The Research Of Load Modeling Based On Component And Measured Approach

Synthesis load modeling has been received widespread attention in power system analysis and control. The paper has explained static model structure and basic identified theory about parameter of load component of electric power. A new Lemke optimal algorithm is applied in identified parameter of the load component. It make up static simulation experiment to different loads component in the paper, using characteristic recording instrument of load, in which can write down its electric voltage and current. The modeling practice shows its validity and feasibility of the method.In the electrical power system simulation computation, the synthesis load model of induction motor and the constant impedance proportion has the important influence to the simulation result extremely. The paper based on the statistical synthesis load modeling mentality, has carried on the detail investigation statistics to the Hunan electrical network load characteristic. In the load characteristic investigation foundation, the paper has obtained the actual proportion of induction motor and the constant impedance load of 48 transformer substation under 3 different operational condition Hunan electrical grid using the synthesis processing method, the conclusion has the vital significance to the electrical power system simulation computation.The paper have put forward a set of system approach of the component-based modeling Approach. On the premise of analysis and procession the data, the paper has used two fuzzy clustering to choose the choice industry consumers, that is, the fuzzy equivalent relation clustering and the fuzzy C means clustering. The paper is analyzed 48 transformers in the two methods and the class synthesis characteristic of the fuzzy equivalent relation is obtained using the weighted average method. The fuzzy C means clustering results is more reasonable and effective than the fuzzy equivalent relation clustering. Two clustering methods have solved the complexity and subjectivity of the transformer load characteristic classification in load modeling.Load measured data is regarded as stochastic disturbance of voltage. The paper applies three layer decomposition and re-construction of wavelet package method to analysis load modeling data, attain characterizes vector of load data, construct characteristics vector, which is used to classify load data. Based on the characteristics vector is standardized, load data is classified by fuzzy subtract clustering in the paper. The method is proved valid, which is high precision and convergence by the example of the attaining characteristics and clustering of dynamic lab and transformer substation data. It is the significant to load modeling processing the amount of load data.To obtain accurate load modeling, a new load modeling method is proposed in the paper, which is fuzzy neural network load modeling based-on Subtractive clustering. By analyzing input and output data, the paper set up the mount function to classify its clustering number and adjust class center. In this way, the method confirms member function parameters of the initial fuzzy load model. The method can obtain fuzzy rules through the neural network study to modeling data, optimize the parameter member function by amending the linkage proportion in the back prevalence algorithm. So it can identify the load model construct and attain optimal parameter of function member. The load model synthesis ability is proved by stimulating other load data.