A Predictive Model of the Generator Output Based on the Learning of Performance Data in Power Plant
발전플랜트 성능데이터 학습에 의한 발전기 출력 추정 모델

HacJin Yang, Seong Kun Kim
2015 Journal of the Korea Academia-Industrial cooperation Society  
Establishment of analysis procedures and validated performance measurements for generator output is required to maintain stable management of generator output in turbine power generation cycle. We developed turbine expansion model and measurement validation model for the performance calculation of generator using turbine output based on ASME (American Society of Mechanical Engineers) PTC (Performance Test Code). We also developed verification model for uncertain measurement data related to the
more » ... ata related to the turbine and generator output. Although the model in previous researches was developed using artificial neural network and kernel regression, the verification model in this paper was based on algorithms through Support Vector Machine (SVM) model to overcome the problems of unmeasured data. The selection procedures of related variables and data window for verification learning was also developed. The model reveals suitability in the estimation procss as the learning error was in the range of about 1%. The learning model can provide validated estimations for corrective performance analysis of turbine cycle output using the predictions of measurement data loss.
doi:10.5762/kais.2015.16.12.8753 fatcat:iq5uc5lwbnhlfa643qxmk275em