LOAD FLOW ANALYSIS OF132/11KV SUBSTATION USING ETAP: A CASE STUDY

In the operation and design planning for the power system, the most significant and beneficial approach for the investigation of problems relating to power systems can be done by means of load flow analysis or design power flow. In light of a predefined structured power system and transmission system, the load flow analysis provides steady state characteristic data for voltage phase angles and its magnitude, the flow of reactive power in the transmission lines, losses in the system, generation and consumption of reactive power in the bus bar load. In this paper, an endeavor has been made to explore power flow in the 132kV grid by utilizing ETAP. The data is collected from Kohat 132KV substation over a period of one year, specifically in summer and winter peak loads

LOAD FLOW ANALYSIS OF132/11KV SUBSTATION USING ETAP: A CASE STUDY

In the operation and design planning for the power system, the most significant and beneficial approach for the investigation of problems relating to power systems can be done by means of load flow analysis or design power flow. In light of a predefined structured power system and transmission system, the load flow analysis provides steady state characteristic data for voltage phase angles and its magnitude, the flow of reactive power in the transmission lines, losses in the system, generation and consumption of reactive power in the bus bar load. In this paper, an endeavor has been made to explore power flow in the 132kV grid by utilizing ETAP. The data is collected from Kohat 132KV substation over a period of one year, specifically in summer and winter peak loads

Subnational mapping of HIV incidence and mortality among individuals aged 15–49 years in sub-Saharan Africa, 2000–18 : a modelling study

Background: High-resolution estimates of HIV burden across space and time provide an important tool for tracking and monitoring the progress of prevention and control efforts and assist with improving the precision and efficiency of targeting efforts. We aimed to assess HIV incidence and HIV mortality for all second-level administrative units across sub-Saharan Africa. Methods: In this modelling study, we developed a framework that used the geographically specific HIV prevalence data collected in seroprevalence surveys and antenatal care clinics to train a model that estimates HIV incidence and mortality among individuals aged 15–49 years. We used a model-based geostatistical framework to estimate HIV prevalence at the second administrative level in 44 countries in sub-Saharan Africa for 2000–18 and sought data on the number of individuals on antiretroviral therapy (ART) by second-level administrative unit. We then modified the Estimation and Projection Package (EPP) to use these HIV prevalence and treatment estimates to estimate HIV incidence and mortality by second-level administrative unit. Findings: The estimates suggest substantial variation in HIV incidence and mortality rates both between and within countries in sub-Saharan Africa, with 15 countries having a ten-times or greater difference in estimated HIV incidence between the second-level administrative units with the lowest and highest estimated incidence levels. Across all 44 countries in 2018, HIV incidence ranged from 2 ·8 (95% uncertainty interval 2·1–3·8) in Mauritania to 1585·9 (1369·4–1824·8) cases per 100 000 people in Lesotho and HIV mortality ranged from 0·8 (0·7–0·9) in Mauritania to 676· 5 (513· 6–888·0) deaths per 100 000 people in Lesotho. Variation in both incidence and mortality was substantially greater at the subnational level than at the national level and the highest estimated rates were accordingly higher. Among second-level administrative units, Guijá District, Gaza Province, Mozambique, had the highest estimated HIV incidence (4661·7 [2544·8–8120·3]) cases per 100000 people in 2018 and Inhassunge District, Zambezia Province, Mozambique, had the highest estimated HIV mortality rate (1163·0 [679·0–1866·8]) deaths per 100 000 people. Further, the rate of reduction in HIV incidence and mortality from 2000 to 2018, as well as the ratio of new infections to the number of people living with HIV was highly variable. Although most second-level administrative units had declines in the number of new cases (3316 [81· 1%] of 4087 units) and number of deaths (3325 [81·4%]), nearly all appeared well short of the targeted 75% reduction in new cases and deaths between 2010 and 2020. Interpretation: Our estimates suggest that most second-level administrative units in sub-Saharan Africa are falling short of the targeted 75% reduction in new cases and deaths by 2020, which is further compounded by substantial within-country variability. These estimates will help decision makers and programme implementers expand access to ART and better target health resources to higher burden subnational areas

Measuring routine childhood vaccination coverage in 204 countries and territories, 1980-2019 : a systematic analysis for the Global Burden of Disease Study 2020, Release 1

Background Measuring routine childhood vaccination is crucial to inform global vaccine policies and programme implementation, and to track progress towards targets set by the Global Vaccine Action Plan (GVAP) and Immunization Agenda 2030. Robust estimates of routine vaccine coverage are needed to identify past successes and persistent vulnerabilities. Drawing from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2020, Release 1, we did a systematic analysis of global, regional, and national vaccine coverage trends using a statistical framework, by vaccine and over time. Methods For this analysis we collated 55 326 country-specific, cohort-specific, year-specific, vaccine-specific, and dosespecific observations of routine childhood vaccination coverage between 1980 and 2019. Using spatiotemporal Gaussian process regression, we produced location-specific and year-specific estimates of 11 routine childhood vaccine coverage indicators for 204 countries and territories from 1980 to 2019, adjusting for biases in countryreported data and reflecting reported stockouts and supply disruptions. We analysed global and regional trends in coverage and numbers of zero-dose children (defined as those who never received a diphtheria-tetanus-pertussis [DTP] vaccine dose), progress towards GVAP targets, and the relationship between vaccine coverage and sociodemographic development. Findings By 2019, global coverage of third-dose DTP (DTP3; 81.6% [95% uncertainty interval 80.4-82 .7]) more than doubled from levels estimated in 1980 (39.9% [37.5-42.1]), as did global coverage of the first-dose measles-containing vaccine (MCV1; from 38.5% [35.4-41.3] in 1980 to 83.6% [82.3-84.8] in 2019). Third- dose polio vaccine (Pol3) coverage also increased, from 42.6% (41.4-44.1) in 1980 to 79.8% (78.4-81.1) in 2019, and global coverage of newer vaccines increased rapidly between 2000 and 2019. The global number of zero-dose children fell by nearly 75% between 1980 and 2019, from 56.8 million (52.6-60. 9) to 14.5 million (13.4-15.9). However, over the past decade, global vaccine coverage broadly plateaued; 94 countries and territories recorded decreasing DTP3 coverage since 2010. Only 11 countries and territories were estimated to have reached the national GVAP target of at least 90% coverage for all assessed vaccines in 2019. Interpretation After achieving large gains in childhood vaccine coverage worldwide, in much of the world this progress was stalled or reversed from 2010 to 2019. These findings underscore the importance of revisiting routine immunisation strategies and programmatic approaches, recentring service delivery around equity and underserved populations. Strengthening vaccine data and monitoring systems is crucial to these pursuits, now and through to 2030, to ensure that all children have access to, and can benefit from, lifesaving vaccines. Copyright (C) 2021 The Author(s). Published by Elsevier Ltd.Peer reviewe

Dimethyl fumarate in patients admitted to hospital with COVID-19 (RECOVERY): a randomised, controlled, open-label, platform trial

Dimethyl fumarate (DMF) inhibits inflammasome-mediated inflammation and has been proposed as a treatment for patients hospitalised with COVID-19. This randomised, controlled, open-label platform trial (Randomised Evaluation of COVID-19 Therapy [RECOVERY]), is assessing multiple treatments in patients hospitalised for COVID-19 (NCT04381936, ISRCTN50189673). In this assessment of DMF performed at 27 UK hospitals, adults were randomly allocated (1:1) to either usual standard of care alone or usual standard of care plus DMF. The primary outcome was clinical status on day 5 measured on a seven-point ordinal scale. Secondary outcomes were time to sustained improvement in clinical status, time to discharge, day 5 peripheral blood oxygenation, day 5 C-reactive protein, and improvement in day 10 clinical status. Between 2 March 2021 and 18 November 2021, 713 patients were enroled in the DMF evaluation, of whom 356 were randomly allocated to receive usual care plus DMF, and 357 to usual care alone. 95% of patients received corticosteroids as part of routine care. There was no evidence of a beneficial effect of DMF on clinical status at day 5 (common odds ratio of unfavourable outcome 1.12; 95% CI 0.86-1.47; p = 0.40). There was no significant effect of DMF on any secondary outcome

Dimethyl fumarate in patients admitted to hospital with COVID-19 (RECOVERY): a randomised, controlled, open-label, platform trial

Dimethyl fumarate (DMF) inhibits inflammasome-mediated inflammation and has been proposed as a treatment for patients hospitalised with COVID-19. This randomised, controlled, open-label platform trial (Randomised Evaluation of COVID-19 Therapy [RECOVERY]), is assessing multiple treatments in patients hospitalised for COVID-19 (NCT04381936, ISRCTN50189673). In this assessment of DMF performed at 27 UK hospitals, adults were randomly allocated (1:1) to either usual standard of care alone or usual standard of care plus DMF. The primary outcome was clinical status on day 5 measured on a seven-point ordinal scale. Secondary outcomes were time to sustained improvement in clinical status, time to discharge, day 5 peripheral blood oxygenation, day 5 C-reactive protein, and improvement in day 10 clinical status. Between 2 March 2021 and 18 November 2021, 713 patients were enroled in the DMF evaluation, of whom 356 were randomly allocated to receive usual care plus DMF, and 357 to usual care alone. 95% of patients received corticosteroids as part of routine care. There was no evidence of a beneficial effect of DMF on clinical status at day 5 (common odds ratio of unfavourable outcome 1.12; 95% CI 0.86-1.47; p = 0.40). There was no significant effect of DMF on any secondary outcome

Open switch fault diagnosis of cascaded H-bridge 5-level inverter using deep learning

Cascaded H-bridge 5-level inverters (CHB-5LIs) have gained significant traction in high-power applications owing to their capacity to produce high-quality output voltage with minimal harmonic distortion. However, their intricate architecture presents notable challenges for fault diagnosis, particularly concerning open switch faults. In this study, we propose a deep learning-based approach for diagnosing open switch faults in CHB-5LIs. We present a simulation model of the CHB-5LI with open switch faults and generate a dataset comprising voltage waveforms for various fault scenarios. Leveraging this dataset, we train a Convolutional-1D Neural Network (CNN-1D) featuring a multi-layer architecture comprising convolutional and fully connected layers, culminating in the Softmax function for classification. Our method achieves an impressive classification accuracy exceeding 98 percent on previously unseen fault scenarios, underscoring the efficacy of our approach for CHB-5LI fault diagnosis. Additionally, we conducted a thorough analysis of CNN-1D performance and compared it with traditional and other deep learning models for fault diagnosis techniques. The accuracy of other deep learning models on the generated dataset is as follows: RNN is 88.9 percent, 1D-ResNet is 88.8 percent, and Time Inception model is 89.4 percent. Simulation results showcase that our proposed CNN-1D based approach surpasses other methods in terms of accuracy and robustness, elucidating the potential of deep learning for fault diagnosis in intricate power electronics systems. The fault diagnosis time for the proposed method as a fault diagnosis tool for the simulation case is 0.060 ms, compared to 0.062 ms for RNN and 0.065 ms for ResNet

Open switch fault diagnosis of cascaded H-bridge 5-level inverter using deep learning

Cascaded H-bridge 5-level inverters (CHB-5LIs) have gained significant traction in high-power applications owing to their capacity to produce high-quality output voltage with minimal harmonic distortion. However, their intricate architecture presents notable challenges for fault diagnosis, particularly concerning open switch faults. In this study, we propose a deep learning-based approach for diagnosing open switch faults in CHB-5LIs. We present a simulation model of the CHB-5LI with open switch faults and generate a dataset comprising voltage waveforms for various fault scenarios. Leveraging this dataset, we train a Convolutional-1D Neural Network (CNN-1D) featuring a multi-layer architecture comprising convolutional and fully connected layers, culminating in the Softmax function for classification. Our method achieves an impressive classification accuracy exceeding 98 percent on previously unseen fault scenarios, underscoring the efficacy of our approach for CHB-5LI fault diagnosis. Additionally, we conducted a thorough analysis of CNN-1D performance and compared it with traditional and other deep learning models for fault diagnosis techniques. The accuracy of other deep learning models on the generated dataset is as follows: RNN is 88.9 percent, 1D-ResNet is 88.8 percent, and Time Inception model is 89.4 percent. Simulation results showcase that our proposed CNN-1D based approach surpasses other methods in terms of accuracy and robustness, elucidating the potential of deep learning for fault diagnosis in intricate power electronics systems. The fault diagnosis time for the proposed method as a fault diagnosis tool for the simulation case is 0.060 ms, compared to 0.062 ms for RNN and 0.065 ms for ResNet