电力系统保护与控制2024，Vol.52Issue(1)：156-165,10.DOI:10.19783/j.cnki.pspc.230593
基于改进秃鹰算法优化极限学习机的谐波发射水平估计
Harmonic emission level estimation method based on an improved bald eagle search optimized extreme learning machine
摘要
Abstract
A harmonic emission level estimation method based on an improved bald eagle search(IBES)optimized extreme learning machine(ELM)is proposed to address the problem that it is difficult to measure that level directly.First,the Tent chaotic mapping and the Cauchy variant operator are introduced into the traditional bald eagle search algorithm,and the input weights and thresholds of the ELM model are optimized using the IBES algorithm.Second,the harmonic voltage and current at the point of common coupling(PCC)are input and substituted into the IBES-ELM model to estimate the customer-side and system-side harmonic emission levels.Finally,simulations and engineering examples are analyzed and the estimation results are compared with those of other algorithms.The results show that the estimation accuracy of the proposed IBES-ELM method is better than that of long short-term memory(LSTM),convolution neural network(CNN),the back propagation neural network(BP)and CNN-LSTM algorithm models.This verifies the effectiveness and stability of the method.关键词
谐波发射水平/秃鹰搜索优化/Tent混沌映射/柯西变异算子/极限学习机Key words
harmonic emission level/bald eagle search optimization/Tent chaotic mapping/Cauchy variant operator/extreme learning machine引用本文复制引用
夏焰坤,朱赵晴,唐文张,任俊杰,张艺凡..基于改进秃鹰算法优化极限学习机的谐波发射水平估计[J].电力系统保护与控制,2024,52(1):156-165,10.基金项目
四川省科技计划项目资助(2020YFG0184) This work is supported by the Science and Technology Project of Sichuan Province(No.2020YFG0184). （2020YFG0184）
