Identification of vibration loads on hydro generator by using hybrid genetic algorithm

Abstract

Vibration dynamic characteristics have been a major issue in the modeling and mechanical analysis of large hydro generators. An algorithm is developed for identifying vibration dynamic characteristics by means of hybrid genetic algorithm. From the measured dynamic responses of a hydro generator, an appropriate estimation algorithm is needed to identify the loading parameters, including the main frequencies and amplitudes of vibrating forces. In order to identify parameters in an efficient and robust manner, an optimization method is proposed that combines genetic algorithm with simulated annealing and elitist strategy. The hybrid genetic algorithm is then used to tackle an ill-posed problem of parameter identification, in which the effectiveness of the proposed optimization method is confirmed by its comparison with actual observation data.

An adaptive reanalysis method for genetic algorithm with application to fast truss optimization

“…All the above characters of the GA have been widely applied in structural optimization [14][15][16]19]. In order to evaluate the implicit constraints Eqs.…”

“…Genetic algorithm (GA) is stochastic, inspired by natural evolution, hereditary and survival of the fittest [13]. It is particularly effective for non-differentiable, discontinuous, global, paralleled and multi-objective optimization problems and has been explored intensively in structural optimization in recent years [14][15][16][17]. The significantly difference between GA and traditionally gradient optimization algorithms is that GA operates on a population of potential solutions rather than improves a single solution.…”

A review of methods for excitation force reconstruction

“…This is the objective function minimization method described in Sub-section 4.1. For minimization of the objective function purposes many different natural optimization methods can be used, including evolutionary and genetic algorithms [59,61,67,68], swarm-intelligence algorithms [29,60] and others. They are used due to the large scale of the problem, the multi-modality of the optimization problem, as well as the existence of many local minima, which in practice hinder the process of force identification (solutions uniqueness).…”

Parameter identification and pressure control of dynamic system in shield tunneling using least squares method

“…Under the frame work, recurrent neural network is developed to accommodate the on-line identification, in which the weights of the neural network are iteratively and adaptively updated through the model errors. The commonly used parameter identification procedures of the dynamic system include sliding-neural network (Ali et al, 2010), PID neural networks (Li and Liu, 2006a), functional series TARMA model (Poulimeno and Fassois, 2009), neural network modeling (Lee and Oh, 1997), hybrid genetic algorithm (Li and Liu, 2006b), frequency domain method (Peng et al, 2004), interval parameter estimation (Nazin and Polyak, 2005) and recursive incremental estimation (Zhou and Willianm, 1996). The simulated experiments were generally adopted to study the control performance of the earth-pressurebalance system of a shield machine economically and safely.…”