Multi-source data processing and fusion method for power distribution internet of things based on edge intelligence

TL;DR

This paper introduces an edge intelligence-based method for processing and fusing multi-source heterogeneous data in power distribution IoT, improving data uniformity and fusion performance for better distribution network management.

Contribution

It proposes a novel multi-source data processing and fusion architecture using Box-Cox transform and PCA for conflict resolution, enhancing data integration in PD-IoT systems.

Findings

Effective data normalization with Box-Cox transform Zscore.

Improved data fusion accuracy through conflict optimization.

Validated method on IEEE39 node system with positive results.

Abstract

With the rapid advancement of the Energy Internet strategy, the number of sensors within the Power Distribution Internet of Things (PD-IoT) has increased dramatically. In this paper, an edge intelligence-based PD-IoT multi-source data processing and fusion method is proposed to solve the problems of confusing storage and insufficient fusion computing performance of multi-source heterogeneous distribution data. First, a PD-IoT multi-source data processing and fusion architecture based on edge smart terminals is designed. Second, to realize the uniform conversion of various sensor data sources in the distribution network in terms of magnitude and order of magnitude. By introducing the Box-Cox transform to improve the data offset problem in the Zscore normalization process, a multi-source heterogeneous data processing method for distribution networks based on the Box-Cox transform Zscore is proposed. Then, the conflicting phenomena of DS inference methods in data source fusion are optimally handled based on the PCA algorithm. A multi-source data fusion model based on DS inference with conflict optimization is constructed to ensure the effective fusion of distribution data sources from different domains. Finally, the effectiveness of the proposed method is verified by an experimental analysis of an IEEE39 node system in a regional distribution network in China.