A Identifying Method for Turbine Load Deviation Based on DE-MLRF

doi:10.3969/j.issn.1008-0198.2025.03.018

Abstract

Abstract: Aiming at the problem of load deviation in turbine variable condition operation, a classification model for the reason of turbine load deviation is proposed based on the combination of differential evolution (DE) algorithm and multi-label random forest (MLRF). The associated variables affecting the turbine load output are analyzed by Spearman correlation coefficient. The DE algorithm is used to optimize the parameters of the MLRF model, and the DE-MLRF-based classification model of the causes of turbine load deviation is established. The experimental validation is carried out by combining the actual operation data of a 660 MW turbine, and the results show that the MLRF model optimized by DE algorithm achieves the lowest false alarm rate and missing alarm rate, which are 1.902 4% and 1.854 1% respectively, compared with MLRF models optimized by the other seven algorithms. The DE-MLRF-based model for classifying the causes of turbine load deviation can provide decision support for locating the causes of turbine load deviation.

Key words: turbine, load deviation, differential evolution (DE) algorithm, multi-label random forest (MLRF)

CLC Number:

TK262

WEN Wen, HUANG Yujie, TANG Mingzhu, DENG Liming, LUO Cui′e, JiANG Xin. A Identifying Method for Turbine Load Deviation Based on DE-MLRF[J]. Hunan Electric Power, 2025, 45(3): 129-134.

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