An application of sequential neural-network approximation for sitting and sizing passive harmonic filters

Abstract

This paper presents a method which is combined by sequential neural-network approximation and orthogonal arrays (SNAOA) for reducing harmonic distortion with passive harmonic filters and determining the optimal locations for harmonic filters among existent capacitor busses in the power network. An orthogonal array is first conducted to obtain the initial solution set. The set is then treated as the initial training sample. Next, a back-propagation sequential neural network is trained to simulate the feasible domain for seeking the optimal filter design. A restart strategy is also incorporated into the SNAOA so that the searching process may have a better opportunity to reach a near global optimum solution. In order to determine a set of weights of objective function to represent the relative importance of each term, the simplest and most efficient form of triangular membership functions has been considered. To illustrate the performance of the SNAOA, a practical harmonic mitigation problem in a 36-bus radial distribution system is studied. The results show that the SNAOA performs better than the original scheme and satisfies the harmonic limitations with respect to the objective of minimizing total harmonic distortion of voltages and the cost of commercially available discrete sizes for sitting and sizing passive harmonic filters.