Reliability-oriented ripple minimization using artificial neural networks in power converter systems

Date

2024-12-26

Author

Alemdar, Öztürk Şahin

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Reducing thermal stresses on components to extend their operational lifespan is an effective strategy for enhancing the reliability of power converter systems. Capacitors are one of the main components that limit the overall reliability of a power converter. The primary parameter limiting the lifetime of a capacitor is the hotspot temperature, which is directly related to the amount of ripple current flowing through the capacitor. The interleaving technique can minimize common input/output capacitors' current ripple in power converters employing paralleled power stages. In multi-input, single-output, single-input, multi-output, and cascaded power converter systems, interleaving can also be employed to minimize common capacitors' current ripple. In these systems, finding the optimal phase shift that provides the minimum ripple operation is not straightforward when converter cells are operated under asymmetric conditions such as different input/output voltages or loads. This study proposes an active ripple minimization method based on Artificial Neural Networks (ANNs), which provide optimal phase shift values to operate the common capacitors at the minimum ripple state. The proposed method can be used as an additional active control scheme to reduce the thermal stresses on capacitors, enabling lifetime extension. The proposed method is first validated experimentally on an asymmetric interleaved two-cell Boost converter. Then, it is applied to two-cell and three-cell three-level (3L) Boost converters to achieve capacitor lifetime extension. The effectiveness of the proposed method is shown through experimental results. The proposed method can be used in multi-input, single-output, single-input, multi-output, and cascaded power converter systems as an active control scheme to enhance reliability metrics by reducing the thermal stress on capacitors.

Subject Keywords

ripple minimization, capacitor, lifetime extension, artificial neural networks, asymmetric, interleaving, multiphase converters, reliability

URI

https://hdl.handle.net/11511/113352

Collections

Graduate School of Natural and Applied Sciences, Thesis