Abstract
Aiming at the anomaly recognition method of large area measurement system (WAMS), a method based on Grey Mrrelatian Aru and Isolation Forest algorithm is proposed, considering the attributes of power data and the big data characteristics of large area measurement system. In this method, the relational matrix of power data attribute sequence is obtained through grey relational analysis, and the dependent attributes are selected according to the relational matrix for attribute sequence merging. Then, the data set is partitioned by cascaded isolated forest model and abnormal data are detected. Taking the data of a province’s wide-area measurement system as an example for experimental simulation, compared with LOF, Kmeans and IForest, under the same inspection time, the GRA-IForest model has achieved good inspection results and achieved a higher overall detection accuracy and lower standard deviation.
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