Transient power quality assessment based on big data analysis

A transient power quality assessment method is proposed in this paper, using Naive Bayes classification method which is based on big data processing architecture. In this architecture, data sources will be extended to the aspects of power grid monitoring data, the power customer data and the public data, and the assessment severity will be classified into the normal state, the abnormal state, the critical state, and the failed state, according to the Naive Bayes classification results. Based on the data type of transient power quality assessment, big data processing architecture used in this paper can be able to process distributed data and streaming data, so that it can ensure not only updates classifier rules regularly, but also the real-time condition assessment. In the classifier training phase, we use the massive historical data as the distributed learning object, and generate assessment rules periodically. In the state assessment phase, each assessment node will update the assessment rules generated by training phase, generate real-time evaluation of samples from stream processing framework, and evaluate the power quality state according to the current rule. On this basis, this paper designs a Naive Bayes classification method based on MapReduce processing, and realizes the map and reduce process method to compute the priori probability and the conditional probability in distributed way. Experiments show that the transient power quality evaluation method based on the big data analysis presented in this paper is feasible, and achieve good results both in classification accuracy and processing speed.