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Diagnosing faults is crucial for ensuring the safety and reliability of medium-voltage direct current (MVDC) systems. In this study, we propose a bidirectional long short-term memory (Bi-LSTM)-based fault diagnosis scheme for the accurate classification of faults occurring in MVDC systems. First, to ensure stability in case a fault occurs, we modeled an MVDC system that included a resistor-based fault current limiter (R-FCL) and a direct current circuit breaker (DCCB). A discrete wavelet transform (DWT) extracted the transient voltages and currents measured using DC lines and AC grids in the frequency–time domain. Based on the digital signal normalized by the DWT, using the measurement data, the Bi-LSTM algorithm was used to classify and learn the types and locations of faults, such as DC line (PTP, P-PTG, and N-PTG) and internal inverter faults. The effectiveness of the proposed fault diagnosis scheme was validated through comparative analysis within the four-terminal MVDC system, demonstrating superior accuracy and a faster diagnosis time compared to those of the existing schemes that utilize other AI algorithms, such as the CNN and LSTM. According to the test results, the proposed fault diagnosis scheme detects MVDC faults and shows a high recognition accuracy of 97.7%. Additionally, when applying the Bi-LSTM-based fault diagnosis scheme, it was confirmed that not only the training diagnosis time (TraDT) but also the average diagnosis time (AvgDT) were 0.03 ms and 0.05 ms faster than LSTM and CNN, respectively. The results validate the superior fault clarification and fast diagnosis performance of the proposed scheme over those of the other methods. [ABSTRACT FROM AUTHOR]

The integration of converter facilities into power systems has warranted a growing need to address the control interference phenomenon. In this study, we propose a facility-based approach to mitigate the control interference between adjacent high-voltage direct current (HVDC) units in a multi-infeed HVDC system. The proposed method uses a series braking resistor (SeBR) as a preventive measure to restore control when a fault occurs in one HVDC unit within a multi-infeed HVDC system, thereby enabling stable operation of adjacent HVDC units. The developed SeBR model incorporates an inductor component and is divided into Stages 1 and 2 to overcome the frequency reduction issue associated with the long-term deployment of conventional R-based SeBRs. Additionally, if the fault continues to affect the control of adjacent HVDC units despite the application of a blocking stage in the faulted HVDC unit, switch controller logic circuits are designed considering the deployment of Stage 2 of the proposed SeBR. The effectiveness of the proposed SeBR is validated based on the detailed modeling and verification of actual HVDC systems via a case study. The results validate the superior performance of the proposed SeBR over other methods in ensuring a continuous stable operation of the system. [ABSTRACT FROM AUTHOR]

Background: There is a growing concern that particulate matter (PM) such as PM2.5 and PM10 has contributed to exacerbating psychological disorders, particularly depression. However, little is known about the roles of these air pollutants on depression in elderly. Therefore, this study aimed to examine the association between PM2.5 and PM10, and depression in the elderly population in South Korea. Methods: We used panel survey data, the Korean Longitudinal Study of Aging (KLoSA), administered by the Labor Institute during the study period of 2016, 2018, and 2020 covering 217 districts in South Korea (n = 7674). Annual district-specific PM2.5 and PM10 concentrations were calculated for the study period from the monthly prediction concentrations produced by a machine-learning-based ensemble model (cross-validated R2: 0.87), then linked to the people matching with year and their residential district. We constructed a generalized estimating equation (GEE) model with a logit link to identify the associations between each of the long-term PM2.5 and PM10 exposures and depression (CES-D 10) after adjusting for individual and regional factors as confounders. Results: In single-pollutant models, we found that long-term 10 μ g / m 3 increments in PM2.5 (OR 1.36, 95% CI 1.20–1.56) and PM10 (OR 1.19, 95% CI 1.10–1.29) were associated with an increased risk of depression in the elderly. Associations were consistent after adjusting for other air pollutants (NO2 and O3) in two-pollutant models. In addition, the impacts substantially differed by regions grouped by the tertile of the population density, for which the risks of particulate matters on depression were substantial in the middle- or high-population-density areas in contrast to the low-population-density areas. Conclusions: Long-term exposure to PM2.5 and PM10 was associated with a higher risk of developing depression in elderly people. The impact was modified by the population density level of the region where they reside. [ABSTRACT FROM AUTHOR]

Background: Previous studies with large data have been widely reported that exposure to fine particulate matter (PM2.5) is associated with all-cause mortality; however, most of these studies adopted ecological time-series designs or have included limited study areas or individuals residing in well-monitored urban areas. However, nationwide cohort studies including cause-specific mortalities with different age groups were sparse. Therefore, this study examined the association between PM2.5 and cause-specific mortality in South Korea using the nationwide cohort., Methods: A longitudinal cohort with 187 917 National Health Insurance Service-National Sample Cohort participants aged 50-79 years in enrolment between 2002 and 2019 was used. Annual average PM2.5 was collected from a machine learning-based ensemble model (a test R2 = 0.87) as an exposure. We performed a time-varying Cox regression model to examine the association between long-term PM2.5 exposure and mortality. To reduce the potential estimation bias, we adopted generalized propensity score weighting method., Results: The association with long-term PM2.5 (2-year moving average) was prominent in mortalities related to diabetes mellitus [hazard ratio (HR): 1.03 (95% CI: 1.01, 1.06)], circulatory diseases [HR: 1.02 (95% CI: 1.00, 1.03)] and cancer [HR: 1.01 (95% CI: 1.00, 1.02)]. Meanwhile, circulatory-related mortalities were associated with a longer PM2.5 exposure period (1 or 2-year lags), whereas respiratory-related mortalities were associated with current-year PM2.5 exposure. In addition, the association with PM2.5 was more evident in people aged 50-64 years than in people aged 65-79 years, especially in heart failure-related deaths., Conclusions: This study identified the hypothesis that long-term exposure to PM2.5 is associated with mortality, and the association might be different by causes of death. Our result highlights a novel vulnerable population: the middle-aged population with risk factors related to heart failure., (© The Author(s) 2024; all rights reserved. Published by Oxford University Press on behalf of the International Epidemiological Association.)