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

Hou, S.; Xu, Yan; Guo, Wenfei

doi:10.3390/pr10102127

Cited by 4 publications

(2 citation statements)

References 30 publications

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“…Unified power fault detection methods bring huge costs (Chen et al, 2022a;Wang, 2022;Yan et al, 2022), so now, many works use artificial intelligence methods to detect, classify, and locate power faults (Hou et al, 2022;Ma et al, 2022). For example, Yuvaraja et al (2022) examined the effect of smart grid systems by implementing the artificial intelligence technique with application of renewable energy sources (Yuvaraja et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

An improved ELM-WOA–based fault diagnosis for electric power

Fang¹,

Liu

Wang

2023

Front. Energy Res.

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Due to its fast learning speed, the extreme learning machine (ELM) plays a very important role in the real-time monitoring of electric power. However, the initial weights and thresholds of the ELM are randomly selected, therefore it is difficult to achieve an optimal network performance; in addition, there is a lack of distance selection when detecting faults using artificial intelligence algorithms. To solve the abovementioned problem, we present a fault diagnosis method for microgrids on the basis of the whale algorithm optimization–extreme learning machine (WOA-ELM). First, the wavelet packet decomposition is used to analyze the three-phase fault voltage, and the energy entropy of the wavelet packet is calculated to form the eigenvector as the data sample; then, we use the original ELM model coupled with the theory of distance selection to locate faults and compared it with the SVM method; finally, the whale algorithm is used to optimize the input weight and hidden layer neuron threshold of the ELM, i.e., the WOA-ELM model, which solves the problem of the random initialization of the input weight and hidden layer neuron threshold that easily affects the network performance, further improves the learning speed and generalization ability of the network, and is conducive to the overall optimization. The results show that 1) the accuracy of selecting the data according to the fault distance is twice that of not selecting data according to it; 2) compared with the BP neural network, RBF neural network, and ELM, the fault diagnosis model based on the WOA-ELM has a faster learning speed, stronger generalization ability, and higher recognition accuracy; and 3) after optimization of the WOA, the WOA-ELM can improve 22.5% accuracy in fault detection when compared to the traditional ELM method. Our results are of great significance in improving the security of smart grid.

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Section: Introductionmentioning

confidence: 99%

An improved ELM-WOA–based fault diagnosis for electric power

Fang¹,

Liu

Wang

2023

Front. Energy Res.

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“…Most of the 3~66 kV power supply systems in China's petrochemical enterprises adopt neutral point non-effective grounding. Among them, due to the compensation effect of the arc suppression coil, the resonant grounding system has the disadvantages of the fault characteristic quantity not being obvious, and the fault detection being difficult after the single-phase grounding fault occurs [1][2][3][4][5], resulting in a worse faulty feeder detection effect [6,7].…”

Section: Introductionmentioning

confidence: 99%

Faulty Feeder Detection Based on Grey Correlation Degree of Adaptive Frequency Band in Resonant Grounding Distribution System

et al. 2023

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Due to the particularity of their process, petrochemical enterprises have high requirements for the reliability of power supply. If a large-scale blackout occurs due to a grounding fault, it will pose a huge threat to safe production. When the resonant grounding system of petrochemical enterprises faults, due to the complex fault process and weak fault signal, it is difficult to accurately detect the faulty feeder by traditional methods. This paper presents a new method of grey correlation degree based on adaptive frequency band. Firstly, the transient zero-sequence current of each feeder is decomposed by coif5 wavelet, and the low frequency band a5 (power frequency component) and high frequency band d1, d2 (noise signal) are removed. By stacking all of the remaining frequency band signals to construct the wavelet area matrix, the faulty feeder detection characteristic scale and the first faulty feeder detection result are obtained. Secondly, based on the faulty feeder detection characteristic scale, the second faulty feeder detection result is obtained by the average grey correlation degree matrix, which detects the faulty feeder according to the waveform correlation degree. Finally, the final faulty feeder detection result is obtained by equal weight voting. In MATLAB/Simulink, the 10 kV resonant grounding system of petrochemical enterprises is modeled. A large number of simulation results show that the faulty feeder detection method is not affected by the initial phase angle (0°, 45° and 90°), transition resistance (10 Ω, 100 Ω and 1000 Ω), fault distance (1 km, 8 km and 15 km) and overcompensation degree (5%, 8% and 10%), and has good sensitivity.

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Parkinson's disease detection and stage classification: quantitative gait evaluation through variational mode decomposition and DCNN architecture

Elumalai,

Sandhiya

et al. 2024

Connection Science

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

Cited by 4 publications

References 30 publications

An improved ELM-WOA–based fault diagnosis for electric power

An improved ELM-WOA–based fault diagnosis for electric power

Faulty Feeder Detection Based on Grey Correlation Degree of Adaptive Frequency Band in Resonant Grounding Distribution System

Parkinson's disease detection and stage classification: quantitative gait evaluation through variational mode decomposition and DCNN architecture

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