# Multisource Heterogeneous Sensor Processing Meets Distribution Networks: Brief Review and Potential Directions

## TL;DR

This paper reviews how sensor data in power networks is growing rapidly and explores new methods to handle it for better reliability and efficiency.

## Contribution

The paper proposes future directions for integrating traditional and AI-based methods to process multisource heterogeneous sensor data in distribution networks.

## Key findings

- Sensor data in distribution networks is growing rapidly due to IoT and automation.
- Traditional methods struggle with the scale and complexity of multisource heterogeneous data.
- Combining conventional stability with AI adaptability can improve data processing and reliability assessment.

## Abstract

The progressive proliferation of sensor deployment in distribution networks (DNs), propelled by the dual drivers of power automation and ubiquitous IoT infrastructure development, has precipitated exponential growth in real-time data generated by multisource heterogeneous (MSH) sensors within multilayer grid architectures. This phenomenon presents dual implications: large-scale datasets offer an enhanced foundation for reliability assessment and dispatch planning in DNs; the dramatic escalation in data volume imposes demands on the computational precision and response speed of traditional evaluation approaches. The identification of critical influencing factors under extreme operating conditions, coupled with dynamic assessment and prediction of DN reliability through MSH data approaches, has emerged as a pressing challenge to address. Through a brief analysis of existing technologies and algorithms, this article reviews the technological development of MSH data analysis in DNs. By integrating the stability advantages of conventional approaches in practice with the computational adaptability of artificial intelligence, this article focuses on discussing key approaches for MSH data processing and assessment. Based on the characteristics of DN data, e.g., diverse sources, heterogeneous structures, and complex correlations, this article proposes several practical future directions. It is expected to provide insights for practitioners in power systems and sensor data processing that offer technical inspirations for intelligent, reliable, and stable next-generation DN construction.