Optimal Denoising and Feature Extraction Methods Using Modified CEEMD Combined with Duffing System and Their Applications in Fault Line Selection of Non-Solid-Earthed Network

Abstract

As the non-solid-earthed network fails, the zero-sequence current of each line is highly non-stationary, and the noise component is serious. This paper proposes a fault line selection method based on modified complementary ensemble empirical mode decomposition (MCEEMD) and the Duffing system. Here, based on generalized composite multiscale permutation entropy (GCMPE) and support vector machine (SVM) for signal randomness detection, the complementary ensemble empirical mode decomposition is modified. The MCEEMD algorithm has good adaptability, and it can restrain the modal aliasing of empirical mode decomposition (EMD) at a certain level. The Duffing system is highly sensitive when the frequency of the external force signal is the same as that of the internal force signal. For automatically identifying chaotic characteristics, by using the texture features of the phase diagram, the method can quickly obtain the numerical criterion of the chaotic nature. Firstly, the zero-sequence current is decomposed into a series of intrinsic mode functions (IMF) to complete the first noise-reduction. Then an optimized smooth denoising model is established to select optimal IMF for signal reconstruction, which can complete the second noise-reduction. Finally, the reconstructed signal is put into the Duffing system. The trisection symmetry phase estimation is used to determine the relative phase of the detection signal. The faulty line in the non-solid-earthed network is selected with the diagram outputted by the Duffing system.

A visual faulty feeder detection method for power distribution network based on spatial image generation and deep learning

“…The conventional fault diagnosis methods cannot guarantee the accuracy of locating fault points, and the difficulty of manual line patrol increases exponentially. Therefore, it is necessary to study fault location technology for the secure and stable operation of the distribution network [4].…”

Distribution Network Fault-Line Selection Method Based on MICEEMDAN–Recurrence Plot–Yolov5

“…However, this method is unavailable when grounding the neutral point via the arc extinguishing coil. The authors of [14] proposed a fault-line selection method based on a chaotic system, which is not affected by fault type, transition resistance, etc. However, since the chaotic system is susceptible to abnormal signals, and the actual distribution network contains many noisy signals, inputting the signals directly into the chaotic system will cause misclassification, and the system's robustness is poor.…”

Fault identification method for distribution network based on parameter optimized variational mode decomposition and convolutional neural network

“…The neutral point ineffectively grounding power system has been adopted universally in the distribution network, namely a non-solid-earthed network [1]. The network structure is increasingly complicated in this system, and the operating states are various [2].…”