A power line loss analysis method based on boost clustering

Chen, Junde; Zeb, Adnan; Sun, Yuena; Zhang, Defu

doi:10.1007/s11227-022-04777-w

Cited by 7 publications

(1 citation statement)

References 23 publications

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“…Power supply enterprises hope that through the calculation and analysis of power line loss, they can dynamically and accurately propose loss reduction targets for power line objects. Inaccurate user metering circuits, such as abnormal behavior of electricity consumption and inaccurate magnification of metering devices, are important reasons for the fluctuation of the power line loss rate [2][3][4][5]. In the intelligent analysis and modeling of abnormal power consumption, there are evaluation indicators like power, load, alarm, and line loss.…”

Section: Introductionmentioning

confidence: 99%

Research on Interpolation Method for Missing Electricity Consumption Data

Chen,

Yuan,

Chen

et al. 2024

CMC

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Missing value is one of the main factors that cause dirty data. Without high-quality data, there will be no reliable analysis results and precise decision-making. Therefore, the data warehouse needs to integrate high-quality data consistently. In the power system, the electricity consumption data of some large users cannot be normally collected resulting in missing data, which affects the calculation of power supply and eventually leads to a large error in the daily power line loss rate. For the problem of missing electricity consumption data, this study proposes a group method of data handling (GMDH) based data interpolation method in distribution power networks and applies it in the analysis of actually collected electricity data. First, the dependent and independent variables are defined from the original data, and the upper and lower limits of missing values are determined according to prior knowledge or existing data information. All missing data are randomly interpolated within the upper and lower limits. Then, the GMDH network is established to obtain the optimal complexity model, which is used to predict the missing data to replace the last imputed electricity consumption data. At last, this process is implemented iteratively until the missing values do not change. Under a relatively small noise level (α = 0.25), the proposed approach achieves a maximum error of no more than 0.605%. Experimental findings demonstrate the efficacy and feasibility of the proposed approach, which realizes the transformation from incomplete data to complete data. Also, this proposed data interpolation approach provides a strong basis for the electricity theft diagnosis and metering fault analysis of electricity enterprises.

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Section: Introductionmentioning

confidence: 99%