A Novel Blended State Estimated Adaptive Controller for Voltage and Current Control of Microgrid against Unknown Noise

© 2013 IEEE. In this study, a novel blended state estimated adaptive controller is designed for voltage and current control of microgrid against unknown noise. The core feature of the microgrid (MG) is its ability to integrate more than one distributed energy resource into the main grid. The state of a microgrid may deteriorate due to many reasons, for example malicious cyber-attacks, disturbances, packet losses, etc. Therefore, it is necessary to achieve the true state of the system to enhance the control requirement and automation of the microgrid. To achieve the true state of a microgrid, this study proposes the use of an algorithm based on the unscented kalman filter (UKF). The proposed state estimator technique is developed using an unscented-transformation and sigma-points measurement technique capable of minimizing the mean and covariance of a nonlinear cost function to estimate the true state of a single-phase, three-phase single-source and three-phase multi-source microgrid system. The advantage of the proposed estimator over using extended kalman filter (EKF) is investigated in simulations. The results demonstrate that the use of the UKF estimator produces a superior estimation of the system compared with the use of the EKF. An adaptive PID controller is also developed and used in system conjunction with the estimator to regulate its voltage and current against the number of loads. Deviation in load parameters hamper the function of the MG system. The performance of the developed controller is also evaluated against number of loads. Results indicate the controller provides a more stable and high-tracking performance with the inclusion of the UKF in the system

Risk factors for depression and anxiety disorders among Bangladeshi dental students: a cross-sectional survey study

Dental students are highly prone to mental health suffering due to the stressors related to their educational, academic, and clinical environment. However, there are no previous studies that evaluated the mental health problems among dental students in Bangladesh. Therefore , the present study assessed the prevalence of depression and anxiety and its covariates. A cross-sectional survey was carried out among the students of five (out of a total of nine) Bangladeshi public dental institutes. The survey included the Patient Health Questionnaire, the Generalized Anxiety Disorder scale, and questions relating to sociodemographic factors , personal suffering, and family mental health history. Results indicated that the prevalence rates for moderate to severe depression and anxiety were 27.4% and 18.2%, respectively. The risk factors for depression and anxiety were being female, coming from city areas, having past-year psychological and physical suffering, having past-year traumatic events, and having a family history of mental illness, as well as a range of suicide-related behaviors. The prevalence rates of depression and anxiety in the present study were relatively lower than other dental student cohorts residing outside Bangladesh. Further investigations are suggested based on the limitations and strengths of the present study

Modeling drivers to big data analytics in supply chains

The recent emergence of data-driven business markets and the ineligibility of traditional data management systems to trace them have fostered the application of Big Data Analytics (BDA) in supply chains of the present decade. Literature reviews reveal that the successful implication of BDA in a supply chain mainly depends on some key drivers considering the size and operations of an organization. However, collective analysis of all these drivers is still neglected in the existing research field. Therefore, the purpose of this research is to identify and prioritize the most significant drivers of BDA in the supply chains. To this aim, a novel Best-worst method (BWM) based framework has been proposed, which has successfully identified and sequenced the twelve most significant drivers with the help of previous literature and experts’ opinions. Theoretically, this study contributes to the BDA literature by offering some unique drivers to BDA in supply chains. The findings show that ‘sophisticated structure of information technology’ and ‘group collaboration among business partners’ are the top most significant drivers. ‘Digitization of society’ is identified as the least significant driver of BDA in this study. The outcome of this study is expected to assist the industry managers to find out the most and least preferable drivers in their supply chains and then take initiatives to improve the overall efficiency of their organizations accordingly

Study of a two steps process for the valorization of PVC-containing wastes

Published online 27 November 2012The presence of organic compounds in wastes, namely polymer based compounds, is considered a potential relevant source of energy. However, the presence of polyvinyl chloride (PVC) in their composition, causes recycling problems when a thermal process is considered for the wastes treatment [1] preventing its use on processes which the main goal is the energy recovery (Zevenhoven et al. in Fuel 81:507–510, 2002; Kim in Waste Manag 21:609–616, 2001). A possible solution should consider a first step for chlorine removal, through a pyrolysis process previously to a subsequent thermal treatment, for energetic valorization. The present work assesses a possible process for treating PVC-containing wastes in an environmentally friendly way. It is based on the effective de-chlorination of PVC-containing wastes through a pyrolysis process at low temperature before the carbonaceous residue (chlorine free fraction) being subjected to a subsequent thermal treatment for energetic valorization with the production of a synthesis gas (syngas). In the end of the process concentrated hydrochloric acid or other chlorine solutions and a syngas, with high energetic potential are obtained. The synthesis gas produced can be used in turbines or gas engines, replacing the gases obtained from fossil non-renewable resources. The validation of the proposed treatment of PVC-containing wastes in pilot scale has also been performed

Extension of aggregation operators to site selection for solid waste management under neutrosophic hypersoft set

With the fast growth of the economy and rapid urbanization, the waste produced by the urban population also rises as the population increases. Due to communal, ecological, and financial constrictions, indicating a landfill site has become perplexing. Also, the choice of the landfill site is oppressed with vagueness and complexity due to the deficiency of information from experts and the existence of indeterminate data in the decision-making (DM) process. The neutrosophic hypersoft set (NHSS) is the most generalized form of the neutrosophic soft set, which deals with the multi-sub-attributes of the alternatives. The NHSS accurately judges the insufficiencies, concerns, and hesitation in the DM process compared to IFHSS and PFHSS, considering the truthiness, falsity, and indeterminacy of each sub-attribute of given parameters. This research extant the operational laws for neutrosophic hypersoft numbers (NHSNs). Furthermore, we introduce the aggregation operators (AOs) for NHSS, such as neutrosophic hypersoft weighted average (NHSWA) and neutrosophic hypersoft weighted geometric (NHSWG) operators, with their necessary properties. Also, a novel multi-criteria decision-making (MCDM) approach has been developed for site selection of solid waste management (SWM). Moreover, a numerical description is presented to confirm the reliability and usability of the proposed technique. The output of the advocated algorithm is compared with the related models already established to regulate the favorable features of the planned study

Evaluation of analytical methods for parameter extraction of PV modules

© 2017 The Authors. Published by Elsevier Ltd. A review and evaluation of the main analytical techniques for parameters extraction of photovoltaic (PV) modules with due account taken of their applications in modelling photovoltaic systems is presented. Six prevalent analytical methods are investigated and assessed using software tools, which have been developed to extract the required parameters of some commercially available PV modules using these methods. The results were subsequently compared with those obtained using well-established numerical methods. It is shown that, despite the fact that analytical methods can involve a fair amount of approximations, some analytical methods can compete in terms of accuracy with their numerical counterparts with much reduced computational complexity. .Published versio

Varietal differences influence arsenic and lead contamination of rice grown in mining impacted agricultural fields of Zamfara State, Nigeria.

In Zamfara state, Nigeria, rice is cultivated in fields contaminated with Pb (lead) from artisanal and illicit mining activities. Rice grown in such contaminated agricultural areas risks not only Pb contamination but also contamination from other toxic elements, like arsenic (As); co-contamination of Pb and As in rice cultivated in mining impacted areas has been previously reported and rice is a hyperaccumulator of As. A field study was conducted with ten different commonly-cultivated Nigerian rice varieties in the mining-impacted farmlands of Dareta village, Zamfara State. The aim was to determine the optimal rice variety for cultivation on these contaminated farmlands; an optimal variety would have the lowest contaminant concentrations and highest essential elements concentrations in the rice grains. A total of 300 paired soil and rice plants were collected. The mean As and Pb concentrations in paddy soils were 0.91 ± 0.82 mg kg-1 and 288.5 ± 464.2 mg kg-1, respectively. Mean As (30.4 ± 15.1 μg kg-1) content in rice grains was an order of magnitude lower than the Codex recommendation of 200 μg kg-1 (for milled rice) while the Pb content in all the rice varieties (overall mean of 743 ± 327 μg kg-1) was approximately four times higher than the Codex recommendation of 200 μg kg-1. Contrary to previous studies, a negative correlation was observed between As and Pb in rice grains across all the varieties. Rice variety Bisalayi was the variety with the lowest Pb transfer factor (TF = 0.08), but the average Pb concentration in rice grain was still above the Codex recommendation. Bisalayi also had the highest TF for iron. Variety ART_15, which had the lowest As uptake (TF = 0.10), had the highest TF for essential elements (magnesium, potassium, manganese, zinc, and copper). In areas of Pb contamination, Bisalayi rice may therefore be a suitable variety to choose for cultivation

Assessing burned areas in wildfires and prescribed fires with spectral indices and SAR images in the Margalla Hills of Pakistan

The extent of wildfires cannot be easily mapped using field-based methods in areas with complex topography and in those areas, the use of remote sensing is an alternative. This study first obtained images from the Sentinel-2 satellites for the period 2015-2020 with the objective of applying multi-temporal spectral indices to assess areas burned in wildfires and prescribed fires in the Margalla Hills of Pakistan using Google Earth Engine (GEE). Using those images, the Normalized Difference Vegetation Index (NDVI) and the Normalized Burn Ratio (NBR), which are often used to assess the severity of fires, were calculated for wildfires and prescribed fires. For each satellite image scene, spectral indices values were extracted for the 5th, 20th, 40th, 60th, 80th and 95th percentiles of pixels of each burned area. Then, box plots representing the distribution of these values were plotted for each satellite image to identify whether the regeneration time subsequent to a fire, also known as the burn scar, and the severity of the fire differed between the autumn and summer wildfires, and with prescribed fires. A statistical test revealed no differences for the re-generation time amongst the three categories of fires, but that the severity of summer wildfires was significantly different from that of prescribed fire, and this, for both indices. Second, SAR images were obtained from the Sentinel-1 mission for the same period as that of the optical imagery. A visual interpretation of the 34 variables of SAR response revealed that the 95th percentile of the Normalized Signal Ratio (NSR p_95) is adequate to detect burned areas in the study area. Moreover, it was found that 95% of the plots analyzed by the 80th percentile NSR (NSR p_80) showed a good response to fires in comparison to the spectral indices