Application Study on CPSO-FLN Model in the Steam Turbine Heat Rate Forecast

doi:10.11930/j.issn.1004-9649.201903078

Abstract

Abstract: The heat rate is one of the important indexes to assess the thermal economy of thermal power units. Regarding the difficulties in accurate heat rate calculation, this paper proposes a short-term heat rate forecast model based on optimized fast learning network (FLN) by cloud particle swarm optimization (CPSO). The cloud model based adaptive weight strategy is introduced into the particle swarm algorithm such that the weight of the particle swarm algorithm can be self-adaptively adjusted to improve the global optimization performance of the PSO algorithm by taking advantage of the randomness and stability of cloud droplets. Furthermore, the CPSO algorithm is used to tune the model parameters of FLN and establish the CPSO-FLN heat rate forecast model. Finally, the CPSO-FLN model is applied to predict the heat rate of a steam turbine with 12 controllable variables of strong correlation as the input parameters. Through the comparison of the results from the proposed model with those from standard FLN model and PSO-FLN model, the CPSO-FLN model has demonstrated higher accuracy and better generalization capacity, and hence is proved to be an effective forecast method.

Key words: steam turbine, heat rate, fast learning network, particle swarm optimization algorithm, cloud model

CLC Number:

TK267

HU Jian, LIU Chao. Application Study on CPSO-FLN Model in the Steam Turbine Heat Rate Forecast[J]. Electric Power, 2019, 52(8): 179-184.

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URL: https://www.electricpower.com.cn/EN/10.11930/j.issn.1004-9649.201903078

https://www.electricpower.com.cn/EN/Y2019/V52/I8/179

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