Abstract
AbstractThis study focuses on the fault diagnosis of a hydroelectric generation system with hydraulic‐mechanical‐electric structures. To achieve this analysis, a methodology combining Bayesian network approach and fault diagnosis expert system is presented, which enables the time‐based maintenance to transform to the condition‐based maintenance. First, fault types and the associated fault characteristics of the generation system are extensively analyzed to establish a precise Bayesian network. Then, the Noisy‐Or modeling approach is used to implement the fault diagnosis expert system, which not only reduces node computations without severe information loss but also eliminates the data dependency. Some typical applications are proposed to fully show the methodology capability of the fault diagnosis of the hydroelectric generation system.
Highlights
2015 United Nations Climate Change Conference promised that the raise of global warming is almost 2°C compared to pre‐industrial levels, which greatly promotes the electricity generation to turn to renewable energy such as hydropower generations.[1]
A complete Bayesian network (BN) is comprised of nodes, connecting arrows and the conditional probability tables (CPTs), which is represented by a directed acyclic graph (DAG)
A complete Bayesian network fault diagnosis model of the generating system is implemented that takes into consideration the comprehensive knowledge of the vibration fault types and the associated fault characteristics
Summary
2015 United Nations Climate Change Conference promised that the raise of global warming is almost 2°C compared to pre‐industrial levels, which greatly promotes the electricity generation to turn to renewable energy such as hydropower generations.[1]. The economic benefit and carbon dioxide mitigation of such hydroelectric generating systems are well known to the general public,[6,7,8,9,10,11] but the stability and safety impacts of themselves still require enough attentions
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