Power grid fault diagnosis model based on the time series density distribution of warning information

Zhang, Xu; Ding, Ruiting; Wang, Zirui; Guo, Zixing; Liu, Bowen; Wei, Juan

doi:10.1016/j.ijepes.2022.108774

Cited by 3 publications

(3 citation statements)

References 16 publications

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“…Ref. [8] proposed a graph-encoding-based method to provide a unified data representation for deep learning models. Ref.…”

Section: Introductionmentioning

confidence: 99%

A Novel Reduced-Order Analytical Fault Diagnosis Model for Power Grid

Lin,

Wang,

et al. 2024

IEEE Access

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Power grid fault diagnosis methods based on analytical models possess advantages such as logical rigor, strong interpretability, and high applicability. However, the existing design of their objective functions is presented as a 0-1 integer nonlinear programming model, making it difficult to approximate the optimal solution. To address this issue, this paper proposes an improvement to the expression of the second backup protection expectation involved in the diagnostic model. Thus, the order of the objective function is successfully reduced, transforming the original 0-1 integer nonlinear programming model into a constrained 0-1 integer linear programming model, which can be efficiently solved by typical commercial solvers based on a linear integer programming framework. Numerical results demonstrate that the solution time of the proposed linearized model is lower than that of existing nonlinear programming models, and the diagnostic accuracy of the proposed model is higher than that of other state-of-the-art methods.

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“…Ref. [8] proposed a graph-encoding-based method to provide a unified data representation for deep learning models. Ref.…”

Section: Introductionmentioning

confidence: 99%

A Novel Reduced-Order Analytical Fault Diagnosis Model for Power Grid

Lin,

Wang,

et al. 2024

IEEE Access

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“…With the rapid development of modern economy, the consumption of energy is increasing high (Zhang et al, 2023). The direct consumption and waste of nonrenewable energy are particularly serious.…”

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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“…In this method, the k-means algorithm [10,11] is widely used and recognized, but it requires predefining the number of clusters and is sensitive to the initial points, which can make diagnosis difficult and hinder the detection of outliers. The density-based approach identifies outliers based on the density distribution of the data [12,13]. Zheng et al [14] referenced a study on automatic modulation classification, which employed spectrum interference and data augmentation techniques to expand the training dataset, potentially improving the anomaly trend analysis.…”

Section: Introductionmentioning

confidence: 99%

An Expert System Based on Data Mining for a Trend Diagnosis of Process Parameters

Wang,

Zhang

et al. 2023

Processes

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In order to diagnose abnormal trends in the process parameters of industrial production, the Expert System based on rolling data Kernel Principal Component Analysis (ES-KPCA) and Support Vector Data Description (ES-SVDD) are proposed in this paper. The expert system is capable of identifying large-scale trend changes and abnormal fluctuations in process parameters using data mining techniques, subsequently triggering timely alarms. The system consists of a rule-based assessment of process parameter stability to evaluate whether the process parameters are stable. Also, when the parameters are unstable, the rolling data-based KPCA and SVDD methods are used to diagnose abnormal trends. ES-KPCA and ES-SVDD methods require adjusting seven threshold parameters during the offline parameter adjustment phase. The system obtains the adjusted parameters and performs a real-time diagnosis of process parameters based on the set diagnosis interval during the online diagnosis phase. The ES-KPCA and ES-SVDD methods emphasize the real-time alarms and the first alarm of process parameter abnormal trends, respectively. Finally, the system validates the experimental data from UniSim simulation and a chemical plant. The results show that the expert system has an outstanding diagnostic performance for abnormal trends in process parameters.

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Power grid fault diagnosis model based on the time series density distribution of warning information

Cited by 3 publications

References 16 publications

A Novel Reduced-Order Analytical Fault Diagnosis Model for Power Grid

A Novel Reduced-Order Analytical Fault Diagnosis Model for Power Grid

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

An Expert System Based on Data Mining for a Trend Diagnosis of Process Parameters

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