“…Under the action of the alternating magnetic flux, the induced electromotive force E2 is generated on the secondary side of the transformer [2]. This signal enters the single-chip microcomputer through the sampling resistor and the signal conditioning circuit to perform the leakage protection function after being sampled and processed.…”
Section: Electromagnetic Current Transformer Methodmentioning
The paper uses a magnetic modulation current transformer method to detect transient waveform residual currents containing pulsating DC components and even smooth DC components. At the same time, it uses IIR-based digital low-pass filters to filter the collected signals, which improves Filtering accuracy. The software design is based on the floating-point DSP-TMS320F28335, which realizes the accurate sampling and analysis of the transient waveform residual current, and the transient waveform residual current detection meets the standard requirements.
“…Under the action of the alternating magnetic flux, the induced electromotive force E2 is generated on the secondary side of the transformer [2]. This signal enters the single-chip microcomputer through the sampling resistor and the signal conditioning circuit to perform the leakage protection function after being sampled and processed.…”
Section: Electromagnetic Current Transformer Methodmentioning
The paper uses a magnetic modulation current transformer method to detect transient waveform residual currents containing pulsating DC components and even smooth DC components. At the same time, it uses IIR-based digital low-pass filters to filter the collected signals, which improves Filtering accuracy. The software design is based on the floating-point DSP-TMS320F28335, which realizes the accurate sampling and analysis of the transient waveform residual current, and the transient waveform residual current detection meets the standard requirements.
“…Literature [7–11] combines wavelet packet transform, energy entropy, quantum genetics, and other artificial NN to establish a related classification model, which provides theoretical support for effective recognition types. In [12], the least squares SVM can accurately identify the electric shock current of the birth object from the total leakage current. Based on the principle of adaptive filtering, the literature [13] establishes an adaptive electric shock current detection model with good noise robustness and can effectively eliminate the dead zone of protection action.…”
Section: Bioelectrical Shock Current Sample Collection and Related mentioning
confidence: 99%
“…Literature [7][8][9][10][11] combines wavelet packet transform, energy entropy, quantum genetics, and other artificial NN to establish a related classification model, which provides theoretical support for effective recognition types. In [12], the least squares SVM can accurately identify the electric shock current of the birth object IET Cyber-Phys. Syst., Theory Appl.…”
Section: Bioelectrical Shock Current Sample Collection and Related Workmentioning
confidence: 99%
“…Based on the principle of adaptive filtering, the literature [13] establishes an adaptive electric shock current detection model with good noise robustness and can effectively eliminate the dead zone of protection action. However, the methods in [7][8][9][10] are mainly used for fault identification of power systems, and the literature [11][12][13] is mainly used for the identification of electric shock currents. Residual current protection is different from power system fault and, it is subject to changes in leakage current caused by various load changes.…”
Section: Bioelectrical Shock Current Sample Collection and Related Workmentioning
“…To identify the residual current defect in low voltage distribution networks, a cooperative training classification model based on an upgraded squirrel search method for a semi-supervised SVM and the k-nearest neighbor is applied in 29 . A protection strategy based on least squares-SVM is designed and developed for residual current and touch current 30 . All aforementioned study deal with SVM based different strategies for fault detection in different systems where the proposed system developed rule-based classifiers for detecting sensor fault and load current fault and MSVM is applied for leakage current fault through proper classification in a household environment.…”
Unsafe electrical appliances can be hazardous to humans and can cause electrical fires if not monitored, analyzed, and controlled. The purpose of this study is to monitor the system’s condition, including the electrical properties of the appliances, and to diagnose fault conditions without deploying sensors on individual appliances and analyzing individual sensor data. Using historical data and an acceptable range of normal and leakage currents, we proposed a hybrid model based on multiclass support vector machines (MSVM) integrated with a rule-based classifier (RBC) to determine the changes in leakage currents caused by installed devices at a certain moment. For this, we developed a sensor-based monitoring device with long-range communication to store real-time data in a cloud database. In the modeling process, RBC algorithm is used to diagnose the constructed device fault and overcurrent fault where MSVM is applied for detecting leakage current fault. To conduct an operational field test, the developed device was integrated into some houses. The results demonstrate the effectiveness of the proposed system in terms of electrical safety monitoring and detection. All the collected data were stored in a structured database that could be remotely accessed through the Internet.
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