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Performance Monitoring of Regenerative System Based on Dominant Factor Method

[+] Author Affiliations
Cheng Chen, Xiaobo Zhong, Jun Xiao, Yong Zhu, Jiao Jiang

JiangSu Power Design Institute Co., Ltd., Nanjing, China

Paper No. POWER-ICOPE2017-3534, pp. V002T08A019; 8 pages
doi:10.1115/POWER-ICOPE2017-3534
From:
  • ASME 2017 Power Conference Joint With ICOPE-17 collocated with the ASME 2017 11th International Conference on Energy Sustainability, the ASME 2017 15th International Conference on Fuel Cell Science, Engineering and Technology, and the ASME 2017 Nuclear Forum
  • Volume 2: I&C, Digital Controls, and Influence of Human Factors; Plant Construction Issues and Supply Chain Management; Plant Operations, Maintenance, Aging Management, Reliability and Performance; Renewable Energy Systems: Solar, Wind, Hydro and Geothermal; Risk Management, Safety and Cyber Security; Steam Turbine-Generators, Electric Generators, Transformers, Switchgear, and Electric BOP and Auxiliaries; Student Competition; Thermal Hydraulics and Computational Fluid Dynamics
  • Charlotte, North Carolina, USA, June 26–30, 2017
  • Conference Sponsors: Power Division, Advanced Energy Systems Division, Solar Energy Division, Nuclear Engineering Division
  • ISBN: 978-0-7918-5761-8
  • Copyright © 2017 by ASME

abstract

Safe and efficient operation of a power plant is the system designers’ target. Regenerative system improves the Rankine Cycle efficiency of a power station. However, it is quite difficult to monitor the regenerative system’s performance in an accurate, economical and real-time way at any operation load. There are two main problems about this. One is that most model based on numerical and statistics approaches cannot be explained by the actual operation mechanism of the actual process. The other is that most mechanism models in the past could not be used to monitor the system performance accurately at real-time.

This paper focuses on solving these two problems and finds a better way to monitor the regenerative system’s performance accurately in a real-time by the analysis of the mechanism models and numerical methods. It is called the dominant factor method. Two important parameters (characteristic parameter and dominant factor) and characteristic functions are introduced in this paper. Also, this paper described the analysis process and the model building process.

In the paper, the mathematics model building process is based on a 1000MW unit’s regenerative system. Characteristic functions are built based on the specific operating data of the power unit. Combing the general mechanism model and the characteristic function together, this paper builds up a regenerative system off-design mathematical model. First, this paper proved the model accuracy by computer simulation. Then, the models were used to predict the pressure of the piping outlet, the temperature of the outlet feedwater and drain water of heaters in a real-time by computers. The results show that the deviation rate between the theoretical predictions and the actual operation data is less than 0.25% during the whole operation load range. At last, in order to test the fault identification ability of this model, some real tests were done in this 1000MW power plant during the actual operation period. The performance changes are identified via the difference between the predict value and the real time value. The result of the tests shows that the performance’s gradient change and sudden change could be found by the model result easily.

In order to verify the adaptability of the model, it was used for another 300MW unit, and done some operation test. The results show that this method can also be used for the 300MW unit’s regenerative system. And it can help the operator to recognize the fault heater.

The results of this paper proved that the dominant factor method is feasible for performance monitoring of the regenerative system. It can be used to monitor and find the fault of the regenerative system at any operation load by an accurate and fast way in real-time.

Copyright © 2017 by ASME

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