Power Quality Evaluation Based On Intelligence Algorithms

With the rapid development of the world’s industrial applications and advanced technologies, people’s demand for power is more and morestrong,general power quality has been difficult to meet the requirements of the people. The power supply department and consumershave paid more attention to the qualityof power. Power trading has already become an important partof the market.Theproper evaluation of power quality can make an accurate quantitative result,which can be the judgment of the power quality.And the price of electricity is pricing-in the power quality. At the same time, as the power market’sfiercecompetition, reasonable assessment of power quality is also promoting the establishment of a favorable market environment。There are so many power quality evaluation index,in order to complete the comprehensive assessment of power quality,this paper uses a projection pursuit technology to establish evaluation model, which has a unique advantage in solving problems on high-dimensional and nonlinear;then, for the uncertainty in the projection process, we use the maximum entropy principle to reduce it; finally,we use particle swarm optimization to find the best projection direction.Therefore, according to the characteristic of the indicators in power quality assessment,select the appropriate method to establish a framework, in the framework, other algorithms are used to compensate for its shortcomings. These three indispensable intelligent algorithms closely construct a complete power quality evaluation model and proposed a new method for power quality assessment. After the method proposed, first it is used into some data to validate this method and compared with some existing assessment methods, this method can be obtained with high reliability; when the reliability verification is completed, the method is used into some substations,thelevel of power quality is obtained.Result ofconcrete example indicates:the Projection Pursuit model with the maximum entropy principle, can successfully overcome the uncertainty in the process of projection. The final evaluation results have higher credibility.Particle Swarm Optimization method can easily find the optimal projection direction.