Electric Power ›› 2022, Vol. 55 ›› Issue (8): 104-112.DOI: 10.11930/j.issn.1004-9649.202101017

• Power System • Previous Articles     Next Articles

Harmonic State Estimation Method Based on Bayesian Optimized Elastic Network Regression

MA Siqi, WANG Zhong   

  1. School of Electrical Engineering, Sichuan University, Chengdu 610065, China
  • Received:2021-01-05 Revised:2021-12-08 Published:2022-08-18
  • Supported by:
    This work is supported by National Natural Science Foundation of China (No.49901013).

Abstract: When the regression algorithm is used to estimate the harmonic state of power system, the high correlation between harmonic sources will cause the ill condition of the normal matrix, which will significantly affect the accuracy of harmonic estimation. In order to accurately estimate the harmonic state of the power system, a harmonic state estimation method is proposed based on Bayesian optimization elastic network regression. Firstly, when the least square method is used to estimate the harmonic state, the weighted 1-norm and 2-norm are added to the penalty function at the same time; In addition, in order to estimate the harmonic state more efficiently and accurately, the Gaussian process and Bayesian optimization are applied to select the weight of 1-norm and 2-norm; Finally, the effectiveness of the proposed method is verified in IEEE14 node. When there is correlation between harmonic sources, the proposed method can still achieve reasonable harmonic source location and harmonic responsibility division.

Key words: harmonic state estimation, multicollinearity, elastic network regression, Bayesian optimization, Gaussian process