Series Dc arc fault detection and location in wind-solar-storage hybrid system based on variational mode decomposition

Li, Xin; Wang, Haoqi; Guo, Ping; Xiong, Wei; Huang, Jianan

doi:10.1016/j.epsr.2022.107991

Cited by 11 publications

(6 citation statements)

References 31 publications

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“…After processing, the SAF signal is decomposed into 10 IMFs. As the order of the decomposed IMFs increases, the frequency range it contains becomes larger and larger, that is, more and more fault information is contained [27]. The correlation coefficients of the IMF components of the current and voltage decomposed by NA-MEMD are shown in Tables 4 and 5.…”

Section: Feature Extraction Resultsmentioning

confidence: 99%

Series Arc Fault Detection under Vibration Condition Based on N-M-M-B

Liu,

Yang,

Wang

2024

Sensors

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Under the conditions of a mechanical fault in a motor, mechanical vibration of a specific frequency can be generated. The electrical contact points directly connected to the motor can vibrate at the same frequency. The electrical contact points with poor contact can easily produce a series arc fault under vibration conditions, which affects the reliability of the power supply. In order to detect the series arc fault under different vibration conditions, the arc fault generator is connected between the back end of the frequency converter and the motor. An arc fault experiment under different vibration conditions was carried out and the fault phase current and arc voltage signals were collected. In this paper, the noise-assisted multivariate empirical mode decomposition and the correlation coefficient between each intrinsic mode function are used to select the fault feature signals. Then, the reconstructed signal is input into the series arc fault model combining a multi-scale convolutional neural network and a bidirectional long short-term memory network for training. The research results show that the series arc fault detection method proposed in this paper can effectively detect the series arc fault and can preliminarily identify the type of motor fault causing the mechanical vibration of the motor; the model has good noise immunity and generalization.

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Section: Feature Extraction Resultsmentioning

confidence: 99%

Series Arc Fault Detection under Vibration Condition Based on N-M-M-B

Liu,

Yang,

Wang

2024

Sensors

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“…Fortescue transformation) has complex factors, but it is not suitable for asymmetric systems. The result of instantaneous symmetric component transformation is complex and has no definite meaning [24]. Biaxial transformation (R.H. Park transformation) is mostly used for synchronous motor analysis and the analysis of symmetrical faults, while E. Clarke transformation has a complex fault model when single-phase grounding is applied [25].…”

Section: Distribution Network Fault Criterion Based On Karrenbauer Tr...mentioning

confidence: 99%

Fault location of distribution network with distributed generation based on Karrenbauer transform and support vector machine regression

2024

Archives of Electrical Engineering

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As the capacity and scale of distribution networks continue to expand, and distributed generation technology is increasingly mature, the traditional fault location is no longer applicable to an active distribution network and "two-way" power flow structure. In this paper, a fault location method based on Karrenbauer transform and support vector machine regression (SVR) is proposed. Firstly, according to the influence of Karrenbauer transformation on phase angle difference before and after section fault in a low-voltage active distribution network, the fault regions and types are inferred preliminarily. Then, in the feature extraction stage, combined with the characteristics of distribution network fault mechanism, the fault feature sample set is established by using the phase angle difference of the Karrenbauer current. Finally, the fault category prediction model based on SVR was established to solve the problem of a single-phase mode transformation modulus and the indistinct identification of two-phase short circuits, then more accurate fault segments and categories were obtained. The proposed fault location method is simulated and verified by building a distribution network system model. The results show that compared with other methods in the field of fault detection, the fault location accuracy of the proposed method can reach 98.56%, which can enhance the robustness of rapid fault location.

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“…In this way a more comprehensive characterization can be obtained. Li et al [14] used VMD to decompose the voltage signal of a fault and used the time-frequency domain features at the time of no fault as thresholds. By comparing the time-frequency domain characteristics and threshold characteristics, the purpose of detecting and localizing SAF was achieved.…”

Section: Introductionmentioning

confidence: 99%

Three-phase series arc fault detection based on two-dimensional attention PoolFormer

Yu,

Zhang,

Zhao

et al. 2024

Meas. Sci. Technol.

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Series arc faults are a major causation of electrical fires. The complexity of load types in low-voltage distribution systems makes arc faults detection more challenging for three-phase circuits with inverters. To solve this problem, this paper proposes a detection method based on two-dimensional attention PoolFormer. Firstly, a low-voltage three-phase series arc faults data acquisition platform is built to collect the required data. The collected current signals are encoded as pictures through image mapping and projected into a more discriminative space, while increasing the magnitude of the dataset. Subsequently, the two-dimensional attention PoolFormer algorithm model is constructed to fully exploit the feature information between different fault categories. This model has multi-scale parallel convolution to extract features of input samples and perform information fusion. Considering also the ability to seize the location characteristics of fault information well, the two-dimensional attention is designed to be added inside the algorithm, to grasp the precise location information to enhance the performance of the algorithm. Finally, the dataset is fed into the two-dimensional attention PoolFormer model for training and testing. The results show that the accuracy of the method proposed in this paper can achieve 99.36%.

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Series Dc arc fault detection and location in wind-solar-storage hybrid system based on variational mode decomposition

Cited by 11 publications

References 31 publications

Series Arc Fault Detection under Vibration Condition Based on N-M-M-B

Series Arc Fault Detection under Vibration Condition Based on N-M-M-B

Fault location of distribution network with distributed generation based on Karrenbauer transform and support vector machine regression

Three-phase series arc fault detection based on two-dimensional attention PoolFormer

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