Evaluation of Generator's Participation Factor Capability to Identify the Small Signal Oscillations Type of Power System using Analytical Method and Simultaneous Prediction by Neural Network

The small signal rotor angle stability which is called the small signal stability is an important issue in planning and operation of power systems. This type of stability is analyzed in four categories which include local, inter-area, control and torsional oscillations. Identification and segregation of these oscillations behavior can be used for applying control actions such as excitation system and power system stabilizer parameters tuning in order to control the oscillations damping. Determining the frequency of the system's critical modes and participation factors of the generators in these modes are one of the conventional methods for analyzing these oscillations which are calculated using analytical methods such as modal analysis. In this paper, capability of the generator's participation factor for determining the inter-area and local oscillations are studied at first by means of analytical methods. Conventional methods (e.g., modal analysis and time–domain simulations) are challenging and time consuming tasks. Thus, the generator's participation factor and power system oscillations type in the critical mode are predicted using a hybrid method containing a feature selection and probabilistic neural network (PNN) part. The advantage of the proposed method is the accuracy, fast calculations, simultaneous prediction of oscillations type and their participation in the dominant state variable of the generators in the critical modes.