Using Machine Learning Tools to Predict Compressor Stall Hipple, Samuel Bonilla-Alvarado, Harry Pezzini, Paolo Shadle, Lawrence Bryden, Kenneth
dc.contributor.department Ames Laboratory
dc.contributor.department Mechanical Engineering 2021-07-06T16:07:43.000 2021-08-14T01:37:30Z 2021-08-14T01:37:30Z 2020-04-08
dc.description.abstract <p>Clean energy has become an increasingly important consideration in today’s power systems. As the push for clean energy continues, many coal-fired power plants are being decommissioned in favor of renewable power sources such as wind and solar. However, the intermittent nature of renewables means that dynamic load following traditional power systems is crucial to grid stability. With high flexibility and fast response at a wide range of operating conditions, gas turbine systems are poised to become the main load following component in the power grid. Yet, rapid changes in load can lead to fluid flow instabilities in gas turbine power systems. These instabilities often lead to compressor surge and stall, which are some of the most critical problems facing the safe and efficient operation of compressors in turbomachinery today. Although the topic of compressor surge and stall has been extensively researched, no methods for early prediction have been proven effective. This study explores the utilization of machine learning tools to predict compressor stall. The long short-term memory (LSTM) model, a form of recurrent neural network (RNN), was trained using real compressor stall datasets from a 100 kW recuperated gas turbine power system designed for hybrid configuration. Two variations of the LSTM model, classification and regression, were tested to determine optimal performance. The regression scheme was determined to be the most accurate approach, and a tool for predicting compressor stall was developed using this configuration. Results show that the tool is capable of predicting stalls 5–20 ms before they occur. With a high-speed controller capable of 5 ms time-steps, mitigating action could be taken to prevent compressor stall before it occurs.</p>
dc.identifier archive/
dc.identifier.articleid 1947
dc.identifier.contextkey 23693358
dc.identifier.s3bucket isulib-bepress-aws-west
dc.identifier.submissionpath ameslab_manuscripts/941
dc.language.iso en
dc.relation.ispartofseries IS-J 10524
dc.source.bitstream archive/|||Sat Jan 15 02:32:56 UTC 2022
dc.source.uri 10.1115/1.4046458
dc.subject.disciplines Energy Systems
dc.subject.keywords energy conversion/systems
dc.subject.keywords natural gas technology
dc.subject.keywords compressor surge and stall
dc.subject.keywords machine learning
dc.subject.keywords long short-term memory
dc.subject.keywords turbomachinery
dc.title Using Machine Learning Tools to Predict Compressor Stall
dc.type article
dc.type.genre article
dspace.entity.type Publication
relation.isOrgUnitOfPublication 25913818-6714-4be5-89a6-f70c8facdf7e
relation.isOrgUnitOfPublication 6d38ab0f-8cc2-4ad3-90b1-67a60c5a6f59
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