The load characteristics classification and synthesis of substations in large area power grid

Abstract

It is very difficult to build load model for every substation since there are numerous substations in large area power grid. A practical method is to classify the substations into several classes, pick out a typical substation from each class and build its load model, then generalize it to other substations of the same class. In this paper, a new method based on SOM (Self-Organization Map) neural network is presented for load characteristics classification and synthesis of substations in large area power grid. SOM neural network is a clustering method with self-organizing characteristics and mapping capability that can classify different input patterns automatically. Besides, the trained SOM neural network can discriminate the new input pattern conveniently without retraining. Therefore, the new substations can be discriminated with the existing classification result unchanged. The effectiveness of the proposed method is verified by a simulation of 183220kV substations in Shandong power grid using MATLAB Neural Network Toolbox. At first, the load composition rate in each substation is chosen as the feature vector, then SOM neural network is introduced to the classification and synthesis of the load characteristics of substations. At last, the synthetic load characteristic of each class is derived from the cluster center. The result is satisfactory since the method not only decreases the randomness and subjectivity of the load characteristic classification and synthesis of substations, but also improves the effectiveness and efficiency of load modeling. The method offers a new way for practical load modeling.