Capacitors are one type of reliability-critical components in power electronic systems. In the last two decades, many efforts in academic research have been devoted to the condition monitoring of capacitors to estimate their health status. Industry applications are demanding more reliable power electronics products with preventive maintenance. Nevertheless, most of the developed capacitor condition monitoring technologies are rarely adopted by industry due to the complexity, increased cost, and other relevant issues. An overview of the prior-art research in this area is therefore needed to justify the required resources and the corresponding performance of each key method. It serves to provide a guideline for industry to evaluate the available solutions by technology benchmarking, as well as to advance the academic research by discussing the history development and the future opportunities. Therefore, this paper first classifies the capacitor condition monitoring methods into three categories, then the respective technology evolution in the last two decades is summarized. Finally, the state-of-the-art research and the future opportunities targeting for industry applications are given.

A Review of the Condition Monitoring of Capacitors in Power Electronic Converters

Capacitors are widely used in dc links of power electronic converters to balance power, suppress voltage ripple, and store short-term energy. Condition monitoring (CM) of dc-link capacitors has great significance in enhancing the reliability of power converter systems. Over the past few years, many efforts have been made to realize CM of dc-link capacitors. This article gives an overview and a comprehensive comparative evaluation of them with emphasis on the application objectives, implementation methods, and monitoring accuracy when being used. First, the design procedure for the CM of capacitors is introduced. Second, the main capacitor parameters estimation principles are summarized. According to these principles, various possible CM methods are derived in a step-by-step manner. On this basis, a comprehensive review and comparison of CM schemes for different types of dc-link applications are provided. Finally, application recommendations and future research trends are presented.

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An Overview of Condition Monitoring Techniques for Capacitors in DC-Link Applications

As electrolytic capacitor is apt to fail in power circuits, it is very important to identify its electrical parameters, mainly the equivalent series resistance (ESR) and capacitance ( C ). A noninvasive online identification method of capacitor's ESR and C for continuous-conduction-mode (CCM) buck converter is proposed in this paper. Based on the ac component of capacitor voltage, the calculation model is founded. By sampling the pulse width modulation signal and output voltage, the method needs no current sensor and is effective for the CCM buck converter operating at different conditions. Implementation of the identification system is presented. The tests are carried out for different aged capacitors and ambient temperatures. The experimental results validate the effectiveness of the method.

A Current-Sensorless Online ESR and C Identification Method for Output Capacitor of Buck Converter

To interconnect low-voltage solar photovoltaics (PV) with dc system, a dc–dc boost converter is required. To minimize the switching frequency oscillations in solar PV voltage, aluminum electrolytic capacitors (AECs) are connected between solar PV and the converter. Operational life of AECs depends on electrical and environmental parameters and is less than that of solar PV panels. Equivalent series resistance (ESR) of AEC increases with life, thereby increasing the voltage ripple across PV terminals. This results in less power extraction from solar PV. This paper proposes a method for health monitoring of AEC using the parameters measured for maximum power point tracking (MPPT) (PV voltage and current). The proposed technique is applicable for both continuous conduction mode and discontinuous conduction mode of operation of the converter. Effect of temperature on ESR is incorporated for accurate determination of the health of capacitor. Key advantage is that no additional current or voltage sensor is required to implement this technique, thereby offering a low-cost solution. Further, the proposed technique does not require significant processing capabilities and is implemented in the same digital controller used for MPPT. Detailed simulation studies are carried out and results are included in this paper. A laboratory prototype of dc–dc boost converter is developed for experimentation. Results are in agreement with that obtained from simulation studies.

Online Monitoring Technique for Aluminum Electrolytic Capacitor in Solar PV-Based DC System

As electrolytic capacitor is apt to fail in power converters, it is very important to monitor its electrical parameters, mainly the equivalent series resistance (ESR) and capacitance (C). A novel non-invasive online monitoring method of DC-link capacitor's ESR and C for boost power factor correction (PFC) converter is proposed in this paper. By analyzing the capacitor voltage ripple which is composed of that of ESR and C, the calculation model is founded, which shows that instead of additional current sensor, only two values of capacitor voltage in particular moments within a line cycle need to be sampled for the calculation of ESR and C. The schematic of the monitoring system together with the design considerations of the trigger circuit, the isolated voltage amplifier, the isolated current amplifier are described in detail. The experimental results show the effectiveness of the method.

An Online Monitoring Scheme of DC-Link Capacitor's ESR and C for a Boost PFC Converter

Aluminum electrolytic capacitors are usually used in DC-DC converters as smoothing capacitors. According to the statistics data, they are the weakest among various power components in the DC-DC converter. With the increase of the running time, the performance degradation of electrolytic capacitors reflects in electrical parameters, mainly the Equivalent Series Resistance (ESR) and capacitance. The best indicator of the output filter capacitor failure is the increase of ESR. And the output ripple voltage of the converter increases with respect to ESR. Monitoring the ESR variation of the electrolytic capacitor, achieving by voltage and current ripple, can estimate the health state of the converter.

ESR estimation method for DC-DC converters based on improved EMD algorithm

Chinese address resolution (CAR) is a key step in geocoding technology, and the resolution results directly affect the service quality of address-based applications. Deep learning models have been widely used in CAR task but they require abundant annotated address data to obtain satisfied performance. In this paper, an active transfer learning method combining uncertainty with diversity for CAR is proposed, for which the main goal is to mitigate the annotation requirement for unlabeled address in the target region and to Improve the utilization of labeled data in the source region. Considering the correlation among Chinese addresses, we propose a clustering method of unlabeled address on the basis of feature words, mined from address data based on LDA model, to reflect the distribution of the address. A metric of comprehensive sample strategy combing uncertainty with diversity (CSSCUD) is constructed to select training samples from the target region, which can obtain high valuable samples by considering informativeness and distribution in feature words space jointly in each batch. Experiments on the address dataset from two different regions show that the comprehensive active transfer learning method achieves a higher resolution accuracy than various baselines by using the same number of labeled training samples, which illustrates that the proposed method is effective and practical for CAR.

An Active Transfer Learning Method Combining Uncertainty with Diversity for Chinese Address Resolution

Portrait recognition is a key task in public security area. Most present portrait recognition methods lay more emphasis on extracting facial features by different algorithms for a variety of scenarios, but focus less on fusion of multiple recognition results. In this paper, we propose a portrait image recommendation method based on collaborative filtering, for which the main task is to improve the hit accuracy by utilizing the portrait recognition results from multiple feature-based models. Considering these models preferences and historical scores on portrait images, user-based collaborative filtering is applied to calculate the similarity between the models. Meanwhile, due to the difference of similarity criteria, an improved comprehensive similarity model based on Auto-Encoder is established to synthesize similarity values calculated by different feature-based models to predict the rating scores of the candidate portrait images. Experiments on portrait images in the wild show that the hit accuracy reaches 85.48% and is 1.11% higher than that best results of the single feature-based models, which illustrates that the proposed method is effective and practical for recommendation of portrait images.

Xueyuan Zheng

Papers

ESR estimation method for DC-DC converters based on improved EMD algorithm

An Active Transfer Learning Method Combining Uncertainty with Diversity for Chinese Address Resolution

A portrait image recommendation method based on collaborative filtering