This paper presents a new approach to shortterm load forecasting in power systems. The proposed method makes use of chaos time series analysis that is based on deterministic chaos to capture characteristics of complicated load behavior. Deterministic chaos allows us to reconstruct a time series and determine the number of input variables. This paper describes chaos time series analysis of daily power system peak loads. The nonlinear mapping of deterministic chaos is identified by the multilayer perceptron of an artificial neural network. The proposed approach is demonstrated in an example.
In this paper, a hybrid method of data precondition techniques and an artificial neural network (ANN) is proposed to deal with fault detection in power systems. The proposed method makes use of FFT and DA clustering as a precondition technique. FFT is used to extract features of fault currents so that faults to be studied are characterized by frequency domain. DA clustering classifies input data into clusters in a sense of global clustering. DA contributes to the universal clustering that is not affected by the initial conditions. For each cluster, an ANN model is constructed to estimate the location and the type of fault. As ANN, this paper focuses on RBFN (Radial Basis Function Network) due to the better nonlinear approximation. DA clustering is also proposed to determine the centers of RBFN appropriately. Thus, the RBFN model results in one with global structure. The proposed method is successfully applied to a sample system. A comparison is made between the proposed and the conventional methods.
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