CIESC Journal ›› 2018, Vol. 69 ›› Issue (9): 3924-3931.doi: 10.11949/j.issn.0438-1157.20180293

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Modeling method of ASOS-ELM and its application in prediction of heat rate of steam turbine

NIU Peifeng1, WANG Xiaofei1, LIU Nan2, WANG Yuanning3, CHANG Lingfang1, ZHANG Xianchen1   

  1. 1. School of Electrical Engineering, Yanshan University, Qinhuangdao 066004, Hebei, China;
    2. Yanshan University, Qinhuangdao 066004, Hebei, China;
    3. College of Environment Science and Engineering, Dalian Maritime University, Dalian 116026, Liaoning, China
  • Received:2018-03-19 Revised:2018-06-03 Online:2018-09-05 Published:2018-07-10
  • Supported by:

    supported by the National Natural Science Foundation of China (61573306).

Abstract:

The extreme learning machine (ELM) problem can not quickly and accurately predict heat rate. Combined with swarm intelligence optimization algorithm, an ameliorated symbiotic organisms search algorithm and extreme learning machine (ASOS-ELM) comprehensive modeling method is proposed. This method uses the ameliorated symbiotic organisms search (ASOS) algorithm to optimize the parameters of the ELM hidden layer activation function to obtain the optimal ELM model. Firstly, the initial heat rate prediction model is established with ELM, and the root mean square error (RMSE) of the output heat rate is used as the fitness value of the algorithm. Then the appropriate ELM parameters are found through the ASOS algorithm to obtain an accurate heat rate prediction model. The performance of the heat rate prediction is compared with the traditional ELM model, support vector regression (SVR) model optimized by the ASOS algorithm, ELM optimized by improved particle swarm optimization (PSO) and basic symbiotic organisms search algorithm (SOS). The results show that the ASOS-ELM model has a precise forecasting ability and rapid convergence speed when dealing with complex data models, which provides a new idea for modeling the heat rate of a steam turbine.

Key words: steam turbine, heat rate, algorithm, extreme learning machine, optimization, model

CLC Number: 

  • TK267

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