Analysis Of Genetic Algorithm And Ant Algorithm In Power System Fault Diagnosis

The rapid development of the power system, the increasing degree of automation, and system structure complex,high-load,high-power continuous operation, and other factors, in a complex network, due to small disturbance will inevitably trigger failure of the power system fault chain large area will lead to the collapse of the power grid disastrous consequences. Timely detection of fault and fault prediction technology equipment to enhance the safety, reliability provide an effective way. Common model fault diagnosis method as a parameter estimation method ARMA model, state estimation method, and so on, need more prior knowledge. Fuzzy expert system, neural network and other knowledge is based on shallow and deep knowledge of combining research methods, the direction of intelligent fault diagnosis to be further developed, sush as a wavelet analysis, fractal geometry, data fusion, genetic algorithm. Fault diagnosis of common method of characteristics, fault formation of numerous factors, the traditional method of fault diagnosis has been difficult to meet the requirements of modern equipment.In order to achieve a comprehensive forecast search, fault analysis presented in this paper distribution network fault analysis in the prestage, using genetic algorithms global search capability rapidly initial solution will be the result of genetic algorithms into ant colony algorithm of the information needed to rapidly optimal solution method.Because genetic algorithm can find the optimal solution, in the practical application of the convergence process in the immature, converge to the optimal solution partial, slow convergence and other issues. Traditional genetic algorithms to improve the speed of convergence on the need to improve search efficiency and improve performance optimization, thus accelerating convergence, in this ant colony algorithm has its advantages, which can effectively reduce the search space and improve search efficiency, which will enable improved genetic algorithm gradually at the optimal solution.In this paper, genetic algorithms and knowledge ant colony algorithm based on the combination of hybrid algorithm can improve the search speed of convergence advantages on how to achieve through a combination of fault diagnosis, and to find ant colony algorithm combining genetic algorithm applied to the analysis of fault diagnosis entry points. Combining ant colony algorithm analysis and fault diagnosis of genetic algorithm analysis of the practical application of significance.