4 results about "Arc-fault circuit interrupter" patented technology

Arc fault protection for a digital power distribution system powering a device. The system includes an arc fault circuit interrupter ("AFCI") and a controller. The controller is connected to the AFCI. The controller is operable to control the AFCI to disable power to the device. The controller includes a processor and a memory. The controller is configured to send a digital power energy packet through the AFCI to the device, measure an error quantity associated with the digital power energy packet, evaluate the error quantity associated with the digital power energy packet, determine whether an arc fault condition exists based on the evaluation of the error quantity associated with the digital power energy packet, and control the AFCI to disable power to the device when it is determined that an arc fault condition exists.

The invention discloses a self-checking system, a self-checking method and an arc fault circuit breaker, and relates to the technical field of electronic power, the self-checking system comprises a current transformer, a sampling unit and a control unit; a primary coil of the current transformer is connected with the DC power supply unit, a first end of a secondary coil is connected with a first end of the resistive load, and a second end is connected with a first end of the capacitive load; the second end of the resistive load is connected with the second end of the capacitive load and the control unit; the input side of the sampling unit is connected with two ends of the capacitive load, and the output side is connected with the control unit; the control unit sends a first electric signal to a secondary coil of the current transformer; the sampling unit collects a first response voltage signal of the current transformer based on the first electric signal; the control unit analyzes the first response voltage signal to determine whether the current transformer works normally. According to the invention, the manufacturing cost of an AFCI product can be reduced, and the accuracy of AFCI arcing detection is improved.

The application discloses a kind of based on deep learning's fault arc accurate detection method and system, belong to low-voltage distribution safety and electrical fire early warning technical field.The present application is aimed at the problem that fault arc is prone to occur, difficult to detect in low-voltage distribution system, and the high false detection and missed detection rate of traditional method, a high-precision, fast detection scheme of fusion current time-frequency domain feature and deep learning model is proposed.The present application quickly locates current disturbance by improving CUSUM algorithm, greatly reduces the amount of calculation;Automatic extraction current time-frequency domain joint feature, input Stacking double-layer integrated deep learning model to realize high-precision fault classification;Combined with double exponential arc model to expand samples, improve generalization ability.The detection accuracy of the present application reaches 99.06%, and the detection time is only 10% of the traditional method, can effectively identify series, parallel, ground fault arc, adapt to residential, commercial, industrial and other complex multi-load scenarios, and support multi-branch line fault positioning, with high precision, low delay, strong robustness, easy deployment and other advantages, can be widely used in arc fault circuit interrupter, electrical fire monitoring system, to prevent electrical fire from the source.

The present disclosure relates to systems and methods for creating a training dataset for an artificial intelligence based arc fault circuit interrupter. According to various embodiments, a method for creating a training dataset for an artificial intelligence based arc fault circuit interrupter is provided. In some embodiments, the method includes: collecting a number of non-arc data frames; calculating a spectral magnitude for each non-arc data frame and summing the spectral magnitudes; calculating a mean of the sum of spectral magnitudes for the non-arc data; collecting a number of arc data frames; calculating a spectral magnitude for each arc data frame and summing the spectral magnitudes; for each arc data frame, comparing the sum of spectral magnitudes to a threshold based on the mean of the sum of spectral magnitudes for the non-arc data; and adding each arc data frame for which the sum of spectral magnitudes exceeds the threshold to an AI data model training dataset.