The unit commitment is one of the most important in the economy scheduling in power system.The unit commitment optimization is one of the fundamental issues in power system operation.It is used to determine when to start up and/or shut down units,how to dispatch the committed units to meet systemwide demand and reserve requirements during the scheduling horizon,and its optimal objective is to reduce the total operating costs.This dissertation focuses on this problem as well as resource scheduling with state constrains.The characteristic,principle,current status and development of the electric power system economic dispatch are generalized in this thesis.In the dissertation,a mixedinteger programming model for unit commitment is presented and some methods of solving this problem are summarized.Then,a new optimization technique originating from artificial life and evolutionary computation is researched,names particle swarm optimization (PSO) algorithm.The basic principle of PSO is introduced at length,and various improvements of PSO are also present.Finally,based of PSO research and the characteristic of unit commitment in power system,improvements of PSO are presented.They are adaptive partical swarm optimization(APSO) algorithm,improved hybrid partical swarm(HPSO) algorithm and improved binary partical swarm algorithm.Based on the mathematical model of the optimal unit commitment,the improvement of PSO principle,contriving means and realization process are presented.Particle swarm optimization is a stochastic global optimization technique.It finds optimal regions of complex search spaces through the interaction of individuals in a population of particles.Particle swarm optimization has become the hotspot of evolutionary computation because of its simple for implement,excellent performance and few parameters need to be tuned. Researching on PSO show that it has preferably searching ability.Applying those improvements PSO to the unit commitment optimization problem,the result of simulation is show that PSO is correct and useful,and the method proved to be practical.Finlly,because of the deficiency of single algorithm,a novel hybrid approach is proposed,namly PSOGA based synthesizeing the merits in both PSO and GA.Simulated experiments for the optimization of nonlinear functions show that the PSOGA algorithm is superior to the PSO and GA in both the speed of convergence and the ability of finding the global optimum.This paper analysis those known fault location arithmetic of discrimination.Based on current matrix arithmetic not locating fault section when faults occur at the ends of feeder branches and multiple faults in different feeder branches,a novel judgement based on this matrix arithmetic is presented and can solve these problem,which can not only locate single fault,but identified ends and multiple faults quickly and correctly.Simulating and analyzing various faults in the single source and multi source power distribution system,the result indictes its validity.
