AHFE Open AccessOptimization of turbine generator through vibration damping for maximum service life in power plants
Abstract
Power plant condition monitoring data is essential in identifying unscheduled maintenance needs. The data obtained from monitoring the condition of a power plant over numerous years of operation indicates that the primary reason for the failure of turbo generators due to vibrations is the misalignment of the turbine centreline. It is crucial to identify problems with steam turbines to prevent load losses and boost the operational reliability of a turbo generator. This paper presents the vibrational characteristics of a 500 MW turbo generator and the performance boost attained through optimized turbine maintenance. Shaft relative vibrations were analyzed at run-up at 500 rpm with no load and at 3000 rpm with approximately 420 MW. The study found that the highest absolute pedestal vibration levels were reduced by 8.5% as a result of maintenance optimization.
Keywords: Vibration, turbo generator, turbine, maintenance, performance improvement, optimization, retrofit
DOI: 10.54941/ahfe1003783
Cite this paper
More from this volume
- Explaining algorithmic decisions: design guidelines for explanations in User Interfaces
- Value-driven architecture enabling new interaction models in Society 5.0
- The Removal of Irrelevant Human Factors in a Multi-Review Corpus through Text Filtering
- Accounting trustworthiness requirements in Service Systems Engineering
- Analysis of the behavior of the floating systems used for boundary of river-sea recreational activities area
- A Data retrieval Model for Distributed Heterogeneous Pharmacy Information Sources
- Short-time taxi demand prediction based on Transformer-LSTM in integrated transportation hub
- Hackathon-based software development: Lessons learned from an internal corporate hackathon
- Improving Internet Advertising Using Click – Through Rate Prediction
- Crowdsourcing for Second Language Learning
- Evaluating embedded semantics for accessibility description of web crawl data
- ETL and ML Forecasting Modeling Process Automation System