Research on Intelligent Monitoring Panel System for Thermal Power Plants in the Context of Big Data

Abstract

The transformation of the energy structure continues to deepen, with the role of thermal power progressively evolving from a primary power source to a flexible regulating source. This shift imposes higher standards for its operational safety, economy, and flexibility. The traditional monitoring panel model relies heavily on the experience of operators and commonly faces prominent challenges such as heavy monitoring workload, delayed warning responses, and one-sided analysis and decision-making. This paper focuses on the pathway for empowering the intelligent upgrade of thermal power plant monitoring panel systems through big data technology. It begins by analyzing the limitations of traditional monitoring and the characteristics of plant data in the big data context. Subsequently, it constructs a four-layer overall architecture encompassing "data collection, computation & storage, intelligent analysis, and application". The paper then provides an in-depth interpretation of core technologies, including real-time data stream processing, massive historical data mining, predictive equipment health management, and operational optimization with intelligent decision-making. Finally, it prospects the system's application potential in areas like proactive early warning, collaborative optimization, and adaptive control, while discussing the associated challenges and development trends. The research indicates that the intelligent monitoring panel system serves as the core vehicle for the digital and intelligent transformation of thermal power plants, playing a pivotal supporting role in enhancing plant operational efficiency and market competitiveness.