Lifetime Prediction of Mica Paper Capacitors Based on an Improved Iterative Grey–Markov Chain Model

Abstract

Mica paper capacitors possess the characteristics of high energy storage density and reliability and have been widely applied in various power systems as energy storage components. The lifetime of capacitors is a critical factor that ensures the reliability of a system. The leakage resistance of a mica paper capacitor can be utilized to characterize its lifetime. However, the decrease of leakage resistance caused by a single test pulse is slight during a lifetime test. A large number of test pulses are needed to obtain complete lifetime data of mica paper capacitors. To obtain lifetime prediction of capacitors based on limited data, an improved iterative Grey–Markov chain model based on unbiased grey system theory, fuzzy classification with fuzzy C-mean (FCM) algorithm, and an iterative method is proposed. It can take advantage of the prediction power of the conventional Grey–Markov model and improve anti-interference performance simultaneously. As an example, six mica paper capacitors are utilized to validate the feasibility and practicability of this model. The working state and remaining lifetime of mica capacitors can be dynamically evaluated through this model in practical applications. This model is also applicable to forecasting in other lifetime testing situations. This helps to assess the reliability of a whole system.