Application of adaptive neuro-fuzzy inference system control in power systems

Abstract

An adaptive neuro-fuzzy inference system (ANFIS) is developed by combining neural-networks and fuzzy system. The ANFIS model uses the advantages possessed by the properties of neural networks and its decision making is based on fuzzy inference. The ANFIS parameters are obtained and updated by training processes. The ANFIS consists of two inputs (by Gaussian or other membership function) and an output (with constant or linear membership function). The ANFIS control is implemented by building a power system stabilizer (PSS) in power systems. The PSS function is to produce an additional stabilizing signal on the reactive mode of the generator. Training data are obtained from the systems that controlled by a conventional PSS with various conditions. The training process is carried out repeatedly until the appropriate ANFIS parameters are found. Next, the PSS based on ANFIS is applied to replace the conventional PSS on a single machine and hybrid power plants. Peak overshoot and settling time of the power systems are smaller and shorter. The ANFIS PSS makes the power system stability improve significantly in small-signal studies.