Heath Monitoring of Capacitors and Supercapacitors Using the Neo-Fuzzy Neural Approach

Despite their great improvements, reliability and availability of power electronic devices always remain a focus. In safety-critical equipment, where the occurrence of faults can generate catastrophic losses, health monitoring of most critical components is absolutely needed to avoid and prevent breakdowns. In this paper, a noninvasive health monitoring method is proposed. It is based on fuzzy logic and the neural network to estimate and predict the equivalent series resistance (ESR) and the capacitance (C) of capacitors and supercapacitors (SCs). This method, based on the neo-fuzzy neuron model, performs a real-time processing (time series prediction) of the measured device impedance and the degradation data provided by accelerated ageing tests. To prove the efficiency of the proposed method, two experiments are performed. The first one is dedicated to the estimation of the ESR and C for a set of 8 polymer film capacitors, while the second one is dedicated to the prediction of the ESR and C for a set of 18 SCs. The obtained results show that combining fuzzy logic and the neural network is an accurate approach for the health monitoring of capacitors and SCs.