1,721,053 research outputs found
Smart grid state estimation and its applications to grid stabilization
Get PDFUniversity of Technology Sydney. Faculty of Engineering and Information Technology.The smart grid is expected to modernize the current electricity grid by commencing a new set of technologies and services that can make the electricity networks more secure, automated, cooperative and sustainable. The smart grid can integrate multiple distributed energy resources (DERs) into the main grid. The need for DERs is expected to become more important in the future smart grid due to the global warming and energy problems. Basically, the smart grid can spread the intelligence of the energy distribution and control system from the central unit to long-distance remote areas, thus enabling accurate state estimation and wide-area real-time monitoring of these intermittent energy sources. Reliable state estimation is a key technique to fulfil the control requirement and hence is an enabler for the automation of power grids. Driven by these motivations, this research explores the problem of state estimation and stabilization taking disturbances, cyber attacks and packet losses into consideration for the smart grid.
The first contribution of this dissertation is to develop a least square based Kalman filter (KF) algorithm for state estimation, and an optimal feedback control framework for stabilizing the microgrid states. To begin with, the environment-friendly renewable microgrid incorporating multiple DERs is modelled to obtain discrete-time state-space linear equations where sensors are deployed to obtain system state information. The proposed smart grid communication system provides an opportunity to address the state regulation challenge by offering two-way communication links for microgrid information collection, estimation and stabilization. Interestingly, the developed least square based centralised KF algorithm is able to estimate the system states properly even at the beginning of the dynamic process, and the proposed H2 based optimal feedback controller is able to stabilize the microgrid states in a fairly short time.
Unfortunately, the smart grid is susceptible to malicious cyber attacks, which can create serious technical, economic, social and control problems in power network operations. In contrast to the traditional cyber attack minimization techniques, this study proposes a recursive systematic convolutional (RSC) code and KF based method in the context of smart grids. The proposed RSC code is used to add redundancy in the microgrid states, and the log maximum a-posterior is used to recover the state information which is affected by random noises and cyber attacks. Once the estimated states are obtained, a semidefinite programming (SDP) based optimal feedback controller is proposed to regulate the system states. Test results show that the proposed approach can accurately mitigate the cyber attacks and properly estimate as well as regulate the system states.
The other significant contribution of this dissertation is to develop an adaptive-then-combine distributed dynamic approach for monitoring the grid under lossy communication links between wind turbines and the energy management system. Based on the mean squared error principle, an adaptive approach is proposed to estimate the local state information. The global estimation is designed by combining local estimation results with weighting factors, which are calculated by minimizing the estimation error covariances based on SDP. Afterwards, the convergence analysis indicates that the estimation error is gradually decreased, so the estimated state converges to the actual state. The efficacy of the developed approach is verified using the wind turbine and IEEE 6-bus distribution system.
Furthermore, the distribution power sub-systems are usually interconnected to each other, so this research investigates the interconnected optimal filtering problem for distributed dynamic state estimation considering packet losses. The optimal local and neighbouring gains are computed to reach a consensus estimation after exchanging their information with the neighbouring estimators. Then the convergence of the developed algorithm is theoretically proved. Afterwards, a distributed controller is designed based on the SDP approach. Simulation results demonstrate the accuracy of the developed approaches.
The penultimate contribution of this dissertation is to develop a distributed state estimation algorithm for interconnected power systems that only needs a consensus step. After modelling the interconnected synchronous generators, the optimal gain is determined to obtain a distributed state estimation. The consensus of the developed approach is proved based on the Lyapunov theory. From the circuit and system point of view, the proposed framework is useful for designing a practical energy management system as it has less computational complexity and provides accurate estimation results.
The distributed state estimation algorithm is further modified by considering different observation matrices with both local and consensus steps. The optimal local gain is computed after minimizing the mean squared error between the true and estimated states. The consensus gain is determined by a convex optimization process with a given local gain. Moreover, the convergence of the proposed scheme is analysed after stacking all the estimation error dynamics. The efficacy of the developed approach is demonstrated using the environment-friendly renewable microgrid and IEEE 30-bus power system.
Overall, the findings, theoretical development and analysis of this research represent a comprehensive source of information for smart grid state estimation and stabilization schemes, and will shed light on green smart energy management systems and monitoring centre design in future smart grid implementations. It is worth pointing out that the aforementioned contributions are very important in the smart grid community as communication impairments have a significant impact on grid stability and the distributed strategies can reduce communication burden and offer a sparse communication network
Study on the effect of S-allyl cysteine on testosterone production and neuroprotection
No full text博士学位論文の要旨及び審査結果の要旨 (Summary of Thesis(DR)
Effects of autonomous vehicles on pavement distress & road safety and pavement distress optimization
No full textThe commercial application of automation technology in passenger and freight transport will bring positive revolutionary changes in transportation mobility. Despite having more advantages, automation in trucking technology has some detrimental effects on the performance of asphalt pavement and highway safety. This study focuses on optimization of asphalt pavement distresses and prediction of rutting induced traffic safety factors for movement of autonomous trucks. This study optimizes the asphalt concrete (AC) pavement distresses by devising traffic input in Mechanistic-Empirical Pavement Design Software, AASHTOWare. An increase in pavement distresses was observed for a small increase in the standard deviation of wheel wander, uniform distribution of truck traffic loading, and equal distribution of vehicle positioning on the road lanes. Permanent deformation of the asphalt concrete layer for roads (PEDRO) model was incorporated to predict AC pavement rutting for a typical pavement section. Hydroplaning speed and skid resistance as traffic safety factors were evaluated from widely accepted empirical equations for the induced rutting. A standard tire rather than a truck tire was considered due to its high susceptibility to traffic safety. A graphical relationship has been proposed to obtain a design threshold value for hydroplaning speed, water film depth and autonomous truck speed. An attempt was made to improve pavement performance by increasing frequency of truck load in low-temperature period of a day.Includes bibliographical references
Rapid breeding, genome-wide characterization and morphophysiological evaluation of a salt tolerant rice
No full textA Decision Tree Algorithm for Peak Load Shaving in Isolated Microgrid Using Hybrid PV- BESS System
No full textSpatial-Longitudinal Bent-Cable Model with an Application to Atmospheric CFC Data
Get PDFSpatial data (also called georeferenced data) arise in a wide range of scientific studies, including geography, agriculture, criminology, geology, urban and regional economics. The underlying spatial effects – the measurement error caused by any spatial pattern embedded in data – may affect both the validity and robustness of traditional descriptive and inferential techniques. Therefore, it is of paramount importance to take into account spatial effects when analysing spatially dependent data. In particular, addressing the spatial association among attribute values observed at different locations and the systematic variation of phenomena by locations are the two major aspects of modelling spatial data.
The bent-cable is a parametric regression model to study data that exhibits a trend change over time. It comprises two linear segments to describe the incoming and outgoing phases, joined by a quadratic bend to model the transition period. For spatial longitudinal data, measurements taken over time are nested within spatially dependent locations. In this thesis, we extend the existing longitudinal bent-cable regression model to handle spatial effects. We do so in a hierarchical Bayesian framework by allowing the error terms to be correlated across space. We illustrate our methodology with an application to atmospheric chlorofluorocarbon (CFC) data. We also present a simulation study to demonstrate the performance of our proposed methodology.
Although we have tailored our work for the CFC data, our modelling framework may be applicable to a wide variety of other situations across the range of the econometrics, transportation, social, health and medical sciences. In addition, our methodology can be further extended by taking into account interaction between temporal and spatial effects. With the current model, this could be done with a spatial correlation structure that changes as a function of time
Obesity Risk Estimation Accounting Spatial Dependency, Error in Covariate Measurement, and Factors Operating at Multiple Levels
No full textDisease mapping has long been a part of public health, epidemiology, and the study of disease in human populations. Hierarchical spatial models for areal data address the competing goals of accurate small area estimation and fine-scale geographic resolution in disease mapping simultaneously, and it has become a fertile area of research over the last two decades. More recently, there has been increased uptake of the methods in applied research. Nonetheless, there is still scope for the methodological developments. This thesis contributes to the uptake of disease mapping in applied health research through key areas: methodological development, implementation, and application. Chapters 1 and 2 of this thesis provide a brief review of literatures on spatial model, measurement error model, and obesity research. These chapters also summarize methods and data to be utilized in this thesis. Chapter 3 presents an applied research work that demonstrates the importance of incorporating spatial autocorrelation from the observed data into a statistical model, via real and simulated data. The analysis of real data across 117 health regions of Canada is of practical interest, as it identified several obesity clusters with discernible spatial patterns throughout Canada. Chapters 4 and 5 of the thesis present two research works on methodological development. First, covariate measurement error provides biased estimates in standard regression model, violating underlying assumption. In Chapter 4, the classical and Berkson measurement error models were integrated with the well-known Besag-York-Mollie (BYM2) model to incorporate covariate measured with error. The simulation results revealed that the use of a measurement error model for an error-prone covariate in BYM2 model has the advantage of producing a superior fit. The results also demonstrate that a BYM2 model without taking into account covariate measurement error may lead to highly biased estimates for certain parameters. The proposed method was applied for estimating socio-economic and environmental factor’s effect on the obesity counts using aggregated data for 117 health regions of Canada. Second, optimal prediction of risk for an adverse health condition risk at population level requires integrating covariates from multiple levels into a single modeling framework. However, it is a common practice to estimate effects of individual and group-level covariates using multiple models independently. To overcome this methodological gap, this thesis formulated the joint BYM2 model in Chapter 5, that integrates individual- and group-level models through association parameter. The simulation results revealed that the joint BYM2 model performed the same or better than the independent estimation for recovering parameter values. The capability of the proposed model was demonstrated through estimating the risk of developing unhealthy health condition among Canadian secondary school students, integrating individual-, school-, and neighbourhood-level covariates. The neighbourhood-level model incorporated spatially correlated count data and covariates measured with error. Finally, in Chapter 6, the overall findings from this thesis and potential directions for future work are discussed
EFFECT OF APPLICATION AND FREQUENCY OF GIBBERELLIC ACID ON GROWTH AND YIELD OF CORIANDER (Coriandrum sativum L.)
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Submitted to the Department of Agricultural Botany
Sher-e-Bangla Agricultural University, Dhaka
In partial fulfillment of the requirements
for the degree of
MASTER OF SCIENCE (MS)
IN
AGRICULTURAL BOTANY
SEMESTER: JANUARY- JUNE, 2016An experiment taking the variety BARI Dhonia-1 was carried out at the research field of Sher-e-Bangla Agricultural University, Dhaka during November 2016 to March 2017 to examine the effect of application and frequency of Gibberellic acid on growth and yield of coriander (Coriandrum sativum L.). The treatments consisted of five concentrations of GA3 viz. 0 (control), 25, 50, 75 and 100 ppm GA3 and two application frequencies viz. spray at 25 days after sowing (DAS) and spray at 50 DAS. There were ten treatments combination in all. The experiment was laid out in factorial Randomized Complete Block Design with three replications. The maximum plant height and leaves per plant were obtained from 100 ppm GA3 closely followed by 75 ppm GA3 while the highest primary and secondary branches per plant were recorded from 75 ppm GA3. Foliar spray of GA3 at 25 DAS produced the maximum plant height, leaves per plant, primary and secondary branches per plant. Days to first and 50% flowering and days to maturity were decreased with the increase of GA3 concentrations. Application of 75 ppm GA3 and GA3 sprayed at 25 DAS independently produced the highest values of primary and secondary branches per plant and yield contributing character. GA3 75 ppm eopled weak spray at 25 DAS gave the best results with regard to primary and secondary branches per plant (11.24 and 21.67), dry weight per plant (34.51g) umbels per plant (25.56), umbellates per umbel (6.24), seeds per umbel (33.90), seeds per plant (866.48), umbel circumference (22.52cm), seed yield (7.61g per plant and 2.03 t/ha) and stover yield (1.38 t/ha)
Organic-Inorganic Nanomaterial Based Highly Efficient Flexible Nanogenerator for Self-Powered Wireless Electronics
Get PDFAs the world progresses towards artificial intelligence and the Internet of Things (IoT), self‐powered sensor systems are increasingly vital for sensing and detection. Nanogenerators, a new technology in energy research, enable the harvesting of normally wasted energy from the environment. This technology scavenges a wide range of ambient energies, meeting the ever-expanding energy demands as conventional fossil fuel sources are depleted. This research involves designing and fabricating high-performance flexible piezoelectric nanogenerators (PENGs) and triboelectric nanogenerators (TENGs), using novel organic-inorganic hybrid nanomaterials for wireless electronics.
Structural health monitoring (SHM) is crucial in the aerospace industry to enhance aircraft safety and consistency through reliable sensor networks. PENGs are promising for powering wireless sensor networks in aerospace SHM applications due to their sustainability, durability, flexibility, high performance, and superior reliability. This research demonstrated a self-powered wireless sensing system based on a porous PVDF (polyvinylidene fluoride)-based PENG, which is ideal for developing auto-operated sensor networks. The porous PVDF film-based PENG, enhanced output current by ~ 11 times and output voltage by ~ 8 times, respectively, compared to a pure PVDF-based PENG. The PENG device generated sufficient electrical energy to power a customized wireless sensing and communication unit and transfer sensor data every ~ 4 minutes. This PENG could harness energy from automobile vibration, reflecting the potential for real-life SHM systems.
Subsequently, a novel, self-assembled, highly porous perovskite (FAPbBr2I)/polymer (PVDF) composite film was designed and developed to fabricate high-performance piezoelectric nanogenerators (PENGs). The porous structure enlarged the bulk strain of the piezoelectric composite film, resulting in a 5-fold enhancement of the strain-induced piezo potential and a 15-fold amplification of the output current. This highly-efficient PENG achieved a peak output power density of 10 µW/cm2 and enabled to run a self-powered integrated wireless electronic node (SIWEN). The PENG was applied to real-life scenarios including wireless data communication, efficient energy harvesting from automobile vibrations as well as biomechanical motion. This low-temperature, full-solution synthesis approach could lead to a paradigm shift in sustainable power sources, expanding the realms of flexible PENGs.
One of the remaining concerns is the highly soluble lead component, which is one of the constituents of the PENGs that poses potential adversary impacts on human health and the environment. To address this concern, lead-free organic-inorganic hybrid perovskite (OIHP) based flexible piezoelectric nanogenerators (PENGs) have been developed. The excellent piezoelectric properties of the FASnBr3 NPs was demonstrated with a high piezoelectric charge coefficient (d33) of ~ 50 pm/V through piezoelectric force microscopy (PFM) measurements. The device’s outstanding flexibility and uniform distribution properties resulted in a maximum piezoelectric peak-to-peak output voltage of 94.5 V, peak-to-peak current of 19.1 μA, and output power density of 18.95 μW/cm2 with a small force of 4.2 N, outperforming many state-of-the-art halide perovskite-based PENGs. For the first time, a self-powered RF wireless communication between smartphones and a nanogenerator solely based on a lead-free PENG was demonstrated and serves as a stepping-stone towards achieving self-powered Internet of Things (IoT) devices using environment-friendly perovskite piezoelectric materials.
Likewise, triboelectric nanogenerators (TENGs) are also promising energy-harvesting devices for powering the next generation of wireless electronics. TENGs’ performance relies on the triboelectric effect between the tribonegative and tribopositive layers. In this study, a natural wood-derived lignocellulosic nanofibrils (LCNF) tribolayer was reported to have high tribonegativity (higher than polytetrafluoroethylene (PTFE)) due to the presence of natural lignin on its surface and its nanofibril morphology. LCNF nanopaper-based TENGs produced significantly higher voltage (160%) and current (120%) output than TENGs with PTFE as the tribonegative material. Assembling LCNF nanopaper into a cascade TENG generated sufficient output to power a wireless communication node to send a radio-frequency signal to a smartphone every 3 mins. This study demonstrates the potential of using LCNF as a more environmentally friendly alternative to conventional tribonegative materials based on fluorine-containing petroleum-based polymers.
Overall, this thesis explores the design and development of highly efficient and flexible nanogenerators for self-powered wireless electronics. By combining highly electroactive nanomaterials with flexible polymer matrix structures, NGs with high electric output performance and flexibility were successfully obtained. The synthesizing process for the electroactive nanomaterials was carefully designed and adopted to sustain the inherent advantages of flexible electronics. The various type of high performance flexible NGs developed in this research work, including ZnO/PVDF porous PENGs, FAPbBr2I/PVDF based PENGs, FASnBr3/PDMS based PENGs, and LCNF nanopaper-based TENGs, provide promising solutions for energy harvesting and self-powered sensing
PROFITABILITY OF POTATO PRODUCTION IN SOME SELECTED AREAS OF JOYPURHAT DISTRICT
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The Department of Agricultural Economics,
Sher-e-Bangla Agricultural University, Dhaka-1207
in partial fulfillment of the requirements for the degree of
MASTER OF SCIENCE
IN
AGRICULTURAL ECONOMICSThe purpose of this study was to identify the profitability of potato growers in three
locations namely Kalai Upazilla, Khetlal Upazilla, and Akkelpur Upazilla under
Joypurhat District. Primary data was acquired from a random sample of 20 farmers in
each study location. A total of 60 farmers was included in the study. In this study, both
tabular and functional analyses were used. The study's main findings demonstrate that
potato production was profitable. The total cost, gross returns and net returns of potato
production was Tk. 173222.52, Tk. 273691 and Tk. 100468.48 per hectare respectively
in the study areas. The Benefit Cost Ratio (BCR) was 1.58, implying that 1 taka
invested in potato production yielded Tk. 1.58 in return. Potato growers faced a number
of problems, including high fertilizer and insecticide prices, lack of quality seed, and a
low price of product during the late harvesting period. The yield of potatoes could be
increased by using a new modern variety. Major recommendations include the need for
both the government and private institutions to take initiatives to assure the availability
of high-quality HYV seeds at reasonable price at farmers' doorsteps. To improve the
current situation, the government should take the necessary steps to train farmers about
the proper use of inputs through DAE personnel. The government should also take
initiatives to search for new markets for potatoes during the harvesting season so that
they can get the desirable price
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