首页|Fault detection method for energy measurement systems equipped with a Rogowski coil using the coil's response to a unit voltage jump and a fully convolutional neural network
Fault detection method for energy measurement systems equipped with a Rogowski coil using the coil's response to a unit voltage jump and a fully convolutional neural network
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NSTL
Elsevier
? 2022 Elsevier LtdThe article presents a method of assessing the condition of a measurement system equipped with a Rogowski coil using the response of the coil to the unit voltage jump in the secondary circuit. The method is based on a direct sensor-microcontroller interface and has been tested on the STM32F745 microcontroller. Unlike traditional direct sensor-microcontroller methods described in literature a Fully Convolutional Neural Network (FCN) is used to extract signal features and estimate the state of the system. The microcontroller is responsible for capturing the coil responses, which are used by the FCN for time series classification. This method allows creating smart sensor with self-testing and identification capabilities. The Class Activation Map (CAM) is used to define class specific contribution regions and verify the performance of the FCN network. The proposed framework is suitable for remote assessment of the system condition in high voltage areas where Rogowski coils are used. Because of the presence of voltages dangerous for humans and the frequent inability to switch off the voltage in a power facility, this method significantly speeds up the location of damage in the measurement system.
Fully Convolutional Neural NetworkMicrocontroller interfacingRogowski coil sensorsTime series ClassificationTime-domain measurement
Dopierala P.
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Instytut Energetyki - Instytut Badawczy Oddzia? Gdańsk
NSTL
Elsevier
