Topology Identification Method of Low-voltage Distribution Network Based on Improved Pearson Correlation Coefficient Method

Qin, Liwen; Huang, Weixiang; Guo, Zhicheng; Zhou, Yangjun; Zhang, Bin

doi:10.1109/ciycee53554.2021.9676722

Cited by 7 publications

(4 citation statements)

References 3 publications

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“…To evaluate the effectiveness of the proposed method in identifying user-transformer relationships, it was compared with the correlation analysis method [17]. We compared the accuracy of the correlation analysis method with the method proposed in this study.…”

Section: User-transformer Relationship Identificationmentioning

confidence: 99%

“…The correlation analysis method determines the connection relationship between the user and transformer or the user and phase by analyzing voltage correlation coefficients. Reference [17] proposed a Kalman filter and a Pearson correlation coefficient method for identifying the topology relationship of low-voltage distribution networks. Reference [18] proposed calibrating the topology of low-voltage distribution networks based on the Pearson correlation coefficient and the K-Nearest Neighbor (KNN) algorithm.…”

Section: Introductionmentioning

confidence: 99%

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Topology Identification of Low-Voltage Distribution Network Based on Deep Convolutional Time-Series Clustering

Jiang

2023

Energies

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Accurate topology relationships of low-voltage distribution networks are important for distribution network management. However, the topological information in Geographic Information System (GIS) systems for low-voltage distribution networks is prone to errors such as omissions and false alarms, which can have a heavy impact on the effective management of the networks. In this study, a novel method for the identification of topology relationships, including the user-transformer relationship and the user-phase relationship, is proposed, which is based on Deep Convolutional Time-Series Clustering (DCTC) analysis. The proposed DCTC method fuses convolutional autoencoder and clustering layers to perform voltage feature representation and clustering in a low-dimensional feature space simultaneously. By jointly optimizing the clustering process via minimizing the sum of the reconstruction loss and clustering loss, the proposed method effectively identifies the network topology relationships. Analysis of examples shows that the proposed method is correct and effective.

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Section: User-transformer Relationship Identificationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Topology Identification of Low-Voltage Distribution Network Based on Deep Convolutional Time-Series Clustering

Jiang

2023

Energies

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“…A large amount of research has shown that there is a high correlation between neutral DC, vibration and noise under the condition of DC bias [26,27]. For evaluating the Pearson correlation coefficient, the criteria are shown in Table 1 [28]. Therefore, in view of the situation of mutated abnormal data in a certain period of time, the correlation of data for synchronous monitoring is defined as an index, and the Pearson correlation coefficient between the monitoring data is calculated to determine whether there is abnormality in the monitoring data.…”

Section: Synchronous Monitoring Of Data Correlationmentioning

confidence: 99%

Identification of Abnormal Data for Synchronous Monitoring of Transformer DC Bias Based on Multiple Criteria

Kou,

Lin,

Wang

et al. 2023

Sensors

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Seriously abnormal data exist in the synchronous monitoring data of transformer DC bias, which causes serious data feature contamination and even affects the identification of transformer DC bias. For this reason, this paper aims to ensure the reliability and validity of synchronous monitoring data. This paper proposes an identification of abnormal data for the synchronous monitoring of transformer DC bias based on multiple criteria. By analyzing the abnormal data of different types, the characteristics of abnormal data are obtained. Based on this, the abnormal data identification indexes are introduced, including gradient, sliding kurtosis and Pearson correlation coefficient. Firstly, the Pauta criterion is used to determine the threshold of the gradient index. Then, gradient is used to identify the suspected abnormal data. Finally, the sliding kurtosis and Pearson correlation coefficient are used to identify the abnormal data. Data for synchronous monitoring of transformer DC bias in a certain power grid are used to verify the proposed method. The results show that the accuracy of the proposed method in identifying mutated abnormal data and zero-value abnormal data is claimed to be 100%. Compared with traditional abnormal data identification methods, the accuracy of the proposed method is significantly improved.

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“…The literature [15,16] extracted voltage data features by K-means clustering and analyzed the users to be detected using the improved Pearson correlation coefficient algorithm. However, the energy meters near the head of the low-voltage bus of the distribution substation but in different outgoing lines are close to the electrical distance from the bus.…”

Section: Introductionmentioning

confidence: 99%

Topology identification of low-voltage station based on robust total least squares method

Yuan,

Cao,

Wang

2023

Second International Conference on Sustainable Technology and Management (ICSTM 2023)

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The low-voltage station area has a long-standing problem of line topology confusion, which leads to difficulties in locating faults in the distribution network and making maintenance difficult. In order to accurately identify the topological relationships of low-voltage stations, we proposes a low-voltage station topology identification algorithm based on the robust total least squares. We use the least squares method as the theoretical basis. Anti-variance estimation is added to the algorithm to further improve its ability to suppress coarse variances and thus improve recognition accuracy. The algorithm collects the active current values of customer meters in the station area at multiple time points. These data are used as the basis for establishing matrix equations to identify the topological relationships in the station area. Finally, the algorithm is compared with the least squares and weighted least squares to verify the accuracy of the algorithm. The results show that the Robust Total Least Squares has high accuracy for the topological identification of low-voltage station and is suitable for the application requirements of low-voltage station area.

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Topology Identification Method of Low-voltage Distribution Network Based on Improved Pearson Correlation Coefficient Method

Cited by 7 publications

References 3 publications

Topology Identification of Low-Voltage Distribution Network Based on Deep Convolutional Time-Series Clustering

Topology Identification of Low-Voltage Distribution Network Based on Deep Convolutional Time-Series Clustering

Identification of Abnormal Data for Synchronous Monitoring of Transformer DC Bias Based on Multiple Criteria

Topology identification of low-voltage station based on robust total least squares method

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