An Intelligent Decision-Support Framework for Substation Safety Using LSTM-OOA Optimization and 3D Virtual Monitoring

Zeyuan Lin; Letian Wang; Fan Zhang; Wei Li

doi:10.31181/dmame8120251465

Authors

Zeyuan Lin Shandong Electric Power Engineering Consulting Institute Corp., Ltd., Jinan Shandong 250013, China https://orcid.org/0009-0006-7896-9024
Letian Wang Shandong Electric Power Engineering Consulting Institute Corp., Ltd., Jinan Shandong 250013, China. https://orcid.org/0009-0006-7896-9024
Fan Zhang Shandong Electric Power Engineering Consulting Institute Corp., Ltd., Jinan Shandong 250013, China. https://orcid.org/0009-0006-7896-9024
Wei Li Shandong Electric Power Engineering Consulting Institute Corp., Ltd., Jinan Shandong 250013, China https://orcid.org/0009-0006-7896-9024

DOI:

https://doi.org/10.31181/dmame8120251465

Keywords:

3D Virtual Environment, Decision Making, Multi-Source Data Fusion, Intelligent Safety Control, LSTM-Orangutan Optimization Algorithm (OOA)

Abstract

The global expansion of power infrastructure has highlighted the growing necessity for advanced safety control mechanisms that exceed the capabilities of conventional manual systems, particularly as the number of substations and their operational complexities continue to rise. This study presents an intelligent integration framework that combines a three-dimensional (3D) virtual environment with automated operation tickets to enhance substation safety and intelligent control. The system adopts a multi-source heterogeneous information fusion approach to achieve improved operational performance and enhanced reliability. Central to the architecture is a 3D visualisation platform for substations, which facilitates real-time simulation of substation operations, scheme login access, equipment status monitoring, and automated management of operation tickets. Real-time data processing and execution of decisions are achieved through the deployment of multiple smart hardware components, including multi-dimensional sensing devices, high-efficiency wearable tools, pre-aligned rods, and substation inspection robots. Substation predictive maintenance capabilities are strengthened through a forecasting model that integrates Long Short-Term Memory (LSTM) neural networks with the Orangutan Optimisation Algorithm (OOA), specifically targeting space-time data prediction of transformer oil temperatures. The Attention-LSTM model demonstrates superior short-term predictive precision, enabling early fault detection and automated diagnostics. To enhance strategic decision-making within the intelligent control system, the Analytic Hierarchy Process (AHP) is incorporated to establish prioritised action plans. Experimental validation confirms the system’s ability to generate timely alerts regarding abnormal equipment conditions within substations. The proposed integrated safety control framework represents a comprehensive and practical solution for substation automation, significantly improving operational effectiveness, system reliability, safety management, and data-informed decision-making

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