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14 October 2021 Research on soft sensing method of pumping unit system efficiency based on KPCA and SVR
Kun Ma
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Proceedings Volume 11930, International Conference on Mechanical Engineering, Measurement Control, and Instrumentation; 1193024 (2021) https://doi.org/10.1117/12.2611076
Event: International Conference on Mechanical Engineering, Measurement Control, and Instrumentation (MEMCI 2021), 2021, Guangzhou, China
Abstract
The system efficiency is an important technical parameter to measure energy consumption of pumping units. It is of great significance to study the simplified calculation method of the system efficiency for saving test cost and objectively evaluating the implementation effect of energy saving and consumption reduction technology of pumping units. In order to solve the problem that the existing calculation methods of pumping unit system efficiency depend on many parameters, and some parameters are complex or difficult to obtain, a soft sensor model building method of system efficiency based on the combination of KPCA and SVR is proposed, Kernel principal component analysis introduces kernel mapping method into principal component analysis, which can effectively deal with nonlinear data. Through nonlinear function transformation, the data is projected into high-dimensional feature space, and the nonlinear distribution characteristics of data are obtained. Then, SVR algorithm is used to establish a soft sensor model which can accurately predict the efficiency of pumping unit system. The test results show that compared with the traditional model building method, the soft sensing model based on KPCA and SVR has higher measurement accuracy and generalization ability, which can effectively reduce the monthly testing workload of pumping units and reduce the production and operation costs of the oilfield.
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Kun Ma "Research on soft sensing method of pumping unit system efficiency based on KPCA and SVR", Proc. SPIE 11930, International Conference on Mechanical Engineering, Measurement Control, and Instrumentation, 1193024 (14 October 2021); https://doi.org/10.1117/12.2611076
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KEYWORDS
Data modeling
Mathematical modeling
Principal component analysis
Statistical modeling
Complex systems
Feature extraction
Sensing systems
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