## 8 Conclusions  
Energy sustainability is a fundamental goal for modern societies, driven by the increasingly massive use of electricity, particularly from renewable sources.  
However, the widespread use of electricity across various sectors is only achievable through power converters, which contain a particularly vulnerable component: the capacitor. Therefore, it is crucial to design fault diagnosis techniques (FDTs) that can evaluate the health status of capacitors to prevent financial losses due to unexpected shutdowns and minimize potential risks to human safety.  
Capacitor aging is a gradual process, marked by an increase in internal resistance and a decrease in capacitance. However, once a certain level of degradation is reached, the risk of catastrophic failure, such as an open circuit, rises significantly. This makes it essential to pinpoint the optimal time for replacement.  
This paper introduces a non-invasive, data-driven FDT that determines the optimal time for replacing the capacitor in the output filter of a buck converter. The proposed solution integrates a signal processing module (SPM) that extracts attributes from raw data, which are then fed into the information processing module (IPM). The IPM uses these attributes to assess the capacitor's condition by applying a machine learning algorithm, the logistic regression model that was trained on 20% of the dataset and tested on the remaining 80%. To enhance the computational efficiency and reduce the risk of overfitting, feature selection was employed. Additionally, cross-validation was conducted during the training phase to validate the relevance of the selected features. The model showed outstanding results on both the training and test datasets, with accuracy, precision, recall, and F1 score all exceeding 0.998.  
Further validation involved two distinct analyses: evaluation on an independent dataset and real-time implementation during capacitor aging. The former yielded accuracy, precision, recall, and F1 scores greater than 0.99, whereas real-time operation during capacitor degradation sustained comparable performance with all metrics above 0.995.  
Additionally, to evaluate the performance of the proposed FDT in real-world applications, the proposed solution was tested in particularly noisy environments, whose signals of interest have SNR levels below 10 dB. Even under these extreme conditions, the proposed solution maintained excellent performance, achieving an accuracy, precision, recall, and F1 score exceeding 0.98.  
The proposed solution is well suited for microcontroller implementation due to its low computational requirements and sensor-free operation. Additionally, it was successfully implemented across two different converters, which demonstrated its adaptability and scalability.  
Future work will involve implementing the proposed solution in an experimental buck converter prototype. Furthermore, the suitability of the proposed approach will be assessed for other DC-link applications, including power factor correction (PFC), adjustable speed drives (ASDs), and photovoltaic (PV) inverters.  
**Funding:** This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The study did not report any data.  
**Conflicts of Interest:** The author declares no conflicts of interest.