• 전기학회논문지
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• 제60권7호
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• pp.1438-1443
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• 2011

The increase of power capacity in the superconducting fault current limiter (SFCL) is essential for application into the power grid. To achieve this, when superconducting units were connected in series and parallel, the unbalanced quenching characteristics between superconducting units generated by different critical current behavior should be improved. In the transformer-type SFCL, the superconducting units connected in series could be simultaneously quenched by the connection of neutral lines between secondary coils and superconducting units. From this the consumed power in superconducting units was equally distributed. In addition, the more the turn ratio of the transformer was reduced, the more consumed power in the superconducting units was reduced by the decrease of the induction voltage generated in the superconducting units. From those results, the transformer-type SFCL using neutral lines could increase the power capacity of the SFCL by the equal power division into the superconducting units.

• Journal of Power Electronics
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• 제11권3호
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• pp.369-374
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• 2011

Usually, a LPF (low pass filter) is used in the feedback loop of a SRF (synchronous reference frame) - PLL (phase locked loop) system because the measured grid voltage contains harmonic distortions and sensor noises. In this paper, it is shown that the cut-off frequency of the LPF should be designed to suppress the harmonic ripples contained in the measured voltage. Also, a new design method for the loop gain of the PI-type controller in the SRF-PLL is proposed with consideration of the dynamics of the LPF. As a result, a better transient response can be obtained with the proposed design method. The LPF frequency and the PI controller gain are designed in coordination according to the steady state and dynamic performance requirements. Furthermore, in the proposed method, the controller gain and the LPF cut-off frequency are changed from their normal value to a transient value when a voltage disturbance is detected. This paper shows the feasibility and usefulness of the proposed methods through the computer simulations and experimental results.

• 한국방송∙미디어공학회:학술대회논문집
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• 한국방송∙미디어공학회 2022년도 하계학술대회
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• pp.1305-1307
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• 2022

자동차 부품의 결함은 시스템 전체의 성능 저하 및 인적 물적 손실이 발생할 수 있으므로 생산라인에서의 불량 검출은 매우 중요하다. 따라서 정확하고 균일한 결과의 불량 검출을 위해 딥러닝 기반의 고장 진단 시스템이 다양하게 연구되고 있다. 하지만 제조현장에서는 정상 샘플보다 비정상 샘플의 발생 빈도가 현저히 낮다. 이는 학습 데이터의 클래스 불균형 문제로 이어지게 되고, 이러한 불균형 문제는 고장을 판별하는 분류 모델의 성능에 영향을 끼치게 된다. 이에 본 연구에서는 모터의 동작음으로부터 불량 모터를 판별하는 불량 검출 시스템 설계를 위한 데이터 불균형 해결 방법을 제안한다. 자동차 사이드 미러 모터의 동작음을 학습 및 테스트를 위한 데이터 셋으로 사용하였으며 손실함수 계산 시 학습 데이터 셋의 클래스별 샘플 수 가 반영되는 label-distribution-aware margin(LDAM) loss 와 Inception, ResNet, DenseNet 신경망 모델의 비교 분석을 통해 불균형 데이터를 처리할 수 있는 가능성을 보여주었다.