A Process-Driven Low-Voltage Acceptance Automation Platform with Visual Workflow Orchestration and Machine Learning Validation

Dandan Qi; Liyu Huang; Jingxin Xia; Jing Zhang; Shan Cha; Zhengyang Peng

doi:10.3791/72580

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Research Article

A Process-Driven Low-Voltage Acceptance Automation Platform with Visual Workflow Orchestration and Machine Learning Validation

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DOI:

10.3791/72580

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September 3rd, 2026

Dandan Qi¹ , Liyu Huang¹ , Jingxin Xia¹ , Jing Zhang¹ , Shan Cha¹ , Zhengyang Peng¹

¹Metrology Center, Guangzhou Power Supply Bureau, Guangdong Power Grid Co., Ltd.

Summary

The proposed Low-Voltage Acceptance Automation Platform integrates the Internet of Things (IoT), visual workflow orchestration, edge and cloud computing, feature engineering, and machine learning to automate low-voltage (LV) system acceptance testing. The platform enables intelligent fault detection, real-time decision-making, automated acceptance, high accuracy, low operational latency, and scalable deployment.

Abstract

The rapid expansion of low-voltage (LV) networks and their integration with distributed energy resources requires intelligent and automated management solutions. Cloud-edge collaborative Internet of Things (IoT) platforms support real-time monitoring, control, and data acquisition. However, existing platforms generally lack workflow automation, visual process orchestration, user-guided decision support, and comprehensive validation. Consequently, they do not provide process-driven solutions for automated LV network acceptance testing. This study presents the design and evaluation of a Low-Voltage Acceptance Automation Platform based on a visualized process canvas. The proposed platform adopts a process-driven architecture in which acceptance workflows are visually created, managed, and executed. The visual process canvas transforms conventional static monitoring into dynamic workflow automation by enabling real-time workflow execution, validation, and decision-making. The framework incorporates the Open LV Network & Smart Meter dataset to support realistic modeling of electrical load behavior. The workflow includes IoT-based data acquisition, data preprocessing, feature engineering, workflow orchestration using the visual process canvas, machine learning-based validation, and real-time dashboard visualization. The proposed framework achieved a fault detection rate of 98.4%, a receiver operating characteristic area under the curve (ROC-AUC) of 0.968, and an operational decision latency of 31 ms, outperforming traditional cloud-centric and IoT-based baseline approaches by approximately 30%–40%.

Introduction

Industry 4.0 (I4.0)¹ is enabled by several emerging technologies, among which the Industrial Internet of Things (IIoT) is one of the most important. The IIoT refers to the application of Internet of Things (IoT) technologies in industrial environments, where interconnected devices and machines communicate primarily through machine-to-machine interactions. Because failures in industrial systems may result in significant operational losses, IIoT applications require highly reliable communication while generating substantially larger volumes of data than conventional IoT systems². Additionally, some studies regard the IIoT ....