基于贝叶斯网络多馈入直流系统相继换相失败概率推断

Probability Inference of Successive Commutation Failure of Multi-DC System Based on Bayesian Network

大规模多馈入高压直流各回路之间紧密复杂的耦合可能恶化高压直流换相条件,增加了多馈入高压直流系统发生相继换相失败的风险。该文基于贝叶斯网络理论,以换流母线电压降、直流电流变化率和谐波畸变率这三大影响因素作为相继换相失败的证据指标,分别建立3个证据指标的证据概率函数和相继换相失败免疫因子,根据这些证据建立多馈入直流系统相继换相失败的贝叶斯网络,推断多馈入直流输电系统相继换相失败的概率。依据该文所提出的多馈入直流系统相继换相失败的概率推断方法和相关性的风险分析,可预警多馈入直流系统相继换相失败风险,也可提出多馈入直流相继换相失败的抑制措施。

The close and complicated interactions between the large-scale multi-infeed HVDC circuits may worsen the commutation conditions of the HVDCs and increase the risks of successive commutation failures in the multi-infeed HVDC systems.In this paper,on the base of the Bayesian network theory,the three influencing factors,i.e.,the voltage drop of the HVDC inverter bus,the DC current change rate and the harmonic distortion rate,are taken as the evidence indicators of successive commutation failures.The evidence probability functions of the three evidence indicators and their immune factors of successive commutation failures are proposed respectively.According to these evidences,the Bayesian networks of successive commutation failures of the multi-infeed DC systems are developed,and the probability of successive commutation failures of the multi-infeed DC transmission systems is inferred.According to the proposed probability inference method and the correlation risk analysis of successive commutation failures of the multi-infeed DC systems,the risks of successive commutation failures of the multi-infeed DC systems is able to be forewarned,and the corresponding suppression measures of successive commutation failures are able to be put forward.