Uncertainty modelling in power system state estimation

This thesis was submitted for the degree of Doctor of Philosophy and was awarded by Brunel University.As a special case of the static state estimation problem, the load-flow problem is studied in this thesis. It is demonstrated that the non-linear load-flow formulation may be solved by real-coded genetic algorithms. Due to its global optimisation ability, the proposed method can be useful for off-line studies where multiple solutions are suspected. This thesis presents two methods for estimating the uncertainty interval in power system state estimation due to uncertainty in the measurements. The proposed formulations are based on a parametric approach which takes in account the meter inaccuracies. A nonlinear and a linear formulation are proposed to estimate the tightest possible upper and lower bounds on the states. The uncertainty analysis, in power system state estimation, is also extended to other physical quantities such as the network parameters. The uncertainty is then assumed to be present in both measurements and network parameters. To find the tightest possible upper and lower bounds of any state variable, the problem is solved by a Sequential Quadratic Programming (SQP) technique. A new robust estimator based on the concept of uncertainty in the measurements is developed here. This estimator is known as Maximum Constraints Satisfaction (MCS). Robustness and performance of the proposed estimator is analysed via simulation of simple regression examples, D.C. and A.C. power system models.Embassy of Kuwai

Uncertainty modelling in power system state estimation

As a special case of the static state estimation problem, the load-flow problem is studied in this thesis. It is demonstrated that the non-linear load-flow formulation may be solved by real-coded genetic algorithms. Due to its global optimisation ability, the proposed method can be useful for off-line studies where multiple solutions are suspected. This thesis presents two methods for estimating the uncertainty interval in power system state estimation due to uncertainty in the measurements. The proposed formulations are based on a parametric approach which takes in account the meter inaccuracies. A nonlinear and a linear formulation are proposed to estimate the tightest possible upper and lower bounds on the states. The uncertainty analysis, in power system state estimation, is also extended to other physical quantities such as the network parameters. The uncertainty is then assumed to be present in both measurements and network parameters. To find the tightest possible upper and lower bounds of any state variable, the problem is solved by a Sequential Quadratic Programming (SQP) technique. A new robust estimator based on the concept of uncertainty in the measurements is developed here. This estimator is known as Maximum Constraints Satisfaction (MCS). Robustness and performance of the proposed estimator is analysed via simulation of simple regression examples, D.C. and A.C. power system models.EThOS - Electronic Theses Online ServiceEmbassy of KuwaitGBUnited Kingdo

GA: A Package for Genetic Algorithms in R

Genetic algorithms (GAs) are stochastic search algorithms inspired by the basic principles of biological evolution and natural selection. GAs simulate the evolution of living organisms, where the fittest individuals dominate over the weaker ones, by mimicking the biological mechanisms of evolution, such as selection, crossover and mutation. GAs have been successfully applied to solve optimization problems, both for continuous (whether differentiable or not) and discrete functions. This paper describes the R package GA, a collection of general purpose functions that provide a flexible set of tools for applying a wide range of genetic algorithm methods. Several examples are discussed, ranging from mathematical functions in one and two dimensions known to be hard to optimize with standard derivative-based methods, to some selected statistical problems which require the optimization of user defined objective functions. (This paper contains animations that can be viewed using the Adobe Acrobat PDF viewer.

Adherence to the Mediterranean diet and its association with glycaemic control in Type 1 diabetes

Introduction: The Mediterranean diet (Mdiet) is defined as the dietary patterns of people living around the Mediterranean regions during the 1950s and 1960s. This thesis aimed to investigate the adherence to the Mdiet by Cypriot and Greek populations, and its association with glycaemic control in people with Type 1 diabetes (T1DM) in Cyprus. Methods: Longitudinal adherence to the Mdiet in Cyprus and Greece was explored in a systematic review. The cumulative adherence, stratified by Mdiet scoring systems, was explored alongside the potential age and gender differences. Adherence to Mdiet, glycaemic control and their association (using linear regression models) were investigated in a cross-sectional study among people with T1DM in Limassol, Cyprus. The methodology of this study was tested in a pilot study. Results: The systematic review included 15 independent studies (18 papers). The adherence to the Mdiet was graded as moderate. The KIDMED and the MedDietScore were the most used scores and indicated cumulative mean adherence of 51.6% (4.3 points) and 52.5% (28.9 points), respectively. There was a suggestion of lower adherence in younger ages and a reducing trend over time; no gender difference was observed. For the cross-sectional study, 103 participants were recruited through random sample selection. The mean adherence was 57.6% (31.7 points); 80% and 19% of the participants had a moderate and high adherence, respectively. The median HbA1c and fasting glucose was 65 mmol/mol and 10.3 mmol/l, respectively. Most participants had suboptimal glycaemic control. Mdiet adherence and glycaemic control were poorer in younger ages; no gender difference was observed. The Mdiet was statistically significantly associated with HbA1c but not with fasting glucose, after adjusting for potential confounders. The fully adjusted model predicted a reduction in HbA1c (mmol/mol) by 1.5% for every additional point in the MedDietScore. Conclusion: Mdiet is associated with a clinically and statistically significant reduction of HbA1c in T1D