Detection for Abnormal Commutation Process State of Converter Based on Temporal and Amplitude Characteristics of AC Current

Li, Xiaohua; Li, Jiewen; Li, Hao; Yin, Shanshan; Cai, Zexiang

doi:10.1109/tpwrd.2022.3204621

Cited by 4 publications

(3 citation statements)

References 16 publications

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“…The existing research methods for detecting abnormal electricity usage can be roughly divided into two types: traditional detection methods and data mining based detection methods. Traditional detection methods usually rely on manual on-site inspections, state estimation, and the rate of change of load curves for detection [19]. These detection schemes are often inefficient and costly.…”

Section: A Matrix Completion Data Protection Methods For Detecting Ab...mentioning

confidence: 99%

“…The calculation formula for the outlier factor i o is as follows (19). The value range of outlier factor i o is between 0 and 1, and values greater than 0.95 are considered outliers and separated from other data.0.95 is the conventional confidence level of Dixon detection method for outlier detection, suitable for binary classification problems in power grid anomaly detection [34].It continues to use the improved Kmeans algorithm to cluster and remove outliers on the remaining data after removing outliers, repeating the process until each cluster no longer has overlapping parts.…”

Section: ˆ( )mentioning

confidence: 99%

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Abnormal Electricity Consumption Detection Method for Smart Grid Using Fusion Matrix Completion and Improved Clustering Algorithm

Zhang,

Zhu

2024

IEEE Access

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With the application and development of new technologies such as the Internet of Things and big data in the energy field, smart grids have gradually become the main trend for the future development of the power system. However, frequent communication between various participants in the smart grid can lead to higher privacy leakage risks, and malicious attackers can cause economic losses and security accidents to the smart grid by injecting abnormal power data. To address the issues of data privacy protection and anomaly detection in smart grids, this study proposed a matrix completion based smart grid data privacy protection scheme and an unsupervised learning based smart grid anomaly data detection scheme. A matrix completion based smart grid data privacy protection scheme was proposed to address the issue of data privacy protection between different trust domains in the smart grid. This scheme repaired missing elements in the relevant matrix through matrix completion, and increased random perturbations by adding noise with the same statistical characteristics as the original data to achieve data privacy protection. To address the issue of missing power data labels and inaccurate principal component analysis caused by abnormal data, an improved K-means clustering algorithm was used to remove outliers and optimize the results, to achieve accurate detection of abnormal data. The experimental results showed that the proposed algorithm has an accuracy of 0.9, an F1 value of 0.7, and a Bayesian detection rate of 0.61, all of which are higher than other algorithms. The AUC value of the proposed method was 0.79, indicating that the scheme established in this chapter has certain advantages in detecting abnormal electricity consumption.

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Section: A Matrix Completion Data Protection Methods For Detecting Ab...mentioning

confidence: 99%

Section: ˆ( )mentioning

confidence: 99%

Abnormal Electricity Consumption Detection Method for Smart Grid Using Fusion Matrix Completion and Improved Clustering Algorithm

Zhang,

Zhu

2024

IEEE Access

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“…According to the literature [21], the current of the converter valve i VTm is obtained by using the three-phase current of the valve side.…”

Section: B Detection Principle Of Valve Current Temporal Featuresmentioning

confidence: 99%

A Location Method for AC Fault in LCC-HVDC System Based on Virtual Conduction Width Characteristics

Yin

2023

IEEE Access

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When an AC fault occurs in HVDC transmission system, the transient response change in the rate of change of current and voltage, which may lead to the unexpected action behavior of converter protection. This is attributed to the electrical quantity similarities between internal and external faults, leading to difficulties in fault location identification and reducing the sensitivity in protection measure activation. To realize internal and external fault identification and quickly deal with external faults, an AC fault detection method for LCC-HVDC systems based on virtual conduction width characteristics is proposed. First, by utilizing the ratio integral of the three-phase AC current on the valve side to the reference current, the virtual conduction width flowing through each valve in the sampling window is calculated. Second, according to the size relationship among virtual conduction widths, internal and external faults are identified. Third, after determining the external faults, the difference in the virtual conduction width for each valve branch in the sampling window is calculated to locate the fault phase. Finally, the simulation results based on EMTDC/PSCAD show that the proposed method can quickly identify internal and external faults and locate fault phases and is not affected by fault moment and fault resistance.

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Converter valve-level fault location method based on the temporal differences among horizontal and longitudinal valve states

Yin,

2023

International Journal of Electrical Power & Energy Systems

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Detection for Abnormal Commutation Process State of Converter Based on Temporal and Amplitude Characteristics of AC Current

Cited by 4 publications

References 16 publications

Abnormal Electricity Consumption Detection Method for Smart Grid Using Fusion Matrix Completion and Improved Clustering Algorithm

Abnormal Electricity Consumption Detection Method for Smart Grid Using Fusion Matrix Completion and Improved Clustering Algorithm

A Location Method for AC Fault in LCC-HVDC System Based on Virtual Conduction Width Characteristics

Converter valve-level fault location method based on the temporal differences among horizontal and longitudinal valve states

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