Application of improved grey model residual modified fusion algorithm in dissolved gas forecasting

Abstract

The power transformer directly affects whether the power system can operate safely and reliably, and the prediction of the dissolved gas content in the transformer oil is an effective means to discover transformer defects and latent faults, which helps to better carry out state maintenance work. This study extracts the time series data in transformer oil and analyses the interactions and effects between various gas components in the oil. Then, combined with the changing trend of the content of each component, the original sequence was processed using an accumulated method to extract the intrinsic characteristics of the sequence, thus establishing the MGM(1,n)(grey multivariate model). As the grey prediction model can reduce the prediction accuracy of the sequence with longer time span, this study combines the adaptive regression and Markov correction models to optimise the grey prediction model from the perspective of residual correction. Finally, the accuracy and effectiveness of the model are verified by comparing with the original prediction method.

Keywords

• regression analysis
• transformer oil
• power transformers
• time series
• markov processes
• grey systems
• dissolved gas content
• power system
• power transformer
• dissolved gas forecasting
• improved grey model residual modified fusion algorithm
• original prediction method
• residual correction
• markov correction models
• longer time span
• prediction accuracy
• grey prediction model
• original sequence
• gas components
• time series data
• state maintenance work
• latent faults
• transformer defects
• transformer oil