A Hierarchical Fuzzy Inference Network for Estimating the Minimum Voltage Magnitude in Radial Distribution Systems

This paper proposes a new method for estimating the minimum voltage magnitude and its node number in distribution networks. The proposed method is based on the hierarchical fuzzy inference network (HFIN) that estimates the minimum voltage magnitude at the multi-levels. The liberalization of the power networks brings about a new aspect that uncertain reverse power flows exist due to distributed generation such as wind power units, etc. As a result, it is important to monitor them in distribution networks appropriately. As a quality index, the minimum voltage magnitude is a useful measure that indicates the deterioration of the voltage quality in the distribution networks. However, it is hard to carry out the power flow calculation for lack of measurements. In this paper, a fuzzy-inference-based method is proposed to estimate the location and the magnitude of the minimum voltage. The proposed method makes use of the fuzzy inference network in a hierarchical way to evaluate the association probability of the location and the magnitude of the minimum voltage