Evaluation of a Local Fault Detection Algorithm for HVDC Systems

A great increase in the amount of energy generated from clean and renewable sources integrated in the electric power system is expected worldwide in the coming years. High Voltage Direct Current (HVDC) systems are seen as a promising alternative to the traditional Alternating Current (AC) systems for the expansion of the electric power system. However, to achieve this vision, there are some remaining challenges regarding HVDC systems which need to be solved. One of the main challenges is related to fault detection and location in HVDC grids. This paper reviews the main protection algorithms available and presents the evaluation of a local fault detection algorithm for DC faults in a multi-terminal Voltage Source Conversion (VSC) based HVDC grid. The paper analyses the influence of the DC voltage sampling frequency and the cable length in the performance of the algorithm. © 2019, European Association for the Development of Renewable Energy, Environment and Power Quality (EA4EPQ).The authors thank the support from the Spanish Ministry of Economy, Industry and Competitiveness (project ENE2016-79145-R AEI/FEDER, UE) and GISEL research group IT1083-16), as well as from the University of the Basque Country UPV/EHU (research group funding PPG17/23)

Fault detection based on ROCOV in a multi-terminal HVDC grid

Protection of a meshed VSC-HVDC grid is a challenge due to the behaviour of DC current and voltage signals during fault conditions. Protection systems must operate in a very short time range. Since fault detection should be very fast, local measurement based algorithms are mostly used; communication based algorithms lack the needed speed as a result of the communication time delay. This way, a ROCOV algorithm is proposed in this paper. This algorithm is analysed for different fault conditions.The authors gratefully acknowledge the support from the Spanish Ministry of Economy, Industry and Competitiveness (project ENE2016-79145-R AEI/FEDER, UE), the Basque Government (GISEL research group IT1191-19), as well as from the University of the Basque Country UPV/EHU (research group funding GIU18/181)

Assessment of distribution grid losses depending on storage location for residential PV systems in three grids in the region of Murcia

This paper shows the results of the studies carried out to determine which system, batteries installed behind the meter (BTM) or batteries installed in front of the meter (FTM), shows the best performance in terms of minimal power losses and energy storage capacity that would have the Battery Energy Storage Systems (BESS) to be installed. The analysis has been carried out over real networks in the region of Murcia (Spain) with different topologies and a variable number and location of customers along the grid. In addition, real consumption profiles of random customers, as well as real measurements of the transformer loading curves recorded at the Secondary Substation (SS), have been considered in the analysis. In this way, it can be ensured that the results represent real situations that can be found nowadays

A Comparative Study of Static VAR Systems for ImprovingVoltage Stability in Expansion of Mining Projects with Gearless Motor Drives

In the next decade, the demand for copper is expected to grow by 25%, posing a challenge for mining facilities to increase their production by opening new mines or upgrading existing ones. To optimize the electrical infrastructure of mining facilities, the integration of flexible AC transmission systems (FACTSs) can be instrumental in mitigating voltage stability issues that may arise during the installation of new gearless motor drives (GMDs). The purpose of this paper is to conduct a comparative analysis of the dynamic performance of static VAR systems (SVSs) as a means of enhancing voltage stability in mining expansion projects, particularly in the context of the integration of new GMDs into the system. This paper presents a case study of an existing three crushing-line mine configuration that has been upgraded with two new GMD systems. The primary contribution of this research is a comprehensive methodology designed to enhance the stability of a mining system through the integration of an SVS, which includes the sizing of the SVS system, an analysis of costs, as well as a determination of the required installation surface and optimal placement of the SVS within the system. The simulation results conclusively demonstrate the effectiveness of the SVS systems in reducing the voltage drop by 2% upon activation of new GMDs, as well as mitigating the adverse impact of transient disturbances on the system. Specifically, the first oscillation voltage peak value is improved by 3.5%, following a three-phase short circuit of 1 second duration, while overvoltage is reduced by 1% in response to sudden load changes. When compared with the system without an installed SVS, these findings highlight the significant advantages and benefits of integrating SVSs into mining operations

Sliding Mode Real-Time Control of Photovoltaic Systems Using Neural Estimators

The maximum power point tracking (MPPT) problem has attracted the attention of many researchers, because it is convenient to obtain the maximum power of a photovoltaic module regardless of the weather conditions and the load. In this paper, a novel control for a boost DC/DC converter has been introduced. It is based on a sliding mode controller (SMC) that takes a current signal as reference instead of a voltage, which is generated by a neuronal reference current generator. That reference current indicates the current (IMPP) at the maximum power point (MPP) for given weather conditions. In order to test the designed control system, a photovoltaic module model based on a second artificial neuronal network (ANN) has been obtained from experimental data gathered during 18 months in the Faculty of Engineering Vitoria-Gasteiz (Spain). We have analyzed the performance of such model and we found that it is very accurate (MSE = 0.062 A and R = 0.991 with test dataset). We also have tested the performance of the overall SMC design with both simulated and real tests, concluding that it guarantees that the power in the output of the converter is very close to the power of the photovoltaic module output

Anti-carbamylated protein antibodies positivity and disease activity in Hispanic patients with established rheumatoid arthritis: An observational study

Objectives We aimed to determine the prevalence of anti-carbamylated protein (anti-CarP) antibodies in Mexican Hispanics with established rheumatoid arthritis (RA) and to assess their relationship with disease activity. Methods A cohort study was conducted in 278 patients with established RA during an 18-month follow-up. We measured IgG/IgM/IgA rheumatoid factor (RF), IgG anticitrullinated protein antibodies (ACPA) and IgG/IgM/IgA anti-CarP antibodies using enzyme-linked immunosorbent assay (ELISA). For disease activity, we performed the 28-joint disease activity score with erythrocyte sedimentation rate (DAS28-ESR). Repeated measures one-way ANOVA was used to test the association between anti-CarP IgG antibody status and longitudinal DAS28-ESR scores. Patients were evaluated at baseline and at 6, 12, and 18 months during follow-up. Results Anti-CarP IgG antibodies were positive in 47.8% of patients and, accounting for all isotypes, in 9.5% of patients with negative RF and ACPA. Triple antibody positivity was present in 42.6% of patients in our sample. Anti-CarP IgG antibody positivity did not show statistically significant differences in mean DAS28-ESR when compared to anti-CarP IgG antibody negative patients at baseline, 6, 12 or 18 months. Conclusion Anti-CarP IgG antibodies are not associated to a higher disease activity in Hispanic patients with established RA. Our findings suggest that the clinical value of measuring anti-CarP antibodies in RA diminishes over time.Transplantation and autoimmunit

Outcomes after perioperative SARS-CoV-2 infection in patients with proximal femoral fractures: an international cohort study

Objectives Studies have demonstrated high rates of mortality in people with proximal femoral fracture and SARS-CoV-2, but there is limited published data on the factors that influence mortality for clinicians to make informed treatment decisions. This study aims to report the 30-day mortality associated with perioperative infection of patients undergoing surgery for proximal femoral fractures and to examine the factors that influence mortality in a multivariate analysis. Setting Prospective, international, multicentre, observational cohort study. Participants Patients undergoing any operation for a proximal femoral fracture from 1 February to 30 April 2020 and with perioperative SARS-CoV-2 infection (either 7 days prior or 30-day postoperative). Primary outcome 30-day mortality. Multivariate modelling was performed to identify factors associated with 30-day mortality. Results This study reports included 1063 patients from 174 hospitals in 19 countries. Overall 30-day mortality was 29.4% (313/1063). In an adjusted model, 30-day mortality was associated with male gender (OR 2.29, 95% CI 1.68 to 3.13, p80 years (OR 1.60, 95% CI 1.1 to 2.31, p=0.013), preoperative diagnosis of dementia (OR 1.57, 95% CI 1.15 to 2.16, p=0.005), kidney disease (OR 1.73, 95% CI 1.18 to 2.55, p=0.005) and congestive heart failure (OR 1.62, 95% CI 1.06 to 2.48, p=0.025). Mortality at 30 days was lower in patients with a preoperative diagnosis of SARS-CoV-2 (OR 0.6, 95% CI 0.6 (0.42 to 0.85), p=0.004). There was no difference in mortality in patients with an increase to delay in surgery (p=0.220) or type of anaesthetic given (p=0.787). Conclusions Patients undergoing surgery for a proximal femoral fracture with a perioperative infection of SARS-CoV-2 have a high rate of mortality. This study would support the need for providing these patients with individualised medical and anaesthetic care, including medical optimisation before theatre. Careful preoperative counselling is needed for those with a proximal femoral fracture and SARS-CoV-2, especially those in the highest risk groups. Trial registration number NCT0432364