Artificial Intelligence Application in Power Generation Industry: Initial considerations

International Conference on Advances in Renewable Energy and Technologies 2016

Ismail, Rahmat Izaizi B.^1 ; Ismail Alnaimi, Firas B.^1 ; Al-Qrimli, Haidar F.^2

Power Generation Research Centre, University Tenaga Nasional, Kajang

43000, Malaysia^1

Department of Mechanical Engineering, Curtin University Sarawak, Miri

98009, Malaysia^2

关键词: Fault detection and diagnosis;    Faults detection;    Intelligent condition monitoring;    Mitigation measures;    Nonlinear iterations;    Plant operations;    Power generation industries;    Problem-solving abilities;   

Others  :  https://iopscience.iop.org/article/10.1088/1755-1315/32/1/012007/pdf
DOI  :  10.1088/1755-1315/32/1/012007

来源: IOP

【 摘 要 】

With increased competitiveness in power generation industries, more resources are directed in optimizing plant operation, including fault detection and diagnosis. One of the most powerful tools in faults detection and diagnosis is artificial intelligence (AI). Faults should be detected early so correct mitigation measures can be taken, whilst false alarms should be eschewed to avoid unnecessary interruption and downtime. For the last few decades there has been major interest towards intelligent condition monitoring system (ICMS) application in power plant especially with AI development particularly in artificial neural network (ANN). ANN is based on quite simple principles, but takes advantage of their mathematical nature, non-linear iteration to demonstrate powerful problem solving ability. With massive possibility and room for improvement in AI, the inspiration for researching them are apparent, and literally, hundreds of papers have been published, discussing the findings of hybrid AI for condition monitoring purposes. In this paper, the studies of ANN and genetic algorithm (GA) application will be presented.