Development Of Practical Software For Bad Data Identification In Power System

With the alteration of power network structure and operation mode, the real time data in power systems has greater influence on its security and stability. Traditionally, the data analysis deals with the data on the surface without obtaining internal relationship. Fuzzy cluster gives a description of the uncertainty of the specimen and builds up a bridge between the discrimination and classification, which can reflect the objective world better. Consequently, it becomes the new mainstream of the data analysis research. Under the guidance of cluster, the fuzzy cluster is feasibile to be introduced into the bad data identification of power systems by researching fuzzy cluster theory. Bad data recognition software written by Visual C++and Fortran languages is applied to the power system and the reliability of data acquisition system can be assured. It is significant. which can provide right guidance for control center.This thesis firstly gives a brief introduction about cluster analysis and provides theoretical base for the fuzzy cluster algorithm by introducing classic set and fuzzy set, fuzzy relationship and fuzzy matrix. The advantages and disadvantages of common fuzzy cluster algorithm are compared, and they are complementary in certain parts. One new integrated cluster method will then be obtained by the optional combination of the two.Secondly, this thesis makes full use of the advantage of Visual C++visualization program, interface development ability and the powerful calculation of FORTRAN language, The cluster algorithm based on fuzzy equivalence, fuzzy ISODATA cluster algorithm and fuzzy integrated cluster algorithm can be realized by programming modelization. In order to further develop the bad data identification software of power systems and make it convenient, visualizable and compatible in use, Excel Office Software is then adopted for the convenience of searching and calculating intermediate data and valid output of the results. Consequently, the interaction and unification of the data is realized, the resource can be saved and the integration and feasibility can be improved as well.Finally, though the testing and the analysing with the actual example, we confirm the feasibility and the usability of the bad data identification software for power systems.