An Improved Machine Learning Scheme for Data-Driven Fault Diagnosis of Power Grid Equipment

In recent power grid systems, data-driven approach has been taken to grid condition evaluation and classification after successful adoption of big data techniques in internet applications. However, the raw training data from single monitoring system, e.g. dissolved gas analysis (DGA), are rarely sufficient for training in the form of valid instances and the data quality can rarely meet the requirement of precise data analytics since raw data set usually contains samples with noisy data. This paper proposes a machine learning scheme (PCA_IR) to improve the accuracy of fault diagnose, which combines dimension-increment procedure based on association analysis, dimension-reduction procedure based on principal component analysis and back propagation neural network (BPNN). First, the dimension of training data is increased by adding selected data which originates from different source such as production management system (PMS) to the original data obtained by DGA. The added data would also inevitably result in more noise. Thus, we then take advantage of the PCA method to reduce the noise in the training data as well as retaining significant information for classification. Finally, the new training data yielded after PCA procedure is inputted into BPNN for classification. We test the PCA_IR scheme on fault diagnosis of power transformers in power grid system. The experimental results show that the classifiers based on our scheme achieve higher accuracy than traditional ones. Therefore, the scheme PCA_IR would be successfully deployed for fault diagnosis in power grid system.