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| Soft Sensor Model of Heat Rate Based on Optimized LSSVM by FEWOA |
| ZUO Zhi-ke1,CHEN Guo-bin1,LIU Chao2,NIU Pei-feng2,LI Yi-long3 |
1.Big Data Institute, Rongzhi College of Chongqing Technology and Business University, Chongqing 400033, China
2. Institute of Electrical Engineering, Yanshan University, Qinhuangdao, Hebei 066004, China
3. Jiangxi College of Engineering, Xinyu, Jiangxi 338000, China |
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Abstract An integrated modeling method was proposed based on feedback elitist whale optimization algorithm (FEWOA) and least square support vector machine (LSSVM). Firstly, a feedback elitist WOA was proposed to solve the problem of low precision of the WOA. Based on the FEWOA, the current optimal solution was mutated to avoid the algorithm falling into local optimal solution by the elite strategy. Meanwhile, a feedback phase was introduced at the end of the position updating, so that the algorithm improves the precision. Numeric simulation results show that the proposed FEWOA exhibited superior performance over the other aforementioned algorithms. Then, a soft sensor model of heat rate based on optimized LSSVM by FEWOA was proposed. Finally, the turbine heat rate prediction model was established by using the operation data collected from a steam turbine generator set. The prediction results of FEWOA-LSSVM model were compared with the others, the result show that the FEWOA-LSSVM prediction model can predict the heat rate of steam turbine more accurately.
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Received: 11 October 2017
Published: 07 March 2019
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2019, Vol. 40 
