with an amount of energy stored in a DC link capacitor provided in the DC link, data indicating whether a protective operation for the motor control apparatus is successful, and data associated with motor output; and a learning step of learning the power failure detection level and the power failure detection time, by repeating update of a function for changing the power failure detection level and the power failure detection time, based on the state variable, wherein the learning step comprises; a reward computation step of computing a reward for the result of changing the power failure detection level and the power failure detection time, based on the state variable, and a function update step of updating, based on the reward, the function, and the learning step comprises learning the power failure detection level and the power failure detection time which achieve the greatest reward, by repeating update of the function in the function update step.
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Specification