An Intelligent Power Distribution Strategy For Customer Side Of The Smart Community

With the constant construction of intelligent community, intelligent residential area gradually become the practice of China’s smart grid development the most typical representative. And intelligent village power behavior differences, leading to the user side unbalanced three-phase load of power grid, the impact of the smart grid security, economic and stable operation. This paper proposes a method based on the user electricity behavior classification of intelligent electric, economic and efficient solution to intelligent village user side unbalanced three-phase load, and through the experimental analysis shows that the effectiveness of the proposed method.First, this paper introduces the intelligent with development present situation domestic and overseas and the smart grid environment the method to solve the problem of unbalanced three-phase load, and put forward a kind of based on user load characteristic curve of the intelligent with electric power use behavior classification method, and briefly introduces the characteristics of the method.Secondly, introduces the characteristics of load characteristic indexes and load characteristic research field, and briefly introduces the research direction in this paper, based on load characteristic is the user electricity behavior classification. At the same time, also introduced the user need to do related preparation before electricity behavior classification: data acquisition, processing, such as load and typical daily load curve.And then, for satisfying the limit unbalance radio of three-phase current and the least switching frequency of automatic commutation device during the commutation process, a mathematical model for optimal commutation was established. And put forward the way, simple but can simplify the process of genetic optimization algorithm. Based on the optimal commutation mathematical model, to distribute the same electricity categories equally to three-phase by automatic commutation, and make intelligent residential area user side A, B, C three-phase load 24 hours to achieve maximum load balancing. Solving the user side three-phase imbalance caused by the user different electricity behavior in the smart grid.Reduce energy losses, improve the quality of power supply, improve service levels, the power supply to realize the smart grid environment intelligent function with electricity. And through to the intelligent village electricity data simulation in the city of nanchang, show that this method is effective.Finally, In order to solve the described insufficient problem of load feature selection and weight calculation in the past clustering analysis of residential electricity behavior, enhance the accuracy of clustering analysis in residential electricity behavior and reduce the time of clustering analysis operation, a data model based on ReliefF algorithm was proposed. The data model such as electricity consumption rate during peak hour, the peak load time, the valley of the power, daily load cycles, the minimum load rate feature, and so on are established. The massive date of residential electricity behavior was researched by the model, and the model was analyzed through k- means algorithm for clustering analysis. Experimental data is from a certain built smart community, the result accuracy reaches 94.61%, and show proposed model based on ReliefF algorithm in clustering analysis of residential electricity behavior is effective.