A Method Based on K-Means and Fuzzy Algorithm for Industrial Load Identification

At present, electrical energy becomes more and more important, and the shortage of electrical energy becomes serious. In order to develop power efficiency and alleviate the problem, people have planned the methods of load identification. This paper is on the basis of many different kinds of methods, and used K-means classification and fuzzy algorithm. This kind of method also needs to store the templates in database in advanced, and compare the features of samples which are extracted from the experiment with templates. Then it utilizes K-means classification to classify and uses fuzzy algorithm to calculate the closeness degree of every cluster. The industrial environment is very complex. E.g. the number of load types, states, some interference factors etc. This method we proposed can handle large amount of data and has another key of advantage. It is the capability to identify major industrial loads accurately. The results are presented in this paper. All in all, industrial load identification needs to be consummated.