Evaluation method of watt-hour meter manufacturer based on metrological big data clustering model

Abstract

The invention discloses an evaluation method of an electric energy meter manufacturer based on a metering big data clustering model, which comprises the following steps: acquiring the original data ofthe electric energy meter and screening the evaluation index of the quality of the electric energy meter; Gaussian mixture model clustering algorithm is used to extract the typical evaluation index data and obtain the clustering center to form the decision matrix of comprehensive quality evaluation of watt-hour meter, which is standardized according to the benefit type and cost type respectively.Based on the weight of AHP, entropy method, Pearson correlation coefficient method and variation coefficient method, the combination weights are obtained by the combination optimization model of index weights, and the decision matrices are weighted to obtain the comprehensive evaluation matrices of the quality of electric energy meters. The quality of watt-hour meter is evaluated comprehensivelyby the method of clip angle measurement, and the quality of each watt-hour meter supplier is sorted from high to low, and the evaluation results are obtained. It can provide support for accurate accounting of energy consumption and energy revenue.

Description

technical field [0001] The invention relates to the field of electric power systems, in particular to an evaluation method for electric energy meter manufacturers based on a metering big data clustering model. Background technique [0002] With the continuous deepening of power market reform, power marketing has been paid more and more attention by power companies. As a supporting link of power marketing, electric energy metering provides basic data for accurate calculation of electric energy consumption and electric energy revenue. The quality of the electric energy meter will have a direct impact on the data quality, application level of the electricity consumption information collection system and the benefit of the electric power enterprise. Therefore, it is of great practical significance to study the comprehensive quality assessment model and method of electric energy meters to improve the data quality of electric energy meters and the benefits of electric power enter...

AI Technical Summary

Problems solved by technology

Due to the different production batches and installation areas, there are many models and types of electric energy meters, so electric energy meters will generate a large amount of structured and unstructured measurement and fault alarm information, and traditional data processing methods are already difficult to handle.

In addition, non-quality factors such as environmental noise and electromagnetic interference may also cause problems such as bad data and empty data in the energy meter

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