A Method for the Oil Chromatographic On-line Data Reconciliation Based on GSO and SVM

To solve the problem of oil chromatographic on-line data distortion caused by outside environment and equipment error, a method for the oil chromatographic on-line data reconciliation based on glowworm swarm optimization (GSO) and support vector machine (SVM) is presented. Firstly, the important parameters that affect the performance of SVM are optimized through GSO. Secondly, SVM regression model is trained by some precise oil chromatographic off-line data. Then the oil chromatographic on-line data is reconciled by SVM regression model when the on-line data is abnormal. Finally, the feasibility and efficiency of the method proposed in the paper is confirmed by the oil chromatographic on-line and off-line data of the power transformer.