Online condition monitoring of MV cable feeders using Rogowski coil sensors for PD measurements

Condition monitoring is a highly effective prognostic tool for incipient insulation degradation to avoid sudden failures of electrical components and to keep the power network in operation. Improved operational performance of the sensors and effective measurement techniques could enable the development of a robust monitoring system. This paper addresses two main aspects of condition monitoring: an enhanced design of an induction sensor that has the capability of measuring partial discharge (PD) signals emerging simultaneously from medium voltage cables and transformers, and an integrated monitoring system that enables the monitoring of a wider part of the cable feeder. Having described the conventional practices along with the authors' own experiences and research on non-intrusive solutions, this paper proposes an optimum design of a Rogowski coil that can measure the PD signals from medium voltage cables, its accessories, and the distribution transformers. The proposed PD monitoring scheme is implemented using the directional sensitivity capability of Rogowski coils and a suitable sensor installation scheme that leads to the development of an integrated monitoring model for the components of a MV cable feeder. Furthermore, the paper presents forethought regarding huge amount of PD data from various sensors using a simplified and practical approach. In the perspective of today's changing grid, the presented idea of integrated monitoring practices provide a concept towards automated condition monitoring.This work is done under the project Smart Condition Monitoring of Power Grid that is funded by the Academy of Finland (Grant No. 309412)

AC Microgrids Protection : A Digital Coordinated Adaptive Scheme

A significant challenge for designing a coordinated and effective protection architecture of a microgrid (MG) is the aim of an efficient, reliable, and fast protection scheme for both the grid-connected and islanded modes of operation. To this end, bidirectional power flow, varying short-circuit power, low voltage ride-through (LVRT) capability, and the plug-and-play characteristics of distributed generation units (DGUs), which are key issues in a MG system must be considered; otherwise, a mal-operation of protection devices (PDs) may occur. In this sense, a conventional protection system with a single threshold/setting may not be able to fully protect an MG system. To tackle this challenge, this work presents a comprehensive coordinated adaptive protection scheme for AC MGs that can tune their protection setting according to the system states and the operation mode, and is able to switch the PDs’ setting. In the first step of the proposed adaptive algorithm, an offline setting will be adopted for selective and sensitive fault detection, isolation, and coordination among proposed protective modules. As any change in the system is detected by the proposed algorithm in the online step, a new set of setting for proposed modules will be performed to adapt the settings accordingly. In this way, a new set of settings are adapted to maintain a fast and reliable operation, which covers selective, sensitive, and adaptive requirements. The pickup current (Ip) and time multiple settings (TMS) of directional over-current relays (DOCR), as well as coordinated time delays for the proposed protection scheme for both of the grid-connected and islanded modes of operation, are calculated offline. Then, an online adaptive protection scheme is proposed to detect different fault types in different locations. The simulation results show that the proposed method provides a coordinated reliable solution, which can detect and isolate fault conditions in a fast, selective and coordinated adaptive pattern.© 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).This research was funded by the Department of Energy Technology, Aalborg University, under the Villum Investigator Grant 25920 as a part of the Villum Investigator Program CROM funded by the Villum Foundation.fi=vertaisarvioitu|en=peerReviewed

Modelling of particulate matter concentrations and source contributions in the Helsinki Metropolitan Area in 2008 and 2010

We refined an urban-scale dispersion modelling system by adding a road dust suspension model, FORE. The deterministic modelling includes both vehicular exhaust emissions (including cold start and cold driving) and suspended road dust. The urban scale modelling system was used in combination with the regional scale chemical transport model LOTOSEUROS, for 2008, and the measured regional background concentrations, for 2010. The predictions were compared against measured concentrations of PM2.5 and PM10. PM2.5 concentrations were slightly and the PM10concentrations substantially under-predicted in 2008, mainly due to the under-predicted regional background concentration. Source contributions of suspended road dust varied from 2% to 8% and from 12% to 38% for PM2.5 and PM10, respectively. Long-range transported contributions at the urban traffic stations were 72% to 92% for PM2.5 and 50% to 83% for PM10. © 2016

Methane budget estimates in Finland from the CarbonTracker Europe-CH4 data assimilation system

This is the final version. Available from the publisher via the DOI in this record.We estimated the CH4 budget in Finland for 2004–2014 using the CTE-CH4 data assimilation system with an extended atmospheric CH4 observation network of seven sites from Finland to surrounding regions (Hyytiälä, Kjølnes, Kumpula, Pallas, Puijo, Sodankylä, and Utö). The estimated average annual total emission for Finland is 0.6 ± 0.5 Tg CH4 yr−1. Sensitivity experiments show that the posterior biospheric emission estimates for Finland are between 0.3 and 0.9 Tg CH4 yr−1, which lies between the LPX-Bern-DYPTOP (0.2 Tg CH4 yr−1) and LPJG-WHyMe (2.2 Tg CH4 yr−1) process-based model estimates. For anthropogenic emissions, we found that the EDGAR v4.2 FT2010 inventory (0.4 Tg CH4 yr−1) is likely to overestimate emissions in southernmost Finland, but the extent of overestimation and possible relocation of emissions are difficult to derive from the current observation network. The posterior emission estimates were especially reliant on prior information in central Finland. However, based on analysis of posterior atmospheric CH4, we found that the anthropogenic emission distribution based on a national inventory is more reliable than the one based on EDGAR v4.2 FT2010. The contribution of total emissions in Finland to global total emissions is only about 0.13%, and the derived total emissions in Finland showed no trend during 2004–2014. The model using optimized emissions was able to reproduce observed atmospheric CH4 at the sites in Finland and surrounding regions fairly well (correlation > 0.75, bias < ± ppb), supporting adequacy of the observations to be used in atmospheric inversion studies. In addition to global budget estimates, we found that CTE-CH4 is also applicable for regional budget estimates, where small scale (1x1 in this case) optimization is possible with a dense observation network.Natural Environment Research Council (NERC)NordFrosk Nordic Centre of ExcellenceAcademy of FinlandEuropean Research Council (ERC)Swiss National Science Foundation (SNSF)Swedish Research Counci

Parameter and model uncertainty in a life-table model for fine particles (PM2.5): a statistical modeling study

<p>Abstract</p> <p>Background</p> <p>The estimation of health impacts involves often uncertain input variables and assumptions which have to be incorporated into the model structure. These uncertainties may have significant effects on the results obtained with model, and, thus, on decision making. Fine particles (PM_2.5) are believed to cause major health impacts, and, consequently, uncertainties in their health impact assessment have clear relevance to policy-making. We studied the effects of various uncertain input variables by building a life-table model for fine particles.</p> <p>Methods</p> <p>Life-expectancy of the Helsinki metropolitan area population and the change in life-expectancy due to fine particle exposures were predicted using a life-table model. A number of parameter and model uncertainties were estimated. Sensitivity analysis for input variables was performed by calculating rank-order correlations between input and output variables. The studied model uncertainties were (i) plausibility of mortality outcomes and (ii) lag, and parameter uncertainties (iii) exposure-response coefficients for different mortality outcomes, and (iv) exposure estimates for different age groups. The monetary value of the years-of-life-lost and the relative importance of the uncertainties related to monetary valuation were predicted to compare the relative importance of the monetary valuation on the health effect uncertainties.</p> <p>Results</p> <p>The magnitude of the health effects costs depended mostly on discount rate, exposure-response coefficient, and plausibility of the cardiopulmonary mortality. Other mortality outcomes (lung cancer, other non-accidental and infant mortality) and lag had only minor impact on the output. The results highlight the importance of the uncertainties associated with cardiopulmonary mortality in the fine particle impact assessment when compared with other uncertainties.</p> <p>Conclusion</p> <p>When estimating life-expectancy, the estimates used for cardiopulmonary exposure-response coefficient, discount rate, and plausibility require careful assessment, while complicated lag estimates can be omitted without this having any major effect on the results.</p

Modelling of the urban concentrations of PM_2.5 on a high resolution for a period of 35 years, for the assessment of lifetime exposure and health effects

Reliable and self-consistent data on air quality are needed for an extensive period of time for conducting long-term, or even lifetime health impact assessments. We have modelled the urban-scale concentrations of fine particulate matter (PM2.5) in the Helsinki Metropolitan Area for a period of 35 years, from 1980 to 2014. The regional background concentrations were evaluated based on reanalyses of the atmospheric composition on global and European scales, using the SILAM model. The high-resolution urban computations included both the emissions originated from vehicular traffic (separately exhaust and suspension emissions) and those from small-scale combustion, and were conducted using the road network dispersion model CAR-FMI and the multiple-source Gaussian dispersion model UDM-FMI. The modelled concentrations of PM2.5 agreed fairly well with the measured data at a regional background station and at four urban measurement stations, during 1999–2014. The modelled concentration trends were also evaluated for earlier years, until 1988, using proxy analyses. There was no systematic deterioration of the agreement of predictions and data for earlier years (the 1980s and 1990s), compared with the results for more recent years (2000s and early 2010s). The local vehicular emissions were about 5 times higher in the 1980s, compared with the emissions during the latest considered years. The local small-scale combustion emissions increased slightly over time. The highest urban concentrations of PM2.5 occurred in the 1980s; these have since decreased to about to a half of the highest values. In general, regional background was the largest contribution in this area. Vehicular exhaust has been the most important local source, but the relative shares of both small-scale combustion and vehicular non-exhaust emissions have increased in time. The study has provided long-term, high-resolution concentration databases on regional and urban scales that can be used for the assessment of health effects associated with air pollution