An Integrated Model-Driven and Data-Driven Method for On-Line Prediction of Transient Stability of Power System With Wind Power Generation

Abstract

The increase of wind power permeability in modern power grid has turned rapid and accurate transient stability (TS) prediction into a more challenging issue. To accurately and promptly perform online TS prediction for power system with doubly fed induction generator (DFIG)-based wind farms, an integrated model-driven and data-driven method is proposed in this paper. The influence of DFIGs is considered in the transformation to guarantee the accuracy of the equivalent one machine infinite bus (OMIB) model transformed from the target system. The P-δ trajectory of the OMIB is fitted with the generator-terminal information to predict TS. To improve the prediction speed, an extreme learning machine (ELM)-based method is utilized to process the other DFIG and system information and evaluate the system status immediately after failure. The simulation results verify that the proposed method can reduce the dependence of the data-driven method on the data sample size and improve the speed and accuracy of online prediction.INDEX TERMS Extreme learning machine (ELM), doubly fed induction generators (DFIG), transient stability, trajectory fitting.

Online Prediction Method for Power System Frequency Response Analysis Based on Swarm Intelligence Fusion Model

“…Since the idea of combining physical-driven and data-driven methods for analysis was proposed [11], [37], there are four typical fusion modes between physical-driven methods and data-driven methods applied in various power system scenarios, including the parallel mode proposed for addressing the issue of joint data and physical modeling in power systems [40]; the serial mode proposed for the problem of high complexity or insufficient accuracy in power system model [35]; the bootstrap mode proposed for the problem of lack of knowledge or engineering experience to guide the construction of power systems data model [41]; and feedback mode proposed for addressing the issue of mismatched power system model parameters [42], as shown in Fig. 1.…”

Mapping of Dynamics Between Mechanical and Electrical Ports in SG-IBR Composite Grids

“…The port-based analysis is case-dependant and may generate different results for different grid conditions and scenarios [22]. The toolbox can help scan through a range cases to identify the common features and exceptions via data-driven methods [23], [24]. On the other hand, it is also possible to establish modular criterion on each port to achieve caseindependent stability.…”

Data-Driven Trajectory Prediction of Grid Power Frequency Based on Neural Models

“…Power systems data analysis is the current frontier of innovation, exploration and productivity, and its research is on the rise. Such data analysis has addressed diverse system areas like coherency groups identification [7], trajectory prediction to identify system dynamics from a noisy measurements [8], short-horizon wind power forecasting [9], online prediction of transient stability with renewables [10], photo-voltaic power production nowcasting in microgrids [11], power system collapse prediction [12], wide area control [13], load frequency control [14], and for renewable integration impact assessment [15].…”