Conference Papers

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Author: search.filters.author.Devi, V.S.K.Has files: No

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    Comparative analysis of different machine learning techniques for condition monitoring of capacitors in a SEPIC converter
    (Institute of Electrical and Electronics Engineers Inc., 2022) RAJENDRAN, S.; Jena, D.; Diaz-D, M.; Devi, V.S.K.
    An efficient condition monitoring technique is essential for power converters to avoid unscheduled maintenance. In this work, the condition monitoring of capacitors in a single-ended primary inductance converter (SEPIC) is proposed based on the following machine learning classifiers: K nearest neighbor, support vector machine, back propagation neural network, Naive Bayes, and deep neural network. The feature of the machine learning algorithms is extracted by three node voltages such as voltage across $C$ 1,C2, and load. These features are utilized for training the algorithms. Moreover, the effectiveness of the different classifies are evaluated by considering the accuracy and area under the curve. Further, each algorithm is trained with a different percentage of a dataset. Finally, a comparative study has been made between the algorithms, and the results exhibit that the deep neural network performs better classification than other algorithms. © 2022 IEEE.
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    Machine learning based condition monitoring of a DC-link capacitor in a Back-to-Back converter
    (Institute of Electrical and Electronics Engineers Inc., 2022) RAJENDRAN, S.; Jena, D.; Diaz-D, M.; Devi, V.S.K.
    The utilization of power electronic converters has increased, and the significance of continuous operation is essential in various applications. Therefore, proper condition monitoring (CM) is vital for power converters to eradicate unpredictable maintenance. However, the existing CM techniques may require additional sensors or injection of controlled voltage to the converters. The following machine learning algorithms, such as a K-nearest neighbors (KNN), Support Vector Machine (SVM), and Naive Bayes (NB), have been proposed to monitor the condition of the dc-link capacitor in a Back-to-Back (BTB) converter. The dc-link voltage is measured, and a wavelet decomposition is employed for the feature extraction. Moreover, the performance index evaluates the efficacy of the different classifiers. Further, different datasets have been considered for the evaluation of the classifiers. From this analysis, it is found that the SVM classifier performs better than others. © 2022 IEEE.
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    Comparative analysis of PID, I-PD and fractional order PI-PD for a DC-DC converter
    (Institute of Electrical and Electronics Engineers Inc., 2022) Shanthini, C.; Devi, V.S.K.; RAJENDRAN, S.; Jena, D.
    This article presents the design of fractional order PI-PD (FOPI-PD) controller for maintaining the output voltage of a buck converter. Initially, some conventional controllers were adapted, and these controllers introduced the overshoot and large settling time. Therefore, a FOPI-PD has been adapted to overcome the above limitations. The advantage of the FOPI-PD controller is that the error passes through the proportional and integral rather than all the controller gains, which reduces the overall control action. Further, the efficacy of the controllers is investigated in different simulation studies. The results show that the fractional order PI-PD controller significantly enhances the settling time in the presence of different load levels and controller disturbances. © 2022 IEEE.