LAUSR

Electrolytic capacitors have large capacity, low price, and fast charge/discharge characteristics. Therefore, they are widely used in various power conversion devices. These electrolytic capacitors are mainly used for temporary storage and voltage stabilization of DC energy and have recently been used in the renewable energy field for linking AC/DC voltage and buffering charge/discharge energy. However, electrolytic capacitors continue to be disadvantageous in their reliability due to their structural weaknesses due to the use of electrolytes and very thin oxide and dielectric materials. Most capacitors are considered a failure when the capacitance has changed by 25% of its initial value. Accurate and fast monitoring or estimation techniques are essential to be used with low cost and no extra hardware. In order to achieve these objectives, an online, reliable, and high-quality technique that continuously monitors the DC-link capacitor condition in a three-phase back-to-back converter is proposed. In this paper, the particle swarm optimization (PSO)-based support vector regression (PSO-SVR) approach is employed for online capacitance estimation based on sensing or deriving the capacitor current. Because the SVR performance alone severely depends on the tuning of its parameters, the PSO algorithm is used, which enables a fast online-based approach with high-parameter estimation accuracy. Experimental results are provided to verify the validity of the method.