Adaptive Neuro-Fuzzy Modeling and Control of IP Drum Level of a Power Plant for Improving Transient Response
| Autor: | Mohsen Montazeri, Pouya Abbasi, Elahe Rezaeifard |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
0209 industrial biotechnology
Adaptive neuro fuzzy inference system Power station Settling time Computer science Combined cycle 020209 energy Boiler (power generation) PID controller 02 engineering and technology law.invention 020901 industrial engineering & automation Control theory law Heat recovery steam generator 0202 electrical engineering electronic engineering information engineering Transient response |
| Zdroj: | 2019 27th Iranian Conference on Electrical Engineering (ICEE). |
| DOI: | 10.1109/iraniancee.2019.8786513 |
| Popis: | Heat recovery steam generator (HRSG) boiler is one of the main components of combined cycle power plants that its proper and safe operation is subject to drum level being in a specified range. In this paper, an application of ANFIS structure is presented for modeling the dynamic behavior of IP drum level changes of Qom Combined Cycle Power Plant, with emphasis on accurate modeling of its transient behavior in order to improve the transient response and consequently prevent steam unit from tripping. Next, the response of the developed model is compared with the experimental data to validate its accuracy. Then, a self-tuning PID controller based on BP neural network is developed to control the drum level changes. Simulation results show improved performance of this controller in terms of less overshoot and settling time, compared to the classic PID controller used in Qom power plant. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|