A Series Arc Fault Detection Method Based on Time-Frequency Markov Permutation Transition Field for Photovoltaic Systems With Power Electronic Devices

Series arc faults (SAFs) are a primary cause of fire incidents in photovoltaic systems. Accurately and rapidly detecting SAF under the interference of power electronic devices remains a significant challenge. This article proposes an SAF detection method based on the time-frequency Markov permutation transition field (TFMPTF). First, variational mode decomposition is used to decompose the current signal into modes containing different frequency components to prevent interference between the information of different frequency bands. Then, the modes are transformed into two-dimensional matrices using TFMPTF. Innovatively, the concept of time-frequency permutation patterns state transition analysis in TFMPTF is proposed, from which the distinct structural information of the current signal can be effectively depicted. Afterward, singular value decomposition is employed to extract fault features from matrices. Finally, fault features are processed using a kernel extreme learning machine to obtain detection results. Offline experimental results show that the average detection accuracy of the proposed method is 98.97%, and the advancedness and adaptability of the proposed method are verified by comparing it with different methods. The proposed method and comparison methods are implemented in a microprogrammed control unit (MCU) for online experiments, further confirming that the detection speed and detection accuracy of the proposed method are reliable.