Small signal stability analysis of power generation system based on time series pattern using fuzzy model predictive control

This paper proposes a Fuzzy Model Predictive Control (FMPC) model as a small signal stability control of a power generation system. Load fluctuations are one of the problems with small signal stability. But in reality, load fluctuations form a dynamic pattern of time series so that it can be predicted. DSARIMA time series models meet predictive criteria. Predicted results are used as load cluster models. Load clustering aims to optimize the operation of the generating system criteria. The FMPC method proposed in this study is the development of the Fuzzy Takagi-Sugeno model. Fuzzy T-S consists of a state estimator (Fuzzy State Estimator) approach to identify each load change and identify the optimal control model (FMPC) for each control input state. The development of Fuzzy T-S in fuzzy state estimators and FMPC functions as multiple soft-switching for each load condition. Through this method, FMPC is able to predict electrical power near real load conditions. FMPC has a performance that can guarantee all load conditions with better frequency and voltage stability than conventional optimal control methods.