Research on Smart Distribution Network Operation State Estimation Based on Naive Bayes
[Purposes]The state estimation of distribution networks requires monitoring and collecting various parameters,which involves a large amount of real-time data.The accuracy,completeness,and real-time performance of data directly affect the accuracy of state estimation.However,due to the large number and wide range of equipment in the distribution network,as well as the difficulty of data collec-tion,the effectiveness of intelligent distribution network operation state estimation has decreased.There-fore,a naive Bayesian based intelligent distribution network operation state estimation method is pro-posed.[Methods]This paper establishes a measurement model for the smart distribution network,uses this model and collection equipment to collect and process the operational status measurement data of the smart distribution network,and obtains reliable and effective data.Then,by processed measurement data and naive Bayesian methods,the prior distribution of unknown variables is corrected to obtain a pos-terior distribution,thereby achieving accurate estimation of the operating status of the distribution net-work.[Findings]The research method can improve the accuracy of state estimation up to 98.5%,and the estimation results can accurately reflect the actual operating state of the distribution network.[Conclu-sions]The application of intelligent distribution network operation state estimation methods helps to im-prove the intelligence level of the distribution network,achieve real-time monitoring,optimized opera-tion,and fault prediction and processing of the distribution network,thereby ensuring the safe and stable operation of the intelligent distribution network.
naive bayessmart distribution networkoperating statusstate estimation
万方数据
维普
