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Integrated Intelligent Maintenance Architectures for Hydro-Generator Excitation Systems: A Knowledge-Driven and Machine-Assisted Diagnostic Paradigm
Issue Vol. 2 No. 01 (2025): VOLUME 02 ISSUE 01 --- Section Articles
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Abstract
The reliability and operational continuity of hydroelectric power plants depend critically on the performance of hydro-generator excitation systems, which regulate voltage stability, reactive power control, and transient response under dynamic grid conditions. Over the past several decades, the evolution of intelligent maintenance systems has transformed the way complex electromechanical infrastructures are monitored, diagnosed, and sustained. This research article develops an extensive theoretical and analytical investigation into intelligent, knowledge-based, and machine-assisted maintenance architectures for hydro-generator excitation systems, with particular emphasis on real-time embedded maintenance frameworks. Grounded strictly in established scholarly literature, the study synthesizes developments in expert systems, knowledge engineering, machine learning, and real-time diagnostics within power generation environments. A central contribution of this work is the conceptual integration of embedded real-time maintenance principles demonstrated in early excitation system research with later advances in expert systems and machine intelligence applied to industrial diagnostics.
The article critically examines how traditional rule-based expert systems, domain ontologies, and knowledge representation methods have been progressively augmented by data-driven learning techniques, while retaining interpretability and operational trustworthiness required in safety-critical energy infrastructures. Drawing on seminal works in artificial intelligence, knowledge engineering, and power plant diagnostics, the study articulates a comprehensive methodological framework for designing intelligent maintenance systems that operate continuously, adapt to evolving system states, and support human decision-makers without displacing them. The discussion highlights the enduring relevance of early embedded diagnostic architectures for excitation systems, particularly those emphasizing real-time signal acquisition, fault symptom mapping, and expert inference mechanisms, and situates them within contemporary debates on human–machine governance and intelligent automation.
By offering a deeply elaborated, non-summarized, and theoretically expansive analysis, this article addresses a significant literature gap: the absence of an integrative, conceptually unified account of intelligent maintenance systems for hydro-generator excitation subsystems that bridges classical expert system design with modern machine-assisted reasoning. The findings underscore that sustainable innovation in power plant maintenance does not arise from algorithmic novelty alone, but from coherent system architectures that harmonize embedded sensing, domain knowledge, and adaptive intelligence within robust socio-technical frameworks.
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References
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