A Stacked Denoising Sparse Autoencoder Based Fault Early Warning Method for Feedwater Heater Performance Degradation

Xingshuo Li, Jinfu Liu, Jiajia Li, Xianling Li, Peigang Yan and Daren Yu
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Xingshuo Li: Harbin Institute of Technology, Harbin 150001, Heilongjiang, China
Jinfu Liu: Harbin Institute of Technology, Harbin 150001, Heilongjiang, China
Jiajia Li: Harbin Institute of Technology, Harbin 150001, Heilongjiang, China
Xianling Li: Science and Technology on Thermal Energy and Power Laboratory, Wuhan 430205, Hubei, China
Peigang Yan: Harbin Institute of Technology, Harbin 150001, Heilongjiang, China
Daren Yu: Harbin Institute of Technology, Harbin 150001, Heilongjiang, China

Energies, 2020, vol. 13, issue 22, 1-21

Abstract: Power grid operation faces severe challenges with the increasing integration of intermittent renewable energies. Hence the steam turbine, which mainly undertakes the task of frequency regulation and peak shaving, always operates under off-design conditions to meet the accommodation demand. This would affect the operation economy and exacerbate the ullage of equipment. The feedwater heater (FWH) plays an important role in unit, whose timely fault early warning is significant in improving the operational reliability of unit. Therefore, this paper proposes a stacked denoising sparse autoencoder (SDSAE) based fault early warning method for FWH. Firstly, the concept of a frequent pattern model is proposed as an indicator of FWH performance evaluation. Then, an SDSAE- back-propagation (BP) based method is introduced to achieve self-adaptive feature reduction and depict nonlinear properties of frequent pattern modeling. By experimenting with actual data, the feasibility and validity of the proposed method are verified. Its detection accuracy reaches 99.58% and 100% for normal and fault data, respectively. Finally, competitive experiments prove the necessity of feature reduction and the superiority of SDSAE based feature reduction compared with traditional methods. This paper puts forward a precise and effective method to serve for FWH fault early warning and refines the key issues to inspire later researchers.

Keywords: stacked denoising sparse autoencoder; feedwater heater; fault early warning; frequent pattern model; feature reduction (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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