Secondary Voltage Control using Singular Value Decomposition by Discovering Community Structures in Power Networks

Voltage and Frequency control are the two fundamental control problems in power systems. Unlike frequency control, voltage control is complicated by the fact that reactive power can\u27t travel far distances from its source of generation. Due to this distributed nature of reactive power, voltage control is usually performed in decentralized manner. Typically, voltage control problem is divided into a three-level hierarchical structure namely primary, secondary and tertiary voltage control.;The aim of this thesis is to present an optimal secondary voltage control by decomposing a large power system into small subsystems called voltage control areas (VCAs) using the fast community detection algorithm. Each VCA is self-sufficient in satisfying its reactive power demand. A load bus, called pilot point/bus, is selected in each VCA as a representative of the voltage profile of the whole area. Singular value decomposition of Fast Decoupled Load Flow (FDLF) Jacobian is used to optimally control the voltages of these pilot buses.;The presented approach is tested on two standard IEEE test power systems i.e. 9-Bus and 39-Bus systems. The computational time comparison of the fast community detection algorithm with another algorithm called original-GN algorithm is also presented. Through simulation results, it is shown that the presented optimal voltage control (Opt-VC) is a better approach compared to sensitivity based voltage control (Sen-VC)

AI-Driven Security Constrained Unit Commitment Using Predictive Modeling And Eigen Decomposition

Security Constrained Unit Commitment (SC-UC) is a complex large scale mix integer constrained optimization problem solved by Independent System Operators (ISOs) in the daily planning of the electricity markets. After receiving offers and bids, ISOs have only few hours to clear the day-ahead electricity market. It requires a lot of computational effort and a reasonable time to solve a large-scale SC-UC problem. However, exploiting the fact that a UC problem is solved several times a day with only minor changes in the system data, the computational effort can be reduced by learning from the historical data and identifying the patterns in the historical data using data mining techniques. In this research study, two data driven approaches based on predictive modeling techniques are proposed to solve a SC-UC problem in a day ahead electricity market which can be used as alternative backup methods for solving a SC-UC problem. In the first approach, the SC-UC is partially modeled using predictive modeling techniques to enhance the computational speed of the problem, while in the second approach, the optimization problem is completely replaced by data driven predictive models to further enhance the computational efficiency, however, at the cost of some optimality loss. The proposed approaches are validated through numerical simulations on different IEEE case studies to demonstrate and study the effectiveness of the developed approaches. The results obtained from the proposed approaches are compared with those obtained from commercial optimization solvers e.g., IBM CPLEX MIQP and GUROBI MIQP solvers

Development of a new detection algorithm to identify acute coronary syndrome using electrochemical biosensors for real-world long-term monitoring

Electrochemically based technologies are rapidly moving from the laboratory to bedside applications and wearable devices, like in the field of cardiovascular disease. Major efforts have focused on the biosensor component in contrast with those employed in creating more suitable detection algorithms for long-term real-world monitoring solutions. The calibration curve procedure presents major limitations in this context. The objective is to propose a new algorithm, compliant with current clinical guidelines, which can overcome these limitations and contribute to the development of trustworthy wearable or telemonitoring solutions for home-based care. A total of 123 samples of phosphate buffer solution were spiked with different concentrations of troponin, the gold standard method for the diagnosis of the acute coronary syndrome. These were classified as normal or abnormal according to established clinical cut-off values. Off-the-shelf screen-printed electrochemical sensors and cyclic voltammetry measurements (sweep between −1 and 1 V in a 5 mV step) was performed to characterize the changes on the surface of the biosensor and to measure the concentration of troponin in each sample. A logistic regression model was developed to accurately classify these samples as normal or abnormal. The model presents high predictive performance according to specificity (94%), sensitivity (92%), precision (92%), recall (92%), negative predictive value (94%) and F-score (92%). The area under the curve of the precision-recall curve is 97% and the positive and negative likelihood ratios are 16.38 and 0.082, respectively. Moreover, high discriminative power is observed from the discriminate odd ratio (201) and the Youden index (0.866) values. The promising performance of the proposed algorithm suggests its capability to overcome the limitations of the calibration curve procedure and therefore its suitability for the development of trustworthy home-based care solutions

Real-Time Hydraulic Modelling of a Water Distribution System in Singapore

This paper describes the implementation of a real-time hydraulic model of a water distribution system in Singapore. This on-line system is based on the Integration of real-time hydraulic data with hydraulic computer simulation models and statistical prediction tools. To facilitate this implementation, a network of wireless sensor nodes continuously sample hydraulic data such as pressure and flow rate, transmitting it to cloud-based servers for processing and archiving. Then, data streams from the sensor nodes are integrated into an on-line hydraulic modeling subsystem that is responsible for on-line estimation and prediction of the water distribution system's hydraulic state for a rolling planning horizon of 24 hours ahead. This online hydraulic model is one of the components of the WaterWiSe (Wierless Water Sentinel) platform which is an end-to-end integrated hardware and software system for monitoring, analyzing, and modeling urban water distribution systems in real-time.Singapore. National Research FoundationSingapore-MIT Alliance for Research and Technology. Center for Environmental Sensing and Modelin

Formulation, in vitro evaluation and characterization of atorvastatin solid dispersion

Purpose: To formulate a polymer-incorporated solid dispersion preparation for enhancing the dissolution and bioavailability of atorvastatin calcium trihydrate (ATV), while maintaining oral compatibility.Method: Four different methods, i.e., physical mixing (PM), fusion (F), solvent evaporation (SE) and kneading (K), as well as three different excipients i.e. croscarmellose sodium (CCS), microcrystalline cellulose (MCC) and lactose (LAC) were used to formulate various drug-carrier combinations.Results: In SE method, the rank order of magnitude of drug release was CCS > LAC > MCC, while in fusion and kneading methods, the rank order of release was MCC > CCS > LAC and MCC > CCS > LAC, respectively. Drug release of atorvastatin was maximum (103 %) in FM2 formulation. However,this formulation was non-compatible based on spectroscopic analysis. In contrast, SC2 formulations at 1:2 ratio were compatible in terms of cumulative drug release (99 %), and based on spectroscopic data, thermal analysis and microscopic evaluation.Conclusion: These results confirm that CCS forms a superior interface with atorvastatin when SE formulation method is used. Thus, solid dispersion is a promising approach for enhancing the oral bioavailability of atorvastatin. Keywords: Atorvastatin, Solid dispersion, Bioavailability, Solvent evaporatio