Prediction Model of Steam Turbine Heat Rate Based on CNGWO-LSSVM

doi:10.11930/j.issn.1004-9649.201803137

Abstract

Abstract: In order to accurately calculate the heat rate of steam turbines, an improved grey wolf optimization (GWO) algorithm is proposed to optimize the soft-sensing modeling method using least squares support vector machine (LSSVM). Firstly, aiming at the issue of low convergence precision of GWO algorithm, the chaotic nonlinear grey wolf optimization algorithm (CNGWO) is proposed to improve the performance of GWO algorithm by virtue of Kent chaotic search strategy and nonlinear dynamic decline strategy. Then the model parameters of LSSVM is optimized by chaotic nonlinear grey wolf algorithm, and the soft sensor model of CNGWO-LSSVM is established. Based on the real-time operation data of a 600 MW supercritical steam turbine unit in a power plant, the soft sensing technology based on CNGWO-LSSVM is applied to predicate the heat rate of the 600 MW supercritical steam turbine unit. The prediction result indicates that the LSSVM optimized by CNGWO algorithm has achieved satisfactory prediction effect, which can provide an effective way for accurate heat rate calculation.

Key words: thermal power plant, steam turbine, heat rate, soft sensing, least squares support vector machine, grey wolf optimization algorithm

CLC Number:

TK267

ZUO Zhike, CHEN Guobin, LIU Chao, NIU Peifeng. Prediction Model of Steam Turbine Heat Rate Based on CNGWO-LSSVM[J]. Electric Power, 2018, 51(8): 148-153.

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URL: https://www.electricpower.com.cn/EN/10.11930/j.issn.1004-9649.201803137

https://www.electricpower.com.cn/EN/Y2018/V51/I8/148

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