Uncertainty analysis of load models in small signal stability

Load modeling plays an important role in power system dynamic stability assessment and enhancement. Uncertainties with respect to different load characteristics and operating conditions have great effects on power system small signal stability analysis and control. However, owing to the time-varied and random load characteristics, it is difficult to construct load model accurately. Therefore, it is important to analyze the effects of the load uncertainty on the power system dynamic analysis. One of the most widely used methods in the uncertainty analysis is Monte-Carlo method, requiring many sample data obtained from repeated simulations, which limits its applications in the power system uncertainty analysis. This paper applies the Probabilistic Collocation Method (PCM) to analyze quantitative uncertainties, such as motor load proportions and nodal injection power on small signal stability analysis. It allows the uncertainty analysis of the power systems to be studied using only a handful of simulations. By using the proposed method, the desired output is directly described as a polynomial of the uncertain parameters. Consequently, high calculation accuracy could be reached by low-dimensionality polynomial functions and fewer simulations. The effects of the load model uncertainty combining the nodal injection uncertainty on the power system dynamic stability are calculated and analyzed based on the IEEE typical test system.