Parallel Computing Applications in Large-Scale Power System Operations

Electrical energy is the basic necessity for the economic development of human societies. In recent decades, the electricity industry is undergoing enormous changes, which have evolved into a large-scale and competitive industry. The integration of volatile renewable energy, and the emergence of transmission switching (TS) techniques bring great challenges to the existing power system operations problems, especially security-constrained unit commitment (SCUC) solution engines. In order to deal with the uncertainty of volatile renewable energy, scenario-based stochastic optimization approach has been widely employed to ensure the reliability and economic of power systems, in which each scenario would represent a possible system situation. Meanwhile, the emergence of TS techniques allows the system operators to change the topology of transmission systems in order to improve economic benefits by mitigating transmission congestion. However, with the introduction of extra scenarios and decision variables, the complexity of the SCUC model increases dramatically and more computational efforts are required, which might make the power system operation problems difficult to solve and even intractable. Therefore, an advanced solution technique is urgently needed to solve both stochastic SCUC problems and TS-based SCUC problems in an effective and fast way. In this dissertation, a decomposition framework is presented for the optimal operation of the large-scale power system, which decomposes the original large-size power system optimization problem into smaller-size and tractable subproblems, and solves these decomposed subproblems in a parallel manner with the help of high performance computing techniques. Numerical case studies on a modified I 118-bus system and a practical 1168-bus system demonstrate the effectiveness and efficiency of the proposed approach which will offer the power system a secure and economic operation under various uncertainties and contingencies

Myeloperoxidase gene-463G > A polymorphism and premature coronary artery disease

We investigated the association between myeloperoxidase gene -463G > A polymorphism and premature coronary artery disease (CAD) in two Chinese population samples: 229 patients and 230 controls. Genotypes were determined by ligase detection reaction-polymerase chain reaction sequencing and the grouping technique. We found lower frequencies of both the A/A genotype and the A allele in patients (p < 0.05). Multivariate logistic regression showed that the risk of premature CAD in subjects carrying the AA genotype was reduced by 83% in relation to individuals carrying the G/G genotype (OR = 0.172, 95% CI: 0.057-0.526, p = 0.002). Our results indicate that -463G > A polymorphism of the myeloperoxidase gene is associated with premature CAD in Chinese individuals, suggesting that the AA genotype is a protective factor against premature CAD

A Clustering Algorithm Combine the FCM algorithm with Supervised Learning Normal Mixture Model

In this paper we propose a new clustering algorithm which combines the FCM clustering algorithm with the supervised learning normal mixture model; we call the algorithm as the FCM-SLNMM clustering algorithm. The FCM-SLNMM clustering algorithm consists of two steps. The FCM algorithm was applied in the first step. In the second step the supervised learning normal mixture model was applied and the clustering result of the first step was used as training data. The experiments on the real world data from the UCI repository show that the supervised learning normal mixture model can improve the performance of the FCM algorithm sharply, and which also show that the FCM-SLNMM perform much better than the unsupervised learning normal mixture model and other comparison clustering algorithms. This indicates that the FCM-SLNMM algorithm is an effective clustering algorithm. 1