scholarly journals An Adaptive Early Fault Detection Model of Induced Draft Fans Based on Multivariate State Estimation Technique

2021 ◽  
Author(s):  
Ruijun Guo ◽  
Guobin Zhang ◽  
Qian Zhang ◽  
Lei Zhou ◽  
Haicun Yu ◽  
...  
Keyword(s):  
Power Plant ◽  
Fault Detection ◽  
State Estimation ◽  
Thermal Power ◽  
Adaptive Strategy ◽  
Normal Operation ◽  
Efficient Production ◽  
Estimation Technique ◽  
Auxiliary Equipment ◽  
Early Fault Detection

The induced draft (ID) fan is important auxiliary equipment in the thermal power plant. It is of great significance to monitor the operation of the ID fan for safe and efficient production. In this paper, an adaptive warning model is proposed to detect early faults of ID fans. First, a non-parametric monitoring model is constructed to describe the normal operation states with the multivariate state estimation technique (MSET). Then, an early warning approach is presented to identify abnormal behaviors based on the results of the MSET model. As the performance of the MSET model is heavily influenced by the normal operation data in the historic memory matrix, an adaptive strategy is proposed by using the samples with a high data quality index (DQI) to manage the memory matrix and update the model. The proposed method is applied to a 300 MW coal-fired power plant for early fault detection, and it is compared with the model without an update. Results show that the proposed method can detect the fault earlier and more accurately.

scholarly journals An Adaptive Early Fault Detection Model of Induced Draft Fans Based on Multivariate State Estimation Technique

Energies ◽  
2021 ◽  
Vol 14 (16) ◽  
pp. 4787
Author(s):  
Ruijun Guo ◽  
Guobin Zhang ◽  
Qian Zhang ◽  
Lei Zhou ◽  
Haicun Yu ◽  
...  
Keyword(s):  
Fault Detection ◽  
State Estimation ◽  
Power Plants ◽  
Similarity Index ◽  
Adaptive Strategy ◽  
Estimation Technique ◽  
Operating Characteristics ◽  
Model Accuracy ◽  
Auxiliary Equipment ◽  
Early Fault Detection

The induced draft (ID) fan is an important piece of auxiliary equipment in coal-fired power plants. Early fault detection of the ID fan can provide predictive maintenance and reduce unscheduled shutdowns, thus improving the reliability of the power generation. In this study, an adaptive model was developed to achieve the early fault detection of ID fans. First, a non-parametric monitoring model was constructed to describe the normal operating characteristics with the multivariate state estimation technique (MSET). A similarity index representing operation status was defined according to the prediction deviations to produce warnings of early faults. To deal with the model accuracy degradation because of variant condition operation of the ID fan, an adaptive strategy was proposed by using the samples with a high data quality index (DQI) to manage the memory matrix and update the MSET model, thereby improving the fault detection results. The proposed method was applied to a 300 MW coal-fired power plant to achieve the early fault detection of an ID fan. In addition, fault detection by using the model without an update was also compared. Results show that the update strategy can greatly improve the MSET model accuracy when predicting normal operations of the ID fan; accordingly, the fault can be detected more than 4 h earlier by using the strategy with the adaptive update when compared to the model without an update.

Modification Research on the Tube Layout of One 300MW Unit Condenser

2013 ◽  
Vol 860-863 ◽  
pp. 1436-1440
Author(s):  
Qi Cheng ◽  
Li Zhang ◽  
Yin Jun Wu
Keyword(s):  
Numerical Simulation ◽  
Power Plant ◽  
Thermal Power Plant ◽  
Thermal Power ◽  
Normal Operation ◽  
Auxiliary Equipment ◽  
Before And After ◽  
Important Means ◽  
Low Efficiency ◽  
Operating Unit

In the thermal power plant, as an important auxiliary equipment of turbine, the condenser plays an important role in the normal operation of power plant. At present, the domestic operating unit has a large number of old condenses, which has low efficiency and emerging problems because of the increasing running time. Therefore, the transformation of old condenser becomes an important means to tap the potential of the old unit. In this paper, we transform one 300MW unit condenser with its tube layout, then through the numerical simulation and date analysis of the condenser before and after transformation to explore the possibility of modification.

Early Fault Detection of Hot Components in Gas Turbines

2016 ◽  
Vol 139 (2) ◽  
Author(s):  
Liu Jinfu ◽  
Liu Jiao ◽  
Wan Jie ◽  
Wang Zhongqi ◽  
Yu Daren
Keyword(s):  
Fault Detection ◽  
Gas Turbine ◽  
Gas Turbines ◽  
Working Environment ◽  
Detection Sensitivity ◽  
Normal Operation ◽  
Large Temperature ◽  
Ambient Conditions ◽  
Fisher Discriminant Analysis ◽  
Early Fault Detection

The working environment of hot components is the most adverse of all gas turbine components. Malfunction of hot components is often followed by catastrophic consequences. Early fault detection plays a significant role in detecting performance deterioration immediately and reducing unscheduled maintenance. In this paper, an early fault detection method is introduced to detect early fault symptoms of hot components in gas turbines. The exhaust gas temperature (EGT) is usually used to monitor the performance of the hot components. The EGT is measured by several thermocouples distributed equally at the outlet of the gas turbine. EGT profile is symmetrical when the unit is in normal operation. And the faults of hot components lead to large temperature differences between different thermocouple readings. However, interferences can potentially affect temperature differences, and sometimes, especially in the early stages of the fault, its influence can be even higher than that of the faults. To improve the detection sensitivity, the influence of interferences must be eliminated. The two main interferences investigated in this study are associated with the operating and ambient conditions, and the structure deviation of different combustion chambers caused by processing and installation errors. Based on the basic principles of gas turbines and Fisher discriminant analysis (FDA), a new detection indicator is presented that characterizes the intrinsic structure information of the hot components. Using this new indicator, the interferences involving the certainty and the uncertainty are suppressed and the sensitivity of early fault detection in gas turbine hot components is improved. The robustness and the sensitivity of the proposed method are verified by actual data from a Taurus 70 gas turbine produced by Solar Turbines.

Fault detection and isolation system for boiler-turbine unit of a thermal power plant

2017 ◽  
Vol 148 ◽  
pp. 237-244 ◽  
Author(s):  
G. Madrigal-Espinosa ◽  
G.-L. Osorio-Gordillo ◽  
C.-M. Astorga-Zaragoza ◽  
M. Vázquez-Román ◽  
M. Adam-Medina
Keyword(s):  
Power Plant ◽  
Fault Detection ◽  
Thermal Power Plant ◽  
Turbine Unit ◽  
Thermal Power ◽  
Fault Detection And Isolation ◽  
Isolation System

Sensor fault detection and isolation in a thermal power plant steam separator

2013 ◽  
Vol 21 (7) ◽  
pp. 908-916 ◽  
Author(s):  
Nasar Aldian Ambark Shashoa ◽  
Goran Kvaščev ◽  
Aleksandra Marjanović ◽  
Željko Djurović
Keyword(s):  
Power Plant ◽  
Fault Detection ◽  
Thermal Power Plant ◽  
Thermal Power ◽  
Fault Detection And Isolation ◽  
Sensor Fault ◽  
Sensor Fault Detection ◽  
Steam Separator

scholarly journals Early fault detection for power plant fans based on dynamic neural network

2021 ◽  
Vol 2005 (1) ◽  
pp. 012203
Author(s):  
Guobin Zhang ◽  
Ronghua Du ◽  
Xiaogang Xin ◽  
Wei Zhao ◽  
Shaojia Dang ◽  
...  
Keyword(s):  
Neural Network ◽  
Power Plant ◽  
Fault Detection ◽  
Dynamic Neural Network ◽  
Early Fault Detection

Fault Detection and Isolation of Thermal Power Plant using Auto-associative Kernel Regression

2018 ◽  
Vol 28 (5) ◽  
pp. 429-435
Author(s):  
Minseok Kim ◽  
Seunghwan Jung ◽  
BaekCheon Kim ◽  
Jaeyel Jang ◽  
Jaeyenog Yoo ◽  
...  
Keyword(s):  
Power Plant ◽  
Fault Detection ◽  
Thermal Power Plant ◽  
Thermal Power ◽  
Kernel Regression ◽  
Fault Detection And Isolation

Running State Assessment for Induced Draft Fans Using Auto Encoder Model Combined With Fuzzy Synthetic

2019 ◽  
Author(s):  
Jiawei Yang ◽  
Di Hu ◽  
Tao Yang ◽  
Wei Gao ◽  
Chunmei Li ◽  
...  
Keyword(s):  
Power Plant ◽  
Large Scale ◽  
Thermal Power ◽  
State Assessment ◽  
Operating Conditions ◽  
Assessment Model ◽  
Fuzzy Evaluation ◽  
Auxiliary Equipment ◽  
Operation And Maintenance ◽  
Induced Draft Fan

Abstract In order to explore the operation and maintenance characteristics of important auxiliary machines in large-scale power plant coal-fired boilers, a running state assessment model for auxiliary equipment is established. In this paper, taking the complex variability of the operating conditions of thermal power equipment into consider, auto encoder model combined with fuzzy synthetic is proposed. Based on the residual of the model results and the actual power plant operation data, combined with the fuzzy evaluation model to establish a state assessment model, and analyze the actual situation of the induced draft fan of the power plant, to make a real-time assessment of operation status. The evaluation results show the advantages of the state assessment strategy proposed in this paper, and it can reflect the deterioration of the induced draft fan status in time, providing guidance for the operation and maintenance of the equipment.

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