Aluminum electrolytic capacitors (AECs) are connected in a bank to meet the requirement of capacitance, voltage, and current rating for dc-link of power electronic converters. The performance of capacitor bank deteriorates with time due to the degradation of AECs in the bank. Further, each AEC in the bank degrades differently based on its core temperature, which in turn depends on the current and its equivalent series resistance (ESR). Therefore, health monitoring of a bank would require monitoring of individual AECs in the bank. However, extending the available literature on health monitoring of an AEC to the capacitor bank would require voltage/current sensors for individual AECs. To address the aforementioned issue, this paper suggests a method to assess the health of individual AECs online in the bank without using sensors for each AECs. The proposed method involves estimation of current through each capacitor, which is used to determine the core temperature by using a thermal model of AEC. The core temperature is used for estimation of ESR and capacitance of each capacitor using physics-of-failure based degradation model. For the consistent matching of the degradation model to the actual degradation of the capacitor, the coefficients of the model are sequentially updated. For updating, an algorithm based on extended Kalman filter is used. The proposed scheme is experimentally validated on laboratory prototype of single-phase, grid-connected solar PV inverter under variable load conditions.

中文翻译：

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降低传感器要求的组中单个电容器的健康评估

铝电解电容器 (AEC) 成组连接，以满足电力电子转换器直流链路的电容、电压和电流额定值要求。由于电容器组中 AEC 的退化，电容器组的性能会随着时间的推移而恶化。此外，组中的每个 AEC 会根据其核心温度而不同程度地降级，而这又取决于电流及其等效串联电阻 (ESR)。因此，银行的健康监控需要对银行中的各个 AEC 进行监控。然而，将有关 AEC 健康监测的可用文献扩展到电容器组将需要用于单个 AEC 的电压/电流传感器。为了解决上述问题，本文提出了一种方法来评估银行中单个 AEC 的健康状况，而无需为每个 AEC 使用传感器。所提出的方法涉及估计通过每个电容器的电流，该方法用于通过使用 AEC 的热模型来确定核心温度。核心温度用于使用基于故障物理的退化模型来估计每个电容器的 ESR 和电容。为了使退化模型与电容器的实际退化一致匹配，模型的系数依次更新。对于更新，使用基于扩展卡尔曼滤波器的算法。所提出的方案在可变负载条件下的单相并网太阳能光伏逆变器的实验室原型上进行了实验验证。核心温度用于使用基于故障物理的退化模型来估计每个电容器的 ESR 和电容。为了使退化模型与电容器的实际退化一致匹配，模型的系数依次更新。对于更新，使用基于扩展卡尔曼滤波器的算法。所提出的方案在可变负载条件下的单相并网太阳能光伏逆变器的实验室原型上进行了实验验证。核心温度用于使用基于故障物理的退化模型来估计每个电容器的 ESR 和电容。为了使退化模型与电容器的实际退化一致匹配，模型的系数依次更新。对于更新，使用基于扩展卡尔曼滤波器的算法。所提出的方案在可变负载条件下的单相并网太阳能光伏逆变器的实验室原型上进行了实验验证。