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Model simulations are becoming more important in dynamic power system analyses as our increasing dependence on electricity supply. However, the general steam models often show some disadvantages in dynamical cases. No enough attention is paid to the non-linear relationships between flow rate and pressure differences. The paper puts forwards a new nonlinear turbine model taking the nonlinearities into...
The problem of Materialized view selection is one key issue in improving response time of complex queries in data warehouse. In this paper we proposed an Ant Colony based algorithm which is efficient compared with Genetic Algorithm (GA), a popular algorithm for materialized view selection. Given space limitation, our algorithm could minimize total query cost as much as possible. Based on evaporation...
An algorithm for evolving recurrent neural network via the genetic algorithm was implemented on the CUDA, resulting in a system called CuParcone (CUDA based Partially Connected Neural Evolutionary). Run on a Nvidia Tesla “GPU supercomputer, ” CuParcone achieves a performance increase of 323 times in face gender recognition compared to the comparable Parcone algorithm on a state-of-the-art, commodity...
More accurate models are adopted for dynamic stability analysis of hydro generation systems but the parameters of these models are often estimated roughly. Inaccurate parameters may decrease the analysis accuracy improved by using an accurate model. An improved genetic algorithm is proposed in this paper to estimate the parameters of a hydro generation system model which contains a series of basic...
The paper gives an improved particle swarm optimal algorithm in which a kind of exponent decreasing inertia weights is given to improve the convergence speed and a kind of stochastic mutations is used to improve the diversity of the swarm in order to overcome the disadvantage of premature convergence and later period oscillatory occurrences. It is shown by five representative benchmarks functionpsilas...
Accuracy parameters of system model are of great importance in stability and security evaluation or simulation for power system. Some of the conventional methods may have inadequate adaptability or effectiveness for identification of different power systems. A new framework for power system identification is proposed based on an improved genetic algorithm with a logarithmic fitness function and an...
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