Remote data acquisition model for abnormal operation of power equipment based on .NET technology

Abstract

In order to solve the problem that the traditional remote data acquisition model can not achieve high-precision data reconstruction, which leads to low success rate of data acquisition, a remote data acquisition model for abnormal operation of power equipment is constructed based on .NET technology. According to the prediction residual results of multiple state parameters, the monitoring indexes of power equipment operation state are extracted, and the operation data of power equipment is accessed by using .NET technology, and the abnormal operation data is identified. According to the recognition results of abnormal data, the remote data acquisition model is constructed from two aspects of sparse compression sampling and reconstruction of abnormal data to ensure high-precision data reconstruction and the reliability of the acquisition results. The experimental results show that the collection success rate of the remote abnormal data collection model is 8.28% and 10.62% higher than that of the traditional collection model, which has good remote collection performance.