Machine Learning Based Fault Type Identification In the Active Distribution Network

Sun, Baicong; Zhang, Hengxu; Shi, Fang

doi:10.1109/itnec.2019.8729054

Cited by 9 publications

(2 citation statements)

References 9 publications

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“…For the two basic learners built herein, the ResNet18 model can extract the complex nonlinear features of the data set, and the LSTM model can extract the time series-related features. The secondary learner fuses and learns the complementary feature results extracted by the two basic learners to generate the final classification result [43]. The secondary classifier selected in this paper is the decision tree model.…”

Section: Construction Of the Secondary Data Sets And Fusion Modelsmentioning

confidence: 99%

Single-Phase Grounding Fault Types Identification Based on Multi-Feature Transformation and Fusion

Min

Xia

Meng

et al. 2022

Sensors

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The frequent occurrence of single-phase grounding faults affects the reliable operation of power systems. When a single-phase grounding fault occurs, it is difficult to accurately identify the fault type because of the weak characterization and subtle distinction between different fault types. Therefore, this paper proposes a single-phase grounding fault type identification method based on the multi-feature transformation and fusion. Firstly, the Hilbert–Huang transform (HHT) was used to preprocess the fault recorded wave data to highlight the characteristics between different fault types. Secondly, the deep learning model ResNet18 and the long short-term memory (LSTM) are designed to extract the complex abstract features and time-series correlation features from the preprocessed data set separately. Finally, it designs a fusion model to combine the advantages of heterogeneous models to identify the type of single-phase grounding fault. Experiments validate that the method is good at fully mining the characteristics of the fault types contained in the fault recorded wave data, so it can identify multiple types of faults with strong robustness and provide a reliable basis for the subsequent formulation of targeted fault-handling measures.

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Section: Construction Of the Secondary Data Sets And Fusion Modelsmentioning

confidence: 99%

Single-Phase Grounding Fault Types Identification Based on Multi-Feature Transformation and Fusion

Min

Xia

Meng

et al. 2022

Sensors

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“…Although this makes fault identification more challenging, it is more realistic. Another ML-based method proposed by [8] for fault identification in a distribution network uses the phasor form of the current and voltage. In their simulation, they took various low-and high-impedance faults into account, as they mentioned.…”

Section: Introductionmentioning

confidence: 99%

ML-Based Intermittent Fault Detection, Classification, and Branch Identification in a Distribution Network

Hojabri,

Nowak,

Papaemmanouil

2023

Energies

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The accurate detection and identification of intermittent cable faults are helpful in improving the reliability of the distribution system. This paper proposes intermittent fault detection and identification for distribution networks based on machine-learning (ML) techniques. For this reason, the IEEE 33 bus system is simulated in the radial and mesh topologies by considering all possible single- and three-phase electrical faults and limitations to collect high-resolution voltage and current waveforms. Moreover, this simulation investigates and considers various cases including low-impedance faults (LIFs) and high-impedance faults (HIFs) with a short and long duration. The collected data from the simulation are used for high-impedance intermittent fault detection, classification, and branch identification using eight supervised learning methods. A comparison between the accuracy and error of these ML classifiers shows that gradient booster (GB) and K-nearest neighbors (KNN) have the best performance for all three objectives. However, GB has a very high computation time compared to KNN.

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Artificial Neural Network for Fault Diagnosis in Active Distribution Network

Raha¹,

Biswas²

2022

Communications in Computer and Information Science

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Machine Learning Based Fault Type Identification In the Active Distribution Network

Cited by 9 publications

References 9 publications

Single-Phase Grounding Fault Types Identification Based on Multi-Feature Transformation and Fusion

Single-Phase Grounding Fault Types Identification Based on Multi-Feature Transformation and Fusion

ML-Based Intermittent Fault Detection, Classification, and Branch Identification in a Distribution Network

Artificial Neural Network for Fault Diagnosis in Active Distribution Network

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