The widespread deployment of renewable energies and non-linear loads has led to serious harmonic pollution in electrical distribution networks.Evaluation of the harmonic contribution(HC)of each customer is a significa...The widespread deployment of renewable energies and non-linear loads has led to serious harmonic pollution in electrical distribution networks.Evaluation of the harmonic contribution(HC)of each customer is a significant task for power quality management.Most previous studies focus on periodic evaluation methods,where numerous data have to be collected in advance over a period(e.g.,one day).However,customer behaviors are time-varying and would lead to dynamic HCs,which can not be captured by traditional periodic evaluation methods.To address this issue,this paper presents a novel real-time HC evaluation method considering multiple dynamic customers.First,a two-stage iteration estimator is proposed based on the information fusion technique to quantify real-time HC of each customer.Then,to mitigate the negative effect of unknown background harmonics,a dominant index method is developed to determine credibility of the measurement data.On this basis,an adaptive gain selection strategy is proposed to improve accuracy of real-time HC evaluation.By doing so,the major harmonic contributor can be identified for implementing harmonic suppression and improving power quality.Finally,a typical IEEE system is utilized to verify the proposed methods.The results show that using the proposed method,evaluation errors can be reduced from about 10%to 2.5%.Moreover,the total harmonic distortion of voltage can be suppressed from 5.564%to 0.702%.Therefore,this research provides practical insights for addressing harmonic problems in power systems.展开更多
基金funded by The Science and Technology Development Fund,Macao SAR,China(File/Project no.SKL-IOTSC-2021-2023,0003/2020/AKP,FDCT/0022/2020/A1).
文摘The widespread deployment of renewable energies and non-linear loads has led to serious harmonic pollution in electrical distribution networks.Evaluation of the harmonic contribution(HC)of each customer is a significant task for power quality management.Most previous studies focus on periodic evaluation methods,where numerous data have to be collected in advance over a period(e.g.,one day).However,customer behaviors are time-varying and would lead to dynamic HCs,which can not be captured by traditional periodic evaluation methods.To address this issue,this paper presents a novel real-time HC evaluation method considering multiple dynamic customers.First,a two-stage iteration estimator is proposed based on the information fusion technique to quantify real-time HC of each customer.Then,to mitigate the negative effect of unknown background harmonics,a dominant index method is developed to determine credibility of the measurement data.On this basis,an adaptive gain selection strategy is proposed to improve accuracy of real-time HC evaluation.By doing so,the major harmonic contributor can be identified for implementing harmonic suppression and improving power quality.Finally,a typical IEEE system is utilized to verify the proposed methods.The results show that using the proposed method,evaluation errors can be reduced from about 10%to 2.5%.Moreover,the total harmonic distortion of voltage can be suppressed from 5.564%to 0.702%.Therefore,this research provides practical insights for addressing harmonic problems in power systems.
