No abstract

One of the future challenges in Modular Multilevel Converters (MMCs) is how to size key components with compromised costs and design margins, while fulfilling specific reliability targets. It demands better thermal modeling compared to the state-of-the-arts in terms of both accuracy and simplicity. Different from two-level power converters, MMCs have inherent dc-bias in arm currents and the power device conduction time is affected by operational parameters. A time-wise thermal modeling for the power devices in MMCs is therefore an iteration process and time-consuming. This paper thus proposes a simply analytical thermal modeling method, which adopts equivalent periodic power loss profiles. More importantly, time-domain simulations are not required in the proposed method. Benchmarking of the proposed methods with the prior-art solutions is performed in terms of parameter sensitivity and model accuracy with a case study on a 30-MW MMC system. Experiments are carried out on a specifically designed scaled-down system to verify the electro-thermal aspects. Index Terms-Insulated gate bipolar transistor (IGBT), modular multilevel converter (MMC), power semiconductor, reliability, thermal stress estimation, thermal design.

This paper proposes a mission profile-based reliability prediction method for Modular Multilevel Converters (MMCs). It includes key modeling steps, such as long-term mission profile, analytical power loss models, system-level and component-level thermal modeling, lifetime modeling, Monte-Carlo analysis, and redundancy analysis. Thermal couplings and uneven thermal stresses among sub-modules are considered. A case study of a 15-kVA down-scale MMC has been used to demonstrate the proposed method and validate the theoretical analysis. The outcomes serve as a first step for developing realistic reliability analysis and model-based design methods for full-scale MMCs in practical applications.

Abstract-Power cycling in semiconductor modules contributes to repetitive thermal-mechanical stresses, which in return accumulate as fatigue on the devices, and challenge the lifetime. Typically, lifetime models are expressed in number-of-cycles, within which the device can operate without failures under predefined conditions. In these lifetime models, thermal stresses (e.g., junction temperature variations) are commonly considered. However, the lifetime of power devices involves in crossdisciplinary knowledge. As a result, the lifetime prediction is affected by the selected lifetime model. In this regard, this paper benchmarks the most commonly-employed lifetime models of power semiconductor devices for offshore Modular Multilevel Converters (MMC) based wind farms. The benchmarking reveals that the lifetime model selection has a significant impact on the lifetime estimation. The use of analytical lifetime models should be justified in terms of applicability, limitations, and underlying statistical properties.

No abstract