Integration and optimisation analysis of PLC and SCADA-HMI–IPC systems in intelligent power distribution monitoring

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Authors: Lingyun Gu, Fuping Wang
Cited by: 1
References: 35

Abstract

• IEPSHOM reached 96.2% fault detection with latency lowered to 174 ms. • Adaptive HMI cut operator response by 435 ms and sped task completion. • Hybrid rule–tree detection kept F1 at 97.9% with precision/recall > 96%. • Usability improved and NASA-TLX dropped, with fewer interface errors. • 90% of users preferred IEPSHOM and reported higher trust and clarity. Tahe use of Programmable Logic Controllers (PLCs), Supervisory Control and Data Acquisition (SCADA) systems, and Human-Machine Interface industrial PCs (HMI IPCs) is still limited by the interface latency, inconsistent terminology, and limited operator support. To address these shortcomings in intelligent power distribution monitoring, this research paper presents and justifies a new Integrated Edge-Aware PLC-SCADA-HMI Optimization Model (IEPSHOM) to overcome them. It is novel because it combines a rule-first, decision-tree-refinement step with an adaptive Human-Machine Interface (HMI) to minimize detection latency and enhance the quality of operator responses. The 4-feeder low-voltage substation testbed consisted of Siemens S7-1200 PLCs and adaptive HMI dashboards to compare IEPSHOM with a traditional PLC-SCADA architecture. The outcomes indicate that the accuracy of fault detection in IEPSHOM improved by 11.6 percent, the false-positive rate decreased by 5.5 percent, the average latency decreased by 321 ms to 174 ms, and the operator alert resolution time decreased. There was a 2.4–3.1-point increase in the usability rating, indicating the usefulness of adaptive interfaces in improving decision precision under challenging situations. These results show that the effectiveness of real-time monitoring and operators in digital substations can be enhanced with the help of IEPSHOM.

Fields of Study & Subjects

Industrial Automation and Control Systems Smart Grid Security and Resilience Power Systems and Technologies

Publication details

Journal / source
International Journal of Electrical Power & Energy Systems
Publisher
Elsevier BV
Volume
175
Pages
111597–111597
Publication date
25 January 2026
Language
EN